TY - Conference Paper T1 - Do-it-Yourself Digital Agriculture applications with semantically enhanced IoT platform A1 - Jayaraman, P P Y1 - 2015/// KW - do- it-yourself KW - internet of things KW - semantic digital agriculture KW - semantic web JF - 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015 DO - 10.1109/ISSNIP.2015.7106951 UR - https://api.elsevier.com/content/abstract/scopus_id/84933544010 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2015) Do-it-Yourself Digital Agriculture applications with semantically enhanced IoT platform.pdf N1 - Cited By (since 2015): 53 N2 - Internet of Things (IoT) enables various applications (crop growth monitoring and selection, irrigation decision support, etc) in Digital Agriculture domain. Semantic enhancements to IoT platforms address challenges of interoperability, data fusion, integration of heterogeneous IoT silos, just to name a few. This paper discusses the recently released OpenIoT platform which demonstrated its applicability to a number of use cases, including a Digital Agriculture use case (Phenonet). An ontology to represent Phenonet domain concepts has been proposed and the results of experimental study, related semantic queries and reasoning using the ontology are presented. A do-it-yourself principle driven zero-programming effort Phenonet user interface demonstrates benefits and efficiency of the approach. ER - TY - HTML T1 - Smart Agriculture for Sustainable Food Security Using Internet of Things (IoT) A1 - Qureshi, T A1 - Saeed, M A1 - Ahsan, K A1 - Malik, A A A1 - ... Y1 - 2022/// PB - hindawi.com JF - … and Mobile Computing UR - https://www.hindawi.com/journals/wcmc/2022/9608394/ N1 - Cited By (since 2022): 1 N2 - … What are the most practiced smart agriculture … of IoT in agriculture? Through this overview, we are trying to highlight the potential of IoT in agriculture for sustainable food security for … ER - TY - Article T1 - IoT based agriculture as a cloud and big data service: The beginning of digital India A1 - Gill, S S Y1 - 2017/// KW - Agriculture as a Service KW - Autonomic Management KW - Big Data KW - Cloud Computing KW - Internet of Things JF - Journal of Organizational and End User Computing VL - 29 IS - 4 SP - 1 EP - 23 DO - 10.4018/JOEUC.2017100101 UR - https://api.elsevier.com/content/abstract/scopus_id/85027976353 N1 - Cited By (since 2017): 61 N2 - Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things IoT and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service AaaS through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service QoS is also better in terms of QoS parameters. ER - TY - Conference Paper T1 - IoT Based Smart Agriculture Management System A1 - Nagaraja, G S Y1 - 2019/// KW - Crop Prediction KW - Data Acquisition KW - Internet of things KW - Machine Learning KW - Precision Agriculture KW - Sensors KW - Soil fertility JF - CSITSS 2019 - 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution, Proceedings DO - 10.1109/CSITSS47250.2019.9031025 UR - https://api.elsevier.com/content/abstract/scopus_id/85083084374 N1 - Cited By (since 2019): 17 N2 - India is a land of agriculture. More than 70% of the population is involved directly or indirectly in crop production activities. This sector contributes to the Indian economy a great deal. It contributes over 17% of the total Gross Domestic Product (GDP). With the introduction of newer seed varieties, new methods of agriculture, and the use of efficient fertilizers, crop production has increased. But without using the smarter methods, the agricultural domain still remains in the backlogs. The conventional method involves a lot of human instincts which at times fail. And thus there is a need for a smarter way of crop production using Internet of Things (IoT) and Machine Learning techniques. The proposed system is a smart agriculture management system (SAMS) which is automated to help farmers to increase the crop production. The system also helps in reduction of resource wastage by adopting a technique called precision agriculture. The system uses different sensors for data acquisition to measure various environmental factors which are required for crop production. The data obtained from these sensors is visualized in the form of graphs. ER - TY - Conference Paper T1 - A survey on Zigbee based wireless sensor networks in agriculture A1 - Kalaivani, T Y1 - 2011/// KW - Agriculture KW - Binary phase shift keying KW - Frequency modulation KW - Monitoring KW - Temperature measurement KW - Temperature sensors JF - TISC 2011 - Proceedings of the 3rd International Conference on Trendz in Information Sciences and Computing SP - 85 EP - 89 DO - 10.1109/TISC.2011.6169090 UR - https://api.elsevier.com/content/abstract/scopus_id/84859979698 N1 - Cited By (since 2011): 76 N2 - This paper focuses on providing an overview of zigbee based wireless sensor network (WSN) as applied in agriculture for intelligent farming. In this research work, a survey on wireless sensor networks and their standards and technologies in the field of agriculture was carried out. Based on the analysis and survey, the need for intelligent farming especially in developing countries like India, has grown to a greater extent. In this paper we try to survey different applications of zigbee based wireless sensor network in agriculture such as monitoring of environmental conditions like weather, soil moisture content, soil temperature, soil fertility, weed-disease detection, monitoring leaf temperature/moisture content and monitoring growth of the crop, precision agriculture, automated irrigation facility, storage of agricultural products etc. This paper also provides the possible research issues existing in Physical layer of ZigBee. ER - TY - Article T1 - Modelling and analysis of IoT technology using neural networks in agriculture environment A1 - Özbilge, E Y1 - 2020/// KW - agriculture KW - intelligent systems KW - internet of things KW - machine learning KW - neural networks JF - International Journal of Computers, Communications and Control VL - 15 IS - 3 DO - 10.15837/IJCCC.2020.3.3885 UR - https://api.elsevier.com/content/abstract/scopus_id/85086874561 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Modelling and analysis of IoT technology using neural networks in agriculture environment.pdf N1 - Cited By (since 2020): 3 N2 - The rapid development of internet, cloud computing and sensor networks lead to develop and deploy the Internet of Things (IoT) which is a hot topic for the researchers. It has started to be used in various areas. Thus, agriculture is one of the most popular IoT research area. In agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air tempera- ture, solar radiations, and relative humidity etc. These extreme weather conditions effect crops and farms very significantly. But with the benefits of Internet of Things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. This paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. This paper proposed a time-delay radial basis function (TDRBF) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environ- ment. The performance of the acquired network model was analysed statistically and presented in this paper. As a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years. ER - TY - Conference Paper T1 - Blockchain Smart Contract for Scalable Data Sharing in IoT: A Case Study of Smart Agriculture A1 - Rahman, M Ur Y1 - 2020/// KW - Agriculture KW - Blockchain KW - Distributed databases KW - Internet of Things KW - Scalability KW - Smart contracts KW - Stakeholders JF - 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2020 DO - 10.1109/GCAIoT51063.2020.9345874 UR - https://api.elsevier.com/content/abstract/scopus_id/85101423617 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Blockchain Smart Contract for Scalable Data Sharing in IoT A Case Study of Smart Agriculture.pdf N1 - Cited By (since 2020): 6 N2 - The emerging Smart Agriculture based on Internet of Things (IoT) is facing major challenges like data sharing, storage, and monitoring, primarily due to the distributed nature of IoT and massive scale. We performed a review of the literature and found that blockchain performance, scalability, cost, and throughput are the major challenges in adopting blockchain for smart agriculture. To overcome these challenges, this paper proposes a scalable and distributed data sharing system integrating access control for smart agriculture. We demonstrate our approach in a smart agriculture setting, which consists of four tiers that are: smart agriculture, smart contract, Interplanetary File System (IPFS) and agriculture stakeholders (remote users). This paper explains in detail the different components of our proposed architecture. Our approach uses anonymous identities to ensure users' privacy. Our approach is fully scalable because a large number of resource owners can use their data sharing smart contracts to create, update or delete data sharing policies. In addition, our approach does not require transaction fees when the smart contract receives a large number of policy evaluation requests. For the sake of simplicity, we publish and test a single data sharing smart contract. However, in practice, multiple smart contracts need to be deployed to allow each resource owner to securely share agriculture data with stakeholders. Finally, we evaluate the performance of our proposed system on the EOS blockchain to show that the resource consumption (in terms of computing power and network bandwidth) introduced by our framework are insignificant compared to its scalability, cost and security benefits. ER - TY - Conference Paper T1 - IoT based System for Smart Agriculture A1 - Marcu, I M Y1 - 2019/// KW - Libelium KW - agriculture KW - meteorological parameters KW - monitoring of crops JF - Proceedings of the 11th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2019 DO - 10.1109/ECAI46879.2019.9041952 UR - https://api.elsevier.com/content/abstract/scopus_id/85084561435 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) IoT based System for Smart Agriculture.pdf N1 - Cited By (since 2019): 9 N2 - Agriculture is the most traditional activity over time. Since the beginning of it, agriculture has suffered many changes to improve productivity and quality of crops. Some of the first significant improvements have been remarked when machines and new tools such as irrigation systems, harvest machines, farmland clearing machines were introduced in the primitive agriculture, where these activities were performed mainly by humans and animals. Over time, agriculture has been affected by weather disasters (such as storms or extreme temperatures) and by natural disasters (such as pests and plant diseases). Thus, the next step in the development of the agriculture domain was to propose the Internet of Things (IoT) solutions for monitoring of many parameters for better precision agriculture. Such a system would provide useful information on plant growth, crops’ diseases, and soil properties that are a benefit for crops. This paper describes a possible solution for a more reliable IoT- based system using Libelium for Smart Agriculture to monitor the parameters that have a direct impact on crops. Moreover, the monitoring system aims to manage agricultural issues related to irrigations and analyses the effect of the measured parameters on agriculture, helping the farmers to have healthy crops. Keywords- ER - TY - Conference Paper T1 - Wireless sensor network for precise agriculture monitoring A1 - Li, S A1 - Cui, Jin A1 - Li, Zighang Y1 - 2011/// KW - intelligent management system KW - precision agriculture KW - wireless sensor networks JF - Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 VL - 1 SP - 307 EP - 310 DO - 10.1109/ICICTA.2011.87 UR - https://api.elsevier.com/content/abstract/scopus_id/79956048266 N1 - Cited By (since 2011): 31 N2 - Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments. ER - TY - Review T1 - IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges A1 - Quy, V K A1 - Hau, Nguyen Van A1 - Anh, Dang Van A1 - Quy, Nguyen Minh A1 - Ban, Nguyen Tien A1 - Lanza, Stefania A1 - Randazzo, Giovanni A1 - Muzirafuti, Anselme Y1 - 2022/// KW - Internet of Things KW - IoT ecosystem KW - Sustainability agriculture KW - food security KW - green technologies KW - natural resources KW - sustainable environment JF - Applied Sciences (Switzerland) VL - 12 IS - 7 SN - 2076-3417 DO - 10.3390/app12073396 UR - https://api.elsevier.com/content/abstract/scopus_id/85127581801 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2022) IoT-Enabled Smart Agriculture Architecture, Applications, and Challenges.pdf N1 - Cited By (since 2022): 4 N2 - The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach. ER - TY - Article T1 - Deep learning predictor for sustainable precision agriculture based on internet of things system A1 - Jin, X B Y1 - 2020/// KW - Gated recurrent unit KW - Internet of things KW - deep learning predictor KW - medium- and long-term prediction KW - precision agriculture KW - sequential two-level decomposition structure JF - Sustainability (Switzerland) VL - 12 IS - 4 DO - 10.3390/su12041433 UR - https://api.elsevier.com/content/abstract/scopus_id/85081009477 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System.pdf N1 - Cited By (since 2020): 46 N2 - Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production. ER - TY - JOUR T1 - A Combo Smart Model of Blockchain with the Internet of Things (IoT) for the Transformation of Agriculture Sector A1 - Awan, S H A1 - Ahmad, S A1 - Khan, Y A1 - Safwan, N A1 - ... Y1 - 2021/// KW - Blockchain Technique KW - Food Tracking KW - Internet of Things KW - Smart Model PB - Springer JF - Wireless Personal … DO - 10.1007/s11277-021-08820-6 UR - https://link.springer.com/article/10.1007/s11277-021-08820-6 N1 - Cited By (since 2021): 5 Q2 N2 - ...Nowadays traditional techniques of living and earning are being transformed to modern smart technologies, taking their inspiration from the emerging trends. Agriculture and its supply chain are also one of the major domains of research that need attention for its growth especially in developing countries like Pakistan. Food safety and its supply are drawing the world's attention towards its importance and people are focusing on it because of health hazards. This research, presents a Combo smart model with a novel scheme for the transformation of traditional agriculture to smart agriculture, taking into consideration both blockchain and Internet of Things ( IoT) characteristics. The system proves to be reliable, automatic, open, and biological food tracks built with the features of Blockchain IoT devices. This system provides equal opportunity to all stakeholders involved in the agricultural food supply chain; even they are not familiar with each other and may not trust. IoT devices are added to the smart model to reduce human interference for data recording and verification. For validation purposes, the proposed scheme is compared to our own scheme, which uses only IoT devices deployed in the monitoring field without a blockchain... ER - TY - Article T1 - Transforming agriculture through pervasive wireless sensor networks A1 - Wark, T Y1 - 2007/// KW - agriculture KW - cattle behavior KW - virtual fencing KW - wireless sensor networks JF - IEEE Pervasive Computing VL - 6 IS - 2 SP - 50 EP - 57 DO - 10.1109/MPRV.2007.47 UR - https://api.elsevier.com/content/abstract/scopus_id/34247379489 N1 - Cited By (since 2007): 269 N2 - A large-scale, outdoor, pervasive computing system based on the Fleck hardware platform applies sensor network technology to farming. Comprising static and animal-borne mobile nodes, the system measures the state of a complex, dynamic system comprising climate, soil, pasture, and animals. This data supports prediction of the land's future state and improved management outcomes through closed-loop control. This article is part of a special issue, Building a Sensor-Rich World. ER - TY - Article T1 - Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study A1 - Popović, T A1 - Latinovic, Nedeljko A1 - Pešic, Ana A1 - Zecˇevic, Zˇarko A1 - Krstajic, Bozˇo A1 - Djukanovic, Slobodan Y1 - 2017/// KW - Cloud computing KW - Ecological monitoring KW - Internet of things KW - Precision agriculture KW - Remote sensing KW - Software architecture JF - Computers and Electronics in Agriculture VL - 140 SP - 255 EP - 265 DO - 10.1016/j.compag.2017.06.008 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169916307566 UR - https://api.elsevier.com/content/abstract/scopus_id/85020907365 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Architecting an IoT-enabled platform for precision agriculture and ecological monitoring A case study.pdf N1 - Cited By (since 2017): 130 N2 - This paper discusses a case study of designing a private Internet of Things (IoT) enabled platform for the research in precision agriculture and ecological monitoring domains. The system architecture is gradually derived using an approach of multiple, concurrent views. Each view represents an architectural perspective describing the solution from the viewpoint of different stakeholders, such as end-users, researchers, developers, and project managers. The end-user requirements have been identified using a set of high-level scenarios, which capture the context and illustrate the motivation for building the platform. The requirements and architecture of the proposed platform have been derived so that the users of the platform, researchers, and developers on the project, can utilize it for prototyping solutions for these high level use cases. The paper further describes the implementation of the platform and its evaluation using various sensor nodes deployed at the research and end-user facilities. The solution is open to further development with respect to supporting additional IoT protocols, data types, and interfacing to various analytics tools. The proposed architecture can also be implemented using different server platforms and cloud technologies. ER - TY - Conference Paper T1 - A novel technology for smart agriculture based on IoT with cloud computing A1 - Mekala, M A1 - Viswanathan, P. Y1 - 2017/// KW - Agriculture Monitoring KW - Cloud Computing KW - Gprs KW - Internet of Things KW - Irrigation KW - Li-Fi KW - Routing Protocol JF - Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017 SP - 75 EP - 82 DO - 10.1109/I-SMAC.2017.8058280 UR - https://api.elsevier.com/content/abstract/scopus_id/85034566928 N1 - Cited By (since 2017): 54 N2 - Internet of Things (IoT) is one of the fastest developing technologies throughout the India. But, most of the population (70%) in India depending on agriculture. This situation is one of the reason, that hindering the development of country. in order to solve this problem only one solution that, smart agriculture by adding new technological methods instead of present traditional agriculture methods. Hence we proposed new IoT technology with cloud computing and Li-Fi. Wi-Fi is great for general wireless coverage within buildings, whereas Li-Fi[10] is wireless data coverage with high density in confined area. Li-Fi provides better bandwidth, efficiency, availability and security than Wi-Fi and has already achieved blisteringly high speed in the lab. First this project includes remote controlled process to perform tasks like spraying, weeding, bird and animal scaring, keeping vigilance, moisture sensing, etc. Secondly it includes smart warehouse management which includes temperature maintenance, humidity maintenance and theft detection in the warehouse. Thirdly, intelligent decision making based on accurate real time field data for smart irrigation with smart control. Controlling of all these operations will be through any remote smart device or computer connected to Internet and the operations will be performed by interfacing cameras, sensors, Li-Fi or ZigBee modules. ER - TY - Article T1 - Advancement and trend of internet of things in agriculture and sensing instrument A1 - He, Y Y1 - 2013/// KW - Internet of things KW - Sensing instrument KW - Spectrum Gauging Control KW - agriculture JF - Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery VL - 44 IS - 10 SP - 216 EP - 226 DO - 10.6041/j.issn.1000-1298.2013.10.035 UR - https://api.elsevier.com/content/abstract/scopus_id/84887298125 N1 - Cited By (since 2013): 60 N2 - The internet of things (IOT) in agriculture consisted three layers, including perceive, transportation and application. The perceive layer were used to acquire the information of crops, soil and environment. The transportation layer was used to establish the transportation network of IOT in agricultural by combing the techniques like GPRS, Zigbee, WIFI, Bluetooth and the intelligent networking methods. The process layer focused on the intelligent management of agriculture, including multidimensional information fusion, intelligent decision and automatic control, et al. The key problems, research emphasis and application fields of IOT in agriculture include three layers were discussed in detail and analyzed. The prospect and development trend of IOT in agricultural in modern agriculture was put forward. ER - TY - Conference Paper T1 - Capacitive Soil Moisture Sensor Node for IoT in Agriculture and Home A1 - Hirsch, C A1 - Bartocci, Ezio A1 - Grosu, Radu Y1 - 2019/// KW - Bluetooth KW - Internet of Things KW - agriculture KW - crops KW - moisture measurement KW - plant diseases KW - sensors KW - software architecture KW - soil JF - 2019 IEEE 23rd International Symposium on Consumer Technologies, ISCT 2019 SP - 97 EP - 102 DO - 10.1109/ISCE.2019.8901012 UR - https://api.elsevier.com/content/abstract/scopus_id/85075632867 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Capacitive Soil Moisture Sensor Node for IoT in Agriculture and Home.pdf N1 - Cited By (since 2019): 6 N2 - Growing food for nine billion people is a challenge we will face in near future. Digital technologies hold a great promise to increase efficiency and use resources effectively. In this paper we present a low-power and scalable IoT-based architecture for home farmers and scientific purposes that enables to verify the environmental impact on plants developments by monitoring the soil moisture and temperature. The measured values are transmitted via Bluetooth Low Energy (BLE) to a gateway, e.g., a smartphone running the gateway app, to a cloud platform that stores and processes the data. This data is useful to give home farmers vital information, e.g., when to water a plant or when the plant is prune to get infected by a disease. This information is of great importance because it helps to decrease crop failures and thus farm more efficient. The hardware and software architecture presented is scalable and designed to consume low power. Experiments show that it is possible to create a high-spatial resolution by putting many sensor nodes on a small area, in order to help research to measure micro-climate. ER - TY - Conference Paper T1 - IoT, big data science & analytics, cloud computing and mobile app based hybrid system for smart agriculture A1 - Roy, S Y1 - 2017/// KW - Android KW - Data Analytics KW - Field Programmable Gate Array KW - GPS KW - Internet of Things JF - 2017 8th Industrial Automation and Electromechanical Engineering Conference, IEMECON 2017 SP - 303 EP - 304 DO - 10.1109/IEMECON.2017.8079610 UR - https://api.elsevier.com/content/abstract/scopus_id/85039946880 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) IoT, Big Data Science & Analytics, Cloud Computing and Mobile App based Hybrid system for Smart Agriculture.pdf N1 - Cited By (since 2017): 34 N2 - Here we presents AgroTick, an innovative hybrid system for smart agriculture. AgroTick is an IoT based system supported with mobile interface and designed using technology modules like cloud computing, embedded firmware, hardware unit and big data analytics. AgroTick is architected and designed to improve the efficiency of agriculture, build a well-connected farming network and create a knowledge sharing platform for farmers. In a longer run, AgroTick will address two key issues plaguing agriculture in India - harvesting rainwater and groundwater, and predicting effective utilization of the same. ER - TY - Conference Paper T1 - IOT agriculture to improve food and farming technology A1 - Jaiganesh, S Y1 - 2017/// KW - Agriculture KW - Internet of Things KW - big data KW - cloud computing JF - 2017 Conference on Emerging Devices and Smart Systems, ICEDSS 2017 SP - 260 EP - 266 DO - 10.1109/ICEDSS.2017.8073690 UR - https://api.elsevier.com/content/abstract/scopus_id/85039919619 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) IOT Agriculture to improve Food and Farming.pdf N1 - Cited By (since 2017): 47 N2 - The paper researches the part of Internet of Things (IOT) in Agricultural Sector. Today agriculture is inserted with propel benefit like GPS, sensors that empower to impart to each other break down the information and further more trade information among them. IT gives benefit as cloud to farming. Agriculture cloud and IT benefit gives an exceptional ability administration to ranchers with respect to development of yields, estimating, composts, maladies detail technique for cure to be utilized Scientist taking a shot at agriculture will give their disclosures, proposals with respect to cutting edge procedures for development, utilization of manures can get the history of the area. The review depended on applying a cloud construct application in light of agriculture. This depends on agro-cloud that upgradeagricultural generation and accessibility of information identified with research extends in the fizzled, the effect of doing this will spare the cost and time make the correspondence simpler and speedier. This paper would advance a ton of research in the region of use of IOT in agriculture. ER - TY - Conference Paper T1 - Intelligent agriculture greenhouse environment monitoring system based on IOT technology A1 - Liu, D Y1 - 2016/// KW - Agriculture KW - CC2530 KW - Greenhouse KW - Internet of Things KW - ZigBee JF - Proceedings - 2015 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2015 SP - 487 EP - 490 DO - 10.1109/ICITBS.2015.126 UR - https://api.elsevier.com/content/abstract/scopus_id/84963811415 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Intelligent agriculture greenhouse environment monitoring system based on IOT technology.pdf N1 - Cited By (since 2016): 94 N2 - In recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (Internet of Things) technology rapid development and wide application. This paper takes CC2530 chip as the core, presents the design and implementation of agriculture Greenhouse Environment monitoring system based on ZigBee technology, the wireless sensor and control nodes takes CC2530F256 as core to control the environment data. This system is made up of front-end data acquisition, data processing, data transmission and data reception. The ambient temperature is real-time processed by the temperature sensor of data terminal node. Processed data is send to the intermediate node through a wireless network. Intermediate node aggregates all data, and then sends the data to the PC through a serial port, at the same time, staff may view, analysis and storage the data by the PC that provide real-time data for agricultural greenhouse, fans and other temperature control equipment, and achieve automatic temperature control. ER - TY - Article T1 - Advanced UAV–WSN system for intelligent monitoring in precision agriculture A1 - Popescu, D Y1 - 2020/// KW - Internet of Things KW - data consensus KW - intelligent data processing KW - precision agriculture KW - relevant data extraction KW - trajectory planning KW - unmanned aerial vehicles KW - wireless sensor networks JF - Sensors (Switzerland) VL - 20 IS - 3 DO - 10.3390/s20030817 UR - https://api.elsevier.com/content/abstract/scopus_id/85079082781 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture.pdf N1 - Cited By (since 2020): 64 N2 - The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV-WSN-IoT approach increases the performances in both precision agriculture and ecological agriculture. ER - TY - Conference Paper T1 - Dynamic IoT management system using K-means machine learning for precision agriculture applications A1 - Stewart, J Y1 - 2017/// KW - K-means KW - dynamic network management system KW - internet of things KW - multimedia wireless sensor network KW - precision agriculture KW - service differentiation KW - smart farming JF - ACM International Conference Proceeding Series DO - 10.1145/3018896.3036385 UR - https://api.elsevier.com/content/abstract/scopus_id/85044670194 N1 - Cited By (since 2017): 7 N2 - Multi-media applications for use in Precision Agriculture (PA) and Smart Farming (SM) require Network Management Systems to deliver Quality of Service (QoS) end-to-end guarantees. This paper presents the second phase of the research in providing a network management system capable of delivering end-to-end QoS guarantees for Internet of Things (IOT) networks. The first phase of this work used a wireless test bed to develop a propagation model to incorporate the attenuation due to foliage in dense vegetation typically found in PA environments. The output of this propagation model will influence the decision making process in the network management system. Wireless Multimedia Sensor Networks (WMSN) operate under the umbrella of the Wireless Sensor Network (WSN) IEEE 802.15.4 Medium Access Control (MAC) and Physical (PHY) protocol to deliver multimedia applications such as voice, video and live streaming. To operate successfully these multi-media applications have high QoS requirements. To enable these QoS requirements to be fulfilled performance metrics such as throughput, end-to-end delay and limited packet loss must be guaranteed. This next phase of the work in developing the intelligent network management system presented in this paper uses an OPNET™ simulation package to implement a modified K-Means algorithm to detect the presence of multi-media traffic. Consequently a signal informs the network management system to adopt pre-configured settings via the Personal Area Network Co-ordinator (PANC). The resulting changes implement service differentiation by manipulating the MAC layer (size of the individual GTS timeslots and duty cycle) to deliver better throughput and end-to-end delay performance. OPNET™ simulation results show that the new algorithm facilitates better performance and meets QoS requirements suitable to multimedia applications. This paper focuses on the derivation and evaluation of the performance of the K-Means algorithm. The sensory nodes are power, memory and computationally restricted. These restrictions coupled with the heterogeneous structure of the wireless network make intelligent network management systems very important if the QoS requirements are to be fulfilled. Upon detection of multimedia traffic with high QoS demands usually triggered in the aftermath of an event of particular interest e.g. security threat etc., a management system must dynamically effect change of the network configuration settings to maintain such guarantees. As real-time applications require an urgent response, the dynamic change must occur during run time automatically. This research work is novel in that it combines the output from the development of the physical layer propagation model to inform a network management system to trigger service differentiation for multimedia traffic in a PA environment. ER - TY - Book Chapter T1 - qIoTAgriChain: IoT Blockchain Traceability Using Queueing Model in Smart Agriculture A1 - Patra, S S Y1 - 2021/// KW - AgriChain KW - Blockchain KW - Internet of things KW - Performance analysis KW - Queueing model KW - Smart agriculture KW - Supply chain JF - EAI/Springer Innovations in Communication and Computing SP - 203 EP - 223 SN - 2522-8595 DO - 10.1007/978-3-030-65691-1_14 UR - https://api.elsevier.com/content/abstract/scopus_id/85108439563 N1 - Cited By (since 2021): 3 N2 - Blockchain is an egressing digitally managed technology which allows ubiquitous monetary transactions with the untrusted entities, without involving the intermediary bodies, viz., banks. The literature reveals that IoT along with blockchain technology (BT) offers various benefits to the functioning of the agriculture supply chain (AgriChain). It is going to be implemented with which BT is going to perform a change in paradigm in which the transactions will be taken care of by reducing the number of intermediaries, making fast payment procedures among the parties. Developing economies with an ever-growing population country like India caters to food security demands and faces lots of challenges pretending agriculture supply chain sustainability. Therefore, it is crucial to follow BT in the agriculture supply chain to leverage multiple profits. In this chapter, we develop a queueing model which evaluates in detail the characteristics of an AgriChain system and measures the performance of the model such as the number of transactions expected that are waiting inside the queue to enter into the block, mean transactions waiting in the block, waiting for the time of a transaction, confirmation time of any transaction, etc. ER - TY - Article T1 - Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines A1 - Gupta, N Y1 - 2020/// KW - Agricultural machine KW - Artificial neural network KW - Edge computation KW - Evolutionary algorithm KW - Green IoT KW - Health-monitoring JF - Applied Intelligence VL - 50 IS - 11 SP - 3990 EP - 4016 DO - 10.1007/s10489-020-01744-x UR - https://api.elsevier.com/content/abstract/scopus_id/85087508569 N1 - Cited By (since 2020): 42 N2 - In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors. ER - TY - Conference Paper T1 - Sensor planning for a symbiotic UAV and UGV system for precision agriculture A1 - Tokekar, P Y1 - 2013/// KW - Fertilizers KW - Ground Vehicles KW - Precision Agriculture KW - Sensors KW - Unmanned Aerial Vehicles KW - unmanned aerial vehicles JF - IEEE International Conference on Intelligent Robots and Systems SP - 5321 EP - 5326 SN - 2153-0858 DO - 10.1109/IROS.2013.6697126 UR - https://api.elsevier.com/content/abstract/scopus_id/84893724859 N1 - Cited By (since 2013): 64 N2 - We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) for a precision agriculture application. In this application, the ground and aerial measurements are used for estimating nitrogen (N) levels on-demand across a farm. Our goal is to estimate the N map over a field and classify each point based on N deficiency levels. These estimates in turn guide fertilizer application. Applying the right amount of fertilizer at the right time can drastically reduce fertilizer usage. Towards building such a system, this paper makes the following contributions: First, we present a method to identify points whose probability of being misclassified is above a threshold. Second, we study the problem of maximizing the number of such points visited by an UAV subject to its energy budget. The novelty of our formulation is the capability of the UGV to mule the UAV to deployment points. This allows the system to conserve the short battery life of a typical UAV. Third, we introduce a new path planning problem in which the UGV must take a measurement within a disk centered at each point visited by the UAV. The goal is to minimize the total time spent in traveling and measuring. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of our system with simulations which use manually collected soil measurements from the field. ER - TY - Article T1 - An improved multilayer perceptron approach for detecting sugarcane yield production in IoT based smart agriculture A1 - Wang, P A1 - Hafshejani, Behzad Aalipur A1 - Wang, Daluyo Y1 - 2021/// KW - Internet of things KW - Multilayer perceptron KW - Precision agriculture KW - Sugarcane yield production JF - Microprocessors and Microsystems VL - 82 DO - 10.1016/j.micpro.2021.103822 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0141933121000028 UR - https://api.elsevier.com/content/abstract/scopus_id/85098947988 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) An improved multilayer perceptron approach for detecting sugarcane yield production in IoT based smart agriculture.pdf N1 - Cited By (since 2021): 6 refer N2 - Internet of Things (IoT) as one of most powerful technologies can provides precision management and intelligent navigation for managers and manufacturing plants’ Smart agriculture to deal a good strategy for improving agricultural productions and maximizing farm efficiency. Sugar production is subsidiary to many diverse and various parameters. Due to a diverse variety of parameters and the lengthy process in precision agriculture, the analytical prediction is difficult and impossible. In such situations, using intelligent systems such as machine learning may be proposed as an alternative solution. This paper proposed an improved Multilayer Perceptron (MLP) approach to predict the amount of sugar yield production in IoT agriculture. Experimental results show that the proposed MLP algorithm has maximum accuracy of 99%, precision of 95%, recall of 96% and Minimum Mean Absolute Error (MAE) of 0.04% and Root mean square error (RMSE) of 0.006% for detecting sugarcane yield production in IoT Agriculture. ER - TY - Conference Paper T1 - Successful deployment of a Wireless Sensor Network for precision agriculture in Malawi A1 - Mafuta, M A1 - Zennaro, Marco A1 - Bagula, Antoine A1 - Ault, Graham A1 - Gombachika, Harry Y1 - 2012/// KW - Batteries KW - Irrigation KW - Soil moisture KW - Valves KW - Wireless sensor networks KW - Zigbee KW - precision agriculture KW - soil moisture KW - solar power KW - water scarcity KW - wireless sensor networks JF - Proceedings - 2012 IEEE 3rd International Conference on Networked Embedded Systems for Every Application, NESEA 2012 DO - 10.1109/NESEA.2012.6474009 UR - https://api.elsevier.com/content/abstract/scopus_id/84875587786 N1 - Cited By (since 2012): 31 N2 - This paper demonstrates how an Irrigation Management System (IMS) can practically be implemented by successfully deploying a Wireless Sensor Network (WSN). Specifically, the paper describes an IMS which was set up in Manja Township, City of Blantyre based on an advanced irrigation scheduling technique. Since the system had to be self-sustained in terms of power, which is a challenge for deployment in rural areas of developing countries like Malawi where grid power supply is scarce, we used solar Photovoltaic (PV) and rechargeable batteries to power all electrical devices in this system. The system incorporated a remote monitoring mechanism through a General Packet Radio Service (GPRS) modem to report soil temperature, soil moisture, WSN link performance and PV power levels. Irrigation valves were activated to water the field. Our preliminary results have revealed engineering weakness of deploying such a system. Nevertheless, the paper shows that it is possible to develop a robust, fully-automated, solar powered, and low cost IMS to suit the socio-economic conditions of small scale farmers in developing countries. ER - TY - JOUR T1 - LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile A1 - Ahmed, M A A1 - Gallardo, J L A1 - Zuniga, M D A1 - Pedraza, M A A1 - ... Y1 - 2022/// PB - mdpi.com JF - Sensors UR - https://www.mdpi.com/1576948 UR - https://www.mdpi.com/1424-8220/22/8/2824/htm N2 - … This section presents the complete IoT-based architecture for smart farming [20,21]. The proposed architecture consists of four layers: farm perception layer, sensors and actuators layer, … ER - TY - Article T1 - A secure user authentication and key-agreement scheme using wireless sensor networks for agriculture monitoring A1 - Ali, R Y1 - 2018/// KW - AVISPA KW - Agriculture monitoring KW - Random oracle model KW - Wireless sensor networks JF - Future Generation Computer Systems VL - 84 SP - 200 EP - 215 DO - 10.1016/j.future.2017.06.018 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0167739X17303862 UR - https://api.elsevier.com/content/abstract/scopus_id/85028085660 N1 - Cited By (since 2018): 89 N2 - Agriculture is the backbone of our economic system and plays an important role in the life of an economy. It does not only provide raw material and food, but also provides large employment opportunities. Therefore, agriculture requires modern technology for increasing the productivity. In this context, wireless sensor networks (WSNs) could be utilized for monitoring the climatic parameters such as (temperature, humidity, light, carbon dioxide, soil moisture, acidity etc.) in an agriculture field. The climatic parameters are very important in terms of growth, quality and productivity of crops. But, any kind of interception, modification, insertion, and deletion on these parameters can have negative effect on crop. Therefore, security and privacy are important issues in agriculture field. In this regard, we design a novel remote user authentication scheme using wireless sensor networks for agriculture monitoring. The protocol is validated through Burrows–Abadi–Needham (BAN) logic and also simulated using Automated Validation Information Security Protocols and Applications (AVISPA) tool. We formally analyze the security of the scheme using random oracle model. In addition, the informal security analysis shows that the proposed protocol is secure and resists various kinds of malicious attacks. As a results, the proposed protocol is applicable in a real life application. ER - TY - Article T1 - Wireless Underground Sensor Networks Path Loss Model for Precision Agriculture (WUSN-PLM) A1 - Sambo, D Wohwe A1 - Forster, Anna A1 - Yenke, Blaise Omer A1 - Sarr, Idrissa A1 - Gueye, Bamba Y1 - 2020/// KW - Wireless underground sensor networks KW - complex dielectric constant KW - path loss model KW - precision agriculture JF - IEEE Sensors Journal VL - 20 IS - 10 SP - 5298 EP - 5313 DO - 10.1109/JSEN.2020.2968351 UR - https://api.elsevier.com/content/abstract/scopus_id/85083845708 N1 - Cited By (since 2020): 29 N2 - Despite a large number of applications in the field of health care, military, ecology or agriculture, the Wireless Underground Sensor Network (WUSN) faces the problem of wireless Underground Communication (WUC) which largely attenuate the signal on the ground. For the case of precision agriculture, the motes are buried and they have to check the good growth of plants by verifying data like the water content. However, due to soil composition, the wave signal is attenuated as it travels across the ground. Thus, before a real deployment of WUSN, the prediction of the path loss due to signal attenuation underground is an important asset for the good network functioning. In this paper, we proposed a WUSN path loss for precision agriculture called WUSN-PLM. To achieve it, the proposed model is based on an accurate prediction of the Complex Dielectric Constant (CDC). WUSN-PLM allows evaluating the path loss according to the different types of communication (Underground-to-Underground, Underground to Aboveground and Aboveground to Underground). On each communication type, WUSN-PLM takes into account reflective and refractive wave attenuation according to the sensor node burial depth. To evaluate WUSN-PLM, intensive measurements on real sensor nodes with two different pairs of transceivers have been conducted on the botanic garden of the University Cheikh Anta Diop in Senegal. The results show that the proposed model outperforms the existing path loss models in different communication types. The results show that our proposed approach can be used on real cheap sensor with 87.13% precision and 85% balanced accuracy. ER - TY - Book Chapter T1 - Precision Agriculture Using Advanced Technology of IoT, Unmanned Aerial Vehicle, Augmented Reality, and Machine Learning A1 - Ponnusamy, V Y1 - 2021/// KW - Augmented reality KW - Data analytics KW - Drone KW - Internet of things KW - Machine learning KW - Precision agriculture KW - Smart farming KW - Virtual reality KW - unmanned aerial vehicles JF - Internet of Things SP - 207 EP - 229 SN - 2199-1073 DO - 10.1007/978-3-030-52624-5_14 UR - https://api.elsevier.com/content/abstract/scopus_id/85101151997 N1 - Cited By (since 2021): 6 N2 - Agriculture is one of the primary processes for quality food production in the globe. Unfortunately, the productivity of agriculture is very low, and many factors affect the yield level of it. Precision agriculture (PA) is one of the solutions for the above problem. PA uses site-specific crop management concept based on measured data using sensors and data analytics to find the root cause of yield reduction. Precision agriculture automates farming which involves the collection of data and analysis of them for better decision-making to gain high yield and quality of the agricultural product. The agriculture system integrated with data analytics and machine learning is called as smart farming or smart agriculture The goal of smart agriculture is to develop a decision-making support system for farming management. The precision smart agriculture can be enhanced with the help of latest technologies of Internet of Technology (IoT), unmanned aerial vehicle (UAV), augmented reality (AR) system, and machine learning (ML) algorithms. This chapter focuses on the illustration and utilization of those advanced technologies for smart farming. ER - TY - Conference Paper T1 - Low cost IoT solutions for agricultures fish farmers in Afirca: A case study from Burkina Faso A1 - Zougmore, T W Y1 - 2018/// KW - Internet of Things KW - LoRa KW - cloud platform JF - ICSCC 2018 - 1st International Conference on Smart Cities and Communities DO - 10.1109/SCCIC.2018.8584549 UR - https://api.elsevier.com/content/abstract/scopus_id/85061068045 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) LowcostIoTsolutionsforagriculturesfishfarmersinAfirca_acasestudyfromBurkinaFaso.pdf N1 - Cited By (since 2018): 10 N2 - In this paper, we share our experience of the deployment of a sensor network. This sensor network was deployed at the Aquaculture and Aquatic Biodiversity Research Unit (UR-ABAQ) at NAZI BONI University. The deployed sensors - permanently monitor the pH, dissolved oxygen and water temperature of a clarias (a fish species) hatchery - measure the soil moisture of banana and papaya fields - and finally measure the meteorological parameters (wind speed, air humidity, rainfall, sunshine ...) of the laboratory site. The hatchery parameters collected make it possible to control the mortality of fry (sending of SMS alerts, twitter, facebook in case of exceeding the threshold of certain parameters). The soil moisture collected will optimize the watering of the fields by the water rich in fertilizers produced by the fish of the aquacultural ponds. Finally, the weather parameters will make it possible to determine the correlations between the weather parameters and the production of the banana and papaya fields. The data collected is sent on a cloud platform through a gateway equipped with a LoRa antenna and a 3G MoDem. The sensors communicate with a radio wave to the gateway using also LoRa technology. These energy-efficient sensors are equipped with a solar power supply and a LoRa radio antenna that can transmit 15 km in rural areas ER - TY - Article T1 - IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot A1 - Brewster, C A1 - Roussaki, Oanna A1 - Kalatzis, Nikos A1 - Doolin, Kevin A1 - Ellis, Keith Y1 - 2017/// KW - Cloudd KW - Internet of Things KW - agriculture KW - food JF - IEEE Communications Magazine VL - 55 IS - 9 SP - 26 EP - 33 DO - 10.1109/MCOM.2017.1600528 UR - https://api.elsevier.com/content/abstract/scopus_id/85029605450 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) IoT in Agriculture designing a earoupe wide large scale pilot.pdf N1 - Cited By (since 2017): 157 N2 - The technologies associated with the Internet of Things have great potential for application in the domain of food and agriculture, especially in view of the societal and environmental challenges faced by this sector. From farm to fork, IoT technologies could transform the sector, contributing to food safety, and the reduction of agricultural inputs and food waste. A major step toward greater uptake of these technologies will be the execution of IoT-based large-scale pilots (LSPs) in the entire supply chain. This article outlines the challenges and constraints that an LSP deployment of IoT in this domain must consider. Sectoral and technological challenges are described in order to identify a set of technological and agrifood requirements. An architecture based on a system of systems approach is briefly presented, the importance of addressing the interoperability challenges faced by this sector is highlighted, and we elaborate on requirements for new business models, security, privacy, and data governance. A description of the technologies and solutions involved in designing pilots for four agrifood domains (dairy, fruit, arable, meat and vegetable supply chain) is eventually provided. In conclusion, it is noted that for IoT to be successful in this domain, a significant change of culture is needed. ER - TY - Article T1 - RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture A1 - Abouzar, P Y1 - 2016/// KW - Precision agriculture KW - Wireless sensor networks KW - distributed localization KW - information aggregation KW - path loss model KW - range-based localization algorithms JF - IEEE Transactions on Wireless Communications VL - 15 IS - 10 SP - 6638 EP - 6650 DO - 10.1109/TWC.2016.2586844 UR - https://api.elsevier.com/content/abstract/scopus_id/84994530185 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) RSSI-Based Distributed Self-Localization for wsn in pa.pdf N1 - Cited By (since 2016): 107 N2 - Node localization algorithms that can be easily integrated into deployed wireless sensor networks (WSNs) and which run seamlessly with proprietary lower layer communication protocols running on off-the-shelf modules can help operators of large farms and orchards avoid the difficulty, cost and/or time involved with manual or satellite-based node localization techniques. Even though the state-of-the-art node localization algorithms can achieve low error rates using distributed techniques such as belief propagation (BP), they are not well suited to WSNs deployed for precision agriculture applications with large number of nodes, few number of landmarks and lack real time update capability. The algorithm proposed here is designed for applications such as pest control and irrigation in large farms and orchards where greater power efficiency and scalability are required but location accuracy requirements are less demanding. Our algorithm uses received signal strength indicator (RSSI) values to estimate the distribution of distance between nodes then updates the location probability mass function (pmf) of nodes in a distributed manner. At every time step, the most recently communicated path loss samples and location prior pmf received from neighbouring nodes is sufficient for nodes with unknown location to update their location pmf. This renders the algorithm recursive, hence results in lower computational complexity at each time step. We propose a particular realization of the method in which only one node multicasts at each time step and neighbouring nodes update their location pmf conditioned on all communicated samples over previous time steps. This is highly compatible with realistic WSN deployments, e.g., ZigBee which are based upon the ad hoc on-demand distance vector (AODV) where nodes flood route request (RREQ) and route reply (RREP) packets. Further, beacon signals transmitted during the network formation and routing table formulation stage can provide the RSSI information required by the localization algorithm. ER - TY - Article T1 - Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture A1 - Pincheira, M Y1 - 2021/// KW - Blockchain KW - Internet of things KW - Precision irrigation KW - Smart contracts KW - Water management JF - Computers and Electronics in Agriculture VL - 180 DO - 10.1016/j.compag.2020.105889 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169920330945 UR - https://api.elsevier.com/content/abstract/scopus_id/85096820172 N1 - Cited By (since 2021): 21 N2 - This paper explores how the energy-efficient integration of IoT-based sensing and blockchains, an innovation in the field of Digital Infrastructure technologies, can be used to incentivize virtuous behaviors in agricultural practices. The novelty of the study lies specifically in the unprecedented use of constrained sensing devices as trustworthy data sources for a permissionless blockchain. Furthermore, we show how our research results, advancing the State-of-the-Art in the IoT and blockchain interactions, can support the interests of a diverse set of water management stakeholders in a concrete use-case implementation. To assess our contribution and validate our results we use a system architecture comprising constrained IoT devices for measuring water consumption used as direct data-source actors, a public blockchain infrastructure, and smart contracts that represent the interests of different water management stakeholders and regulate the distribution of incentives amongst virtuous farmers. Further validation on the usability of our results is obtained through the real implementation of a complete use case featuring the Ethereum network as a public blockchain and where six different types of IoT platforms are individually assessed for impact on the IoT devices, in terms of energy, processing time, and available memory. The findings show how solutions based on the proposed architecture can be implemented with only 6% of additional energy budget compared to the normal operations of the IoT devices. Besides showing new means to energy-efficiently integrate IoT data sources in a permissionless blockchain, the validation results make our contribution a strong candidate for use in automated and incentive-based irrigation water management systems as well as a key component in fostering increased sustainability of the whole agricultural sector. ER - TY - Conference Paper T1 - Smart IoT Monitoring System for Agriculture with Predictive Analysis A1 - Araby, A A Y1 - 2019/// KW - Internet of Things KW - Machine Learning KW - Message Queuing Telemetry Transport KW - Precision Agriculture JF - 2019 8th International Conference on Modern Circuits and Systems Technologies, MOCAST 2019 DO - 10.1109/MOCAST.2019.8741794 UR - https://api.elsevier.com/content/abstract/scopus_id/85068562766 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Smart IoT Monitoring System for Agriculture with Predictive Analysis.pdf N1 - Cited By (since 2019): 24 N2 - The Internet of Things (IoT) technology has the means to shape the future of many industries. Data is the language of the communication between different nodes through the network; the networks are the communication channel. The cloud is the home and destination of the data which adds intelligence through data analytics software, Precision agriculture uses the IoT features to help in managing crops production, by optimizing the quality of the crops through applying required nutrients and reduce the harmful impacts on the environment due to the application of excess pesticides. In this paper, we deployed a sensing network to gather the field data of some crops (Potatoes, Tomatoes, etc.), then fed these data to a machine learning algorithm to get a warning message finally displaying both the data and the warning message through a Graphical User Interface (GUI). ER - TY - Conference Paper T1 - A novel and smart IoT system for real time agriculture applications with IaaS cloud computing A1 - Thalluri, L N A1 - Ayodhya, Jitendra Prasad A1 - Prasad, T B Anjaneya A1 - Raju, CH Yuva A1 - Vadlamudi, SaiDivya A1 - Babu, P Bose Y1 - 2020/// KW - Internet of Things KW - cloud computing KW - communication modules KW - sensors JF - 2020 International Conference on Computer Communication and Informatics, ICCCI 2020 DO - 10.1109/ICCCI48352.2020.9104160 UR - https://api.elsevier.com/content/abstract/scopus_id/85087045355 N1 - Cited By (since 2020): 4 N2 - In this paper, we have designed an IoT system for real time agriculture application with IaaS cloud architecture. Overall system design involves, preparation of six Wi-Fi enabled sensor nodes, design of server with In this agriculture sector, to improve the database including dedicated IP, for visualization we have designed an application with data processing ability, finally all section's are interfaced to create eventual agriculture IoT system. In the first phase, we have designed six Wi-Fi enabled sensor node's each node is connected with five sensor's i.e., temperature, humidity, pH, pressure and flow sensors. Each sensor is connected to ARM11 processor with 1GHz clock speed. In second phase, created server with predefined database. Database is generalized as a organizing the collection of data. This database which is created, only authorized persons are allowed to access the database. Database can be created by using the MySQL which is an oracle backed open source relational database The database management system(RDBMS). Database is mainly used for storing the corresponding output values which are obtained from the sensors and analyze the data for day by day. Server is a networked computer that can be run a database. Here the sever is a web server which is allocated with public IP (i.e., internet protocol) which website can host. IP is mainly two types public IP and private IP, here we are using public IP which can be accessed over the internet. Public IP is generally having unique address that can be allocated to a computing device. Web application is designed by using HTML (hypertext markup language) programming for creating web pages with text, images. PHP (hypertext preprocessor) it will be embedded in a HTML code and it can supported in a webserver. Here, using protocols are HTTP(hypertext transfer protocol) protocol it provides communication to the world wide web. ER - TY - Article T1 - An effective novel IOT framework for water irrigation system in smart precision agriculture A1 - Suresh, P Y1 - 2019/// KW - Internet of Things KW - Precision Agriculture KW - Sensors KW - Smart Water management KW - cloud JF - International Journal of Innovative Technology and Exploring Engineering VL - 8 IS - 6 SP - 558 EP - 564 UR - https://api.elsevier.com/content/abstract/scopus_id/85067079750 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) An Effective Novel IOT Framework For Water Irrigation Systems in Smart Precision Agriculture.pdf N1 - Cited By (since 2019): 7 N2 - In order to accommodate growing population, the call for more food will increase and new strategies should be designed to create more reliable agricultural production strategies. There is a need to create new agricultural manufacturing methods with the smarter water management point on productiveness and rational usage of environmental assets. Considering the need to collect new records about the rural cycle, that is the set of occasions happening at crop all through its lifetime, the scientific community started exploring new era that might be applied to fulfil the necessities of Precision Agriculture. The continuous research on inexpensive, smaller, extra energy-green community nodes will result in the need of Internet of Things. Water irrigation control is one of the typical usages of computer systems in Agriculture. In this paper, an effective novel IoT framework for water irrigation system is designed and evaluated. It is justified that the proposed methodology yields good results. ER - TY - Article T1 - A framework for wireless sensor networks management for precision viticulture and agriculture based on IEEE 1451 standard A1 - Fernandes, M A Y1 - 2013/// KW - Gateway KW - IEEE 1451 KW - IEEE 802.15.4 KW - Precision agriculture KW - Precision viticulture KW - Wireless sensor networks JF - Computers and Electronics in Agriculture VL - 95 SP - 19 EP - 30 DO - 10.1016/j.compag.2013.04.001 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169913000756 UR - https://api.elsevier.com/content/abstract/scopus_id/84877013801 N1 - Cited By (since 2013): 29 N2 - Precision viticulture (PV) and precision agriculture (PA) requires the acquisition and processing of a vast collection of data coming typically from large scale and heterogeneous sensor networks. Unfortunately, sensor integration is far from being simple due to the number of incompatible network specifications and platforms. The adoption of a common, standard communication interface would allow the engineer to abstract the relation between the sensor and the network. This would reduce the development efforts and emerge as an important step towards the adoption of “plug-and-play” technology in PA/PV sensor networks. This paper explores this need and introduces a framework for smart data acquisition in PA/PV that relies on the IEEE 1451 family of standards, which addresses the transducer-to-network interoperability issues. The framework includes a ZigBee end device (sMPWiNodeZ), as an IEEE 1451 WTIM (Wireless Transducer Interface Module), and an IEEE 1451 NCAP (Network Capable Application Processor) that acts as gateway to an information service provider and WSN (Wireless Sensor Network) coordinator. The paper discusses the proposed IEEE 1451 system architecture and its benefits in PA/PV and closes with results/lessons learned from in-field trials towards smarter WSN. ER - TY - Article T1 - A survey on wireless sensor network infrastructure for agriculture A1 - Yu, X Y1 - 2013/// KW - Agriculture KW - Hybrid wireless sensor network KW - Information collection KW - Monitoring KW - wireless underground sensor networks JF - Computer Standards and Interfaces VL - 35 IS - 1 SP - 59 EP - 64 DO - 10.1016/j.csi.2012.05.001 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0920548912000608 UR - https://api.elsevier.com/content/abstract/scopus_id/84867885452 N1 - Cited By (since 2013): 81 N2 - The hybrid wireless sensor network is a promising application of wireless sensor networking techniques. The main difference between a hybrid WSN and a terrestrial wireless sensor network is the wireless underground sensor network, which communicates in the soil. In this paper, a hybrid wireless sensor network architecture is introduced. The framework to deploy and operate a hybrid WSN is developed. Experiments were conducted using a soil that was 50% sand, 35% silt, and 15% clay; it had a bulk density of 1.5g/cm^3 and a specific density of 2.6cm^-^3. The experiment was conducted for several soil moistures (5, 10, 15, 20 and 25%) and three signal frequencies (433, 868 and 915MHz). The results show that the radio signal path loss is smallest for low frequency signals and low moisture soils. Furthermore, the node deployment depth affected signal attenuation for the 433MHz signal. The best node deployment depth for effective transmission in a wireless underground sensor network was determined. ER - TY - Review T1 - Applicability of Wireless Sensor Networks in Precision Agriculture: A Review A1 - Thakur, D Y1 - 2019/// KW - Irrigation KW - Monitoring KW - Precision agriculture KW - Sensors KW - Wireless sensor networks JF - Wireless Personal Communications SN - 0929-6212 DO - 10.1007/s11277-019-06285-2 UR - https://api.elsevier.com/content/abstract/scopus_id/85064452588 N1 - Cited By (since 2019): 49 N2 - Presently, wireless sensor network (WSN) plays important role in engineering, science, agriculture and many other field like surveillance, military applications, smart cars etc. Precision agriculture (PA) is one of the field in which WSN is widely adopted. The aim of the adoption of WSNs in PA is to measure the different environmental parameters such as humidity, temperature, soil moisture, PH value of soil etc., for enhancing the quantity and quality of crops. Further, the WSNs are also helped to reduce the consumptions of the natural resources used in farming. Hence, the aim of this review is to identify the various WSNs technologies adopted for precision agriculture and impact of these technologies to achieve smart agriculture. This review also focuses on the different environmental parameters like irrigation, monitoring, soil properties, temperature for achieving precision agriculture. Further, a detailed study is also carried out on different crops which are covered using WSNs technologies. This review also highlights on the different communication technologies and sensors available for PA. To analyze the impact of the WSNs in agriculture field, several research questions are designed and through this review, we are tried to find the solutions of these research questions. ER - TY - Conference Paper T1 - IoT-Based Agriculture Monitoring System A1 - Raviteja, K Y1 - 2020/// KW - Android KW - DC motor KW - Internet of things KW - NodeMCU KW - Soil moisture sensor KW - Temperature sensors JF - Advances in Intelligent Systems and Computing VL - 1079 SP - 473 EP - 483 SN - 2194-5357 DO - 10.1007/978-981-15-1097-7_40 UR - https://api.elsevier.com/content/abstract/scopus_id/85078437158 N1 - Cited By (since 2020): 5 N2 - Internet of things (IoT) plays a crucial role in smart agriculture. Smart farming is an emerging concept, as IoT sensors are capable of providing information about their agriculture fields. Our focus is to provide farmers with an IoT-based Web application for monitoring the agriculture fields and its conditions. With the arrival of open supply NodeMCU boards beside low-cost wet sensors, it is viable to make devices that monitor the temperature/humidity sensor and soil wet content associated, consequently irrigating the fields or the landscape as and when required. The projected system makes use of microcontroller NodeMCU platform and IoT that alert farmers to remotely monitor the standing of sprinklers placed on the agricultural farms by knowing the sensing element values, thereby making the farmers’ work a lot of easier when considering different farm-related activities. ER - TY - Article T1 - Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture A1 - Alghazzawi, D A1 - BAMASAQ, OMAIMA A1 - BHATIA, SURBHI A1 - KUMAR, ANKIT A1 - DADHEECH, PANKAJ A1 - ALBESHRI, AIIAD Y1 - 2021/// KW - Internet of Things KW - agricultural engineering KW - gas sensors KW - pH measurement KW - telecommunication congestion control KW - wireless sensor networks JF - IEEE Access VL - 9 SP - 151401 EP - 151420 DO - 10.1109/ACCESS.2021.3124791 UR - https://api.elsevier.com/content/abstract/scopus_id/85118613456 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture.pdf N1 - Cited By (since 2021): 2 N2 - Wireless sensor networking is being used extensively in agricultural activities to increase productivity and reduce losses in various ways. The greenhouse simplifies the concept of planting, which has several benefits in agriculture. In agricultural models, soil pH sensors and gas sensors are commonly used. These sensors are applicable in various Internet of Things (IoT) integrated agricultural activities. The paper discusses the hardware design and working of the proposed model. In addition, various agricultural models used for evapotranspiration are also explained. The key factors such as congestion control are evaluated using the Penman-Monteith equation. This paper focuses on implementing more than two references parameters like evapotranspiration and humidity under different conditions, which aids in splitting the relationship evenly by the number of sources. Furthermore, the paper shows the implementation done with MATLAB and values are adjusted using the code. The paper claims to achieve similar variations with the same source value, validating the proposed model’s efficiency and fairness. In an optimal region, these schemes also demonstrate higher throughput and lower delay rates. The improved packet propagation through the IoT network is demonstrated using visualization tools, and the feedback is computed to determine the overall access amount (A1 + A2) obtained. The experimental results show that the propagation rate is 1.24, more significant than the link capacity value. The claims are verified by showing the improved congestion control as it outperforms different parameters, considering an additive increase condition by 0.3% and multiplicative decrease condition by 1.2 %. ER - TY - Book Chapter T1 - Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors A1 - Corwin, D L Y1 - 2019/// KW - Airborne sensor KW - Apparent soil electrical conductivity KW - Electrical resistivity KW - Electromagnetic induction KW - Proximal sensor KW - Remote sensing KW - Response surface sampling KW - Satellite sensors KW - Soil salinity mapping KW - Soil spatial variability JF - Advances in Agronomy VL - 158 SP - 1 EP - 130 SN - 0065-2113 DO - 10.1016/bs.agron.2019.07.001 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0065211319300690 UR - https://api.elsevier.com/content/abstract/scopus_id/85072202232 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Review of soil salinity assessment for agriculture across multiple.pdf N1 - Cited By (since 2019): 35 N2 - Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyper-spectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment. ER - TY - Article T1 - IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry A1 - Misra, N N Y1 - 2022/// KW - blockchain KW - digital KW - gene KW - internet KW - precision agriculture KW - robotics KW - sensors KW - sequencing KW - social media JF - IEEE Internet of Things Journal VL - 9 IS - 9 SP - 6305 EP - 6324 DO - 10.1109/JIOT.2020.2998584 UR - https://api.elsevier.com/content/abstract/scopus_id/85129568445 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2022) IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry.pdf N1 - Cited By (since 2022): 47 N2 - Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supply- chain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes. ER - TY - Review T1 - Ion-specific nutrient management in closed systems: The necessity for ion-selective sensors in terrestrial and space-based agriculture and water management systems A1 - Bamsey, M Y1 - 2012/// KW - bioregenerative life support KW - hydroponics KW - inorganic ion monitoring KW - ion-selective sensors KW - space exploration KW - water quality JF - Sensors (Switzerland) VL - 12 IS - 10 SP - 13349 EP - 13392 SN - 1424-8220 DO - 10.3390/s121013349 UR - https://api.elsevier.com/content/abstract/scopus_id/84868248700 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2012) Ion-Specific Nutrient Management in Closed Systems The Necessity for Ion-Selective Sensors in Terrestrial and Space-Based Agriculture and Water Management Systems.pdf N1 - Cited By (since 2012): 50 N2 - The ability to monitor and control plant nutrient ions in fertigation solutions, on an ion-specific basis, is critical to the future of controlled environment agriculture crop production, be it in traditional terrestrial settings (e.g., greenhouse crop production) or as a component of bioregenerative life support systems for long duration space exploration. Several technologies are currently available that can provide the required measurement of ion-specific activities in solution. The greenhouse sector has invested in research examining the potential of a number of these technologies to meet the industry’s demanding requirements, and although no ideal solution yet exists for on-line measurement, growers do utilize technologies such as high-performance liquid chromatography to provide off-line measurements. An analogous situation exists on the International Space Station where, technological solutions are sought, but currently on-orbit water quality monitoring is considerably restricted. This paper examines the specific advantages that on-line ion-selective sensors could provide to plant production systems both terrestrially and when utilized in space-based biological life support systems and how similar technologies could be applied to nominal on-orbit water quality monitoring. A historical development and technical review of the various ion-selective monitoring technologies is provided. ER - TY - Conference Paper T1 - Conversational user interface integration in controlling IoT devices applied to smart agriculture: Analysis of a chatbot system design A1 - Symeonaki, E Y1 - 2020/// KW - Chatbots KW - Conversational User Interfaces KW - Dialog systems KW - Interaction systems KW - Internet of Things KW - Natural language processing KW - Smart Agriculture JF - Advances in Intelligent Systems and Computing VL - 1037 SP - 1071 EP - 1088 SN - 2194-5357 DO - 10.1007/978-3-030-29516-5_80 UR - https://api.elsevier.com/content/abstract/scopus_id/85072844543 N1 - Cited By (since 2020): 2 N2 - Smart Agriculture is a considerably novel approach which attempts to encounter the climate change challenges as well as the increased global nutritional needs through the adoption of automated and ICT directed innovative technologies. Since the latest solutions for Smart Agriculture employ cloud services and advanced interconnectivity methods which consolidate the Internet of Things (IoT) technology features along with the ubiquity and mobility attributes granted by smart devices, the necessity of extreme customization, enabling the remote interaction of the objects, is creating the need for sophisticated interfaces. Moreover, in order to achieve the maximum possible penetration of IoT technologies in the agricultural sector it is essential to establish interaction methods among users, applications and systems through interfaces which are simple and friendly in end-usage. Herewith, in this paper an attempt is made to encounter this issue through the integration of Conversational User Interfaces (CUI) in controlling IoT devices for Smart Agriculture. The proposed approach introduces the design and indicative usage of a chatbot system which employs a messenger service platform environment in natural language so as to provide an efficient, secure and user-friendly framework of interaction with the IoT devices deployed for agricultural purposes. ER - TY - Conference Paper T1 - BIoT: Blockchain based IoT for Agriculture A1 - Umamaheswari, S Y1 - 2019/// KW - Internet of things KW - agriculture KW - blockchain KW - smart contracts JF - Proceedings of the 11th International Conference on Advanced Computing, ICoAC 2019 SP - 324 EP - 327 DO - 10.1109/ICoAC48765.2019.246860 UR - https://api.elsevier.com/content/abstract/scopus_id/85086260304 N1 - Cited By (since 2019): 20 N2 - Blockchain's most basic promise for the agriculture industry is that it removes the need for third parties otherwise required to ensure trust within buyer-seller relationships, or for that matter any source-destination relationship. In an environment enabled by blockchain technology, transactions become peer-to-peer with no use for intermediaries.Apart from providing the means to transact peer-to-peer, blockchain can create `smart contracts' that execute the terms of any agreement when specified conditions are met. Every time value changes hands, whether physical products, services or money, the transaction can be documented, creating a permanent history of the product or transaction, from source to ultimate destination. Blockchain can be of great help in this sector. A transparent and trusted system can be built by putting all the information about agricultural events on a blockchain. Farmers can also get instant data related to the seed quality, climate environment related data, payments, soil moisture, demand and sale price, etc. all on a single platform.The intent of this project is to store the sensor data in a blockchain and build a smart contract deployed in the Ethereum blockchain to facilitate buying and selling of crops and land. ER - TY - Conference Paper T1 - Blend of Cloud and Internet of Things (IoT) in agriculture sector using lightweight protocol A1 - Raikar, M Y1 - 2018/// KW - Internet of Things KW - agriculture KW - artificial intelligence KW - cloud computing KW - data analysis KW - irrigation KW - telemetry JF - 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018 SP - 185 EP - 190 DO - 10.1109/ICACCI.2018.8554406 UR - https://api.elsevier.com/content/abstract/scopus_id/85060063164 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Blend of Cloud and Internet of Things (IoT) in agriculture sector using lightweight protocol.pdf N1 - Cited By (since 2018): 10 N2 - The emergence of the disruptive technologies such as cloud computing, IoT (Internet of Things), machine learning and data analytics has made its mark in different sectors like transportation, agriculture, healthcare, environment monitoring, renewable energy systems, retail, and industry. The IoT provides connectivity of things dynamically to the network whereas cloud computing provides virtualization in storage and processing. In CloudIoT paradigm, cloud and IoT are merged together to provide complementary features in smart applications/services. CloudIoT solutions make it possible to envisage ubiquitous and pervasive connectivity to the users. The source of livelihood for a majority of the population is agriculture; it mantles a dominant role in the economy of the country. The proposed work focuses on CloudIoT architecture for providing any smart solutions in different sectors. A case study of a smart irrigation system is discussed in the paper. A smart irrigation system is developed using the lightweight protocol, MQTT (Message Queue Telemetry Transport). MQTT protocol is 22% more energy efficient and 15% faster when compared with other protocols. The temperature and soil moisture data are collected and managed by Amazon cloud. The data analysis is performed using the Weka (Waikato Environment for Knowledge Analysis) tool. The cost-effective solution is demonstrated and results speak the strength and performance of the system. ER - TY - Conference Paper T1 - Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning A1 - Garg, S A1 - Pundir, Pradyumn A1 - Jindal, Himanshu A1 - Saini, Hemraj A1 - Garg, Somya Y1 - 2021/// KW - Internet of Things KW - Machine learning KW - Multimodal system KW - Pre-Trained CNN KW - Precision Agriculture JF - 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 DO - 10.1109/ICCCNT51525.2021.9579646 UR - https://api.elsevier.com/content/abstract/scopus_id/85124730795 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning.pdf N1 - Cited By (since 2021): 3 N2 - Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The automation requires the assortment of information given by the sensors such as soil, water, light, humidity, temperature for additional information to furnish the operator with exact data to acquire excellent yield to farmers. In this work, a study is proposed that incorporates all common state-of-the-art approaches for precision agriculture use. Technologies like the Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. The data collection using IoT is responsible for the measure of moisture levels for smart irrigation, n, p, k estimations of fertilizers for best yield development. For crop damage prediction, various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not ER - TY - Conference Paper T1 - Web based service to monitor automatic irrigation system for the agriculture field using sensors A1 - Rani, M Usha Y1 - 2014/// KW - Arduino KW - GSM KW - Grove Moisture Sensor KW - Water flow sensor KW - Web portal JF - 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014 DO - 10.1109/ICAEE.2014.6838569 UR - https://api.elsevier.com/content/abstract/scopus_id/84904162542 N1 - Cited By (since 2014): 25 N2 - The paper describes the automatic irrigation system using the Arduino microcontroller with grove moisture sensor and water flow sensor. The communication will be established using the Zigbee protocol and the control will be sent based on the moisture level of the soil using Arduino microcontroller. The two xbee radios's used in the network will be treated as master and slave in combination with the Arduino microcontroller. Here when a particular moisture level is reached, depending on the value of the moisture level water flow will be allowed in the pipe and the flow range, water pressure will be updated along with the time in a database and also displayed in the web portal. The owner of the agricultural field can anytime check the moisture level and the motor status. The motor's functionality status will also be a sent to the farmer's mobile using GSM. ER - TY - Article T1 - The role of RFID in agriculture: Applications, limitations and challenges A1 - Ruiz-Garcia, L A1 - Lunadei, Loredana Y1 - 2011/// KW - Animal identification KW - Cold chain KW - Food traceability KW - Precision agriculture KW - Radio frequency identification JF - Computers and Electronics in Agriculture VL - 79 IS - 1 SP - 42 EP - 50 DO - 10.1016/j.compag.2011.08.010 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169911001876 UR - https://api.elsevier.com/content/abstract/scopus_id/80052703535 N1 - Cited By (since 2011): 238 N2 - The recent advances in RFID offer vast opportunities for research, development and innovation in agriculture. The aim of this paper is to give readers a comprehensive view of current applications and new possibilities, but also explain the limitations and challenges of this technology. RFID has been used for years in animal identification and tracking, being a common practice in many farms. Also it has been used in the food chain for traceability control. The implementation of sensors in tags, make possible to monitor the cold chain of perishable food products and the development of new applications in fields like environmental monitoring, irrigation, specialty crops and farm machinery. However, it is not all advantages. There are also challenges and limitations that should be faced in the next years. The operation in harsh environments, with dirt, extreme temperatures; the huge volume of data that are difficult to manage; the need of longer reading ranges, due to the reduction of signal strength due to propagation in crop canopy; the behavior of the different frequencies, understanding what is the right one for each application; the diversity of the standards and the level of granularity are some of them. ER - TY - Conference Paper T1 - Design of intelligent agriculture management information system based on IoT A1 - Yan-E, D Y1 - 2011/// KW - Agriculture MIS KW - Internet of Things KW - Radio frequency identification KW - wireless sensor networks JF - Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 VL - 1 SP - 1045 EP - 1049 DO - 10.1109/ICICTA.2011.262 UR - https://api.elsevier.com/content/abstract/scopus_id/79955994400 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2011) Design of intelligent agriculture management information system based on IoT.pdf N1 - Cited By (since 2011): 100 N2 - Agricultural information technology (AIT) has been broadly applied to every aspect of agriculture and has become the most effective means & tools for enhancing agricultural productivity and for making use of full agricultural resources. As an important sub-technology of AIT, the using of technology of Agriculture Information Management directly affects the degree of agricultural informatization and efficiency of agricultural production's decision. In this paper, on the basis of introducing the concept of agricultural information management and analyzing the features of Agricultural data, the designing method and architecture of Intelligent Agriculture MIS was discussed in detail, finally, this paper gives an implementation example of system in agricultural production. ER - TY - Review T1 - Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture A1 - Khanna, A Y1 - 2019/// KW - Internet of Things KW - Precision agriculture KW - Radio frequency KW - Radio frequency identification KW - Smart agriculture KW - Wireless communication protocol KW - Wireless network infrastructure KW - frequency Identification JF - Computers and Electronics in Agriculture VL - 157 SP - 218 EP - 231 SN - 0168-1699 DO - 10.1016/j.compag.2018.12.039 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169918316417 UR - https://api.elsevier.com/content/abstract/scopus_id/85059458348 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Evolution of Internet of Things (IoT) and its significant impact in the field of PA.pdf N1 - Cited By (since 2019): 241 N2 - During recent years, one of the most familiar name scaling new heights and creating a benchmark is Internet of Things (IoT). It is indeed the future of communication that has transformed Things (Objects) of the real world into smarter devices. The functional aspect of IoT is to unite every object of the world in such a manner that humans have the ability to control them via Internet. Furthermore, these objects also provide regular as well as timely updates on their current status to its end user. Although IoT concepts were proposed a couple of years ago, it may not be incorrect to quote that this term has become a benchmark for establishing communication among objects. In context to the present standings of IoT, identification of the most prominent applications in the field of IoT have been highlighted and a comprehensive review has been done specifically in the field of Precision Agriculture. This article evaluates contributions made by various researchers and academicians over the past few years. Furthermore, existing challenges faced while performing agricultural activities have been highlighted along with future research directions to equip novel researchers of this domain to assess the current standings of IoT and to further improve upon them with more inspiring and innovative ideas. ER - TY - Article T1 - Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas A1 - Ahmed, N A1 - De, Debashis A1 - Hussain, Md. Iftekhar Y1 - 2018/// KW - Cloud computing KW - Fog computing KW - Internet of Things KW - Long distance network KW - Smart Agriculture KW - Wi-Fi JF - IEEE Internet of Things Journal VL - 5 IS - 6 SP - 4890 EP - 4899 DO - 10.1109/JIOT.2018.2879579 UR - https://api.elsevier.com/content/abstract/scopus_id/85056186063 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas.pdf N1 - Cited By (since 2018): 204 N2 - Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of Fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT based agriculture and farming solutions, the propose solution reduces network latency up to a certain extent. In this, a cross layer based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency ER - TY - Review T1 - Applications of smartphone-based sensors in agriculture: A systematic review of research A1 - Pongnumkul, S Y1 - 2015/// KW - Agriculture KW - Applications KW - Sensors KW - Smartphone JF - Journal of Sensors VL - 2015 SN - 1687-725X DO - 10.1155/2015/195308 UR - https://api.elsevier.com/content/abstract/scopus_id/84939174977 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2015) Applications of Smartphone-Based Sensors in Agriculture.pdf N1 - Cited By (since 2015): 105 N2 - Smartphones have become a useful tool in agriculture because their mobility matches the nature of farming, the cost of the device is highly accessible, and their computing power allows a variety of practical applications to be created. Moreover, smartphones are nowadays equipped with various types of physical sensors which make them a promising tool to assist diverse farming tasks. This paper systematically reviews smartphone applications mentioned in research literature that utilize smartphone built-in sensors to provide agricultural solutions. The initial 1,500 articles identified through database search were screened based on exclusion criteria and then reviewed thoroughly in full text, resulting in 22 articles included in this review. The applications are categorized according to their agricultural functions. Those articles reviewed describe 12 farming applications, 6 farm management applications, 3 information system applications, and 4 extension service applications. GPS and cameras are the most popular sensors used in the reviewed papers. This shows an opportunity for future applications to utilize other sensors such as accelerometer to provide advanced agricultural solutions. ER - TY - Article T1 - A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues A1 - Ruan, J Y1 - 2019/// KW - Actuators KW - Agriculture KW - Aquaculture KW - Food production KW - Green products KW - Monitoring KW - Temperature sensors JF - IEEE Communications Magazine VL - 57 IS - 3 SP - 90 EP - 96 DO - 10.1109/MCOM.2019.1800332 UR - https://api.elsevier.com/content/abstract/scopus_id/85062952706 N1 - Cited By (since 2019): 85 N2 - The increasing population in the world forces humans to improve farm yields using advanced technologies. The Internet of Things (IoT) is one promising technique to achieve precision agriculture, which is expected to greatly increase yields. However, the large-scale application of IoT systems in agriculture is facing challenges such as huge investment in agriculture IoT systems and non-tech-savvy farmers. To identify these challenges, we summarize the applications of IoT techniques in agriculture in four categories: controlled environment planting, open-field planting, livestock breeding, and aquaculture and aquaponics. The focus on implementing agriculture IoT systems is suggested to be expanded from the growth cycle to the agri-products life cycle. Meanwhile, the energy concern should be considered in the implementation of agriculture IoT systems. The construction of green IoT systems in the whole life cycle of agri-products will have great impact on farmers' interest in IoT techniques. With the life cycle framework, emerging finance, operation, and management (FOM) issues in the implementation of green IoT systems in agriculture are observed, such as IoT finance, supply chain and big data financing, network nodes recharging and repairing, and IoT data management. These FOM issues call for innovative farm production modes and new types of agribusiness enterprises. ER - TY - Article T1 - Performance Management of IEEE 802.15.4 Wireless Sensor Network for Precision Agriculture A1 - Kone, C A1 - Hafid, Abdelhakim A1 - Boushaba, Mustapha Y1 - 2015/// KW - Wireless sensor networks KW - analytical modeling KW - controlling KW - network performance JF - IEEE Sensors Journal VL - 15 IS - 10 SP - 5734 EP - 5747 DO - 10.1109/JSEN.2015.2442259 UR - https://api.elsevier.com/content/abstract/scopus_id/84939857391 N1 - Cited By (since 2015): 45 N2 - The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters (macMinBE and macMaxCSMABackoffs) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements. ER - TY - Article T1 - Performance analysis of IoT based smart agriculture system A1 - Ramprabu, G Y1 - 2019/// KW - Agriculture KW - Cloud Computing KW - Internet of Things KW - Li-Fi KW - Smart KW - Wi-Fi JF - International Journal of Engineering and Advanced Technology VL - 8 IS - 4 SP - 1342 EP - 1344 UR - https://api.elsevier.com/content/abstract/scopus_id/85067838028 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Performance analysis of IoT based smart agriculture system.pdf N1 - Cited By (since 2019): 2 N2 - Internet of Things (IoT) is the greatest emergent technique throughout the World. Large amounts of the people (70%) in India are depending on farming. This circumstance is a motivation, that encumbering the improvement of nation. In order to resolve this difficulty smart agriculture could be executed by appending innovative technical systems as an alternative of current conventional farming schemes. Therefore we proposed innovative IoT scheme with cloud computing and Li-Fi. Wi-Fi is immense for universal wireless exposure inside structures, while Li-Fi is wireless data exposure with elevated concentration in restricted area. Li-Fi offers enhanced bandwidth, effectiveness, accessibility and protection than Wi-Fi and has previously attained blisteringly elevated rate in the lab. Initially this research work embraces remote proscribed procedure to execute assignments like weeding, spraying, animal and bird scaring, moisture sensing, keeping vigilance, etc. Secondly it embraces smart warehouse supervision which embraces humidity protection, temperature preservation and burglary revealing in the stockroom. Finally, intellectual assessment creation depends on perfect actual instance meadow information for elegant irrigation with elegant manage. Scheming of all these procedures would be during any isolated smart mechanism or computer associated to Internet and the procedures will be executed by edging sensors, cameras, ZigBee or Li-Fi modules. ER - TY - Article T1 - An IoT and Blockchain-based approach for the smart water management system in agriculture A1 - Zeng, H A1 - Dhiman, Gaurav A1 - Sharma, Ashutosh A1 - Sharma, Amit A1 - Tselykh, Alexey Y1 - 2021/// KW - Blockchain KW - Internet of Things KW - data analytics KW - sensor deployment KW - smart agriculture JF - Expert Systems DO - 10.1111/exsy.12892 UR - https://api.elsevier.com/content/abstract/scopus_id/85119871897 N1 - Cited By (since 2021): 2 N2 - Agriculture in rural areas facing critical issues such as irrigation with the increase in water crises followed by some other issues line seed quality, poor fertilizers and many others. The recent advances suggest that IoT and Blockchain Technology along with artificial intelligence will be most dominant technologies in near future. In this article, the integration of Internet of Things (IoT) with Blockchain technology is implemented for monitoring agricultural fields efficiently. An efficient seed quality monitoring and smart water management system is design using IoT and Blockchain Technology for managing and coordinating the use of good quality seeds and water resources among communities. The Blockchain network is implemented for securing the information and supporting trust among the members of community. The Blockchain network is also implemented for sporting trust among commercial resource constrained systems, which are communicating with the Blockchain network consisting of a hardware platform. The design of a prototype and its performance evaluation based on implementation is also presented. ER - TY - Review T1 - Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges A1 - Torky, M Y1 - 2020/// KW - Blockchain technology KW - Challenges and solutions KW - Internet of things KW - Precision agriculture JF - Computers and Electronics in Agriculture VL - 178 SN - 0168-1699 DO - 10.1016/j.compag.2020.105476 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169919324329 UR - https://api.elsevier.com/content/abstract/scopus_id/85091535711 N1 - Cited By (since 2020): 81 N2 - Blockchain quickly became an important technology in many applications of precision agriculture discipline. The need to develop smart P2P systems capable of verifying, securing, monitoring, and analyzing agricultural data is leading to thinking about building blockchain-based IoT systems in precision agriculture. Blockchain plays the role of pivotal in replacing the classical methods of storing, sorting and sharing agricultural data into a more reliable, immutable, transparent and decentralized manner. In precision farming, the combination of the Internet of Things and the blockchain will move us from only smart farms only to the internet of smart farms and add more control in supply-chains networks. The result of this combination will lead to more autonomy and intelligence in managing precision agriculture in more efficient and optimized ways. This paper exhibits a comprehensive survey on the importance of integrating both blockchain and IoT in developing smart applications in precision agriculture. The paper also proposed novel blockchain models that can be used as important solutions for major challenges in IoT-based precision agricultural systems. In addition, the study reviewed and clearly discussed the main functions and strengths of the common blockchain platforms used in managing various sub-sectors in precision agriculture such as crops, livestock grazing, and food supply chain. Finally, the paper discussed some of the security and privacy challenges, and blockchain-open issues that obstacles developing blockchain-IoT systems in precision agriculture. ER - TY - Article T1 - Prototyping the visualization of geographic and sensor data for agriculture A1 - Kubicek, P Y1 - 2013/// KW - Adaptive visualization KW - Cartographic visualization KW - Data integration KW - Sensors KW - Wireless sensor networks JF - Computers and Electronics in Agriculture VL - 97 SP - 83 EP - 91 DO - 10.1016/j.compag.2013.07.007 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169913001579 UR - https://api.elsevier.com/content/abstract/scopus_id/84881297366 N1 - Cited By (since 2013): 34 N2 - The effectiveness of decision-making processes in the agricultural domain can be improved by integrating current local environmental and agromonitoring with the Geographic Information System (GIS) and wireless sensor networks (WSNs). The presented paper describes conceptual approaches to context-based cartographic visualization methods for agricultural and metrological data acquired by WSN and a portal prototype for integrated visualization. Each sensor used for agricultural applications has a location and can be placed within a broader spatial context. In our study, sensor characteristics (soil temperature and moisture, atmospheric temperature and moisture) were automatically monitored at frequent intervals and these readings were aggregated with geospatial data from both local and remote (Web Map Service) sources. An experimental portal for the integration and visualization of sensor data and geospatial data was designed and prototyped on the basis of an open source interoperable platform. Conceptual approaches were successfully implemented on four experimental small-plot fields planted with different crop species and operated using different soil tillage practices. Experimental fields were situated in the southeast of the Czech Republic. Very Long range Identification Tag (VLIT) technology was used for wireless communication. Sensor observations verified differences in agrometeorological variables for the conventional tillage of soil and the no-tillage variant. Contextual cartographic visualization was successfully deployed for map view, dynamic cartographic symbology, and the dynamic measurement chart. ER - TY - Article T1 - Software architecture of the internet of things (IoT) for smart city, healthcare and agriculture: analysis and improvement directions A1 - Gavrilović, N Y1 - 2021/// KW - Agriculture KW - Architectural paradigms KW - Healthcare KW - Internet of things KW - Smart city KW - Software architecture JF - Journal of Ambient Intelligence and Humanized Computing VL - 12 IS - 1 SP - 1315 EP - 1336 DO - 10.1007/s12652-020-02197-3 UR - https://api.elsevier.com/content/abstract/scopus_id/85086783239 N1 - Cited By (since 2021): 11 N2 - Internet of things (IoT) enables organizations to automate the process and improves service delivery through Internet technology and transferring the data at the cloud level. IoT does not allow the use of a universal software architecture for different fields in which it is used, but needs to be adjusted according to the requirements of users. This paper presents an analysis of currently available types of software architectures of the IoT systems in the field of smart cities, healthcare, and agriculture. It provides a proposal for solutions and improvements of different software architecture types, interactions between identified software architecture elements that will provide better performance and simplicity. The novelty of the study is the analysis of different types of IoT software architecture such as: layered, service-oriented and cloud-based software architecture application in these areas of IoT. Based on the analysis, the study proposed the type of software architecture of the IoT system for the relevant area of application (smart city, healthcare, and agriculture). Specific points of research are: analysis of different types of software architecture applied in IoT systems, identification of functionalities available in IoT systems through different types of software architecture, the proposal for enhancement of the above functionalities, and proposal of software architecture that is most relevant to the IoT system of a particular area. ER - TY - Book Chapter T1 - IoT in Agriculture Investigation on Plant Diseases and Nutrient Level Using Image Analysis Techniques A1 - Suganya, E Y1 - 2019/// KW - Image processing KW - Internet of Things KW - Segmentation JF - Internet of Things in Biomedical Engineering SP - 117 EP - 130 DO - 10.1016/B978-0-12-817356-5.00007-3 UR - https://api.elsevier.com/content/article/eid/3-s2.0-B9780128173565000073 UR - https://api.elsevier.com/content/abstract/scopus_id/85103790245 N1 - Cited By (since 2019): 6 N2 - Smart farming based on recent Internet of Things (IoT) technologies is the most advanced method to grow food cleanly while making it sustainable. This method applies modern information and communication technologies to agriculture, targeted not only to reduce waste but also to increase agricultural productivity to an optimum level. Agriculture is considered the backbone of the world’s economy. The end-product’s quantity and quality both play a vital role. Using the proposed IoT technology, the identification of diseases in plants with focus on the affected area is more accurate. It also reduces the number of incorrect conclusions that may be drawn, which can lead to incorrect actions taken for sustaining the plants in farms. The proposed technique will also be able to predict the damage level by pests on plants to take appropriate actions to improve productivity. Digital images taken from the plants will be processed and examined using pattern recognition and digital image processing techniques. Proposed image analysis techniques will segment these images to identify diseases and the affected level. This will help to prevent plant disease and increase the nutrient level of the plant, with the help of IoT. The automatic detection of plant disease will be beneficial in monitoring a large field of crops. ER - TY - JOUR T1 - Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge A1 - Ouafiq, E M A1 - Saadane, R A1 - Chehri, A Y1 - 2022/// PB - mdpi.com JF - Agriculture UR - https://www.mdpi.com/2077-0472/12/3/329 UR - https://www.mdpi.com/2077-0472/12/3/329/pdf N1 - Cited By (since 2022): 3 N2 - … Section 10 demonstrates the data science part and what we propose as algorithms for smart farming analysis and predictive maintenance. Finally, Section 11 presents our conclusions. … ER - TY - Article T1 - B+WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring A1 - Edwards-Murphy, F Y1 - 2016/// KW - Decision tree analysis KW - Honey bee monitoring KW - Internet of Things KW - Precision agriculture KW - Precision apiculture KW - Wireless Sensor Networks JF - Computers and Electronics in Agriculture VL - 124 SP - 211 EP - 219 DO - 10.1016/j.compag.2016.04.008 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169916301235 UR - https://api.elsevier.com/content/abstract/scopus_id/84963830865 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) B+WSN Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring.pdf N1 - Cited By (since 2016): 66 N2 - United Nations reports throughout recent years have stressed the growing constraint of food supply for Earth’s growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. In this paper heterogeneous wireless sensor networks are utilised to collect data on a range of parameters from a beehive with the aim of accurately describing the internal conditions and colony activity. The parameters measured were: CO2, O2, pollutant gases, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were also collected to provide an additional analysis dimension. Using a data set from a deployment at a field-deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. Finally, a correlation between meteorological conditions and beehive data was observed. This led to the development of an algorithm for predicting short term rain based on the parameters within the hive. Envisioned applications of this algorithm include agricultural and environmental monitoring for short term local forecasts (95.4% accuracy). Experimental results shows the low computational and energy overhead (5.35% increase in energy consumption) of the classification algorithm when deployed on one network node, which allows the node to be a self-sustainable intelligent device for smart bee hives. ER - TY - Review T1 - Recent Developments of the Internet of Things in Agriculture: A Survey A1 - Kour, V P Y1 - 2020/// KW - Artificial Intelligence KW - Cloud Computing KW - Internet of Things KW - Precision Agriculture KW - Wireless Sensor Networks JF - IEEE Access VL - 8 SP - 129924 EP - 129957 SN - 2169-3536 DO - 10.1109/ACCESS.2020.3009298 UR - https://api.elsevier.com/content/abstract/scopus_id/85089491834 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Recent developments of the Internet of Things in Agriculture a Survey.pdf N1 - Cited By (since 2020): 35 N2 - A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup’s started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article. ER - TY - Conference Paper T1 - Topology optimization in wireless sensor networks for precision agriculture applications A1 - Konstantinos, K Y1 - 2007/// KW - agricultural engineering KW - electrical conductivity measurement KW - telecommunication network topology KW - wireless sensor networks JF - 2007 International Conference on Sensor Technologies and Applications, SENSORCOMM 2007, Proceedings SP - 526 EP - 530 DO - 10.1109/SENSORCOMM.2007.4394974 UR - https://api.elsevier.com/content/abstract/scopus_id/46449115263 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2007) Topology optimization in wireless sensor networks for precision agriculture applications.pdf N1 - Cited By (since 2007): 34 N2 - In this paper a new way to build a wireless sensor network is proposed, which is based on measuring the field's electrical conductivity, staying away from the classic network grid implementation. Furthermore it is explained how a typical WSN works, which are the pros and cons and the technical characteristics, as well as how electrical conductivity can influence the decision to build the WSN topology and the advantage of this approach comparing to the typical ones. ER - TY - Conference Paper T1 - IoT Based low-cost weather station and monitoring system for precision agriculture in India A1 - Math, R K M Y1 - 2019/// KW - Internet of things KW - Monitoring System KW - Precision Agriculture KW - Weather Station JF - Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018 SP - 81 EP - 86 DO - 10.1109/I-SMAC.2018.8653749 UR - https://api.elsevier.com/content/abstract/scopus_id/85063461387 N1 - Cited By (since 2019): 26 N2 - In recent times it is seen that the climatic and weather conditions not only in India but also in other countries have become uncertain and unpredictable, which may have devastating effects on the agriculture production. India being an agricultural country, most of the farmers largely rely on monsoons and agricultural production is weather dependent. The environmental factors like temperature, humidity, moisture, precipitation and many other parameters keep on changing rapidly and unpredictably. This unpredictable nature, variability of climatic or weather conditions makes the life of farmers quite miserable as they are unable to take proper decisions at the right time. Thus, it is the need of the hour to have a real-time, local weather station which would keep the farmers informed well in advance about the prevailing weather conditions so that they can take appropriate decisions at the right time and save their crops from loss. Precision Agriculture (PA) is an art of using the latest available technologies in the agriculture domain so as to make traditional agriculture more profitable and sustainable while reducing the wastage of resources. The penetration of internet into India is very deep and very fast, especially due to the Jio mania by Reliance Jio Infocomm Limited last year, high speed internet is now possible even in rural areas. This paper proposes a IoT based real-time local weather station for PA, that would provide farmers a means of automizing their agricultural practices (irrigation, fertilization, harvesting) at the right time. The proposed system would also aid the farmers to carry out the agricultural tasks on real-time bases, which in turn helps them to use the agricultural resources in efficient way and at the time when needed by the crops. The proposed weather system is a small step towards the development of PA system considering the Indian scenarios. ER - TY - Conference Paper T1 - IoT based Soil Nutrition and Plant Disease Detection System for Smart Agriculture A1 - Suhag, S Y1 - 2021/// KW - Internet of Things KW - agriculture KW - crops KW - farming KW - image classification KW - image recognition KW - intelligent sensors KW - plant diseases KW - soil KW - temperature sensors KW - water quality JF - Proceedings - 2021 IEEE 10th International Conference on Communication Systems and Network Technologies, CSNT 2021 SP - 478 EP - 483 DO - 10.1109/CSNT51715.2021.9509719 UR - https://api.elsevier.com/content/abstract/scopus_id/85124684200 N1 - Cited By (since 2021): 4 N2 - In the coming years, farmers will face challenges to feed the increasing number of populations. They need to ensure food security and reduce the dependency on imports. The effective use of new technologies to increase the efficiency of farming will help the farmers to meet the need of increased population AI and IOT related automation to be designed to improve the way a farmer operates for various tasks. We propose a framework for IoT based Soil Nutrition and Plant Disease detection which uses various sensors to collect the plant-related data in form of images at different time intervals using MY THINGS smart sensor and Soil sensors such as proximal soil sensor (PSS) to test the soil fertility which helps to analyze the condition of soil new cultivation, ploughing, water or the land for harvesting. Temperature sensors are also used. Water quality sensors are used that will keep monitoring the quality of the water. All the data will we be sent to the farmer with the help of the IoT. For image classification, Local binary thresholding is used. At harvesting time robot performs image recognition and classification. The farmer will enter the required data to use the robotic arm to automatically harvest the crop. The arm is proposed to have four degrees of freedom and will be driven by the motors. Robotic arms will identify the crop using image recognition and will put that batch in the appropriate basket to be considered by the farmer for analysis. With regular monitoring, this proposed framework can greatly aid the farmers in maintaining crop health as well as quality. ER - TY - Article T1 - Data Traffic Management Based on Compression and MDL Techniques for Smart Agriculture in IoT A1 - Al-Qurabat, A K M Y1 - 2021/// KW - Differential encoding KW - Huffman encoding KW - Internet of things KW - Lifetime KW - MDL KW - Wireless sensor networks JF - Wireless Personal Communications VL - 120 IS - 3 SP - 2227 EP - 2258 DO - 10.1007/s11277-021-08563-4 UR - https://api.elsevier.com/content/abstract/scopus_id/85106250541 N1 - Cited By (since 2021): 4 N2 - The sector of agriculture facing numerous challenges for the proper utilization of its natural resources. For that reason, and to the growing risk of changing weather conditions, we must monitor the soil conditions and meteorological data locally in order to accelerate the adoption of appropriate decisions that help the culture. In the era of the Internet of Things (IoT), a solution is to deploy a Wireless Sensor Network (WSN) as a low-cost remote monitoring and management system for these kinds of features. But WSN is suffering from the motes’ limited energy supplies, which decrease the total network’s lifetime. Each mote collects periodically the tracked feature and transmitting the data to the edge Gateway (GW) for further study. This method of transmitting massive volumes of data allows the sensor node to use high energy and substantial usage of bandwidth on the network. In this research, Data Traffic Management based on Compression and Minimum Description Length (MDL) Techniques is proposed which works at the level of sensor nodes (i.e., Things level) and at the edge GW level. In the first level, a lightweight lossless compression algorithm based on Differential Encoding and Huffman techniques which is particularly beneficial for IoT nodes, that monitoring the features of the environment, especially those with limited computing and memory resources. Instead of trying to formulate innovative ad hoc algorithms, we demonstrate that, provided general awareness of the features to be monitored, classical Huffman coding can be used effectively to describe the same features that measure at various time periods and locations. In the second level, the principle of MDL with hierarchical clustering was utilized for the purpose of clustering the sets of data coming from the first level. The strategy used to minimize data sets transmitted at this level is fairly simple. Any pair of data sets that can be compressed according to the MDL principle is combined into one cluster. As a result of this strategy, the number of data sets is gradually decreasing and the process of merging similar sets into a single cluster is stopped if no more pairs of sets can be compressed. Results utilizing temperature measurements indicate that it outperforms common methods developed especially for WSNs in reducing the amount of data transmitted and saving energy, even though the suggested system does not reach the theoretical maximum. ER - TY - Conference Paper T1 - (CPS)2: Integration of center pivot systems with wireless underground sensor networks for autonomous precision agriculture A1 - Silva, A R Y1 - 2010/// KW - Precision agriculture KW - cyber physical system KW - scross layer design and adaptation KW - underground electromagnetic propagation KW - wireless underground sensor networks JF - Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS '10 SP - 79 EP - 88 DO - 10.1145/1795194.1795206 UR - https://api.elsevier.com/content/abstract/scopus_id/77954589054 N1 - Cited By (since 2010): 36 N2 - Precision agriculture (PA) refers to a series of practices and tools necessary to correctly evaluate farming needs and a high density of soil sensors is an essential part of this effort. The accuracy and effectiveness of PA solutions are highly dependent on accurate and timely analysis of the soil conditions. Traditional soil measurements techniques, however, do not provide real-time data and hence, cannot fully satisfy these requirements. Moreover, the use of wired sensors, which usually must be installed and removed frequently, impacts the deployment of a high density of sensor nodes for a certain area. In this paper, a novel cyber-physical system (CPS) is developed through the integration of center pivot systems with wireless underground sensor networks, i.e., (CPS)2 for precision agriculture (PA). The Wireless Underground Sensor Networks (WUSNs) consist of wirelessly connected underground sensor nodes that communicate untethered through soil. A CP provides one of the highest efficient irrigation solutions for agriculture and the integration of WUSNs with the CP structure can provide autonomous irrigation capabilities that are driven by the physical world, i.e., conditions of the soil. However, the wireless communication channel for the soil-air path is significantly affected by many spatio-temporal aspects, such as the location and burial depth of the sensors, the soil texture and moisture, the vegetation canopy, and also the speed of the center pivot engine. Due to the high number of real-time parameters to be considered, a cyber-physical system (CPS) must be developed. In this paper, as a proof-of-concept, the results of empirical experiments with these components are provided. The main characteristics of a precision agriculture CPS are highlighted as a result of the experiments realized with a WUSN built on top of a real-life center pivot system. The experiment results show that the concept of (CPS)2 is feasible and can be made highly reliable using commodity wireless sensor motes. Moreover, it is shown that the realization of (CPS)2 requires non-trivial management due to stochastic real-time communication constraints. Accordingly, guidelines for the development of an efficient (CPS)2 solution are provided. To the best of our knowledge, this is the first work that considers a CPS solution based on WUSNs for precision agriculture. ER - TY - JOUR T1 - Internet of Things Empowered Smart Greenhouse Farming A1 - Rayhana, R A1 - Xiao, G A1 - Liu, Z Y1 - 2020/// KW - IoT KW - RFID KW - agriculture KW - greenhouse farming KW - sensors KW - smart farming JF - IEEE Journal of Radio Frequency Identification VL - 4 IS - 3 SP - 195 EP - 211 DO - 10.1109/JRFID.2020.2984391 N2 - The rapid change of climate, population explosion, and reduction of arable lands are calling for new approaches to ensure sustainable agriculture and food supply for the future. Greenhouse agriculture is considered to be a viable alternative and sustainable solution, which can combat the future food crisis by controlling the local environment and growing crops all year round, even in harsh outdoor conditions. However, greenhouse farms persist many challenges for efficient operation and management. The evolving Internet of Things (IoT) technologies, which encompass the smart sensors, devices, network topologies, big data analytics, and intelligent decision is believed to be the solution in addressing the key challenges facing the greenhouse farming, such as greenhouse local climate control, crop growth monitoring, crop harvesting and etc. This paper reviews the current greenhouse cultivation technologies as well as the state-of-the-art of IoT technologies for smart greenhouse farms. The paper also highlights the major challenges that need to be addressed. ER - TY - Review T1 - Internet of Things in agriculture, recent advances and future challenges A1 - Tzounis, A A1 - Katsoulas, Nikolaos A1 - Bartzanas, Thomas A1 - A, Constantinos Kittas Y1 - 2017/// KW - Cloud KW - Food supply chain KW - Internet of things KW - Radio frequency identification KW - Wireless sensor networks JF - Biosystems Engineering VL - 164 SP - 31 EP - 48 SN - 1537-5110 DO - 10.1016/j.biosystemseng.2017.09.007 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1537511017302544 UR - https://api.elsevier.com/content/abstract/scopus_id/85031943069 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Internet of Things in agriculture, recent advances.pdf N1 - Cited By (since 2017): 349 N2 - The increasing demand for food, both in terms of quantity and quality, has raised the need for intensification and industrialisation of the agricultural sector. The “Internet of Things” (IoT) is a highly promising family of technologies which is capable of offering many solutions towards the modernisation of agriculture. Scientific groups and research institutions, as well as the industry, are in a race trying to deliver more and more IoT products to the agricultural business stakeholders, and, eventually, lay the foundations to have a clear role when IoT becomes a mainstream technology. At the same time Cloud Computing, which is already very popular, and Fog Computing provide sufficient resources and solutions to sustain, store and analyse the huge amounts of data generated by IoT devices. The management and analysis of IoT data (“Big Data”) can be used to automate processes, predict situations and improve many activities, even in real-time. Moreover, the concept of interoperability among heterogeneous devices inspired the creation of the appropriate tools, with which new applications and services can be created and give an added value to the data flows produced at the edge of the network. The agricultural sector was highly affected by Wireless Sensor Network (WSN) technologies and is expected to be equally benefited by the IoT. In this article, a survey of recent IoT technologies, their current penetration in the agricultural sector, their potential value for future farmers and the challenges that IoT faces towards its propagation is presented ER - TY - Conference Paper T1 - IoT agriculture system based on LoRaWAN A1 - Davcev, D Y1 - 2018/// KW - Internet of Things KW - LoRaWAN KW - agriculture KW - data streams JF - IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS VL - 2018 SP - 1 EP - 4 DO - 10.1109/WFCS.2018.8402368 UR - https://api.elsevier.com/content/abstract/scopus_id/85050019246 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) IoT agriculture system based on LoRaWAN.pdf N1 - Cited By (since 2018): 89 N2 - In the last years, besides the implementation in the smart city applications, IoT has also found significant place in the agricultural and food production process. In the paper we present an innovative, power efficient and highly scalable IoT agricultural system. This system is based on LoRaWAN network for long range and low power consumption data transmission from the sensor nodes to the cloud services. Our system of cloud services is highly scalable and utilizes data stream for analytics purposes. In our case study we show some preliminary results for grape farm. ER - TY - Conference Paper T1 - Precision agriculture monitoring framework based on WSN A1 - Jiber, Y Y1 - 2011/// KW - Agriculture Monitoring KW - Decision Support System KW - Farming KW - Precision Agriculture KW - Wireless sensor networks JF - IWCMC 2011 - 7th International Wireless Communications and Mobile Computing Conference SP - 2015 EP - 2020 DO - 10.1109/IWCMC.2011.5982844 UR - https://api.elsevier.com/content/abstract/scopus_id/80052440939 N1 - Cited By (since 2011): 30 N2 - Wireless Sensor Networks (WSNs) are nowadays widely used in building decision support systems for better monitoring. One of the most interesting fields having an increasing need in decision support systems is agriculture. Inefficient and wasteful methods of agricultural monitoring lead to extra time and cost loss for farmers. This paper presents the iFarm framework system, an easy-to-use and expandable agricultural monitoring solution to enhance land productivity by better managing water, improving the socio-economic factor of farmers and their awareness, predicting and planning the crop yields. The iFarm system proposes WSNs as a promising mechanism to agricultural resources optimization, decision making, and land monitoring. WSNs make it possible to know at any time information about the land and crop conditions, so that farmers can be assisted with various notifications and suggestions during their farming tasks. It addresses the advantage of the precision agriculture approach to help making valuable decisions which could not only improve the land productivity but also optimize the use of resources. The paper gives a description of the precision agriculture monitoring approach that provides meaningful services to farmers. ER - TY - Conference Paper T1 - Integration of RFID and sensor in agriculture using IOT A1 - Wasson, T A1 - Choudhury, Tanupriya A1 - Sharma, Shilpi A1 - Kumar, Praveen Y1 - 2018/// KW - Agriculture KW - Humidity KW - Irrigation KW - Moisture KW - Radio frequency identification KW - Sensors KW - Temperature KW - internet of things JF - Proceedings of the 2017 International Conference On Smart Technology for Smart Nation, SmartTechCon 2017 SP - 217 EP - 222 DO - 10.1109/SmartTechCon.2017.8358372 UR - https://api.elsevier.com/content/abstract/scopus_id/85048041434 N1 - Cited By (since 2018): 30 N2 - Agriculture is the backbone of the Indian economy. As all know that demand of agricultural products are increasing day by day as the population is ever increasing, so there is a need to minimize labor, limit the use of water and increase the production of crops. So there is a need to switch from traditional agriculture to the modern agriculture. The introduction of internet of things into agriculture modernization will help solve these problems. This paper presents the IOT based agriculture production system which will monitor or analyze the crop environment like temperature humidity and moisture content in soil. This paper uses the integration of RFID technology and sensors. As both have different objective sensors are for sensing and RIFD technology is for identification This will effectively solve the problem of farmer, increase the yield and saves his time, power, money. ER - TY - Article T1 - Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops A1 - Garcia-Sanchez, A Y1 - 2011/// KW - Precision agriculture KW - Video-surveillance KW - wireless sensor networks JF - Computers and Electronics in Agriculture VL - 75 IS - 2 SP - 288 EP - 303 DO - 10.1016/j.compag.2010.12.005 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169910002553 UR - https://api.elsevier.com/content/abstract/scopus_id/79551688180 N1 - Cited By (since 2011): 213 N2 - Monitoring different parameters of interest in a crop has been proven as a useful tool to improve agricultural production. Crop monitoring in precision agriculture may be achieved by a multiplicity of technologies; however the use of Wireless Sensor Networks (WSNs) results in low-cost and low-power consumption deployments, therefore becoming a dominant option. It is also well-known that crops are also negatively affected by intruders (human or animals) and by insufficient control of the production process. Video-surveillance is a solution to detect and identify intruders as well as to better take care of the production process. In this paper, a new platform called Integrated WSN Solution for Precision Agriculture is proposed. The only cost-effective technology employed is IEEE 802.15.4, and it efficiently integrates crop data acquisition, data transmission to the end-user and video-surveillance tasks. This platform has been evaluated for the particular scenario of scattered crops video-surveillance by using computer simulation and analysis. The telecommunications metrics of choice are energy consumed, probability of frame collision and end-to-end latency, which have been carefully studied to offer the most appropriate wireless network operation. Wireless node prototypes providing agriculture data monitoring, motion detection, camera sensor and long distance data transmission (in the order of several kilometers) are developed. The performance evaluation of this real tests-bed scenario demonstrates the feasibility of the platform designed and confirms the simulation and analytical results. ER - TY - Article T1 - Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture A1 - Feng, X Y1 - 2019/// KW - Internet of things KW - Precision agriculture KW - Wireless communication technology KW - Wireless sensor networks JF - Wireless Personal Communications VL - 108 IS - 3 SP - 1785 EP - 1802 DO - 10.1007/s11277-019-06496-7 UR - https://api.elsevier.com/content/abstract/scopus_id/85065520790 N1 - Cited By (since 2019): 57 N2 - Precision agriculture is a suitable solution to these challenges such as shortage of food, deterioration of soil properties and water scarcity. The developments of modern information technologies and wireless communication technologies are the foundations for the realization of precision agriculture. This paper attempts to find suitable, feasible and practical wireless communication technologies for precision agriculture by analyzing the agricultural application scenarios and experimental tests. Three kinds of Wireless Sensor Networks (WSN) architecture, which is based on narrowband internet of things (NB-IoT), Long Range (LoRa) and ZigBee wireless communication technologies respectively, are presented for precision agriculture applications. The feasibility of three WSN architectures is verified by corresponding tests. By measuring the normal communication time, the power consumption of three wireless communication technologies is compared. Field tests and comprehensive analysis show that ZigBee is a better choice for monitoring facility agriculture, while LoRa and NB-IoT were identified as two suitable wireless communication technologies for field agriculture scenarios. ER - TY - Book Chapter T1 - Recent advancements in multimedia big data computing for IoT applications in precision agriculture: Opportunities, issues, and challenges A1 - Verma, S Y1 - 2020/// KW - Data analytics KW - Data collection in agriculture KW - Digital revolution KW - Internet of Things KW - Multimedia Big Data KW - Plant pathology KW - Precision agriculture KW - Sensors KW - Smart farming JF - Intelligent Systems Reference Library VL - 163 SP - 391 EP - 416 SN - 1868-4394 DO - 10.1007/978-981-13-8759-3_15 UR - https://api.elsevier.com/content/abstract/scopus_id/85069494079 N1 - Cited By (since 2020): 12 N2 - This chapter aims to present a survey on the existing techniques and architectures of Multimedia Big Data (MMBD) computing for Internet of Things (IoT) applications in Precision Agriculture, along with the opportunities, issues, and challenges it poses in the context. As a consequence of the digital revolution and ease of availability of electronic devices, a massive amount of data is being acquired from a variety of sources. On one hand, this overwhelming quantity of multimedia data poses several challenges, from its storage to transmission, and on the other, it presents an opportunity to provide an insight into the business trends, intelligence and render rich decision support. One of the key applications of MMBD Computing is Precision Agriculture. The chapter focuses on major agricultural applications, cyber-physical systems for smart farming, multimedia data collection approaches, and various IoT sensors along with wireless communication technologies employed in the field of Precision Agriculture. ER - TY - Conference Paper T1 - Agriculture internet of things: AG-IoT A1 - Uddin, M A Y1 - 2017/// KW - Clustering KW - Dynamic routing KW - Internet of things KW - Smart agriculture KW - Wireless sensor networks JF - 2017 27th International Telecommunication Networks and Applications Conference, ITNAC 2017 VL - 2017 SP - 1 EP - 6 DO - 10.1109/ATNAC.2017.8215399 UR - https://api.elsevier.com/content/abstract/scopus_id/85046664574 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Agriculture internet of things AG-IoT.pdf N1 - Cited By (since 2017): 28 N2 - The Internet of Things (IoT) for agriculture is a rapidly emerging technology where seamless connected sensors device make it possible to monitor and control crop parameters to get quality and quantity of food. This research proposes a new dynamic clustering and data gathering scheme for harnessing the IoT in agriculture. In this paper, an Unmanned Aerial Vehicle (UAV) is used to locate and assist ground IoT devices to form themselves in cluster formation then establishes a reliable uplink communication backbone for data transmission. Use of multifrequency, multi power transmission, and mobile sink make it possible to reduce power utilization of IoT devices as much as possible. The proposed scheme is evaluated by using simulation models and practical experiments. It is found working outclass as compare to all existing systems. ER - TY - Article T1 - A review of wireless sensors and networks' applications in agriculture A1 - Aqeel-Ur-Rehman Y1 - 2014/// KW - Actuators KW - Agriculture KW - Frame KW - Sensor and Actuator Network KW - sensors JF - Computer Standards and Interfaces VL - 36 IS - 2 SP - 263 EP - 270 DO - 10.1016/j.csi.2011.03.004 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0920548911000353 UR - https://api.elsevier.com/content/abstract/scopus_id/84890119176 N1 - Cited By (since 2014): 475 N2 - Due to advancement in technologies and reduction in size, sensors are becoming involved in almost every field of life. Agriculture is one of such domains where sensors and their networks are successfully used to get numerous benefits. Selection of sensors and their effective utilization to solve agricultural domain problems has been an arduous task for novice users due to unavailability of conglomerated information in literature. The aim of this paper is to review the need of wireless sensors in Agriculture, WSN technology and their applications in different aspects of agriculture and to report existing system frameworks in agriculture domain. ER - TY - Conference Paper T1 - Adaptive power system for IoT-based smart agriculture applications A1 - Emira, S S A Y1 - 2019/// KW - Agriculture KW - Energy management KW - Internet of things KW - Load Scheduling KW - Solar irradiance KW - Solar power KW - State of charging KW - Sustainability JF - ICENCO 2019 - 2019 15th International Computer Engineering Conference: Utilizing Machine Intelligence for a Better World SP - 126 EP - 131 DO - 10.1109/ICENCO48310.2019.9027393 UR - https://api.elsevier.com/content/abstract/scopus_id/85083092976 N1 - Cited By (since 2019): 3 N2 - Adaptive power utilization service plays a significant role in the enablement of Internet of Things (IoT) systems in critical energy applications in particular the agricultural environments. These environments are characterized by the wide range of lands with absence of commercial power lines in most of the area. In addition, there is difficulty to reach some deep or high points of sensing in such environments. The adaptive power system shall pave the way to realize the intelligent service for users to build a real-time interaction platform. It can improve the sustainability, stability and reliability of power supply, and provide users with more humanized and multiple intelligent service. IoT technology can effectively improve the adaptive power utilization with its reliable communication and strong data processing. In this paper, a technique is proposed to provide a sustainable power service to ensure continuous operation of smart agriculture system. It depends on renewable energy management and control against load demands. A model governing the technique is built on Matlab and simulation results are discussed. ER - TY - Article T1 - Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening: A review A1 - Madushanki, A A R Y1 - 2019/// KW - Internet of Things KW - agricultural KW - automation KW - business KW - sensor data KW - smart farming JF - International Journal of Advanced Computer Science and Applications VL - 10 IS - 4 SP - 11 EP - 28 DO - 10.14569/ijacsa.2019.0100402 UR - https://api.elsevier.com/content/abstract/scopus_id/85065842088 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening A review.pdf N1 - Cited By (since 2019): 61 N2 - It is essential to increase the productivity of agricultural and farming processes to improve yields and cost- effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016- 2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub- vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies. ER - TY - Conference Paper T1 - SWAMP: An IoT-based smart water management platform for precision irrigation in agriculture A1 - Kamienski, C Y1 - 2018/// KW - Internet of Things KW - Precision Irrigation KW - Smart Water Management JF - 2018 Global Internet of Things Summit, GIoTS 2018 DO - 10.1109/GIOTS.2018.8534541 UR - https://api.elsevier.com/content/abstract/scopus_id/85059074516 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) SWAMP An IoT-based smart water management platform for precision irrigation in agriculture.pdf N1 - Cited By (since 2018): 34 N2 - Irrigation for agriculture is the biggest consumer of freshwater in the world, which makes a case for the intensive use of technology to optimize the use of water, reduce the consumption of energy and improve the quality of crops. While the Internet of Things (IoT) and other associated technologies are the natural choice for smart water management applications, their appropriateness is still to be proven in real settings with the deployment of on-site pilots. Also, IoT-based application development platforms should be generic enough to be adapted to different crops, climates, and countries. The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain and pilots them in Italy, Spain, and Brazil. In this paper, we present the SWAMP view, architecture, pilots and the scenario-based development process adopted in the project. ER - TY - Article T1 - Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture A1 - Jawad, H Y1 - 2020/// KW - Particle swarm optimization KW - Wireless sensor networks KW - ZigBee KW - correlation coefficient KW - exponential equation KW - farm field KW - path loss model KW - polynomial equation JF - IEEE Sensors Journal VL - 20 IS - 1 SP - 552 EP - 561 DO - 10.1109/JSEN.2019.2940186 UR - https://api.elsevier.com/content/abstract/scopus_id/85077977808 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Accurate Empirical Path-loss Model Based on PSO for WSN in Smart Agriculture.pdf N1 - Cited By (since 2020): 42 N2 - Wireless sensor networks (WSNs) have received significant attention in the last few years in the agriculture field. Among the major challenges for sensor nodes’ deployment in agriculture is the path loss in the presence of dense grass or the height of trees. This results in degradation of communication link quality due to absorption, scattering, and attenuation through the crop’s foliage or trees. In this study, two new path-loss models were formulated based on the MATLAB curve-fitting tool for ZigBee WSN in a farm field. The path loss between the router node (mounted on a drone) and the coordinator node was modeled and derived based on the received signal strength indicator (RSSI) measurements with the particle swarm optimization (PSO) algorithm in the farm field. Two path-loss models were formulated based on exponential (EXP) and polynomial (POLY) functions. Both functions were combined with PSO, namely, the hybrid EXPPSO and POLY-PSO algorithms, to find the optimal coefficients of functions that would result in accurate path-loss models. The results show that the hybrid EXP-PSO and POLY-PSO models noticeably improved the coefficient of determination (R2 ) of the regression line, with the mean absolute error (MAE) found to be 1.6 and 2.7 dBm for EXP-PSO and POLY-PSO algorithms. The achieved R2 in this study outperformed the previous state-of-theart models. An accurate path-loss model is essential for smart agriculture application to determine the behavior of the propagated signals and to deploy the nodes in the WSN in a position that ensures data communication without unnecessary packets’ loss between nodes. ER - TY - Conference Paper T1 - Internet of things in smart agriculture: Enabling technologies A1 - Salam, A Y1 - 2019/// KW - Agriculture KW - Education KW - Internet of Things KW - Sensors KW - Soil KW - Technological innovation KW - Wireless sensor networks JF - IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings SP - 692 EP - 695 DO - 10.1109/WF-IoT.2019.8767306 UR - https://api.elsevier.com/content/abstract/scopus_id/85068231963 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Internet of Things in Smart Agriculture_ Enabling Technologies.pdf N1 - Cited By (since 2019): 36 N2 - In this paper, an IoT technology research and innovation roadmap for the field of precision agriculture (PA) is presented. Many recent practical trends and the challenges have been highlighted. Some important objectives for integrated technology research and education in precision agriculture are described. Effective IoT based communications and sensing approaches to mitigate challenges in the area of precision agriculture are presented. ER - TY - Conference Paper T1 - Data mining and wireless sensor network for agriculture pest/disease predictions A1 - Tripathy, A K Y1 - 2011/// KW - Data Mining KW - Precision Agriculture KW - Wireless Sensor Networks KW - pest control JF - Proceedings of the 2011 World Congress on Information and Communication Technologies, WICT 2011 SP - 1229 EP - 1234 DO - 10.1109/WICT.2011.6141424 UR - https://api.elsevier.com/content/abstract/scopus_id/84863158659 N1 - Cited By (since 2011): 41 N2 - Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions. ER - TY - Article T1 - Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk A1 - Ayaz, M A1 - AMMAD-UDDIN, MOHAMMAD A1 - SHARIF, ZUBAIR A1 - MANSOUR, ALI A1 - AGGOUNE, EL-HADI M. Y1 - 2019/// KW - Food quality and quantity KW - Internet of Things KW - advanced agriculture practices KW - agriculture robots KW - automation KW - future food expectation KW - smart agriculture KW - urban farming JF - IEEE Access VL - 7 SP - 129551 EP - 129583 DO - 10.1109/ACCESS.2019.2932609 UR - https://api.elsevier.com/content/abstract/scopus_id/85077971142 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Internet-of-Things_IoT-Based_Smart_Agriculture_Toward_Making_the_Fields_Talk.pdf N1 - Cited By (since 2019): 258 N2 - Despite the perception people may have regarding the agricultural process, the reality is that today’s agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges. ER - TY - Conference Paper T1 - A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture A1 - Ribeiro, F M Y1 - 2020/// KW - Internet of things KW - data filtering KW - fog computing KW - machine learning KW - smart agriculture JF - 2020 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2020 - Proceedings SP - 63 EP - 67 DO - 10.1109/MetroAgriFor50201.2020.9277661 UR - https://api.elsevier.com/content/abstract/scopus_id/85099049931 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) A Nearest Neighbors based Data Filter for Fog Computer in IoT Smart Agriculture.pdf N1 - Cited By (since 2020): 4 N2 - In smart agriculture, the Internet of Things (IoT) makes it possible to analyze and manage agricultural yield to increase productivity, reduce wasted resources, and decrease irrigation costs. In IoT systems, if data management is entirely performed in the cloud, the system may not work correctly due to connectivity problems, which is common in some remote regions where the agribusiness thrives. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, a high number of packets sent from the fog to the cloud can cause link congestion with mostly useless data traffic. Dealing with fog data filtering is a challenge because it requires knowing which data is essential to send to the cloud. This paper proposes an approach to collect and store data in a smart agriculture environment and two different methods filtering data in the fog. We designed an experiment for each filtering method, using a real dataset containing temperature and humidity values. In both experiments, the fog filters the data using the k-Nearest-Neighbors (kNN) algorithm, which classifies data into categories according to their value ranges. In the first experiment, the fog classifies the data and generates an output of the number of data categories. In the second experiment, data is classified and also compressed based on the previously obtained categories using the runlength encoding (RLE) technique to preserve the data time series nature. Our results show that data filtering reduces the amount of data sent by the fog to the cloud. ER - TY - Conference Paper T1 - IoT Based Pest Controlling System for Smart Agriculture A1 - Saranya, K Y1 - 2019/// KW - Internet of Things KW - agricultural products KW - agrochemicals KW - control engineering computing KW - image processing KW - pest control JF - Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019 SP - 1548 EP - 1552 DO - 10.1109/ICCES45898.2019.9002046 UR - https://api.elsevier.com/content/abstract/scopus_id/85081157568 N1 - Cited By (since 2019): 5 N2 - In India, the agricultural economic loss is mainly due to insects and pests. Therefore, pesticides are widely used by farmers to control weeds, insects and plant diseases. Excess usage of pesticides is not only an adverse for the environment but also for human and economy of the nation. In this paper, we proposed a pest control system that makes use of IOT (Internet of Things) and image processing technologies to control pests, thereby reducing the use of pesticides. The proposed system uses infrared sensor (PIR) to detect the presence of insect by the heat radiated by their body. Image processing is used to capture images of the pest to confirm their presence in the field. After confirming the presence of insect by Image processing and PIR sensor, the ultrasonic generator is used to generate ultrasonic waves which are intolerable to insects and pests, drive them away from the agricultural field. The proposed system helps the farmers to improve the agricultural production and management in an eco friendly way. ER - TY - Article T1 - A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture A1 - Castrignanò, A Y1 - 2018/// KW - Change of support KW - Data fusion KW - Factorial cokriging KW - Precision Agriculture KW - Proximal soil sensing KW - Spatial data JF - Catena VL - 167 SP - 293 EP - 304 DO - 10.1016/j.catena.2018.05.011 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0341816218301772 UR - https://api.elsevier.com/content/abstract/scopus_id/85047000882 N1 - Cited By (since 2018): 25 N2 - Application of Precision Agriculture requires an accurate assessment of fine-resolution spatial variation. At present, advances in proximal sensing and spatial data analysis are available to characterize soil systems and detect changes in physical or chemical properties useful to understand and manage the variation within fields in a site-specific way. The objective of this work was to verify the suitability of geostatistical techniques to fuse data measured with different geophysical sensors for delineating homogeneous within-field zones for Precision Agriculture. A geophysical survey, using electromagnetic induction (EMI) and ground penetrating radar (GPR), was carried out at Montecorvino Rovella in the southern Apennines (Salerno, Italy). Both sensors (EMI and GPR) enabled the assessment of variation of soil dielectric properties both laterally and vertically. The study area is a 5 ha terraced olive grove under organic cropping. The sensor surveys were carried out along the terraces and over the entire field. The multi-sensor data were analyzed using geostatistical techniques to estimate synthetic scale-dependent regionalized factors. The results allowed the division of the study area into smaller areas, characterized by different properties that could impact agronomic management. In particular, a large area was delineated in the northern part of the grove, where apparent soil electrical conductivity and radar attenuation were greater. Through soil profiling it was shown that soils of the northern macro-area refer to deep, well developed, clayey Luvic Phaezem, whereas soils of the southern macro-area are shallower and less developed, sandy loam Leptic Calcisol. The proposed geostatistical approach effectively combined the complementary 2D EMI and 3D GPR measurements, to delineate areas characterized by different soil horizontal and vertical conditions. This within-olive grove partition might be advantageously used for site-specific tillage and fertilization. ER - TY - Conference Paper T1 - Investigation of wireless sensor networks for precision agriculture A1 - Zhang, Z Y1 - 2004/// KW - Precision agriculture KW - Wireless sensor networks JF - ASAE Annual International Meeting 2004 SP - 1157 EP - 1164 UR - https://api.elsevier.com/content/abstract/scopus_id/30044447594 N1 - Cited By (since 2004): 28 N2 - Wireless Sensor Network (WSN) is a promising data mining solution of precision agriculture. Instrumented with wireless sensors, it will become available to monitor the plants in real time, such as air temperature, soil water content, and nutrition stress. The real time information of the fields will provide a solid base for farmers to adjust strategies at any time. WSN will revolutionize the data collection in agricultural research. However, there have been few researches on the applications of WSN for agriculture. This work was focused on the investigation of wireless sensor networks in agricultural applications. With a 2.4GHz wireless sensor node, the factors such as the coverage area and the agricultural environment effects (bare soil, soybean, and corn fields) on the radio were studied. The datasets were obtained from experiments. They could give an estimation of the sensors to be deployed in different environments given a certain area. ER - TY - Article T1 - Smart agriculture monitoring system using IoT A1 - Priya, P Lashitha Vishnu Y1 - 2018/// KW - Agriculture Monitoring KW - Internet of Things KW - Wireless Sensor Networks JF - International Journal of Engineering and Technology(UAE) VL - 7 SP - 308 EP - 311 UR - https://api.elsevier.com/content/abstract/scopus_id/85067852020 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Smart agriculture monitoring system using IoT.pdf N1 - Cited By (since 2018): 8 N2 - Atmospheric changes have been sporadic over the previous decade. Because of this in late period, atmosphere shrewd techniques called as savvy agribusiness is embraced by numerous Indian farmers. Keen farming is a robotized and coordinated data innovation executed with the Internet of Things. IOT is growing quickly and broadly connected in every remote condition. This paper presents an efficient sensor innovation and remote systems coordination of IOT innovation has been contemplated and looked into in light of the real circum-stance of agricultural activities. Real goal is to gather ongoing information of agriculture that gives simple access to the farmer. Our task screens the yield development utilizing advanced means giving the precise esteems of different parameters where upon the development depends. Additionally, it will help the farmer to screen more than one rural field in the meantime. Since, the vast majority of the observ-ing is done remotely, it will help the person to pick up data. Since, observing through our framework requires less labor, individuals with physical handicaps can be utilized for checking fields. Our task, not just tries to relieve the primitive methods identified with farming yet additionally serve the group by opening new roads for work. ER - TY - JOUR T1 - Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture A1 - Jamil, F A1 - Ibrahim, M A1 - Ullah, I A1 - Kim, S A1 - Kahng, H K A1 - ... Y1 - 2022/// KW - Agriculture KW - Blockchain KW - Greenhouse KW - Internet of things KW - smart contracts PB - Elsevier JF - … Electronics in Agriculture UR - https://www.sciencedirect.com/science/article/pii/S0168169921005901 N1 - Cited By (since 2022): 10 Q1 N2 - The Internet of Things (IoT) has been widely adopted in many smart applications such as smart cities, healthcare, smart farms, industry etc. In recent few years, the greenhouse industry has earned significant consideration from the agriculture community due to its ability to produce fresh agricultural products with immense growth and production rate. However, labour and energy consumption costs increase the production cost of the greenhouse by 40–50% approximately. Moreover, the security and authenticity of agriculture data, particularly for yield monitoring and analysis, is also a challenging issue in current greenhouse systems.The greenhouse require optimal parameter settings with controlled environment to produce increase food production. Therefore, slight advancement can bring remarkable improvements concerning the increase in production with reduced overall cost. In this work, we contributed blockchain enabled optimization approach for greenhouse system. The proposed approach works in three steps to provide optimal greenhouse environment that are; prediction, optimization, and finally controlling. Initially, the Kalman filter algorithm is employed for predicting the greenhouse sensor data. In next step, the optimal parameters are computed for the indoor greenhouse environment. Finally, the optimized parameters are utilized by the control module to operate and regulate the actuator’s state to meet the desired settings in the indoor environment. To evaluate the performance of our proposed greenhouse system, we have developed an emulation tool. The proposed system has been investigated and compared against baseline approach concerning production rate and energy consumption. The obtained results reveal that the proposed optimization approach has improved the energy consumption by 19% against the prediction based approach and 41% against the Baseline scheme. Furthermore, the proof-of-concept based on the Hyperledger Fabric network is implemented on the top of the proposed greenhouse platform. For experimental analysis, we have conducted a series of experiments using Hyperledger calliper concerning throughput, latency, and resource utilization. These results advocates the efficiency of the proposed optimal greenhouse system. ER - TY - Article T1 - Energy efficient automated control of irrigation in agriculture by using wireless sensor networks A1 - Nikolidakis, S A Y1 - 2015/// KW - Agriculture KW - Energy Efficiency KW - Irrigation KW - Wireless sensor networks JF - Computers and Electronics in Agriculture VL - 113 SP - 154 EP - 163 DO - 10.1016/j.compag.2015.02.004 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169915000460 UR - https://api.elsevier.com/content/abstract/scopus_id/84924034757 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2015) Energy efficient automated control of irrigation in agriculture by using wireless sensor networks.pdf N1 - Cited By (since 2015): 129 N2 - Many agricultural activities can be highly enhanced by using digital technologies. One of these activities is the regulation of the quantity of water in cultivated fields, a process which is directly interwoven with the sustainability and the productivity of crops, since insufficient or excessive irrigation may not only be obstructive, but also destructive. This paper proposes a scheme based on the collaboration of an integrated system for automated irrigation management with an advanced novel routing protocol for Wireless Sensor Networks (WSNs), named ECHERP (Equalized Cluster Head Election Routing Protocol). At its core, the proposed system aims at efficiently managing water supply in cultivated fields in an automated way. The system takes into consideration the historical data and the change on the climate values to calculate the quantity of water that is needed for irrigation. In case that the change on the collected values is above a threshold more frequent data collection is proposed to minimize the necessary quantity of water. On the other hand, in case that the change of the values is below a preset threshold then the time interval to collect data can increase to save sensor energy, leading to a prolonged sensor lifetime. The results show that network lifetime using ECHERP is improved up to 1825 min and if a round is 110 s the model provides energy efficiency using smaller water quantities. ER - TY - Conference Paper T1 - A System for the Monitoring and Predicting of Data in Precision Agriculture in a Rose Greenhouse Based on Wireless Sensor Networks A1 - Rodríguez, S A1 - Tatiana, Gualotuña A1 - Carlos, Grilo Y1 - 2017/// KW - Data Mining KW - Greenhouses KW - Internet of Things KW - Precision Agriculture KW - Wireless Sensor Networks JF - Procedia Computer Science VL - 121 SP - 306 EP - 313 SN - 1877-0509 DO - 10.1016/j.procs.2017.11.042 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1877050917322330 UR - https://api.elsevier.com/content/abstract/scopus_id/85040246122 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/2017 A System for the Monitoring and Predicting of Data in Precision.pdf N1 - Cited By (since 2017): 47 N2 - In order to provide the best growing conditions for roses in a greenhouse, a Wireless Sensor Network has been designed and implemented that allows for agricultural environment data collection such as temperature, humidity and light. Each sensor node can transmit monitoring data to the cloud. Data mining techniques were used with the purpose of identifying behavioral patterns given the environment conditions captured by the sensor network. The operationalization of this research was taken as a case study within the rose greenhouses available to Universidad de las Fuerzas Armadas – ESPE, Ecuador. ER - TY - Conference Paper T1 - An IoT Agriculture System Using FIWARE A1 - Corista, P Y1 - 2018/// KW - Agriculture KW - FIWARE KW - FoF KW - Internet of Things KW - Sensors KW - Smart Farming KW - vf-OS JF - 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 - Proceedings DO - 10.1109/ICE.2018.8436381 UR - https://api.elsevier.com/content/abstract/scopus_id/85052497500 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) An IoT Agriculture System Using FIWARE.pdf N1 - Cited By (since 2018): 11 N2 - The industrial panorama is evolving. Using IoT sensors and actuators it is possible to increase product's value, by controlling its production chain. Internet technologies such as FIWARE can provide the services that modern industry needs to process and evaluate sensor data to apply higher production standards, which increase product value. Although FIWARE enablers provide the tools to fulfil modern industry needs, there is an interoperability gap a gap between applications and enablers, as different enablers have different protocols and needs. This article describes an agriculture system developed using vf-OS (virtual factory Operating System), a platform that aims to become the bridge between applications and enablers, as it provides the means to interact with them. The developed system is composed of different applications, that use enablers (integrated using the vf-OS system), current context management and fruit quality theories, to control product quality during the whole fruit production chain. ER - TY - Article T1 - Performance Analysis of Android-Based Smart Agriculture Monitor and Control Applications A1 - Helmy A1 - Rahmasari, Fenny A1 - Nursyahid, Arif A1 - Setyawan, Thomas Agung A1 - Nugroho, Ari Sriyanto Y1 - 2022/// KW - Android Application KW - Internet of Things KW - Performance Analysis KW - Smart Agriculture JF - Jurnal Nasional Teknik Elektro dan Teknologi Informasi VL - 11 IS - 1 SP - 23 EP - 30 UR - https://api.elsevier.com/content/abstract/scopus_id/85058109715 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Performance analysis of IoT based smart sensors in agriculture applications.pdf N1 - Cited By (since 2018): 2 N2 - The ever-evolving digital era leads to an industrial revolution in the internet of things (IoT)-based smart agriculture and smart farm. Of many uses is the use of an Android-based app that monitors and controls parameters in the cultivation process in this digital era. An unstable internet connection can interfere with the monitoring process. For this reason, a system integration into a single app running even in an offline condition is needed; therefore, the user can monitor and control the Android-based smart agriculture app in two modes, namely online and offline. A performance analysis is also necessary to know the app's reliability in sending and receiving data. This system integration used two modes of operation, i.e. online and offline, wherein the online mode, the app will communicate with the server when connected with the internet using representational state transfer application programming interface (REST API). Meanwhile, the app will communicate directly with the system through a local access point in the offline mode. This app interacts with the system with the MQTT protocol where the app acts as an MQTT client. The performance analysis was conducted in the black box test, load activity test, and app performance test from the Android profiler. The acquired test from the app functionality test (black box) showed that the user could monitor and control the smart agriculture in online and offline mode through the app. The average load time for all the activities was 3.507 seconds with a network bandwidth of 4.54 Mbps. At the same time, the average load time in a network bandwidth of 35.35 Mbps was 1.4 seconds. The system performance test indicated the app was relatively light as the CPU usage for the app was 31%, with a memory usage of 453.8 MB. ER - TY - Conference Paper T1 - Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: A review A1 - Rudd, J D Y1 - 2017/// KW - agricultural aviation KW - hyperspectral imagery KW - multispectral imagery KW - remote sensing KW - satellite imagery JF - 2017 ASABE Annual International Meeting DO - 10.13031/aim.201700272 UR - https://api.elsevier.com/content/abstract/scopus_id/85035111844 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Application of satellite, unmanned aircraft system, and.pdf N1 - Cited By (since 2017): 26 N2 - Data resources for precision agriculture applications are expanding. Image capturing satellite constellations are growing in number, unmanned aircraft systems (UAS) are seeing widespread use as they become more powerful and easier to use, and ground-based units are becoming less expensive to produce. Moreover, the different sensors types that can be used with these three platforms are increasing in variety and capability. Although, the three different remote sensing platforms can supply some similar types of data, each system delivers some unique capabilities suitable for specific uses. This review seeks to demonstrate the potential benefits and shortcomings of data gathered from satellites, UAS, and ground sensors, and how they can be used for different applications. Satellite data can be obtained at varying spatial and temporal resolutions, but the data is easily corrupted by cloud cover. It can also be several days between usable flight paths. Small UAS provide flexibility regarding sensor types and flight timings, and they produce imagery at a higher spatial resolution. These platforms have a wide range in cost and most cannot be used during rain or high wind; moreover, the data quality can be influenced by light conditions. Ground sensors can produce ultra-high resolution data that is less affected by environmental conditions but gathering the data is labor intensive and time consuming. Out of the three systems, UAS are the most versatile, but there are circumstances where data from the other two is more suitable for specific applications. ER - TY - Conference Paper T1 - Smart Village: Solar Based Smart Agriculture with IoT Enabled for Climatic Change and Fertilization of Soil A1 - Maheswari, R Y1 - 2019/// KW - internet of things KW - smart cities KW - smart village KW - solar based smart agriculture JF - 2019 IEEE 5th International Conference on Mechatronics System and Robots, ICMSR 2019 SP - 102 EP - 105 DO - 10.1109/ICMSR.2019.8835454 UR - https://api.elsevier.com/content/abstract/scopus_id/85073192537 N1 - Cited By (since 2019): 6 N2 - In smart villages, access to sustainable energy services acts as a catalyst for development. Enabling facility of internet connection for the new possibilities of increasing agricultural cultivation with proper information and guidance, access to clean water, sanitation and nutrition, the growth of productive enterprises to boost farmer's income. The development of a country depends on the village's development. Most of the agriculture productivity suffer greatly with unforeseen change in climate. Therefore, farmers need to get appropriate information's if any sudden climatic disruption occur, it should notify on time to avoid any major damage in agricultural field. As part of the smart village concept, an intelligent system is designed that may help a farmer to get basic facilities/infrastructure by agricultural development. Here an intelligent system is proposed on the fact of farmers getting all relevant details about the improvement in fertilization of soil and agriculture by delivering climate change information's through an IoT (Internet of Things) devices. These information's could be handled through website and mobile phones. To ease for farmer understandings all the facts and information related to soil fertilization and climatic alerts are delivered as per their native language/language of their interest. This system may help its members to collaborate and take it to another level of requirement in improving their production capacity. These IoT devices are operated either through solar panel or electric supply appropriately to balance the power requirement across the field. ER - TY - Conference Paper T1 - A smart sensor for precision agriculture powered by microbial fuel cells A1 - Sartori, D A1 - Brunelli, Davide Y1 - 2016/// KW - Energy consumption KW - Fuel cells KW - Monitoring KW - Soil KW - Temperature sensors KW - Wireless sensor networks JF - SAS 2016 - Sensors Applications Symposium, Proceedings SP - 42 EP - 47 DO - 10.1109/SAS.2016.7479815 UR - https://api.elsevier.com/content/abstract/scopus_id/84977554503 N1 - Cited By (since 2016): 47 N2 - The level of underground freshwater plays a key role in human activities, in particular in agriculture. Monitoring the level of phreatic aquifers is very important to protect and to preserve this resource. We present a smart, ultra-low power (in the order of mJ), cheap and energy neutral system capable to monitor periodically and remotely the level of phreatic aquifers. The power supply is given by a single terrestrial Microbial Fuel Cell (MFC) and the measurements are carried out by means of a low cost capacitive phreatimeter and can be sent from km away through a long range radio. The overall power consumption is kept low, and power losses are mitigated thank to transient computing paradigm. ER - TY - Conference Paper T1 - Smart Farming Using Internet of Thing(IoT) in Agriculture by Tangible Progarmming for Children A1 - Meadthaisong, S Y1 - 2020/// KW - Internet of Things KW - smart farm KW - tangible programming JF - 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020 SP - 611 EP - 614 DO - 10.1109/ECTI-CON49241.2020.9158083 UR - https://api.elsevier.com/content/abstract/scopus_id/85091849016 N1 - Cited By (since 2020): 4 N2 - The internet of thing(IoT) applied in many applications such as smart industry, smart city, smart life, and Intelligent Agriculture normally the system design by expert. Which system design and programming maybe difficult for children or novices as they cannot learning and program it. This research present our vision tangible programming for children developmenting of internet of thing(IoT) in agriculture. Using tangible programming without program by computer or tablet. Which this encourages children to learn and apply concept for smart farm systems such as monitor temperature and humidity , on web online temperature, on web online humidity. We found that the children could understand idea smart farming using internet of thing(IoT) in agriculture and algorithm of programming. ER - TY - Article T1 - A testbed to evaluate the fiware-based iot platform in the domain of precision agriculture A1 - Martínez, R Y1 - 2016/// KW - FIWARE KW - Internet of Things KW - precision agriculture KW - wireless sensor networks JF - Sensors (Switzerland) VL - 16 IS - 11 DO - 10.3390/s16111979 UR - https://api.elsevier.com/content/abstract/scopus_id/84997228993 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/sensors-16-01979.pdf N1 - Cited By (since 2016): 39 N2 - Wireless sensor networks (WSNs) represent one of the most promising technologies for precision farming. Over the next few years, a significant increase in the use of such systems on commercial farms is expected. WSNs present a number of problems, regarding scalability, interoperability, communications, connectivity with databases and data processing. Different Internet of Things middleware is appearing to overcome these challenges. This paper checks whether one of these middleware, FIWARE, is suitable for the development of agricultural applications. To the authors’ knowledge, there are no works that show how to use FIWARE in precision agriculture and study its appropriateness, its scalability and its efficiency for this kind of applications. To do this, a testbed has been designed and implemented to simulate different deployments and load conditions. The testbed is a typical FIWARE application, complete, yet simple and comprehensible enough to show the main features and components of FIWARE, as well as the complexity of using this technology. Although the testbed has been deployed in a laboratory environment, its design is based on the analysis of an Internet of Things use case scenario in the domain of precision agriculture. ER - TY - Conference Paper T1 - IoT based Soil Nutrients Analysis and Monitoring System for Smart Agriculture A1 - Pyingkodi, M Y1 - 2022/// JF - 3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022 - Proceedings SP - 489 EP - 494 DO - 10.1109/ICESC54411.2022.9885371 UR - https://api.elsevier.com/content/abstract/scopus_id/85139551639 ER - TY - Conference Paper T1 - IoT Based Low-cost Weather Station and Monitoring System for Smart Agriculture A1 - Marwa, C Y1 - 2020/// KW - Environmental monitoring KW - Internet of Things KW - Smart Agriculture KW - Weather Station JF - Proceedings - STA 2020: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering SP - 349 EP - 354 DO - 10.1109/STA50679.2020.9329292 UR - https://api.elsevier.com/content/abstract/scopus_id/85100514588 N1 - Cited By (since 2020): 6 N2 - It is estimated that the world's population will be about 9.1 billion by 2050. The UN FAO has reported that food production would need to be increased by approximately 70 percent to feed this increased population. Therefore, to ensure high yields and farm profitability, it is very important to improve agricultural productivity. In this sense, the technology of the Internet of Things (IoT) has become the key road towards novel practice in agriculture. In the agriculture sector, climate change is also a major concern. A solution to completely satisfy the requirements of automated and real-time monitoring of environmental parameters such as humidity, temperature and rain is proposed in this paper. The proposed platform, which collects environmental data (temperature, humidity and rain) over a period of one year was tested on a real farm in Tunisia. The results show that the proposed solution can be used as a reference model to meet the requirements for large-scale agricultural farm calculation, transmission and storage. ER - TY - Article T1 - Developing ubiquitous sensor network platform using internet of things: Application in precision agriculture A1 - Ferrández-Pastor, F Y1 - 2016/// KW - internet of things KW - precision agriculture KW - ubiquitous sensor network JF - Sensors (Switzerland) VL - 16 IS - 7 DO - 10.3390/s16071141 UR - https://api.elsevier.com/content/abstract/scopus_id/84979208484 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Developing Ubiquitous Sensor Network Platform Using Internet of Things Application in Precision Agriculture.pdf N1 - Cited By (since 2016): 117 N2 - The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched. ER - TY - Article T1 - Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming A1 - Rosell-Polo, J Y1 - 2015/// KW - 3D crop modeling KW - Animal phenotyping KW - Depth cameras KW - Kinect sensor KW - Low-cost tools KW - Plant organ classification KW - Plant phenotyping KW - Precision agriculture KW - Structured light sensors KW - Weed detection JF - Advances in Agronomy VL - 133 SP - 71 EP - 112 DO - 10.1016/bs.agron.2015.05.002 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0065211315001078 UR - https://api.elsevier.com/content/abstract/scopus_id/84943360523 N1 - Cited By (since 2015): 49 N2 - The sustained growth of the world's population in the coming years will require an even greater role for agriculture to meet the food needs of humankind. To improve the productivity and competitiveness of the agricultural industry, it is necessary to develop new and affordable sensing technologies for agricultural operations. This kind of innovations should be implemented in a framework considering the farm, the crops, and their surroundings, with the aim of providing the farmer with information to take better decisions to enhance the production. This is the case of precision agriculture and precision livestock farming. This chapter reviews and discusses the use of structured light sensors in the characterization and phenotyping of crops in orchards and groves, weeds, and animals. As a result of a collaboration between researchers from Spain and Chile, opportunities for this type of sensors have been identified in these countries as examples of South American and European agriculture. In this context, several empirical case studies are presented regarding the use of structured light sensors for flower, fruit, branch, and trunk characterization considering depth and RGB (red-green-blue colors) information in avocados, lemons, apple, and pear orchards. Applications to weed detection and classification as well as to livestock phenotyping are also illustrated. Regarding the presented case studies, experimental and statistical results are provided showing the pros and cons of structured light sensors applied to agricultural environments. Additionally, several considerations are included for the use of this type of sensors to improve the agricultural process. ER - TY - Review T1 - Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production A1 - Halachmi, I Y1 - 2019/// KW - PLF KW - animal welfare KW - farm management KW - precision livestock farming JF - Annual Review of Animal Biosciences VL - 7 SP - 403 EP - 425 SN - 2165-8102 DO - 10.1146/annurev-animal-020518-114851 UR - https://api.elsevier.com/content/abstract/scopus_id/85061669636 N1 - Cited By (since 2019): 82 N2 - Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical. ER - TY - Conference Paper T1 - An IoT-based innovative real-time pH monitoring and control of municipal wastewater for agriculture and gardening A1 - Khatri, N Y1 - 2018/// KW - Internet of things KW - Real time controlling KW - Smart supervision and control KW - Wastewater KW - Water quality KW - Wireless JF - Smart Innovation, Systems and Technologies VL - 79 SP - 353 EP - 362 SN - 2190-3018 DO - 10.1007/978-981-10-5828-8_34 UR - https://api.elsevier.com/content/abstract/scopus_id/85041508479 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) An IoT-based innovative real-time pH monitoring and control of municipal wastewater for agriculture and gardening.pdf N1 - Cited By (since 2018): 7 N2 - This paper presents an internet of thing (IoT)-based innovative real-time pH monitoring and control of municipal wastewater for agriculture and gardening application. During the past few decades after the green revolution in India, water requirement is increased exponentially in all sectors, viz., agriculture, gardening, industry, etc. The demand and supply relationship is very essential for every country in present time, and is also a big challenge to satisfy this requirement around the world. Regular change in the climate and the urbanization makes the lavish use of the available resources has exhausted the available resources. Water is necessary for the survival of the human being on the earth. So for the survival the conservation and management of the available water resource are also equally important. Moreover, for the healthier society, the access of the clean and safe water resource is also imperative. Nowadays municipal wastewater is recycled and reused for agriculture and gardening application after treatment. This paper describes a smart solution to control the water quality through its pH, thus to treat the municipal wastewater and its reuse in the agriculture and gardening purpose. The idea is to develop a low-cost electronic system and its application with such a quality of maintaining (monitoring and control) the water quality within the prescribed standard. ER - TY - Article T1 - A model for smart agriculture using IOT A1 - Anusha, A Y1 - 2019/// KW - Internet of Things KW - agriculture KW - electronic messaging KW - information technology KW - intelligent sensors KW - wireless sensor networks JF - International Journal of Innovative Technology and Exploring Engineering VL - 8 IS - 6 SP - 1656 EP - 1659 UR - https://api.elsevier.com/content/abstract/scopus_id/85065240539 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) A Model for Smart Agriculture Using IOT.pdf N1 - Cited By (since 2019): 14 N2 - Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Messaging Service (SMS) and advices on weather pattern, crops etc. ER - TY - Conference Paper T1 - A wireless sensor network platform optimized for assisted sustainable agriculture A1 - Concepcion, A R De La Y1 - 2014/// KW - Ad hoc networks KW - Agriculture KW - Internet KW - Monitoring KW - Power demand KW - Transceivers KW - Wireless sensor networks JF - Proceedings of the 4th IEEE Global Humanitarian Technology Conference, GHTC 2014 SP - 159 EP - 165 DO - 10.1109/GHTC.2014.6970276 UR - https://api.elsevier.com/content/abstract/scopus_id/84936803696 N1 - Cited By (since 2014): 26 N2 - The paper illustrates an efficient wireless sensor network platform, suitable for application to assisted agriculture in (but not only in) developing Countries and remote regions. The platform has been conceived in order to minimize power consumption, during all the phases of data acquisition, sampling, and compression, with an efficient and performing communication protocol, with extended transmission range and radio coverage optimization. Sensor nodes have been provided with energy harvesting facilities, to avoid any need for direct power supply or battery replacement. The resulting nodes are consequently autonomous, easy to locate and relocate, and scalable. A further work has been done to minimize dimensions and costs, in order to deploy capillary installations. Furthermore, thanks to the work done from the side of the channel optimization, it has been possible to acquire not only standard environmental parameters, but also high definition pictures. Images of plants, trees, as well as fruits and leaves are taken every hour, and forwarded to a central gateway, interfaced with the Internet. A team of agronomists and biologists checks the state of the cultivation from remote, providing the farmer with continuous assistance at a reasonable cost. This is extremely important in Developing Countries, taking into account that in those locations experts cannot reach the fields and cannot provide the farmer with specialized, continuous consultancy, both for economical and logistic reasons. In a global scenario, where new diseases arise rapidly and continuously, the remote assistance provided by an expert can minimize farmer's risks to loose his harvest and reduce his revenues. The set of environmental parameters, together with the visual collection of cultivation conditions, is useful also to generate a culture database, particularly useful in developing regions, where there is almost never historical recorded trace, in particular about possible associated diseases and infections. Last but not least, the platform allows a significant improvement of the sustainability. Thanks to the assistance of the agronomist, the farmer can minimize the use of pesticides and chemicals, as well as reducing the number of additional treatments, resulting in significant advantages, in terms of production costs and organic quality. ER - TY - Review T1 - The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review A1 - Kumar, S A A1 - Ilango, Paramasivam Y1 - 2018/// KW - Precision agriculture KW - Remote monitoring KW - Sensors KW - Wireless sensor networks JF - Wireless Personal Communications VL - 98 IS - 1 SP - 685 EP - 698 SN - 0929-6212 DO - 10.1007/s11277-017-4890-z UR - https://api.elsevier.com/content/abstract/scopus_id/85028775029 N1 - Cited By (since 2018): 66 N2 - Precision agriculture (PA) is an interdisciplinary concept of integrating information technology in agriculture to increase the production and quality of the crops. One of the most important and interesting information of technology is Wireless Sensor Network (WSN). This technology is used to collect, monitor and analyse the data from the field of agriculture. This interdisciplinary technology will boost the crop productivity and maintain quality for example, monitoring the pest and disease control, animal tracking and strength of the crop. In this paper, we have surveyed the importance of sensor in PA and the importance of WSN technologies for remote monitoring in the various applications of the agriculture field. ER - TY - Review T1 - Review - Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture A1 - Mekonnen, Y A1 - Namuduri, Srikanth A1 - Burton, Lamar A1 - Sarwat, Arif A1 - Bhansali, Shekhar Y1 - 2020/// JF - Journal of the Electrochemical Society VL - 167 IS - 3 SN - 0013-4651 DO - 10.1149/2.0222003JES UR - https://api.elsevier.com/content/abstract/scopus_id/85081584518 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Review - Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture.pdf N1 - Cited By (since 2020): 64 N2 - The use of sensors and the Internet of Things (IoT) is key to moving the world's agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system. ER - TY - Book Chapter T1 - Study Report on Using IoT Agriculture Farm Monitoring A1 - Balakrishna, G Y1 - 2019/// KW - Component KW - Formatting KW - Insert KW - Internet of Things KW - Smart agriculture KW - Style KW - Styling KW - temperature JF - Lecture Notes in Networks and Systems VL - 74 SP - 483 EP - 491 SN - 2367-3370 DO - 10.1007/978-981-13-7082-3_55 UR - https://api.elsevier.com/content/abstract/scopus_id/85067670224 N1 - Cited By (since 2019): 8 N2 - The Internet of things (IoT) is rebuilding an agribusiness empowering the ranchers with the extensive variety of strategies, for example, accuracy and supportable farming to confront challenges in the farm. IOT innovation helps in gathering data regarding a situation like climate, dampness, temp, and richness of soil, monitoring crop through internet by farmer empowers discovery of weed, level of water, bug recognition, creature interruption into the field, trim development, and farming. IOT use agriculturists to get associated with his ranch from anyplace and whenever. Remote sensor systems are utilized for observing the ranch conditions and smaller scale reviewer are utilized to control and mechanize the homestead forms. To see remotely the conditions as picture and video, remote cameras have been utilized. An advanced mobile phone enables the rancher to keep refreshed with the continuous states of his rural land utilizing IOT whenever and any piece of the worldwide. IOT innovation can lessen the cost and upgrade the efficiency of conventional cultivating. ER - TY - JOUR T1 - Accuracy and reliability of data in IoT system for smart agriculture A1 - Omar, N A1 - Zen, H A1 - Aldrin, NNAAA A1 - ... Y1 - 2020/// KW - Internet of Things KW - Monitor Crop KW - Remote Monitoring KW - Sensors KW - Smart Precise Agriculture PB - publisher.uthm.edu.my JF - International … UR - https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6390 UR - https://publisher.uthm.edu.my/ojs/index.php/ijie/article/download/6390/3641 L1 - file:///C:/Users/sonsu/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Omar et al. - 2020 - Accuracy and reliability of data in IoT system for smart agriculture.pdf N1 - Cited By (since 2020): 6 Q3 N2 - ...This paper presents the design and development of low-cost Internet of Things (IoT) system for remote monitoring of agriculture ecosystem. The aims of the research are to evaluate the accuracy and reliability of data collected and transmitted with developed IoT system and access from remote location. These data are then compared with the readings of the conventional stand-alone sensors or meters. The system is set in the laboratory and utilises low cost microcontroller, sensors, Wi-Fi network communication, cloud storage, mobile and web application. Microcontroller Arduino Uno ATMega328P is used with ESP8266 Wi-Fi module to enable the device to be connected to the Internet. The sensors used in this system are selected base on the plant’s growth factor. All data collected from the sensors are sent to the Cloud platform such as ThingSpeak and Blynk. Periodical monitoring is carried out on laptop and mobile phone. The results showed that the data taken from the soil moisture, soil temperature, light intensity, surrounding temperature and humidity are accurate and reliable ER - TY - Article T1 - Vicarious radiometric calibration of a multispectral sensor from an aerial trike applied to precision agriculture A1 - Herrero-Huerta, M Y1 - 2014/// KW - Multispectral sensor KW - Precision agriculture KW - Radiometric calibration KW - Remote sensing KW - Vegetation index images KW - Vicarious calibration JF - Computers and Electronics in Agriculture VL - 108 SP - 28 EP - 38 DO - 10.1016/j.compag.2014.07.001 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169914001732 UR - https://api.elsevier.com/content/abstract/scopus_id/84904907547 N1 - Cited By (since 2014): 28 N2 - This article proposes a vicarious calibration as a radiometric calibration method using an onboard multispectral sensor and a low-cost manned aerial platform, PPG (powered paraglider) trike. The statistical analysis of the errors shows the precision reached with this methodology. The greatest advantage offered by this type of manned platforms is its flexibility of flight, autonomy and payload capacity, allowing multiple sensors to be integrated without constraints to weight and volume. The results were validated at two different heights in order to verify the solution obtained with the method, demonstrating the insignificance of relative atmospheric influence between the aerial platform and ground using this platform according to the radiative transfer model on a clear and sunny day. At the same time, the study aims to develop a new trend for remote sensing that will assists in decision making for the sustainable management of extensive crop areas using low-cost geomatic techniques. As a result of the radiometric calibration process, georeferenced images with different vegetation indices over vineyards are obtained. ER - TY - Conference Paper T1 - Improved the efficiency of IoT in agriculture by introduction optimum energy harvesting in WSN A1 - Saxena, M Y1 - 2020/// KW - Internet of Things KW - agriculture KW - computerised monitoring KW - energy harvesting KW - wireless sensor networks JF - 2020 International Conference on Innovative Trends in Information Technology, ICITIIT 2020 DO - 10.1109/ICITIIT49094.2020.9071549 UR - https://api.elsevier.com/content/abstract/scopus_id/85084326558 N1 - Cited By (since 2020): 7 N2 - Internet of Things is the most effective area of research where sensor nodes and smart devices can collect the information from different sources and communicate it with the server without human involvement. In IoT, the main important concept is wireless sensor networks in which data is shared and communicate with the help of sensor nodes. These sensors nodes are distributed randomly in the specified area for collecting and sensing the information of different parameters. This network now a day's effectively used for advance agriculture monitoring and managing many applications automatically through the technology. Sensor nodes have limited energy or back up power so here we use the concept of energy harvesting. These sensor nodes will charge by solar energy and used to monitor the crop management, water management, pesticide monitoring, and climate monitoring. In this paper an IoT based wireless sensor system is proposed which uses the concept of energy harvesting in the area of agriculture for developing, monitoring and controlling the growth and productivity of the system. ER - TY - Conference Paper T1 - Regulation of water in agriculture field using Internet Of Things A1 - Ram, V Vijay Hari Y1 - 2015/// KW - Agriculture KW - Belts KW - GSM KW - Hoses KW - Internet of things KW - Soil JF - Proceedings - 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2015 SP - 112 EP - 115 DO - 10.1109/TIAR.2015.7358541 UR - https://api.elsevier.com/content/abstract/scopus_id/84988942066 N1 - Cited By (since 2015): 35 N2 - Indian agriculture is diverse; ranging from impoverished farm villages to developed farms utilizing modern agricultural technologies. Facility agriculture area in China is expanding, and is leading the world. However, its ecosystem control technology and system is still immature, with low level of intelligence. Promoting application of modern information technology in agriculture will solve a series of problems facing by farmers. Lack of exact information and communication leadsto the loss in production. Our paper is designed to over come these problems. This regulator provides an intelligent monitoring platform framework and system structure for facility agriculture ecosystem based on IOT[3]. This will be a catalyst for the transition from traditional farming to modern farming. This also provides opportunity for creating new technology and service development in IOT (internet of things) farming application. The Internet Of Things makes everything connected. Over 50 years since independence, India has made immense progress towards food productivity. The Indian population has tripled, but food grain production more than quadrupled[1]: there has thus been a substantial increase in available food grain per ca-pita. Modern agriculture practices have a great promise for the economic development of a nation. So we have brought-in an innovative project for the welfare of farmers and also for the farms. There are no day or night restrictions. This is helpful at any time. ER - TY - Conference Paper T1 - Agriculture monitoring and prediction using Internet of Things (IoT) A1 - Saini, M K Y1 - 2020/// KW - Cloud KW - Clustering KW - Internet of things KW - Sensors KW - Smart farming JF - PDGC 2020 - 2020 6th International Conference on Parallel, Distributed and Grid Computing SP - 53 EP - 56 DO - 10.1109/PDGC50313.2020.9315836 UR - https://api.elsevier.com/content/abstract/scopus_id/85100572153 N1 - Cited By (since 2020): 2 N2 - Agriculture has become the most significant growing sector all over the world because of increasing the population. The main challenge in the agriculture industry is to improving farming efficiency and quality without constant physical monitoring to full fill the speedily increasing demand for the food. Apart from the mounting population, the climate circumstance is also a huge challenge in the agricultural industry. The aim of this research paper is to propose a smart farming model based on the Internet of Things using the clustering to deal with the adverse condition. in this model, we use the different types of the sensors like soil moisture, air pressure, rain detection and humidity sensors for a different purpose. The data will collect on the cloud and calculated automatically. The smart agriculture can be adopted from the crop control, collection of useful data, and analysis automatically. The purpose of this paper is how the implement the Internet of Things (IoT) in the monitoring of humidity, soil condition, temperature, and supply water to the field, level of water, climate condition. The IoT based Smart Farming System being planned via this report is integrated with different Sensors and a Wi-Fi module producing live data feed that can be obtained online. ER - TY - Conference Paper T1 - Analysis of Precision Agriculture Technique by Using Machine Learning and IoT A1 - Devi, Y Sasi Supritha A1 - Prasad, T. Kesava Durga A1 - Saladi, Krishna A1 - Durgesh, Nandan Y1 - 2020/// KW - Arduino Uno KW - Disease detection KW - Greenhouses KW - Internet of things KW - Precision agriculture KW - Smart irrigation KW - Support vector machine JF - Advances in Intelligent Systems and Computing VL - 1154 SP - 859 EP - 867 SN - 2194-5357 DO - 10.1007/978-981-15-4032-5_77 UR - https://api.elsevier.com/content/abstract/scopus_id/85088309285 N1 - Cited By (since 2020): 6 N2 - IoT is one of the best among the emerging technologies. Its scope has into the field of agriculture in which farmers learn to control his farm using IoT. Due to the lack of continuous human effort and optimal climatic conditions, many crops go waste every year. This paper discusses various methods that prevent manual action and added automatic control of the farm by using machine learning algorithms and IoT sensors. For example, support vector machine (SVR) is the method to check the weather conditions in every interval of time and gives data to the farmer and automatically takes the respective action. To detect the pests, image processing is the best-used technique. For controlling pests, we use pesticides spray by Drones and agricultural robots. This reduces costs, human effort and saves time. Irrigation can also be controlled by using the k-means clustering algorithm. Greenhouses are the best technique, which is being used nowadays in the precision agriculture that takes information about the growth of a particular plant and gives that information to the farmer. It also checks the internal climatic conditions. ER - TY - Book Chapter T1 - IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review A1 - Majumdar, P Y1 - 2021/// KW - Environment Parameters KW - Machine Learning KW - Real Time JF - Agricultural Informatics: Automation Using the IoT and Machine Learning SP - 89 EP - 115 DO - 10.1002/9781119769231.ch5 UR - https://api.elsevier.com/content/abstract/scopus_id/85124031431 N1 - Cited By (since 2021): 2 N2 - Agriculture monitoring is a promising domain for the economy as it is the primary contributor of job market and food production. Farmers are facing challenges in reducing water consumption and formulating the best irrigation schedules due to discontinuous monsoon, changing weather conditions for improvising crops yield and soil fertility. IoT-based decision making gives real time insight of weather parameters based on cost-effective sensor data acquisition and intelligent processing that reduces manual labor and saves time in Agriculture. Here in this chapter, we present an empirical review on real time visualization and on demand access of weather parameters even from remote locations and intelligent processing using IoT-based solutions like Machine Learning (ML). The ever-augmenting technologies like Machine Learning paved the way for identifying and adapting changes of crop design and irrigation patterns taking into account multi-dimensional large variety of weather data to accurately predict climate conditions suitable for crop irrigation. Hence, this chapter offers a detailed review of IoT-based Machine Learning solutions for precision Agriculture depending on weather and irrigation schedules. This chapter also highlights security solution based on Machine learning capable of handling illegal data access by intruders during cloud data storage. ER - TY - Conference Paper T1 - A monitoring system based on wireless sensor network and an SoC platform in precision agriculture A1 - Lin, J S Y1 - 2008/// KW - Agriculture KW - Circuits KW - Monitoring KW - Signal design KW - Signal processing KW - System on a chip KW - Transceivers KW - Web server KW - Wireless sensor networks KW - ZigBee JF - International Conference on Communication Technology Proceedings, ICCT SP - 101 EP - 104 DO - 10.1109/ICCT.2008.4716133 UR - https://api.elsevier.com/content/abstract/scopus_id/58449083835 N1 - Cited By (since 2008): 34 N2 - This paper proposed a field signals monitoring system with wireless sensor network (WSN) which integrates a System on a Chip (SoC) platform and Zigbee wireless network technologies in precision agriculture. The designed system was constituted by three parts which include field-environment signals sensing units, Zigbee transceiver module and web-site unit. Firstly we used acquisition sensors for field signals, an MCU as the front-end processing device, and several amplifier circuits to process and convert signals of field parameter into digital data. Secondly, Zigbee module was used to transmit digital data to the SoC platform with wireless manner. Finally, an SoC platform, as a Web server additionally, was used to process field signals. Then, we created a system in which field signal values are displayed on Web page or collected into control center in real-time through RJ-45 with the SoC platform. The experimental results show our proposed field-environment signals monitoring system is very feasible for future applications in precision agriculture. ER - TY - Conference Paper T1 - An IoT proposal for monitoring vineyards called senviro for agriculture A1 - Oliver, S T Y1 - 2018/// KW - edge computing KW - internet of things KW - open sensorized platform KW - precision agriculture KW - vineyard KW - wireless sensor networks JF - ACM International Conference Proceeding Series DO - 10.1145/3277593.3277625 UR - https://api.elsevier.com/content/abstract/scopus_id/85056652896 N1 - Cited By (since 2018): 7 N2 - During the last decade, a massive deployment of sensing devices using the Internet protocol to transfer data, called the Internet of Things, has penetrated considerably in all areas; the field of agriculture is not an exception. This fact has led to a new concept called "smart agriculture", and it contemplates activities such as field monitoring, which offer support to make decisions or perform actions, such as irrigation or fertilization. In this scenario, the current work shows a full Internet of Things environment to monitor and predict some vineyard diseases to help farmers to improve the product quality and reduce losses in vineyard fields. Different nodes have been deployed in some vineyard parcels located in the province of Castelló (Spain). ER - TY - Review T1 - Internet of things in agriculture A1 - Verdouw, C Y1 - 2016/// KW - Consumer information KW - Control KW - Food chains KW - Information technology KW - Internet KW - Precision farming KW - Sensing JF - CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources VL - 11 SN - 1749-8848 DO - 10.1079/PAVSNNR201611035 UR - https://api.elsevier.com/content/abstract/scopus_id/85013999344 N1 - Cited By (since 2016): 51 N2 - This literature review on Internet of Things (IoT) in agriculture and food, provides an overview of existing applications, enabling technologies and main challenges ahead. The results of the review show that this subject received attention by the scientific community from 2010 on and the number of papers has increased since then. The literature on IoT in agriculture and food is very much dominated by Asian scientists, especially from China. In other continents, the concept of IoT was up to recently mainly adopted by non-agricultural scientists. The application area of food supply chains is addressed most frequently, followed by arable farming. Most papers report the results of explorative studies or they present IoT systems that are designed or implemented in prototypes and pilots. The literature reviewed focuses on sensing and monitoring, while actuation and remote control is much less addressed. The findings indicate that IoT is still in its infancy in the agriculture and food domain. Applications are often fragmentary, lack seamless integration and especially more advanced solutions are in an experimental stage of development. Important challenges to overcome this situation include (i) integration of existing IoT solutions by open IoT architectures, platforms and standards, (ii) upscaling the usage of interoperable IoT technologies beyond early adopters especially by the simplification of existing solutions and make it more affordable for end users, and (iii) further improvement of IoT technologies to ensure a broad usability in the diversity of the agri-food domain. ER - TY - Conference Paper T1 - Application of the internet of things technology in precision agriculture irrigation systems A1 - Li, S Y1 - 2012/// KW - internet of things KW - modern agriculture KW - precision irrigation KW - wireless sensor networks JF - Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012 SP - 1009 EP - 1013 DO - 10.1109/CSSS.2012.256 UR - https://api.elsevier.com/content/abstract/scopus_id/84873831392 N1 - Cited By (since 2012): 33 N2 - Our country is one of the scarce water resources in 13 countries in the world, shortage of water resources as well as the low utilization of water resources restricts our country economy developing sustainably. In order to effectively reduce the impact of inadequate water resources on China's economy, from modern agricultural cultivation and management perspective, according to the basic principles of Internet, with wireless sensor technology, this article proposes precision agriculture irrigation systems based on the internet of things (IOT) technology, and focuses on the hardware architecture, network architecture and software process control of the precision irrigation system. Preliminary tests showed this system. is rational and practical. ER - TY - Conference Paper T1 - IoT and mechanization in agriculture: Problems, solutions, and prospects A1 - Mentsiev, A U Y1 - 2020/// KW - agriculture KW - internet of things KW - protected agriculture JF - IOP Conference Series: Earth and Environmental Science VL - 548 IS - 3 SN - 1755-1307 DO - 10.1088/1755-1315/548/3/032035 UR - https://api.elsevier.com/content/abstract/scopus_id/85091232355 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT and mechanization in agriculture problems, solutions, and prospect.pdf N1 - Cited By (since 2020): 3 N2 - The IoT, acronym for the Internet of Things, is a coordination of interconnected digital and mechanical devices, people, animals or objects that have been offered with the talent of sharing information without the assistance of human to machine communication, with the help of unique identifiers. The IoT has faced remarkable victory in the fields of business, medicine, defence, smart city and many more. Agriculture is a main sector that has a vast functional potential while considering the Internet of Things. In order to generate environmental states that are compatible for the growth of plants and animals, protected agriculture uses artificial devices and modern development to manipulate best suited climatic behaviours. In this study, the main focus will be on the recent problems, and suitable solutions faced by agricultural sector and provide prospective high tech and modern IoT applications, structures and technologies. ER - TY - Conference Paper T1 - Smart Farming: The IoT based Future Agriculture A1 - Saraswathi, R Vijaya Y1 - 2022/// KW - Arduino KW - Internet of Things KW - Microcontroller KW - Sensors KW - Smart Farming KW - ThingsBoard JF - Proceedings - 4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022 SP - 150 EP - 155 DO - 10.1109/ICSSIT53264.2022.9716331 UR - https://api.elsevier.com/content/abstract/scopus_id/85127335676 N1 - Cited By (since 2022): 1 N2 - Agriculture is backbone of any country. About 60% of our country's population works in agriculture or the primary sector. It contributes more to our country's GDP. It employs the majority of India's population. The internet of things research presents a framework in which farmers may obtain extensive information on the soil, crops growing in specific areas, and agricultural yield and productivity. By utilizing resource optimization and smart planning, this technology-based farming solution will assist farmers in making wise agricultural decisions. The development of IOT based intelligent Smart Farming using smart devices is changing the agriculture production by not only increasing the quality and yield but also to make farming cost effective. The goal of this smart Agriculture or farming is to get live data like temperature, soil moisture and humidity to monitor the surrounding environment. All of this is accomplished with the use of temperature, humidity, and moisture sensors. The system being proposed by this paper is done using microcontroller and various sensors. This system is capable of monitoring the parameters in various soil conditions. ER - TY - Review T1 - Sensors key to advances in precision agriculture A1 - Bogue, R Y1 - 2017/// KW - agriculture KW - food production KW - imaging KW - sensors KW - wireless JF - Sensor Review VL - 37 IS - 1 SP - 1 EP - 6 SN - 0260-2288 DO - 10.1108/SR-10-2016-0215 UR - https://api.elsevier.com/content/abstract/scopus_id/85009754967 N1 - Cited By (since 2017): 26 N2 - Purpose This study aims to illustrate the growing role that sensors play in agriculture, with an emphasis on precision agricultural practices. Design/methodology/approach Following a short introduction, this study first provides an overview of agricultural measurements and applications. It then discusses the importance of airborne and land-based optical sensing techniques and the role of the normalised difference vegetation index. Sensors used on conventional and robotic agricultural machines are considered next, and fixed sensors and sensor networks are then discussed. Finally, brief concluding comments are drawn. Findings This shows that much modern agriculture is a high-technology business which relies on a multitude of sensor-based measurements. Sensors are based on a diversity of optical and other technologies and measure a wide range of physical and chemical variables. They are deployed in the air, on agricultural machines and in the field and generate data that can be used to enhance productivity and reduce both costs and the impact on the environment. Originality/value This provides a detailed insight into the important role played by sensors in modern agricultural practices. ER - TY - Article T1 - A fog computing-based IoT framework for precision agriculture A1 - Guardo, E Y1 - 2018/// KW - Cloud computing KW - Fog computing KW - Internet of Things KW - LoRa KW - Precision agriculture JF - Journal of Internet Technology VL - 19 IS - 5 SP - 1401 EP - 1411 DO - 10.3966/160792642018091905012 UR - https://api.elsevier.com/content/abstract/scopus_id/85054953531 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) A fog computing-based IoT framework for precision agriculture.pdf N1 - Cited By (since 2018): 24 N2 - The challenge of analyzing and processing a huge amount of data is becoming increasingly important in this fourth industrial revolution era. In this scenario, Cloud Computing and Internet of Things (IoT) allow to build up an interconnected network of smart things. These two paradigms do not allow solving the Computing problems yet. Fog Computing aims at moving the processing abilities closer to the end users, avoiding an excessive exploitation of Cloud resources, further reducing computational loads. In this work, we propose a Fog- based IoT framework, which exploits the two-tier Fog and their resources, reducing the transmitted data to the Cloud, improving the computational load balancing and reducing the waiting times. The proposed Fog Computing approach is applied to the emerging area of precision agriculture, including all the techniques of agricultural land management. Furthermore, based on this framework, we have simulated and highlighted how the two-tier Fog Computing approach is able to reduce significantly the amount of transmitted data to the Cloud. We also propose and describe an application prototype, based on the previous framework, able to manage and monitor farmland, with a strong impact on both the business and environmental performance. ER - TY - Conference Paper T1 - Application research of WSN in precise agriculture irrigation A1 - Xiong, S M Y1 - 2009/// KW - data fusion KW - multi-hop routing KW - precision irrigation KW - wireless sensor networks JF - Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009 VL - 2 SP - 297 EP - 300 DO - 10.1109/ESIAT.2009.231 UR - https://api.elsevier.com/content/abstract/scopus_id/71049164214 N1 - Cited By (since 2009): 29 N2 - In order to accurately get extent of the water deficit and therefore realize effective and water-saving irrigation, the application of wireless sensor networks (WSN) to precision irrigation system is explored based on the acoustic emission principle for crop water stress. Cluster based multi-hop routing algorithm is proposed to reduce energy consumption of node transmitting data. By the newest gateway in WSN, the system realizes bridging between wireless networks and wired networks. It runs distributed and possesses many advantages, such as good robustness, extensibility, scalability, and so forth. Simulation results show that the application is correct and reasonable and enables user to precisely acquire the crop water requirement information. The system can be effectively applied to some water-saving agriculture areas, for example, the cropland, the nursery garden, the greenhouse, etc. ER - TY - Conference Paper T1 - Interactive cultivation system for the future IoT-based agriculture A1 - Veloo, K Y1 - 2019/// KW - Artificial Intelligence KW - Internet of Things KW - embedded system KW - smart agriculture KW - urban agriculture JF - Proceedings - 2019 7th International Symposium on Computing and Networking Workshops, CANDARW 2019 SP - 298 EP - 304 DO - 10.1109/CANDARW.2019.00059 UR - https://api.elsevier.com/content/abstract/scopus_id/85078836269 N1 - Cited By (since 2019): 5 N2 - As initiatives to increase Japan's declining food self-sufficiency rate and revitalize the field of agriculture, the concept of smart agriculture and urban agriculture are currently being implemented. Automation via Artificial Intelligence is expected to overcome the labor shortage in the agricultural industry. However, the number of skilled farmers who can contribute to gathering crop growth data required for machine learning is restricted, and these data are also limited to local-and environmental-based conditions. In this paper, we propose a system for obtaining composite growth data in various environments and crops targeted for home gardens and paddy fields. An interactive cultivation sensing system consisting of IoT-based technologies is designed and realized to ensure the continuous growth of crops in optimum conditions daily. With this, progress will be made in determining the efficient cultivation conditions for machine learning, and in finding solutions to future problems of agriculture. ER - TY - Conference Paper T1 - Development of IoT based smart security and monitoring devices for agriculture A1 - Baranwal, T Y1 - 2016/// KW - Agriculture KW - Internet of Things KW - Raspberry Pi KW - Security KW - Sensors KW - Wireless Sensor Networks JF - Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016 SP - 597 EP - 602 DO - 10.1109/CONFLUENCE.2016.7508189 UR - https://api.elsevier.com/content/abstract/scopus_id/85017305538 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Development of IoT based smart security and monitoring devices for agriculture.pdf N1 - Cited By (since 2016): 109 N2 - Agriculture sector being the backbone of the Indian economy deserves security. Security not in terms of resources only but also agricultural products needs security and protection at very initial stage, like protection from attacks of rodents or insects, in fields or grain stores. Such challenges should also be taken into consideration. Security systems which are being used now a days are not smart enough to provide real time notification after sensing the problem. The integration of traditional methodology with latest technologies as Internet of Things and Wireless Sensor Networks can lead to agricultural modernization. Keeping this scenario in our mind we have designed, tested and analyzed an 'Internet of Things' based device which is capable of analyzing the sensed information and then transmitting it to the user. This device can be controlled and monitored from remote location and it can be implemented in agricultural fields, grain stores and cold stores for security purpose. This paper is oriented to accentuate the methods to solve such problems like identification of rodents, threats to crops and delivering real time notification based on information analysis and processing without human intervention. In this device, mentioned sensors and electronic devices are integrated using Python scripts. Based on attempted test cases, we were able to achieve success in 84.8% test cases. ER - TY - Conference Paper T1 - The application of cloud computing and the internet of things in agriculture and forestry A1 - Bo, Y Y1 - 2011/// KW - Cloud Computing KW - Internet of Things KW - agriculture KW - forestry JF - Proceedings - 2011 International Joint Conference on Service Sciences, IJCSS 2011 SP - 168 EP - 172 DO - 10.1109/IJCSS.2011.40 UR - https://api.elsevier.com/content/abstract/scopus_id/80052215639 N1 - Cited By (since 2011): 68 N2 - Cloud Computing and The Internet of Things are the two hot points in the Internet field. The application of the two new technologies is in hot discussion and research, but quite less on the field of agriculture and forestry. Thus, in this paper, we analyze the study and application of Cloud Computing and The Internet of Things on agriculture and forestry. Then we put forward an idea that making a combination of the two techniques and analyze the feasibility, applications and future prospect of the combination. ER - TY - Conference Paper T1 - Design and deploy a wireless sensor network for precision agriculture A1 - Le, T Dinh Y1 - 2015/// KW - agriculture KW - humidity KW - wireless sensor networks JF - Proceedings of 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science, NICS 2015 SP - 294 EP - 299 DO - 10.1109/NICS.2015.7302210 UR - https://api.elsevier.com/content/abstract/scopus_id/84959862191 N1 - Cited By (since 2015): 33 N2 - This paper describes the design, implementation, and deployment of wireless sensor network for precision agriculture. A wireless sensor network was built to use for precision agriculture in which the farmers can monitor and control the agricultural and environmental parameters such as air temperature, air humidity, light, soil moisture, pH, etc. The collected data is stored and transmitted wirelessly to the farmers, which they can use to control and decide appropriate actions for their farm to manage the production and quality. At the hardware side, the system consists of three components: wireless sensor nodes LAU-WSN, wireless sensor management node LAU-WMN, and a server. Software was built at each node to carry out their tasks. At the software side, we proposed a WSN Management Framework for Precision Agriculture, called MFPA, which consists of 3 modules: data collection module, controller module, and data prediction module. The interaction among these modules ensures that user is provided real time environmental and agricultural information which are used to manage the agriculture field. The deployed system has been working properly and promising to bring significant benefits in the agricultural field. Our experimental results show that the data prediction module with dynamic Bayesian network on average can achieve 77.5% and 67.6% accuracy for the temperature prediction, and for the humidity prediction, respectively. ER - TY - Conference Paper T1 - IoT in Precision Agriculture applications using Wireless Moisture Sensor Network A1 - Mat, I Y1 - 2017/// KW - Automatic Irrigation KW - Greenhouse KW - Moisture Sensor KW - Sensor Network KW - Wireless Moisture Sensor Network JF - ICOS 2016 - 2016 IEEE Conference on Open Systems SP - 24 EP - 29 DO - 10.1109/ICOS.2016.7881983 UR - https://api.elsevier.com/content/abstract/scopus_id/85017283091 N1 - Cited By (since 2017): 65 N2 - Internet of Things (IoT) is a network of sensors and connectivity to enable application like agriculture optimum irrigation. Wireless sensor network (WSN) and Wireless Moisture Sensor Network (WMSN) are components of IoT. One of the important processes in agriculture is irrigation. Improper irrigation will result in waste of water. Proper irrigation system could be achieved by using WSN technology. Monitoring and control applications have been tremendously improved by using WSN technology. It enabled efficient communication with many sensors. WMSN is a WSN with moisture sensors. In this study, Precision Agriculture (PA) used WMSN to enable efficient irrigation. In this paper, we describe about IoT and WMSN in agriculture applications particularly in greenhouse environment. This paper explained and proved the efficiency of feedback control method in greenhouse crop irrigation. A test was conducted to see the different these two methods. The methods are irrigation by schedule or feedback based irrigation. Irrigation by schedule is to supply water to the plant at specific time periods. Feedback based irrigation is to irrigate plant when the moisture or level of media wetness reached predefined value. The test shows that there is an average savings of 1,500 ml per day per tree. ER - TY - Conference Paper T1 - An IOT Based Agriculture Monitoring System A1 - Boobalan, J A1 - Jacintha, V A1 - Nagarajan, J. A1 - Thangayogesh, K. A1 - Tamilarasu, S. Y1 - 2018/// KW - Raspberry pi KW - ThingSpeak KW - cloud KW - internet of things KW - python JF - Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018 SP - 594 EP - 598 DO - 10.1109/ICCSP.2018.8524490 UR - https://api.elsevier.com/content/abstract/scopus_id/85057767088 N1 - Cited By (since 2018): 13 N2 - To meet the growing demand of irrigation in India due to uncertain climatic conditions it is necessary to focus on sustainable irrigation approaches and improving the efficiency of the existing irrigation systems. The main purpose of this paper is to analyze the soil moisture level and to afford with auto irrigation to the crops. This system also senses Humidity, temperature and to detect if there are any obstacles in the concerned area and to reduce the human intervention (farmers) for complete automation of the system. The proposed system consist f Raspberry Pi, various sensors, Pi camera and motor driver. In smart supervising system the pi camera captures the video and transfers it to cloud through Raspberry Pi. Here Pi camera is used to provide live video streaming. The soil moisture sensor detects the moisture level and irrigates the various crops in the controlled manner. If any variation in moisture level is sensed then the sensor will update the observed value to the microcontroller and store in the cloud. Depending upon the observed value the crops are automatically provided with water to the preferred level which maintains the humidity of the soil. The moisture level of the industrial area is detected using the Temperature and Humidity sensor and transfer it to cloud through Raspberry Pi. The PIR sensor senses the entry of obstacles in the restricted area and updates it to cloud. The user can obtain these data's from cloud via mobile. Based on these data's the user can control the operation of motor by passing the command YES/NO through motor driver. Here Raspberry pi interfaces with sensors, camera, motor driver and controls the entire system. ER - TY - Conference Paper T1 - A Survey: Smart agriculture IoT with cloud computing A1 - Mekala, M S Y1 - 2017/// KW - Agriculture Monitoring KW - Cloud Computing KW - Gprs KW - Internet of Things KW - Irrigation KW - Li-Fi KW - Routing Protocol JF - 2017 International Conference on Microelectronic Devices, Circuits and Systems, ICMDCS 2017 VL - 2017 SP - 1 EP - 7 DO - 10.1109/ICMDCS.2017.8211551 UR - https://api.elsevier.com/content/abstract/scopus_id/85046018499 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) A Survey Smart agriculture IoT with cloud computing.pdf N1 - Cited By (since 2017): 117 N2 - IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network. ER - TY - Conference Paper T1 - WSN as a tool for supporting agriculture in the precision irrigation A1 - Lima, G.H.E.L. De Y1 - 2010/// KW - agriculture KW - automation KW - irrigation KW - sensor networks KW - simulation environment JF - 6th International Conference on Networking and Services, ICNS 2010, Includes LMPCNA 2010; INTENSIVE 2010 SP - 137 EP - 142 DO - 10.1109/ICNS.2010.26 UR - https://api.elsevier.com/content/abstract/scopus_id/77953107540 N1 - Cited By (since 2010): 36 N2 - The Wireless Sensor Networks (WSN) are composed of many devices, called sensor nodes. Such nodes are able to perceive changes in the environment and perform certain actions. The current technologies in WSN were specially devised based on results from computational models and using tool as TinyOS e Tossim, united with the resources of a high level programming language, in this case Python and its PyGame library. However, such tools focus only in a specific task, for example, either allow the creation of the architecture structure of the sensor nodes (TinyOS) or simulate the WSN behavior in text mode (Tossim). The absence of integrated environments for verifying and validating those networks impairs their advancement. Thus, this work focus its research on the integration of existing computer tools in order to estabilish an application development environment for WSN, uniting the robustness of programming languages with the usability of a friendly interface. Many areas can be beneficiated with WSN. Due to the great agriculture potential of northeast Brazil, the application domain in this research is assisted agricultura, once no similar attempt was found in the literature. Therefore, this paper's study case encompasses the developed simulation environment applied to irrigation of soccer fields. ER - TY - Article T1 - Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture A1 - Tokekar, P Y1 - 2016/// KW - Agriculture KW - path planning KW - robot sensing systems JF - IEEE Transactions on Robotics VL - 32 IS - 6 SP - 1498 EP - 1511 DO - 10.1109/TRO.2016.2603528 UR - https://api.elsevier.com/content/abstract/scopus_id/84991045084 N1 - Cited By (since 2016): 242 N2 - We study two new informative path planning problems that are motivated by the use of aerial and ground robots in precision agriculture. The first problem, termed sampling traveling salesperson problem with neighborhoods (SAMPLINGTSPN), is motivated by scenarios in which unmanned ground vehicles (UGVs) are used to obtain time-consuming soil measurements. The input in SAMPLINGTSPN is a set of possibly overlapping disks. The objective is to choose a sampling location in each disk and a tour to visit the set of sampling locations so as to minimize the sum of the travel and measurement times. The second problem concerns obtaining the maximum number of aerial measurements using an unmanned aerial vehicle (UAV) with limited energy. We study the scenario in which the two types of robots form a symbiotic system-the UAV lands on the UGV, and the UGV transports the UAV between deployment locations. This paper makes the following contributions. First, we present an O((r max )/(r min )) approximation algorithm for SAMPLINGTSPN, where r min and r max are the minimum and maximum radii of input disks. Second, we show how to model the UAV planning problem using a metric graph and formulate an orienteering instance to which a known approximation algorithm can be applied. Third, we apply the two algorithms to the problem of obtaining ground and aerial measurements in order to accurately estimate a nitrogen map of a plot. Along with theoretical results, we present results from simulations conducted using real soil data and preliminary field experiments with the UAV ER - TY - Article T1 - Application of IoT and Cloud Computing in Automation of Agriculture Irrigation A1 - Phasinam, K Y1 - 2022/// KW - Internet of Things KW - agriculture KW - smart irrigation JF - Journal of Food Quality VL - 2022 DO - 10.1155/2022/8285969 UR - https://api.elsevier.com/content/abstract/scopus_id/85124018450 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2022) Application of IoT and Cloud Computing in Automation of.pdf N1 - Cited By (since 2022): 6 N2 - All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world’s fresh water. People who work in agriculture are not only the world’s biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation. ER - TY - Article T1 - Internet of things based automated agriculture system for irrigating soil A1 - Kibria, M G A1 - Seman, Mohamad Tarmizi Abu Y1 - 2022/// KW - Blynk application KW - Internet of things KW - IoT cloud storage KW - Microcontroller KW - Smart agriculture KW - Smart irrigation JF - Bulletin of Electrical Engineering and Informatics VL - 11 IS - 3 SP - 1752 EP - 1764 DO - 10.11591/eei.v11i3.3554 UR - https://api.elsevier.com/content/abstract/scopus_id/85131518334 L1 - file:///C:/Users/sonsu/Downloads/3554-9527-1-PB.pdf N1 - Cited By (since 2022): 1 N2 - This work describes the design of an internet of things (IoT)-based prototype system for outdoor agriculture farmland that employs low-cost sensors for detecting basic agriculture parameters such as soil moisture level, air temperature, and air humidity to irrigate the farmland automatically and manually while tracing the water volume. Therefore, since the water requirement varies according to plant type and plant growth stages, this research focuses on a feature where user can adjust the soil moisture level from software according to the plant type and age stages so that a suitable moisture level can be maintained for plants. Apart from that, the system also has a feature to adjust the temperature threshold limit from the application software so that when the temperature sensor value exceeds the respective threshold limits, it sprinkles the water for a limited time so that plants are not damaged due to overheating. Google sheets is used for cloud storage, and Wi-Fi is used to send data to the cloud using the HTTP protocol. The Blynk application software is used to monitor and control purposes. ER - TY - Review T1 - A comparative study on Internet of Things (IoT) and its applications in smart agriculture A1 - Srilakshmi, A Y1 - 2018/// KW - ANFIS and PLSR KW - Internet of things KW - Radio frequency identification JF - Pharmacognosy Journal VL - 10 IS - 2 SP - 260 EP - 264 SN - 0975-3575 DO - 10.5530/pj.2018.2.46 UR - https://api.elsevier.com/content/abstract/scopus_id/85041104577 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) A comparative study on Internet of Things (IoT) and its applications in smart agriculture.pdf N1 - Cited By (since 2018): 15 N2 - Agriculture plays a vital role in country’s economy and it has an extensive contribution to- wards human civilization. Due to the growing expansions in sensor devices, RFID and Inter- net protocols the architecture of Internet of Things (IoT) has been made to support agriculture by making a Smart agriculture. This paper describes the implementation of various IoT tech- niques and intelligent decision support systems used in agriculture. It provides a wide review on methods and technologies like ANFIS and PLSR Model predictions, experiences in various challenges as well as further work are discussed through the review article. ER - TY - Conference Paper T1 - Blockchain and IoT based food traceability for smart agriculture A1 - Lin, J Y1 - 2018/// KW - Blockchain KW - Food Traceability KW - Internet of Things KW - LPWAN JF - ACM International Conference Proceeding Series DO - 10.1145/3126973.3126980 UR - https://api.elsevier.com/content/abstract/scopus_id/85056715655 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Blockchain and IoT based food traceability for smart agriculture.pdf N1 - Cited By (since 2018): 24 N2 - Food safety is becoming more and more serious topic worldwide. To tackle the food safety issues from the technical aspect, people need a trusted food traceability system that can track and monitor the whole lifespan of food production, including the processes of food raw material cultivation/breeding, processing, transporting, warehousing, and selling etc. In this paper, we propose a trusted, self-organized, open and ecological food traceability system based on blockchain and Internet of Things (IoT) technologies, which involves all parties of a smart agriculture ecosystem, even if they may not trust each other. We use IoT devices to replace manual recording and verification as many as possible, which can reduce the human intervention to the system effectively. Furthermore, we plan to use the smart contract technology to help the law-executor to find problems and process them timely. ER - TY - Article T1 - Soil monitoring and evaluation system using EDL-ASQE: Enhanced deep learning model for IoT smart agriculture network A1 - Sumathi, P Y1 - 2021/// KW - agriculture IoT network KW - enhanced deep learning KW - soil moisture KW - soil quality KW - weight factor JF - International Journal of Communication Systems VL - 34 IS - 11 DO - 10.1002/dac.4859 UR - https://api.elsevier.com/content/abstract/scopus_id/85106209826 N1 - Cited By (since 2021): 2 N2 - The enormous growth of the Internet of Things (IoT) network provides abundant support to agriculture and development, which states the future scope of IoT-based agriculture. In a recent scenario, agriculture IoT can be integrated with sensors, communication protocols, and microcontrollers for automated process executions to increase productivity. Moreover, deep learning effectiveness produces appropriate results and solves several real-time issues related to agriculture-based advancements. The proposed system presents the design of an IoT network communication system to estimate the soil conditions. Soil quality is an important factor in modernized agriculture, productivity enhancement, and hydrological cycles. By the soil quality analysis, the accurate prediction is very significant for sensible usage of resources. An enhanced deep learning model for IoT network-based automated soil quality evaluation observes the complex soil features and meteorological factors with those concerns. Here, the real-time samples are collected from the local area sensor network for analysis. The deep learning model is developed with big data fitting ability for soil quality prediction. The weight factors (W.F.) are derived for measuring the soil quality accurately. The proposed IoT network-based agriculture structure allows a flexible approach to different types of crops and implementation in agricultural areas. Experimental results obtained in the laboratory and onsite confirmed the performance and reliability of the system. The result evaluations are carried out based on precision, accuracy, and processing time, and results show that the model achieves better results than compared models. ER - TY - Conference Paper T1 - Research on the agriculture intelligent system based on IOT A1 - Fu, B Y1 - 2012/// KW - Agriculture KW - Expert systems KW - Internet KW - Production KW - Radio frequency identification KW - Temperature sensors JF - Proceedings of 2012 International Conference on Image Analysis and Signal Processing, IASP 2012 SP - 386 EP - 389 DO - 10.1109/IASP.2012.6425066 UR - https://api.elsevier.com/content/abstract/scopus_id/84874563997 N1 - Cited By (since 2012): 28 N2 - According to the need for transition from traditional agriculture to modern agriculture in China and the Spirits of 2012 Central No. 1 Document of the People's Republic of China, the agriculture intelligent system based on IOT is introduced for organic melon and fruit production. A number of new technologies were used in the system, such as RFID, sensors and so on. This system contains three platforms. The expert system service platform set up a mathematical model to capture the data of the growing melons, and then make a decision. The intelligent production management platform could control the plant environment, the supply of water and fertilizer. The Internet trading platform with traceability function is an extended service for fruit growers and consumers. It is significative that the Agriculture Intelligent System was developed to control the crop growth environment, and to optimize fruit planting management, etc. If the system is adopted in a large region, it will provide benefits to fruit growers. ER - TY - Article T1 - Feasibility analysis proposal for an iot infrastructure for the efficient processing of data in agriculture, case study on cocoa A1 - Martinez, M A Q A1 - Espinoza, Hugo Fransisco Vera A1 - Vazquez, Maikel Yelandi Leyva A1 - Rios, Monica Daniela Gomez Y1 - 2020/// KW - Agriculture data KW - Efficient data processing KW - Internet of Things KW - LWPM KW - Smart agriculture JF - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao VL - 2020 SP - 413 EP - 426 UR - https://api.elsevier.com/content/abstract/scopus_id/85094874881 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Feasibility Analysis Proposal for an IoT infrastructure for efficient processing of data in agriculture case studi on cacao.pdf N1 - Cited By (since 2020): 4 N2 - IoT information in agriculture and its benefits were analyzed to determine a proposal in the management of information generated in the sowing and harvesting of cocoa. The problem is the lack of knowledge of farmers and support entities about the concepts and application of IoT in agriculture. The objective of this paper is to propose a feasibility analysis of an IoT infrastructure for efficient data processing in agriculture in the case of cocoa. The exploration and deductive method were applied for the analysis of the information of the referenced articles. It resulted in a Conceptual Model of IoT in agriculture, a four-layer Architecture, an Execution Algorithm and a Proposal for infrastructure feasibility. It was concluded that the proposal based on IoT is an efficient option for collecting, transporting, processing and delivering information in the process of sowing and harvesting cocoa; feasibility shows that the proposal is viable, simulation tests measured the architecture efficiency with a minimum value of 95.70%. A collective feasibility evaluation based on the LWPM operator shows very high feasibility according to the experts. ER - TY - Conference Paper T1 - IoT Based Intelligent Agriculture Field Monitoring System A1 - Ashifuddinmondal, M Y1 - 2018/// KW - Agriculture KW - Internet of Things KW - Smart Farming KW - ThingSpeak cloud JF - Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018 SP - 625 EP - 629 DO - 10.1109/CONFLUENCE.2018.8442535 UR - https://api.elsevier.com/content/abstract/scopus_id/85053684163 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) IoT Based Intelligent Agriculture Field Monitoring System.pdf N1 - Cited By (since 2018): 36 N2 - Agriculture is becoming an important growing sector throughout the world due to increasing population. Major challenge in agriculture sector is to improve farm productivity and quality of farming without continuous manual monitoring to meet the rapidly growing demand for food. Apart from increasing population, the climate change is also a big concern in agricultural sector. The purpose of this research work is to propose a smart farming method based on Internet of Things (IoT) to deal with the adverse situations. The smart farming can be adopted which offer high precision crop control, collection of useful data and automated farming technique. This work presents an intelligent agriculture field monitoring system which monitors soil humidity and temperature. After processing the sensed data it takes necessary action based on these values without human intervention. Here temperature and moisture of the soil are measured and these sensed values are stored in ThingSpeak [11] cloud for future data analysis. ER - TY - Review T1 - Machine learning techniques with IoT in agriculture A1 - More, S Y1 - 2019/// KW - Internet of things KW - Wireless sensor networks KW - crop prediction KW - deep learning. JF - International Journal of Advanced Trends in Computer Science and Engineering VL - 8 IS - 3 SP - 742 EP - 747 SN - 2278-3091 DO - 10.30534/ijatcse/2019/63832019 UR - https://api.elsevier.com/content/abstract/scopus_id/85068788490 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Machine learning techniques with IoT in agriculture.pdf N1 - Cited By (since 2019): 10 N2 - Traditionally methods developed for agriculture focused on the specific functionality/ domain-dependent such as temperature, humidity pressure, etc and lacks of knowledge base for smart irrigation. In modern generation, the volume of information gathered by numerous sensors over a period, with a diverse series of applications nowadays, is acknowledged by means of Internet of things. Grounded by the properties of an application, the IoT strategies drive outcome in large volume and instantaneous streams of data. Implementing analytics for a large volume of data stream to find novel information, further predict understandings to produce precise and decisions to control a vigorous method that introduces IoT in a well-meaning model for industrial production besides a eminence of life refining technology. Machine learning (deep learning) eases the analytics and knowledge in the IoT domain, the major perspective is to use machine learning (deep learning) in IoT. Hence, in this paper we discuss a systematic review to determine different methods in agriculture practices. ER - TY - Article T1 - Developing a new wireless sensor network platform and its application in precision agriculture A1 - Aquino-Santos, R Y1 - 2011/// KW - precision agriculture KW - routing algorithm KW - sensor networks KW - technological platform JF - Sensors VL - 11 IS - 1 SP - 1192 EP - 1211 DO - 10.3390/s110101192 UR - https://api.elsevier.com/content/abstract/scopus_id/79251645898 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2011) Developing a new wireless sensor network platform and its application in precision agriculture.pdf N1 - Cited By (since 2011): 44 N2 - Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of "smart dust" offer great advantages due to their small size, low power consumption, easy integration and support for "green" applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modified version of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network. ER - TY - Conference Paper T1 - Implementation of an Automated Irrigation System for Agriculture Monitoring using IoT Communication A1 - Rani, D Y1 - 2019/// KW - Automated Irrigation System KW - Internet of Things KW - Sensors JF - Proceedings of IEEE International Conference on Signal Processing,Computing and Control VL - 2019 SP - 138 EP - 143 SN - 2643-8615 DO - 10.1109/ISPCC48220.2019.8988390 UR - https://api.elsevier.com/content/abstract/scopus_id/85085468205 N1 - Cited By (since 2019): 10 N2 - In the recent existence, one of the best and familiar technologies scaling innovative heights and making a standard scale is the Internet of Things (IoT). It is definitely the opportunity of digital communication that has altered things of authentic humanity into smarter electronic devices. In India, regrettably, farmers still use conventional techniques of agricultural supervision leading to inputs wastages and near to the ground production due imprecise types and amount of water functional to the field which depends upon soil investigation and plant capitulate. Irrigation processes are as old as humans because agriculture is the most advanced occupation of civilian humanity and need to change irrigation technology. The main target of this paper is to overcome the previously mentioned tribulations by planning a automated water system structure grounded on the idea of IoT and to provide a smart and sustainable solution to a farmer for maintaining their crop health besides yield. We have considered the grain crop that is rice because water is essential for the growth and development of rice plants. Development of the concept of IoT with sensor technology and the solar system is an innovative and future trend in the agriculture filed. Watering large areas of plants is a difficult task and needs an automatic system to reduce human effort. In order to overcome this problem, many irrigation planning methods have been developed based on soil, crop and weather monitoring. Irrigation planning depends on how much water is used during irrigation. The water system control is the way toward altering the capability of Hydrogen (pH), temperature and moisture of the dirt soil. Smart and automated irrigation is a current farming procedure that has been broadly cultivated in created nations to assemble the difficulties of expanding demand of food supply. In this work, we use the concept of a dashboard, it operates via http protocol and by using this concept we can turn on/off water ER - TY - Article T1 - Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices A1 - Alyahya, S Y1 - 2022/// JF - Electronics (Switzerland) VL - 11 IS - 6 DO - 10.3390/electronics11060963 UR - https://api.elsevier.com/content/abstract/scopus_id/85126727956 N1 - Cited By (since 2022): 1 ER - TY - Article T1 - A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation A1 - Balivada, S Y1 - 2022/// JF - Sensors VL - 22 IS - 10 DO - 10.3390/s22103913 UR - https://api.elsevier.com/content/abstract/scopus_id/85130369006 ER - TY - Review T1 - Root Zone Sensors for Irrigation Management in Intensive Agriculture A1 - Pardossi, A Y1 - 2009/// KW - Dielectric soil moisture sensors KW - irrigation efficiency KW - irrigation scheduling KW - smart water application technology KW - soil matric potential KW - tensiometer KW - wireless sensor networks JF - Sensors VL - 9 IS - 4 SP - 2809 EP - 2835 SN - 1424-8220 DO - 10.3390/s90402809 UR - https://api.elsevier.com/content/abstract/scopus_id/79956330547 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2009) Root Zone Sensors for Irrigation Management in Intensive Agriculture.pdf N1 - Cited By (since 2009): 94 N2 - Crop irrigation uses more than 70% of the world's water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower's experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS' (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy. ER - TY - Article T1 - Sensors driven ai-based agriculture recommendation model for assessing land suitability A1 - Vincent, D R Y1 - 2019/// KW - Internet of things KW - agricultural data KW - agriculture KW - land suitability using sensors KW - multi-layer perceptron KW - sensor data KW - smart agriculture JF - Sensors (Switzerland) VL - 19 IS - 17 DO - 10.3390/s19173667 UR - https://api.elsevier.com/content/abstract/scopus_id/85071714641 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability.pdf N1 - Cited By (since 2019): 56 N2 - The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation. ER - TY - Conference Paper T1 - A low power IoT network for smart agriculture A1 - Heble, S A1 - Kumar, Ajay A1 - Prasad, K.V.V Durga A1 - Samirana, Soumya A1 - P.Rajalakshmi A1 - Desai, U. B. Y1 - 2018/// KW - Internet of Things KW - agriculture KW - computerised monitoring KW - moisture measurement KW - soil KW - wireless sensor networks JF - IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings VL - 2018 SP - 609 EP - 614 DO - 10.1109/WF-IoT.2018.8355152 UR - https://api.elsevier.com/content/abstract/scopus_id/85050372976 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) A Low Power IoT Network for Smart Agriculture.pdf N1 - Cited By (since 2018): 71 N2 - Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field. ER - TY - Conference Paper T1 - Precision Agriculture using Data Mining Techniques and IOT A1 - Sneha, N Y1 - 2019/// KW - Global Positioning System KW - Internet of Things KW - agriculture KW - crops KW - data mining KW - pattern clustering KW - regression analysis JF - 1st IEEE International Conference on Advances in Information Technology, ICAIT 2019 - Proceedings SP - 376 EP - 381 DO - 10.1109/ICAIT47043.2019.8987333 UR - https://api.elsevier.com/content/abstract/scopus_id/85081099862 N1 - Cited By (since 2019): 3 N2 - The objective of the paper is to improve the crop yield in agriculture by measuring the factors affecting, by reducing the consumption and labor work, increase the crop productivity using IOT smart farming and Data mining techniques. Precision agriculture makes use of the technology like IOT sensors, GPS services for communication, (M2M) machine to machine and Data mining techniques. The research concentrates on the extension of two paper which focuses on improving the agriculture using data mining techniques such as DBSCAN, PAM, CLARA, Chameleon, regression techniques. The extended work focuses on identifying the critical factors from the results of clustering techniques and adapting the different sensors for each cluster associated with critical factors. The results include analysis of the cluster results and selection of sensors based on critical factors. Also provides an overview of the selection of sensors. ER - TY - Conference Paper T1 - Design and development of precision agriculture system using wireless sensor network A1 - Nandurkar, S Y1 - 2014/// KW - Irrigation KW - Microcontroller KW - Moisture KW - Radio frequency identification KW - Soil moisture KW - Temperature sensors JF - 1st International Conference on Automation, Control, Energy and Systems - 2014, ACES 2014 DO - 10.1109/ACES.2014.6808017 UR - https://api.elsevier.com/content/abstract/scopus_id/84901248438 N1 - Cited By (since 2014): 89 N2 - Crop farming in India is labour intensive and obsolete. Farming is still dependent on techniques which were evolved hundreds of years ago and doesn't take care of conservation of resources. The newer scenario of decreasing water tables, drying up of rivers and tanks, unpredictable environment present an urgent need of proper utilization of water. We have the technology to bridge the gap between water usage and water wastage. Technology used in some developed countries is too expensive and complicated for a common farmer to understand. Our project is to give cheap, reliable, cost efficient and easy to use technology which would help in conservation of resources such as water and also in automatizing farms. We proposed use of temperature and moisture sensor at suitable locations for monitoring of crops. The sensing system is based on a feedback control mechanism with a centralized control unit which regulates the flow of water on to the field in the real time based on the instantaneous temperature and moisture values. The sensor data would be collected in a central processing unit which would take further action. Thus by providing right amount of water we would increase the efficiency of the farm. The farmer can also look at the sensory data and decide course of action himself. We have made the interface of our project keeping in view the educational and financial background of average Indian farmer. In this paper we are proposed a low cost and efficient wireless sensor network technique to acquire the soil moisture and temperature from various locations of farm and as per the need of crop controller take the decision to make irrigation ON or OFF. ER - TY - Conference Paper T1 - Machine learning Implementation in IoT based Intelligent System for Agriculture A1 - Bhanu, K N Y1 - 2020/// KW - Cloud computing KW - Internet of Things KW - Machine Learning KW - Smart Agriculture KW - wireless sensor networks JF - 2020 International Conference for Emerging Technology, INCET 2020 DO - 10.1109/INCET49848.2020.9153978 UR - https://api.elsevier.com/content/abstract/scopus_id/85090573028 N1 - Cited By (since 2020): 14 N2 - Innovation in Internet of Things (IoT) has acquired changes in everyday life. Agriculture plays a major role in most of the countries and the need for this sector is to become "Smart". A primary inference is the absence of knowledge with respect to soil. There are many types of soil present and each kind of soil has various qualities. A thorough knowledge about soil conditions give rise to various information of soil that can be handled to obtain better yields of crop. Machine learning is a trending technology and it helps in the agricultural area to build the exactness and gives solutions for the crop yield problem. Machine learning (ML) is combined with enormous informational advancements and superior figuring to make new chances to unwind, measure, and understand the data intensive prediction in agricultural environments. This paper conducts a thorough study of various concepts of machine learning for IoT based Smart Agriculture system. ER - TY - Conference Paper T1 - LoRa-based Visual Monitoring Scheme for Agriculture IoT A1 - Ji, M Y1 - 2019/// KW - LoRa KW - Smart Agriculture KW - Visual Monitoring JF - SAS 2019 - 2019 IEEE Sensors Applications Symposium, Conference Proceedings DO - 10.1109/SAS.2019.8706100 UR - https://api.elsevier.com/content/abstract/scopus_id/85065909839 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) LoRa-based Visual Monitoring Scheme for Agriculture IoT.pdf N1 - Cited By (since 2019): 26 N2 - LoRa is a low-power wide-range wireless networking technology suitable for low-rate long-range applications in the Internet of Things (IoT). For example in the agriculture industry, LoRa-based environmental sensing system enables farmers to remotely monitor the status of a large farm in near real-time. However, there had been only a few explorations to transfer multimedia data such as images or video using LoRa because of its low data rate and restricted bandwidth. To this end, we introduce a novel system to transmit continuous images taken from a camera on a static environment through LoRa. The key challenge is to reduce the amount of transmitted data while preserving the image quality and the quality of service delivered to the application. We develop a technique that splits image to grid patches, and transmits only the modified area of an image based on their dissimilarity measure. We implement and evaluate our scheme on a real LoRa device to show its performance and image quality. ER - TY - Conference Paper T1 - The study and application of the IOT technology in agriculture A1 - Zhao, J C Y1 - 2010/// KW - Internet of things KW - agriculture KW - data collection KW - monitoring JF - Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010 VL - 2 SP - 462 EP - 465 DO - 10.1109/ICCSIT.2010.5565120 UR - https://api.elsevier.com/content/abstract/scopus_id/77958590986 N1 - Cited By (since 2010): 179 N2 - In recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (internet of things) technology rapid development and wide application. In the paper, control networks and information networks integration of IOT technology has been studied based on the actual situation of agricultural production. Remote monitoring system with internet and wireless communications combined is proposed. At the same time, taking into account the system, information management system is designed. The collected data by the system provided for agricultural research facilities. ER - TY - Article T1 - IoT Smart Agriculture for Aquaponics and Maintaining Goat Stall System A1 - Effendi, M K R; A1 - Kassim, Murizah; A1 - Sulaiman, Norakmar Arbain; A1 - Shahbudin, Shahrani Y1 - 2020/// KW - Aquaponics KW - Arduino KW - Goat Stall KW - Internet of things KW - Node-RED KW - Smart Agriculture KW - sensors JF - International Journal of Integrated Engineering VL - 12 IS - 8 SP - 240 EP - 250 DO - 10.30880/IJIE.2020.12.08.023 UR - https://api.elsevier.com/content/abstract/scopus_id/85107755479 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT Smart Agriculture for Aquaponics and Maintaining Goat Stall System.pdf N1 - Cited By (since 2020): 3 N2 - This present a project development on smart farm and agriculture. The surge in global population is compelling a shift towards smart farm and agriculture practices. This coupled with the diminishing natural resources increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of internet of things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the farm and agriculture sector. The objective is to design a prototype that used internet of things in the farm and agriculture. Next is to have a monitoring and controlling or automation system that will benefits the farmer. Then collect all the data to be analyses on the rainfall, temperature, humidity and light intensity. The methodology comprised of hardware, software, programming, sensors such as water sensor, light depending resistor sensor, temperature and humidity sensor and weight sensor for collected data. Result presents a prototype on aquaponics and goat stall that implement the concepts of internet of things for monitoring, controlling or automation system while data analytics is presented from all the sensors. Analytic data on the temperature, light intensity, humidity and rainfall rate are analyzed. Surrounding temperature are important for both plant and fish because if it too hot, they can die easily. While optimum light is needed by the plant for their photosynthesis process. Thus, by monitoring and collecting these parameters, data can be used for analyzing purpose. This project can benefits agriculture and farm sector. Prototype also can be used for small size like at the backyard or balcony of the house for person that likes gardening. ER - TY - Conference Paper T1 - Reliable Administration Framework of Drones and IoT Sensors in Agriculture Farmstead using Blockchain and Smart Contracts A1 - Chinnaiyan, R Y1 - 2020/// KW - Blockchain KW - Drones KW - Internet of things KW - Security KW - Sensors KW - Smart Contracts JF - ACM International Conference Proceeding Series SP - 106 EP - 111 DO - 10.1145/3378904.3378918 UR - https://api.elsevier.com/content/abstract/scopus_id/85083302400 N1 - Cited By (since 2020): 10 N2 - IOT adoption is significantly increasing across different industries in the recent decade and the security is the biggest concern for the enterprise and industries to safe guard the data which is emanating out of IOT sensors, Drones and aggregators. In this paper we discuss how blockchain's smart contracts will help to manage the security challenges of IOT sensors and devices. We provide the literature review and approaches on how we build Agro's drone specific smart contracts with different ledger and platform in section II and III. We will elaborate the high-level approach with conceptual framework in section V. we will share the deployment considerations and issues in section VI. In section VII we will present our significance and in section VIII we will share our recommendations and conclusion ER - TY - Conference Paper T1 - Design and implementation of a cloud-based IoT scheme for precision agriculture A1 - Khattab, A Y1 - 2016/// KW - Internet of Things KW - cloud computing KW - platform implementation KW - precision agriculture KW - sensor networks JF - Proceedings of the International Conference on Microelectronics, ICM SP - 201 EP - 204 DO - 10.1109/ICM.2016.7847850 UR - https://api.elsevier.com/content/abstract/scopus_id/85014938278 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Design and implementation of a cloud-based IoT scheme for precision agriculture.pdf N1 - Cited By (since 2016): 79 N2 - The Internet of Things (IoT) technology is currently shaping different aspects of human life. Precision agriculture is one of the paradigms which can use the IoT advantages to optimize the production efficiency and uniformity across the agriculture fields, optimize the quality of the crops, and minimize the negative environmental impact. In this paper, we present an IoT architecture customized for precision agriculture applications. The proposed three-layer architecture collects the needed data and relays it to a cloud-based back-end where it is processed and analyzed. Feedback actions based on the analyzed data can be sent back to the front-end nodes. We built a prototype of the proposed architecture to demonstrate its performance advantages. ER - TY - Conference Paper T1 - Using Cloud IOT for disease prevention in precision agriculture A1 - Foughali, K Y1 - 2018/// KW - Cloud-IOT KW - Decision support system KW - Late blight KW - Wireless sensor networks JF - Procedia Computer Science VL - 130 SP - 575 EP - 582 SN - 1877-0509 DO - 10.1016/j.procs.2018.04.106 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S187705091830468X UR - https://api.elsevier.com/content/abstract/scopus_id/85051242934 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Using Cloud IOT for disease prevention in precision agriculture.pdf N1 - Cited By (since 2018): 47 N2 - The application of decision support system (DSS) for potato late blight disease prevention has proven its benefit. In fact the DSS permits efficiency, minimizes cost and environment impact by estimating the exact requirement fungicide quantity to apply. This prediction using weather condition based late blight forecast model. The required weather information is collected from costly weather station or imprecise historical data. However, with the emergence of the IOT, huge number of low cost and low power sensors nodes can easily be deployed in farmlands in order to gather a precise climate data. Moreover, the collected data can be forwarded by Internet connection to the so called cloud IOT framework. In this paper we present a new prototype of late blight prevention decision support system based on sensor network and cloud IOT. ER - TY - Article T1 - The Role of Geospatial Technology with IoT for Precision Agriculture A1 - Bhanumathi, V Y1 - 2019/// KW - Crop production KW - Internet of Things KW - Precision agriculture KW - Smart farming KW - Wireless sensor networks JF - Studies in Big Data VL - 49 SP - 225 EP - 250 DO - 10.1007/978-3-030-03359-0_11 UR - https://api.elsevier.com/content/abstract/scopus_id/85062477059 N1 - Cited By (since 2019): 9 N2 - Precision agriculture is mainly used to make the farming as user-friendly to achieve the desired production of a crop. With the latest Geospatial technologies, the analysis related to any type of application using the Internet of Things (IoT) made each and everyone, to materialize the things whatever is imagined. The geographic information collected from various sources and with this, IoT establishes a communication to the entire world through an Internet. The information will be helpful in the maintenance of the farmland by applying the required amount of fertilizer at the right time in the right place. It is expected that in the future, this type of smart agriculture with the application of information and communication technologies including IoT will definitely bring a revolution in the global agricultural scenario to make it more resource-efficient and productive. The main goal in combining the Geospatial technology with IoT for precision is to monitor and predict the critical parameters such as water quality, soil condition, ambient temperature and moisture, irrigation, and fertilizer for improving the crop production. It can be expected that with the help of Geospatial and IoT in smart farming, the prediction of the amount of fertilizer, weeds, and irrigation will be accurate and it helps the farmers in making decisions related to all the requirements in terms of control and supply. ER - TY - Conference Paper T1 - IoT-empowered smart agriculture: A real-time light-weight embedded segmentation system A1 - Abouzahir, S A1 - Sadik, Mohamed A1 - Sabir, Essaid Y1 - 2017/// KW - Back Propagation Neural Network KW - Fuzzy C means KW - Internet of Things KW - Precision agriculture KW - Segmentation KW - Smart agriculture KW - Vegetation color index JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) VL - 10542 SP - 319 EP - 332 SN - 0302-9743 DO - 10.1007/978-3-319-68179-5_28 UR - https://api.elsevier.com/content/abstract/scopus_id/85034570804 N1 - Cited By (since 2017): 4 N2 - Internet of Things (IoT) is an emerging technology where standalone equipments and autonomous devices are connected to each other and users via Internet. When IoT concept meets agriculture, the future of farming is pushed to the next level, giving birth to what is called “Smart Agriculture” or “Precision Agriculture”. The most important benefit from IoT is that a user can daily monitor his crop online in a seamless fashion. High quality data gathered from various sensors and transferred wirelessly to farm database will increase farmers understanding to their landuse leading to increasing income and product quality. One of the monitoring process is weeds detection and crop yield estimation using camera sensors. The acquired images help farmers to build map of weeds distribution or yield quantity all over the field, these maps can be used either for real-time processing or to predetermine weeds regions based on field maps history of the previous seasons. This process is referred to as segmentation problem. Several algorithms have been proposed for that purpose, however, these algorithms were run only on high performance computers. In this paper, we evaluate performance and the robustness of the most used legacy algorithms under local conditions. We focused on implementing these schemes within real-time application constraint. For instance, these algorithms were implemented and run in a low-cost embedded system. ER - TY - Article T1 - Information service system of agriculture IoT A1 - Minbo, L Y1 - 2013/// KW - Agriculture KW - Information Discovery KW - Internet of things KW - Object Naming Service JF - Automatika VL - 54 IS - 4 SP - 415 EP - 426 DO - 10.7305/automatika.54-4.413 UR - https://api.elsevier.com/content/abstract/scopus_id/84891328963 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2013) Information Service System Of Agriculture IoT.pdf N1 - Cited By (since 2013): 28 N2 - Internet of Things (IoT) was faced with some difficulties which contained mass data management, various standards of object identification, data fusion of multiple sources, business data management and information service providing. In China, some safety monitoring systems of agricultural product always adopt centralized system architecture in which the data is stored concentratively. These systems could not be connected with or accessed by each other. This paper proposed an information system of agriculture Internet of Things based on distributed architecture. A distributed information service system based on IoT-Information Service, Object Naming Service, Discovery Service is designed to provide public information service including of capturing, standardizing, managing and querying of massive business data of agriculture production. A coding scheme for agricultural product, business location and logistic unit is provided for data identification. A business event model of agriculture IoT is presented for business data management. The whole system realizes the tracking and tracing of agricultural products, and quality monitoring of agriculture production. The implementation of this information service system is introduced. ER - TY - Conference Paper T1 - An Internet of Things (IoT) solution framework for agriculture in India and other Third World countries A1 - Nalinaksh, K Y1 - 2018/// KW - India KW - Internet of Things KW - agriculture KW - architecture KW - smart power management KW - wireless sensor networks JF - Proceedings - 2018 4th International Conference on Advances in Computing, Communication and Automation, ICACCA 2018 DO - 10.1109/ICACCAF.2018.8776792 UR - https://api.elsevier.com/content/abstract/scopus_id/85070536832 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) An Internet of Things (IoT) solution framework for agriculture in India and other Third World countries.pdf N1 - Cited By (since 2018): 2 N2 - Modern agricultural solutions have been, mostly, developed with focus on “Western farmlands”. However, there are severe disparities between farms in India and those in, for instance, Europe. Applying Western solutions in the Third World will not work. In this context we have developed prototype of an IoT simulator capturing situation in Indian agriculture. ER - TY - Conference Paper T1 - An IoT-based Smart Agriculture System with Locust Prevention and Data Prediction A1 - Salim, S A Y1 - 2021/// KW - NodeMCU KW - Raspberry Pi KW - ThingSpeak KW - agricultural factors KW - locust monitoring KW - machine learning KW - support vector regression KW - temperature prediction JF - 2021 8th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2021 SP - 201 EP - 206 DO - 10.1109/ICITACEE53184.2021.9617550 UR - https://api.elsevier.com/content/abstract/scopus_id/85123638889 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) AnIoT-basedSmartAgricultureSystemwithLocustPreventionandDataPrediction.pdf N1 - Cited By (since 2021): 3 N2 - Locust and grasshopper infestation have a long history of affecting crops and human lives. From ancient Egypt to the Bronze age, everywhere, we have seen the manifestation of locust outbreaks and how humans have fought against it for their survival generations after generations. The latest locust eruption began in June 2019 and has continued through 2020. It has been the worst one in the last 70 years in Middle Africa, Middle East, South Asia, and South America. Countries are taking precautions to be safe from this outbreak because, after this corona pandemic, no nation is willing to face another economic pandemic. In advances of facing the consequences of the locust swarms, we need to find an effective and smart solution. In this paper, we have come up with the idea of monitoring important agricultural factors such as soil moisture, temperature, and humidity using sensors to provide real-time information to the farmers about imminent locust infestation to their mobile. Also, to ease their work, our proposed system will provide water and pesticides automatically to the fields by using Raspberry Pi and Node MCU. Our proposed system will generate ultraviolet light and loud noise to kill the insects in case of a locust outbreak. As locust's habitats are closely related to different agricultural factors, linear regression, logistic regression, and support vector regression, machine learning algorithms have been implemented to predict the temperature and humidity so that the farmers can anticipate these factors well ahead of time and plan accordingly. Overall a next-generation solution to fight the locusts has been implemented in this paper. ER - TY - Conference Paper T1 - IoT and Energy Efficiency for Smart Agriculture using Adcon Telemetry Devices A1 - Suciu, G Y1 - 2018/// KW - Internet KW - Internet of Things KW - agriculture KW - computerised monitoring KW - crops KW - diseases KW - telemetry JF - 2018 International Symposium on Fundamentals of Electrical Engineering, ISFEE 2018 DO - 10.1109/ISFEE.2018.8742433 UR - https://api.elsevier.com/content/abstract/scopus_id/85068696628 N1 - Cited By (since 2018): 3 N2 - The IoT concept has grown in recent years and has transformed people's lives by making them easier, regardless of the scope of application. The global population is projected to grow more and more, reaching 9 billions by the year 2050, so IoT devices are needed in agriculture to increase crop yield and therefore feed sources. However, this solution is not exempt from challenges. Weather conditions, climate change and the environment have a great impact on farming practices. To reduce the costs and losses involved in these activities, we analyze the use of the equipment provided by Adcon Telemetry, such as solutions for plant monitoring, disease detection, frost warning, decision support, management and water quality. All data collected by Adcon equipment can be viewed on the addVANTAGE Pro platform, which can be accessed anywhere using a web browser and Internet connection, in graphical or tabular form. ER - TY - Article T1 - Sensors for agriculture and the food industry A1 - Li, S Y1 - 2010/// KW - food industry KW - precision agriculture KW - sensors KW - wireless sensor networks JF - Electrochemical Society Interface VL - 19 IS - 4 SP - 41 EP - 46 DO - 10.1149/2.F05104if UR - https://api.elsevier.com/content/abstract/scopus_id/79953770496 N1 - Cited By (since 2010): 35 N2 - ntensively investigat remote spectral sensing of crops has proven to be an important tool in modern agricultural management. Agricultural remote spectral sensing typically refers to imagery taken from above a field where the incident electromagnetic radiation is generally sunlight. The remote sensing is characterized by spatial resolution, spectral resolution, and temporal resolution. Spatial resolution refers to the smallest area that can be distinguished in the image. Advances in hyperspectral imaging have led to improvements in spectral resolution over the past two decades. Today, hyperspectral imaging systems can measure numerous very narrow contiguous spectral bands throughout the visible, near-infrared, mid-infrared, and thermal infrared portions of the electromagnetic spectrum. The food industry has used remote spectral sensing to monitor food quality and detect possible food contaminants. ER - TY - Article T1 - FarmFox: A Quad-Sensor-Based IoT Box for Precision Agriculture A1 - Sengupta, A A1 - Debnath, Biswajit A1 - Das, Abhijit A1 - De, Debashis Y1 - 2021/// KW - Agriculture KW - Internet of Things KW - Monitoring KW - Sensors KW - Soil KW - Temperature measurement KW - Temperature sensors KW - Wireless sensor networks JF - IEEE Consumer Electronics Magazine VL - 10 IS - 4 SP - 63 EP - 68 DO - 10.1109/MCE.2021.3064818 UR - https://api.elsevier.com/content/abstract/scopus_id/85102687086 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(sengupta 2021) FarmFox A Quad-Sensor based IoT box.pdf N1 - Cited By (since 2021): 7 N2 - The amalgamation of traditional farming methodology with IoT can lead to sustainable agriculture. With this rationale, we have designed and developed an IoT-enhanced device-FarmFox, which can analyze the sensed information and transmitting it to the user via the internet. FarmFox thrives in real-time data collection, soil health monitoring via in-situ analysis, and controlling the whole architecture from a remote location. Compared to existing devices, FarmFox is an economic alternative as it utilizes Arduino-based hardware. Turbidity and pH, these two parameters are first incorpporated in FarmFox. The results confirm the success of soil health in terms of the four parameters. It is expected that the real-life implementation of FarmFox will lead toward a cost-effective smart solution for sustainable agriculture. ER - TY - Review T1 - IoT-based smart irrigation systems: An overview on the recent trends on sensors and iot systems for irrigation in precision agriculture A1 - García, L Y1 - 2020/// KW - Internet of things KW - irrigation KW - precision agriculture KW - sensors JF - Sensors (Switzerland) VL - 20 IS - 4 SN - 1424-8220 DO - 10.3390/s20041042 UR - https://api.elsevier.com/content/abstract/scopus_id/85079571638 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT-Based Smart Irrigation Systems An Overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture.pdf N1 - Cited By (since 2020): 143 N2 - Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems. ER - TY - Conference Paper T1 - A survey of wireless sensor technologies applied to precision agriculture A1 - Barcelo-Ordinas, J M Y1 - 2013/// KW - mobility networks KW - smartphone KW - tag based systems KW - wireless sensor networks JF - Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013 SP - 801 EP - 808 UR - https://api.elsevier.com/content/abstract/scopus_id/84893358485 N1 - Cited By (since 2013): 38 N2 - This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture. The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are: (1) scalar wireless sensor networks; (2) wireless multimedia sensor networks; (3) mobility of nodes; (4) tag-based systems; and (5) smart-phone applications. ER - TY - Conference Paper T1 - Development of WSN system for precision agriculture A1 - Santoshkumar Y1 - 2015/// KW - Agriculture farm KW - Wireless sensor networks KW - ZigBee JF - ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems DO - 10.1109/ICIIECS.2015.7192904 UR - https://api.elsevier.com/content/abstract/scopus_id/84956971596 N1 - Cited By (since 2015): 25 N2 - The agriculture sector is changing rapidly pointing to the future of automated and embedded systems with an array of sensors to monitor and control the growing plants in a way to protect workers, the environment and profits associated with it. The continuous monitoring and controlling of distantly located plants is labour intensive and technically challenging business. In modern precision agriculture, a Wireless Sensor Network (WSN) provides a simple cost effective solution to monitor and control. The basic parameters to be monitored are temperature and humidity (moisture content in the soil). A smart low cost WSN system for precision agriculture is proposed for monitoring and control using open software and electronic prototype - ARDUINO. ER - TY - Conference Paper T1 - An ISM-Band automated irrigation system for agriculture IoT A1 - Bogdanoff, M A1 - Tayeb, Shahab Y1 - 2020/// KW - Agriculture KW - Automation KW - ISM KW - Internet of things JF - IEMTRONICS 2020 - International IOT, Electronics and Mechatronics Conference, Proceedings DO - 10.1109/IEMTRONICS51293.2020.9216351 UR - https://api.elsevier.com/content/abstract/scopus_id/85096365600 N1 - Cited By (since 2020): 2 N2 - In this paper, an inexpensive and user-friendly agriculture automation system is proposed by networking a collection of sensors and actuators to sense the moisture content of the soil and control the water valves for multiple irrigation zones. Using freely available frequencies in the Industrial, Scientific, and Medical (ISM) bands, multiple sensors and actuators can be networked to communicate with one another without the need to pay for subscriptions to existing cellular networks. Each sensor and actuator connect to a central communication node which connects the end-user to the sensors and actuators through a cloud server. The proposed system can act as a base for large-scale smart agriculture deployments. ER - TY - Article T1 - Application mode construction of internet of things (IOT) for facility agriculture in Beijing A1 - Yan, X Y1 - 2012/// KW - agriculture KW - facilities KW - intelligent control KW - internet of things KW - sensors JF - Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering VL - 28 IS - 4 SP - 149 EP - 154 DO - 10.3969/j.issn.1002-6819.2012.04.024 UR - https://api.elsevier.com/content/abstract/scopus_id/84863129192 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2012) Application_mode_construction_of_internet_of_things（IOT）for_facility_agriculture_in_Beijing.pdf N1 - Cited By (since 2012): 25 N2 - Focusing on the issues on the quality and safety of agricultural products and ecological environment safety in Beijing, with the "shopping basket" program requirements of the Beijing government, in accordance with the whole industrial chain of facilities agriculture which includes before/during/post producing, by using technologies of biological, sensor, wireless communication and automatic control so on, the application pattern of IOT was constructed in the needs of the current situation and development of the facility agriculture in Beijing. Mainly using IOT technology with independent intellectual property rights, the perception environment, low cost wireless self-organized network, cloud services platform, intelligent decision-making service and feedback control system were constructed were based on facility agricultural IOT in Beijing, realizing remote diagnosis, monitor and early warning, command decision about the diseases and pests, intelligence control of fertilizer/water/drug, quality and safety supervision and tracing about facility agricultural products, formulating the related technical standards to provide a reference for the establishment of technology standards for facility agricultural IOT in our country. Successful construction application mode of facility agricultural IOT in Beijing will provide the reference model for construction of IOT system in other parts of the country. ER - TY - Article T1 - BlockChain with IoT, an emergent routing scheme for smart agriculture A1 - Awan, S H Y1 - 2020/// KW - Internet of Things KW - agriculture KW - efficient KW - energy scheme JF - International Journal of Advanced Computer Science and Applications VL - 11 IS - 4 SP - 420 EP - 429 DO - 10.14569/IJACSA.2020.0110457 UR - https://api.elsevier.com/content/abstract/scopus_id/85085335029 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) BlockChain_with_IoT_an_Emergent_Routing_Scheme_for smart agriculture.pdf N1 - Cited By (since 2020): 11 N2 - Blockchain is an emerging field of study in a number of applications and domains. Especially when combine with Internet of Things (IoT) this become truly transformative, opening up new plans of action, improving engagement and revolutionizing many sectors including agriculture. IoT devices are intelligent and have high critical capabilities but low-powered and have less storage, and face many challenges when used in isolation. Maintaining the network and consuming IoT energy by means of redundant or fabricated data transfer lead to consumption of high energy and reduce the life of IoT network. Therefore, an appropriate routing scheme should be in place to ensure consistency and energy efficiency in an IoT network. This research proposes an efficient routing scheme by integrating IoT with Blockchain for distributed nodes which work in a distributed manner to use the communicating links efficiently. The proposed protocol uses smart contracts within heterogeneous IoT networks to find a route to Base Station (BS). Each node can ensure route from an IoT node to sink then base station and permits IoT devices to collaborate during transmission. The proposed routing protocol removes redundant data and blocks IoT architecture attacks and leads to lower consumption of energy and improve the life of network. The performance of this scheme is compared with our existing scheme IoT-based Agriculture and LEACH in Agriculture. Simulation results show that integrating IoT with Blockchain scheme is more efficient, uses low energy, improves throughput and enhances network lifetime. ER - TY - Conference Paper T1 - Precision Agriculture Monitoring System Using Green Internet of Things (G-IoT) A1 - Ali, T A A Y1 - 2018/// KW - Internet KW - Internet of Things KW - agriculture KW - air pollution control KW - computerised monitoring KW - crops KW - decision support systems KW - green computing KW - health care KW - user interfaces JF - Proceedings of the 2nd International Conference on Trends in Electronics and Informatics, ICOEI 2018 SP - 481 EP - 487 DO - 10.1109/ICOEI.2018.8553866 UR - https://api.elsevier.com/content/abstract/scopus_id/85059986280 N1 - Cited By (since 2018): 21 N2 - The existing agriculture applications incorporating IoT are helping to increase the productivity of the crops; however, these applications are also introduced some disadvantages, therefore the current researchers are working to defeat these challenges in the futuristic applications by deploying new technologies with no or minimum negative impact on the environment and human health; Green IoT (G-IoT) and green nanotechnology are appear. These technologies are not easy to be used by farmers. So, this research work aims to build a realtime, cost-effective Precision Agriculture Monitoring System with less power consumption, less Green House Gas (GHG) emissions, and a user-friendly interface to help the farmers monitor the parameter variations of their farm (weather, water, soil, pest detection, intrusion detection, fire detection) periodically from anywhere and at any time using their smart-phones. The proposed system will act as a decision support system helps farmers to take the appropriate actions based on the farm parameter's variation; by sending an alert email to the farmer when it is required. ER - TY - Conference Paper T1 - An autonomous multi-sensor UAV system for reduced-input precision agriculture applications A1 - Katsigiannis, P Y1 - 2016/// KW - agricultural engineering KW - autonomous aerial vehicles KW - crops KW - geophysical image processing KW - infrared imaging KW - robot vision KW - sensor fusion KW - spectral analysis JF - 24th Mediterranean Conference on Control and Automation, MED 2016 SP - 60 EP - 64 DO - 10.1109/MED.2016.7535938 UR - https://api.elsevier.com/content/abstract/scopus_id/84986224259 N1 - Cited By (since 2016): 46 N2 - The constant innovation and advancement in unmanned aerial vehicle (UAV) sensing technology has facilitated a series of applications in the field of agriculture. The adoption of precision agriculture and reduced-input farming technics entails higher level of input data, with enhanced spatial and spectral resolution, and increased frequency of information delivery. Whereas satellite remote sensing still has decisive limitations for use in farm management applications, especially in small-scale agriculture, the comparative advantages of UAVs in these aspects propelled them as an alternative data collection platform. However, automation in the deployment of UAV sensing systems for operational in-field use, integration of visible, near-infrared and thermal spectral ranges, standardization of data collection, data processing and analysis workflow, production of readily available services, and credibility of reliable economic return from their incorporation into agronomical practices are components still relatively absent from the agriculture industry. In this paper, we demonstrate the operational use of a recently developed autonomous multi-sensor UAV imaging system, which is designed to provide spectral information related to water management for a pomegranate orchard. Vegetation and water stress indices were derived from both multispectral and thermal spectral data collected simultaneously from the system, and were used as indicators for crop water stress and crop health condition. It is concluded that the developed system addresses the needs and challenges identified for the incorporation of UAV sensing technology into reduced-input precision agriculture applications. ER - TY - Conference Paper T1 - IoT based multi-sensor data acquisition system for the application of smart agriculture A1 - Kumar, S Y1 - 2020/// KW - Internet of things KW - Irrigation system KW - Mobile phone KW - Sensors JF - Communications in Computer and Information Science VL - 1192 SP - 329 EP - 342 SN - 1865-0929 DO - 10.1007/978-981-15-3666-3_27 UR - https://api.elsevier.com/content/abstract/scopus_id/85082392650 N1 - Cited By (since 2020): 2 N2 - In today’s world Internet-of-Things (IoT) is creating fascinating applications by providing the common platform for different domains. This paper proposes an IoT based Multi-Sensor Data Acquisition System to control, monitor and manage different devices together for the application of smart agriculture. The system is designed to measure some of the important parameters related to the crop yield such as soil moisture, temperature, relative humidity, and light intensity. It provides an automated irrigation to crop based on the data obtained from different sensors. In addition, the present system provides an added future for monitoring data and controlling the irrigation system in real-time without losing data during transmission. The irrigation system can be controlled from any location through a mobile phone in two ways one is automatic and the other one is manual control. Further, it is made to provide ease of operation and doesn’t require the knowledge of modern technology for the same. The developed system is very economical and it makes use of solar energy to increase the autonomy of embedded and its associated devices to provide perpetual operation. The aim of the system is to improve the crop yield by the effective use of natural resources. ER - TY - Conference Paper T1 - A2S: Automated agriculture system based on WSN A1 - Yoo, S E Y1 - 2007/// KW - agriculture KW - greenhouse KW - wireless sensor networks JF - Proceedings of the International Symposium on Consumer Electronics, ISCE DO - 10.1109/ISCE.2007.4382216 UR - https://api.elsevier.com/content/abstract/scopus_id/50249183522 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2007) A2S Automated agriculture system based on WSN.pdf N1 - Cited By (since 2007): 76 N2 - This paper describes the results of real deployment of A2S which consists of WSN(Wireless Sensor Network) to monitor and control the environments and a management sub-system to manage the WSN and provide various and convenient services to consumers with hand-held devices such as a PDA living a farming village. The WSN were deployed in greenhouses with melon and cabbage in Dongbu Handong Seed Research Center. A2S was used to monitor the growing process of them and control the environment of the greenhouses. We acquired valuable experiences and ideas from this real deployment and operation of A2S and believe that they can be useful in consumer electronics field such as home network as well as automated agriculture field. ER - TY - Review T1 - A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture A1 - Anisi, M H Y1 - 2015/// KW - Energy consumption KW - Power source KW - Topologies KW - Wireless sensor networks JF - Precision Agriculture VL - 16 IS - 2 SP - 216 EP - 238 SN - 1385-2256 DO - 10.1007/s11119-014-9371-8 UR - https://api.elsevier.com/content/abstract/scopus_id/84928676430 N1 - Cited By (since 2015): 95 N2 - Precision agriculture (PA) is the use of information and communication technology together with best agricultural practices for farm management. PA requires the acquisition, transmission and processing of large amounts of data from farm fields. A wireless sensor network (WSN) is a system for monitoring agriculture fields. Several researchers have used WSNs to collect the required data from the regions of interest for their intended usages in various applications. In a WSN, the energy consumption of the sensor nodes is the main issue, due to its direct impact on the lifetime of the network. Many approaches have been proposed to address this issue using different power sources and types of nodes. Specifically, in PA, because of the extended time period that is required to monitor fields, using an appropriate WSN approach is important. There is a need for a comprehensive review of WSN approaches for PA. The aim of this paper is to classify and describe the state-of-the-art of WSNs and analyze their energy consumption based on their power sources. WSN approaches in PA are categorized and discussed according to their features. ER - TY - Review T1 - A review on innovations in polymeric nanocomposite packaging materials and electrical sensors for food and agriculture A1 - Idumah, C I Y1 - 2020/// KW - Polymer nanocomposites KW - biosensors KW - interfacial interactions KW - nano-sensors JF - Composite Interfaces VL - 27 IS - 1 SP - 1 EP - 72 SN - 0927-6440 DO - 10.1080/09276440.2019.1600972 UR - https://api.elsevier.com/content/abstract/scopus_id/85066883767 N1 - Cited By (since 2020): 35 N2 - The application of polymer nanocomposites packaging materials in industrial, food and agricultural products is a superior alternative to traditional packaging materials such as glass, paper, and metals due to their functionalization, flexibility, and minimal cost. However, usage of these materials has been hindered due to their inferior mechanical and barrier behaviors, which are susceptible to improvement through inclusion of functionalized reinforcing macro- or nanofillers. Furthermore, most reinforced materials exhibit inferior matrix–filler interfacial interactions, which are enhanced with reducing filler dimensions. Hence, this review elucidates functionalization of composites interfacial interaction and its relationship to enhancement of the properties of packaging materials, especially antimicrobial tendencies, enzyme immobilization behavior, biosensing affinity, and so on. Thus, a fundamental understanding of interfacial structure and its relationship to the overall improvement of properties are presented. Therefore, nanomaterials, such as cellulose, nanoclay, halloysite nanotubes, carbon allotropes (graphene and carbon nanotubes), silica, and so on, are discussed relative to their surface treatment approaches and effects on composites films properties for effective packaging. Recently, emerging innovations in nanostructured polymeric composite materials and electrical-sensors, their current applications and future outlook as food, agricultural and industrial packaging materials are also herewith elucidated. ER - TY - Conference Paper T1 - NB-IoT for Smart Agriculture: Experiments from the Field A1 - Valecce, G Y1 - 2020/// KW - Internet KW - Internet of Things KW - cellular radio KW - farming KW - packet radio networks KW - process control JF - 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020 SP - 71 EP - 75 DO - 10.1109/CoDIT49905.2020.9263860 UR - https://api.elsevier.com/content/abstract/scopus_id/85098269328 N1 - Cited By (since 2020): 5 N2 - Internet of Things (IoT) is shaping the agricultural industry to enhance process control, boost business efficiency, and improve product quality. Digital agriculture has the potential to fulfill the climate change adaptation with optimized natural resources use and achieve economic benefits through increased agricultural productivity. Field monitoring and agricultural processes automation can lead to alternative options for managing natural resources and environment. In addition, social and cultural benefits can be fostered by means of enhanced communication infrastructure. The agriculture domain usually requires long-range communications, extended battery lifetime, and high reliability of sensors devices. Narrowband IoT (NB-IoT) is growing as a key Low-Power Wide-Area Network (LPWAN) technology for IoT applications. Smart Agriculture proves a natural use of NB-IoT, as a typical industrial IoT application. In this paper, a NB-IoT agricultural field test is reported, within a real system architecture, comparing its network performances with General Packet Radio Service (GPRS) standard. The experimental campaign exposes gains and challenges of the technology highlighting the most attractive aspects for the farming context. ER - TY - Conference Paper T1 - Smart farming: Iot based smart sensors agriculture stick for live temperature and moisture monitoring using arduino, cloud computing & solar technology A1 - Nayyar, A Y1 - 2017/// KW - Agriculture KW - Agriculture IoT KW - Agriculture Precision KW - Arduino Mega 2560 KW - Cloud Computing KW - DS18B20 Temperature Sensor KW - ESP8266 KW - Internet of Things KW - Smart Farming KW - Soil Moisture Sensor KW - Solar Technology KW - ThingSpeak JF - Communication and Computing Systems - Proceedings of the International Conference on Communication and Computing Systems, ICCCS 2016 SP - 673 EP - 680 DO - 10.1201/9781315364094-121 UR - https://api.elsevier.com/content/abstract/scopus_id/85018450043 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Smart farming Iot based smart sensors agriculture stick for live temperature and moisture monitoring using arduino, cloud computing & solar technology.pdf N1 - Cited By (since 2017): 59 N2 - Internet of Things (IoT) technology has brought revolution to each and every field of common man’s life by making everything smart and intelligent. IoT refers to a network of things which make a self-configuring network. The development of Intelligent Smart Farming IoT based devices is day by day turning the face of agriculture production by not only enhancing it but also making it cost-effective and reducing wastage. The aim / objective of this paper is to propose a Novel Smart IoT based Agriculture Stick assisting farmers in getting Live Data (Temperature, Soil Moisture) for efficient environment monitoring which will enable them to do smart farming and increase their overall yield and quality of products. The Agriculture stick being proposed via this paper is integrated with Arduino Technology, Breadboard mixed with various sensors and live data feed can be obtained online from Thingsspeak.com. The product being proposed is tested on Live Agriculture Fields giving high accuracy over 98% in data feeds. ER - TY - Review T1 - State-of-the-art internet of things in protected agriculture A1 - Shi, X Y1 - 2019/// KW - Internet of things KW - integrated application KW - protected agriculture KW - state-of-the-art JF - Sensors (Switzerland) VL - 19 IS - 8 SN - 1424-8220 DO - 10.3390/s19081833 UR - https://api.elsevier.com/content/abstract/scopus_id/85065086090 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) sensors-19-01833.pdf N1 - Cited By (since 2019): 124 N2 - The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future. ER - TY - Review T1 - Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges A1 - Ferrag, M A Y1 - 2020/// KW - Security KW - authentication KW - blockchain KW - greenhouse KW - privacy KW - smart agriculture JF - IEEE Access VL - 8 SP - 32031 EP - 32053 SN - 2169-3536 DO - 10.1109/ACCESS.2020.2973178 UR - https://api.elsevier.com/content/abstract/scopus_id/85081112914 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Security and Privacy for Green IoT-Based Agriculture Review, Blockchain Solutions, and Challenges.pdf N1 - Cited By (since 2020): 101 N2 - This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture. ER - TY - Article T1 - A Survey on the Role of IoT in Agriculture for the Implementation of Smart Livestock Environment A1 - Farooq, M S Y1 - 2022/// KW - Internet of things KW - animal monitoring KW - animal tracking KW - cattle monitoring KW - cloud computing KW - feeding KW - livestock KW - poultry management JF - IEEE Access VL - 10 SP - 9483 EP - 9505 DO - 10.1109/ACCESS.2022.3142848 UR - https://api.elsevier.com/content/abstract/scopus_id/85123272585 N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) is an emerging paradigm that is transforming real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains including healthcare, smart grid, and agriculture. This domain has started revolutionizing the agriculture industry by providing smart solutions for precision farming, greenhouse management, and livestock monitoring. This article aims to present a comprehensive survey on the role of IoT in the Livestock field by categorizing and synthesizing existing research work in this area. To this end, a detailed discussion has been provided on IoT network infrastructure, topologies and platforms employed for livestock management. In addition, a list of communication protocols and connections of IoT-based livestock systems with relevant technologies have also been explored. Furthermore, numerous IoT-based livestock monitoring, controlling, and tracking applications have been discussed. Apart from this, it also analyses distinct security issues in IoT-based livestock field and developed a collaborative security model to detect and minimize the security risk. Lastly, pertinent open research challenges in the domain of IoT-based livestock management have been presented with future research directions. ER - TY - Article T1 - Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring A1 - Sharma, H A1 - Haque, Ahteshamul A1 - Jaffery, Zainul Abdin Y1 - 2019/// KW - Network throughput KW - Sensor network lifetime KW - Smart agriculture KW - Solar energy harvesting KW - Wireless sensor networks JF - Ad Hoc Networks VL - 94 DO - 10.1016/j.adhoc.2019.101966 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1570870519300952 UR - https://api.elsevier.com/content/abstract/scopus_id/85069699130 N1 - Cited By (since 2019): 79 N2 - The wireless sensor networks (WSNs) are used for the real-life implementation of the Internet of Things (IoT) in smart agriculture, smart buildings, smart cities, and online industrial monitoring applications. Generally, traditional WSN nodes are powered by limited energy capacity, non-rechargeable batteries. The WSN lifetime (days) depends upon, duty cycle, type of application deployment, and battery state of charge (SoC) level. We propose an innovative solution to the limited energy availability design problem by utilizing the ambient solar energy harvesting for battery charging of WSN nodes. However, there are many challenges in solar energy harvesting like intermittency of available power, solar energy prediction, thermal issues, solar panel conversion efficiency, and other environmental issues. The objective of this research work is to maximize the WSN network lifetime using solar energy harvesting technique. From our simulation results, it is proved that the sensor network lifetime is increased from 5.75 days to 115.75 days @ 25% duty cycle and higher, ideally up to infinite network lifetime. Furthermore, the network throughput is also increased from 100 K bits/s to 160 K bits/s. in SEH-WSNs. ER - TY - Conference Paper T1 - Automation in Agriculture and IoT A1 - Puranik, V A1 - Sharmila A1 - Ranjan, Ankit A1 - Kumari, Anamika Y1 - 2019/// KW - Automation KW - Efficiency KW - Internet of Things KW - Productivity KW - Smart Farming JF - Proceedings - 2019 4th International Conference on Internet of Things: Smart Innovation and Usages, IoT-SIU 2019 DO - 10.1109/IoT-SIU.2019.8777619 UR - https://api.elsevier.com/content/abstract/scopus_id/85070664061 N1 - Cited By (since 2019): 19 N2 - We are living in a world of digitization. Almost everything around us is touch by digitisation. The role the Technology has to play in agriculture sector is becoming more and more visible day by day. Since year of its inception communication has played an important part in agriculture, it was not just limited to in area of crop diagnostics but it has played pivotal role in the modification of age old agricultural practices. One can also witness development in various methodologies and technologies being used in the agricultural system. On the contrary, the agriculture sector in India is witnessing losing ground every day that has affected the production capacity of the ecosystem. There is an emerging need to solve the problem in the said domain to restore vibrancy and put it back on higher growth. A large-scale agricultural system requires a lot of maintenance, knowledge, and supervision. In the given paper we are aiming to automate the Maintenance, Control of Insecticides and pesticides, Water Management and Crop Monitoring. ER - TY - Article T1 - Smart water-saving irrigation system in precision agriculture based on wireless sensor network A1 - Xiao, K A1 - Deqin, Xiao A1 - Xiwen, Luo Y1 - 2010/// KW - Smart Irrigation KW - architecture KW - precision agriculture KW - wireless sensor networks JF - Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering VL - 26 IS - 11 SP - 170 EP - 175 DO - 10.3969/j.issn.1002-6819.2010.11.030 UR - https://api.elsevier.com/content/abstract/scopus_id/78650938728 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2010) Smart water-saving irrigation system in precision agriculture based on wireless sensor network.pdf N1 - Cited By (since 2010): 44 N2 - Based on investigation and applications in precision agriculture, a self-designed moisture wireless sensor was presented in the paper, a wireless sensor network was established for monitoring moisture content and water height of field soil. The architecture of the wireless sensor network was constructed, and the smart irrigation control system was designed based on the network. The irrigation test was implemented by real-time moisture data and expert data. The system was proved to be applicable and feasible for applying in the rice growth process and to be a good exploration in the field of precision agriculture and sustainable water resources ER - TY - Conference Paper T1 - Smart Agriculture Implementation using IoT and Leaf Disease Detection using Logistic Regression A1 - Rajeshwari, T Y1 - 2021/// KW - Internet of Things KW - agricultural machinery KW - agriculture KW - artificial intelligence KW - cloud computing KW - diseases KW - irrigation KW - plant diseases KW - regression analysis KW - wireless LAN JF - 2021 4th International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2021 SP - 619 EP - 623 DO - 10.1109/RDCAPE52977.2021.9633608 UR - https://api.elsevier.com/content/abstract/scopus_id/85123864400 N1 - Cited By (since 2021): 2 N2 - The need of robotics, automation, IoT and its implementation based on precision agriculture is very much necessary to eliminate the usage of manpower in farming. In order to make this more efficient system, adding machine learning based leaf disease detection is very much needy. This can result in saving the time, energy of the farmer and thereby improve productivity and efficiency. Usage of various electronic sensors and use of cloud-based service can also facilitate accurate measurement of any parameters and fast processing in regard to farming. A prototype of an agricultural rover to perform ploughing and seed sowing, a separate automatic irrigation, and fertilizer sprinkling system are made to facilitate effective cultivation. The rover is controlled by a smartphone through Wi-Fi module. The irrigation, fertilizer sprinkling system is automated and the parameters are made to be displayed on a cloud based IoT analytics service called thing speak and OLED. For the leaf disease detection part, machine learning based logistic regression is used with some optimizations to improve the accuracy. ER - TY - Article T1 - Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture A1 - Kulmány, I M Y1 - 2022/// KW - internet of things KW - Precision Agriculture KW - Low-cost capacitive soil moisture sensor KW - Thermo-gravimetric method KW - Repeatability and Reproducibility study KW - Non-linear regression JF - Journal of Hydrology and Hydromechanics VL - 70 IS - 3 SP - 330 EP - 340 DO - 10.2478/johh-2022-0014 UR - https://api.elsevier.com/content/abstract/scopus_id/85138158339 N1 - Cited By (since 2022): 1 N2 - Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermo gravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures. ER - TY - Conference Paper T1 - Precision Agriculture for Greenhouses Using a Wireless Sensor Network A1 - Hamouda, Y E M Y1 - 2017/// KW - Android KW - greenhouse KW - irrigation KW - microcontroller KW - precision agriculture KW - wireless sensor networks JF - Proceedings - 2017 Palestinian International Conference on Information and Communication Technology, PICICT 2017 SP - 78 EP - 83 DO - 10.1109/PICICT.2017.20 UR - https://api.elsevier.com/content/abstract/scopus_id/85032285355 N1 - Cited By (since 2017): 30 N2 - Agriculture is one of the most crucial needs of the life since it supports human and animals with food supplies and benefits the human employment and the national economy. Wireless Sensor Network (WSN) has recently applied in precision agriculture to improve the crop yields and apply the agricultural resources at right time and place. In this paper, Greenhouse Smart Management System (GSMS) using WSNs is designed and developed to automatically control, manage and monitor the agricultural parameters and activities inside the greenhouses. The ambient relative humidity and temperature of the greenhouse are measured using sensor nodes. When the sensed parameters exceed threshold values, the irrigation and cooling activities are triggered by activating the fan and water pump devices. GSMS includes also an algorithm to compute the period of irrigation and cooling according to the measured agricultural parameters. Hardware and software for the proposed GSMS are developed in this paper. The results show that the GSMS can save the agricultural resources and improve the crop yields, compared with other traditional schemes. ER - TY - Article T1 - Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision A1 - Qureshi, K Y1 - 2020/// KW - Agriculture KW - Sensors KW - Wireless sensor networks JF - Journal of Sensors VL - 2020 DO - 10.1155/2020/9040395 UR - https://api.elsevier.com/content/abstract/scopus_id/85079350717 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020), Optimized Cluster-Based Dynamic Energy-Aware Routing.pdf N1 - Cited By (since 2020): 48 N2 - Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector. ER - TY - Article T1 - Collaborative actuation of wireless sensor and actuator networks for the agriculture industry A1 - Bai, X Y1 - 2017/// KW - Coordinative control KW - Kalman filter KW - data fusion KW - fuzzy neural network KW - wireless sensor and actuator network JF - IEEE Access VL - 5 SP - 13286 EP - 13296 DO - 10.1109/ACCESS.2017.2725342 UR - https://api.elsevier.com/content/abstract/scopus_id/85023641142 N1 - Cited By (since 2017): 38 N2 - This paper investigates the deployment of collaborative estimation and actuation scheme of wireless sensor and actuator networks for the agriculture industry. In our proposed scheme, sensor nodes conduct a local estimation based on the Kalman filter for enhancing the estimation stability and further transmit data to the actuator nodes under a multi-rate transmission mode for enhancing the overall energy efficiency of the wireless network. Considering the mutual effect of related clusters, a collaborative actuation scheme of actuator nodes is integrated into our proposed scheme for improving the estimation accuracy and convergence speed. With an accurate estimation of the changes in the environmental parameters, combining the fuzzy neural network with the PID control algorithm, the actuator exerts reliable control over the environmental parameters. Performance evaluations and simulation analysis conducted based on the effects of temperature demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes for in the agriculture industry. ER - TY - Conference Paper T1 - Enabling smart agriculture in Nigeria: Application of IoT and data analytics A1 - Elijah, O Y1 - 2018/// KW - Agriculture KW - Nigeria. KW - data analytics KW - internet of things JF - 2017 IEEE 3rd International Conference on Electro-Technology for National Development, NIGERCON 2017 VL - 2018 SP - 762 EP - 766 DO - 10.1109/NIGERCON.2017.8281944 UR - https://api.elsevier.com/content/abstract/scopus_id/85046936999 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Enabling smart agriculture in Nigeria Application of IoT and data analytics.pdf N1 - Cited By (since 2018): 21 N2 - The Nigeria agriculture sector is receiving a lot of attention in recent years due to the need to diversify its economy. Two key gaps facing the agriculture sector have been identified. They include the inability to meet domestic food requirements and the inability to export at quality levels required for market success. We envisage that these problems can be solved by using internet of things (IoT) and data analytics (DA). In this paper, the application of IoT technologies and DA in agriculture are discussed. The benefits and challenges of deploying the IoT and DA are presented. Finally, methods that can be adopted towards solving the problems facing Nigeria's agriculture sector are proposed. ER - TY - Conference Paper T1 - Remote sensing and controlling of greenhouse agriculture parameters based on IoT A1 - Pallavi, K Y1 - 2018/// KW - Internet KW - Internet of Things KW - agriculture KW - crops KW - greenhouses KW - remote sensing KW - wireless sensor networks JF - 2017 International Conference on Big Data, IoT and Data Science, BID 2017 VL - 2018 SP - 44 EP - 48 DO - 10.1109/BID.2017.8336571 UR - https://api.elsevier.com/content/abstract/scopus_id/85047397284 N1 - Cited By (since 2018): 59 N2 - The new era in computer communication is Internet of Things (IoT), gaining its importance because of wide variety of application in oriented project developments. The IoT is furnishing people with smart and remote approach, the remote applications such as smart agriculture, smart environment, smart security, and smart cities etc. These are the upcoming technologies now a day, making the things easy. The IoT has essentially, increased the remote distance control and variety of interconnected things or devices, which becomes an interesting aspect. The IoT includes the hardware and internet connection to the real time application. The main components of IoT are sensors, actuators, embedded system, and internet connection. Therefore, we are interested in developing an IoT application for smart agriculture. The paper proposed a remote sensing of agriculture parameters and control system to the greenhouse agriculture. The plan is to control CO 2 , soil moisture, temperature, and light, based on the soil moisture the controlling action is accomplished for the greenhouse windows/doors based on crops once a quarter complete round the year. The objective is to increase the yield and to provide organic farming. The result shows the remote control of CO2, soil moisture, temperature, and light for the greenhouse. ER - TY - Article T1 - AgriOn: A comprehensive ontology for Green IoT based agriculture A1 - Urkude, G Y1 - 2020/// KW - Internet of Things KW - Ontology KW - Semantic reasoner rules KW - Smart farming KW - Soil moisture KW - moisture JF - Journal of Green Engineering VL - 10 IS - 9 SP - 7078 EP - 7101 UR - https://api.elsevier.com/content/abstract/scopus_id/85094597247 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) AgriOn A comprehensive ontology for Green IoT based agriculture.pdf N1 - Cited By (since 2020): 2 N2 - AgriOn ontology is the advancement of the agriculture ontology. The ontology for agriculture already published is either taxonomy such as AGROVOC that defines common terms or ontologies that have been developed for smart farming but have not published their ontology for reuse. AgriOn promises a description of ontology to be reused as it follows the best practices for ontology development. In this paper, we presented the 'AgriOn' agricultural ontology concept along with the IoT concept of smart farming with nearreal-time activities. Along with AgriOn Ontology, the data annotation for smart agriculture with lightweight IoT-Lite ontology instead of SSN ontology is presented in this paper. Some knowledge-based taxonomies for soil moisture data are defined using the Semantic Reasoner Rules. The M3 ontology and the M3-Lite taxonomy are used to express the sensor data unit and the quantity type information. To make the annotated data sensor lighter, a notation 3 (n3) data format is used, which takes less space than XML and JSON. To demonstrate the logical knowledge extract from the sensor data, the Reasoner Rules and the SPARQL queries are included, which extract the soil moisture knowledge from the field partition. An open-source protected tool is used to develop ontology. ER - TY - Article T1 - A study of LoRaWAN protocol performance for IoT applications in smart agriculture A1 - Miles, B Y1 - 2020/// KW - Internet of Things KW - agribusiness KW - communication network KW - data transmission rates KW - low power consumption JF - Computer Communications VL - 164 SP - 148 EP - 157 DO - 10.1016/j.comcom.2020.10.009 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0140366420319575 UR - https://api.elsevier.com/content/abstract/scopus_id/85093671826 N1 - Cited By (since 2020): 23 N2 - The use of Internet of Things (IoT) is becoming increasingly common in agribusiness to increase food production capacity for the expanding global population. Recently, low-power wide-area networks (LPWANs) have been used in the development of IoT applications that require low power consumption and low data transmission rates. LoRaWAN is considered the most suitable communication network for LPWANs for IoT applications in smart agriculture. In this paper, we present an in-depth study of the performance of the LoRaWAN communication network in the context of an IoT application for a pilot farm. We consider several scenarios and analyze simulation results by using Network Simulator 3. We then propose a mathematical model that precisely predicts the successful packet delivery rate for this type of network considering the number of nodes and the transmission interval duration. Finally, we validate the results of our model by comparing them with other simulation results under different scenarios. ER - TY - Conference Paper T1 - A review of IoT techniques and devices: Smart agriculture perspective A1 - Rani, D Y1 - 2020/// KW - Actuators KW - Intercommunication KW - Internet of things KW - Sensors KW - Smart agriculture JF - Lecture Notes in Electrical Engineering VL - 597 SP - 113 EP - 123 SN - 1876-1100 DO - 10.1007/978-3-030-29407-6_10 UR - https://api.elsevier.com/content/abstract/scopus_id/85076643261 N1 - Cited By (since 2020): 2 N2 - Internet of things (IoT) is the hot point in the Internet field. The concepts help to intercommunicate physical objects furnished with sensing, actuating, computing power and hence connect to Internet. With the help of sensor, actuators and embedded microcontrollers, the verdict of smart object is realized. Wherein these smart objects colligate data from the environment of development, process them, and take reasonable actions. Thus, the IoT may generate unbelievable benefits and helps human beings in living a smart and luxurious life. Due to the potential utilizations of Internet of things (IoT), it has ended up being an unmistakable subject of logical research. The significance and the utility of these advances are in sizzling exchange and research, yet on the field of agribusiness and ranger service, it is very less. In this paper, utilizations of IoT on farming and silviculture has been well perused and broke down; additionally, this paper briefly presented the innovation IoT, agribusiness IoT, rundown of some potential applications areas where IoT is exercisable in the horticulture part, advantages of IoT in farming, and displays a survey of some literature survey. ER - TY - Article T1 - A Long-range context-aware platform design for rural monitoring with IoT In precision agriculture A1 - Taskin, D A1 - YAZAR, Selçuk Y1 - 2020/// KW - Internet of things KW - LoRa KW - context-awareness KW - precision agriculture KW - rural monitoring JF - International Journal of Computers, Communications and Control VL - 15 IS - 2 SP - 1 EP - 11 DO - 10.15837/IJCCC.2020.2.3821 UR - https://api.elsevier.com/content/abstract/scopus_id/85084320137 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) A Long-range context-aware platform design for rural monitoring with IoT In precision agriculture.pdf N1 - Cited By (since 2020): 8 N2 - The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of pro- duction time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measure- ments of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed. ER - TY - Conference Paper T1 - Wireless sensor network in precision agriculture: A survey A1 - Deepika, G Y1 - 2016/// KW - Image Processing KW - Microcontroller KW - Plant Monitoring KW - wireless sensor networks JF - 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 - Proceedings DO - 10.1109/ICETETS.2016.7603070 UR - https://api.elsevier.com/content/abstract/scopus_id/84997207716 N1 - Cited By (since 2016): 25 N2 - Wireless Sensor Networks (WSNs) consist of multiple unassisted embedded devices which process and transmit data collected from different on-board physical sensors (temperature, humidity, pressure, etc.,). There are several applications in WSN such as agriculture, industrial monitoring, etc.,. In agriculture, plant diseases are regularly monitored by WSN. This survey paper explains about the existing methods and new methods and the development of WSN. Plant monitoring with the image processing and sensor networks using Field Programmable Gate Array (FPGA) based control is the new method. The figures in this paper consist of the development and progress in WSN. ER - TY - Article T1 - Robust Soil Water Potential Sensor to Optimize Irrigation in Agriculture A1 - Menne, D Y1 - 2022/// JF - Sensors VL - 22 IS - 12 DO - 10.3390/s22124465 UR - https://api.elsevier.com/content/abstract/scopus_id/85132151752 ER - TY - Conference Paper T1 - Precision agriculture monitoring system using wireless sensor network and Raspberry Pi local server A1 - Flores, K O Y1 - 2017/// KW - Precision Agriculture KW - Wireless sensor networks JF - IEEE Region 10 Annual International Conference, Proceedings/TENCON SP - 3018 EP - 3021 SN - 2159-3442 DO - 10.1109/TENCON.2016.7848600 UR - https://api.elsevier.com/content/abstract/scopus_id/85015406691 N1 - Cited By (since 2017): 32 N2 - Precision Agriculture is utilized to improve the productivity and efficiency of limited agricultural resources by monitoring the relevant data in the field. The main objective of this study is to deploy a low-cost sensor system, gather field data, and display the data through a graphical user interface (GUI). Sensors such as humidity, temperature, moisture, luminosity, electrical conductivity, and pH was used for data acquisition and the Raspberry Pi, acting as a local server, was used for data processing and transfer. The data sent was stored in a main server and organized using SQL. A GUI was developed to provide visualization of the data gathered. The trends of data gathered revealed pattern such as the occurrence of a local maximum for humidity right after dawn and the inverse relationship of humidity and temperature. The whole system was tested and proven to work by the application of fertilizer to the soil and seeing its response in the GUI. ER - TY - Conference Paper T1 - Connecting agriculture to the internet of things through sensor networks A1 - Ma, J Y1 - 2011/// KW - Internet of Things KW - agriculture KW - design KW - sensor networks JF - Proceedings - 2011 IEEE International Conferences on Internet of Things and Cyber, Physical and Social Computing, iThings/CPSCom 2011 SP - 184 EP - 187 DO - 10.1109/iThings/CPSCom.2011.32 UR - https://api.elsevier.com/content/abstract/scopus_id/84863229965 N1 - Cited By (since 2011): 66 N2 - The Internet of Things (IOT), the idea of getting real-world objects connected with each other, will change the ways we organize, obtain and consume information radically. Through sensor networks, agriculture can be connected to the IOT, which allows us to create connections among agronomists, farmers and crops regardless of their geographical differences. With the help of the connections, the agronomists will have better understanding of crop growth models and farming practices will be improved as well. This paper reports on the design of the sensor network when connecting agriculture to the IOT. Reliability, management, interoperability, low cost and commercialization are considered in the design. Finally, we share our experiences in both development and deployment. ER - TY - Conference Paper T1 - An Hybrid Novel Layered Architecture and Case Study: IoT for Smart Agriculture and Smart LiveStock A1 - Houngue, P Y1 - 2020/// KW - Big data KW - E-agriculture KW - Farmer KW - Internet of things KW - Livestock KW - Prototype KW - Sensors KW - Smart KW - Smart-village JF - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST VL - 318 SP - 71 EP - 82 SN - 1867-8211 DO - 10.1007/978-3-030-45293-3_6 UR - https://api.elsevier.com/content/abstract/scopus_id/85085387140 N1 - Cited By (since 2020): 4 N2 - The meteoric rise in the number of connected objects in our daily lives is proof that data transmission and improvement of services related to our activities are a permanent and urgent concern. Communicating objects transform our behaviors, our habits and our society in general. Despite the significant progress in the field of Internet of Things (IoT), much remains to be done especially in developing countries. In the field of e-agriculture, digital production techniques are not enough to guarantee a better yield and safeguard crops. Thus, in this work, we have focused on the resolution of the problems related to transhumance, given the expansion of the phenomenon in developing countries. Indeed, during transhumance, passages intended for animals, called corridors may not be followed by the breeders. This can lead to deadly clashes between herders and farmers. Our vision is to help through the implementation of a smart guidance system based on IoT Technologies, herders to better control their livestock following the predefined corridors from north to south of Benin and vice versa. In order to help farmers to save their crops in case of flood, our system will integrate a prediction module that will enable them to anticipate natural events such as flooding in the heavy rainy season. In this paper, our researches will therefore focus on the proposal of a multi-level architecture that can enable us to achieve the aforementioned objectives. ER - TY - Conference Paper T1 - End-to-end reliability analysis of an IoT based smart agriculture A1 - Kamyod, C Y1 - 2018/// KW - Internet of Things KW - agriculture KW - computer network reliability KW - sensors JF - 3rd International Conference on Digital Arts, Media and Technology, ICDAMT 2018 SP - 258 EP - 261 DO - 10.1109/ICDAMT.2018.8376535 UR - https://api.elsevier.com/content/abstract/scopus_id/85049902243 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) End-to-End Reliability Analysis of an IoT based smart agriculture.pdf N1 - Cited By (since 2018): 16 N2 - End-to-end reliability of a computer network has been widely researched to improve the performance of the system. This paper evaluates end-to-end reliability characteristics of two main IoT communication network architectures through main reliability network parameters by using OPNET. The reliability effects when increasing the number of sensor nodes are simulated and analyzed. The simulation results show exciting reliability features of the intended IoT communication architecture. ER - TY - Conference Paper T1 - WSN sensors for precision agriculture A1 - Kodali, R K Y1 - 2014/// KW - agricultural fields KW - precision agriculture KW - statistical parameters KW - wireless sensor networks JF - IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium SP - 651 EP - 656 DO - 10.1109/tenconspring.2014.6863114 UR - https://api.elsevier.com/content/abstract/scopus_id/84911966417 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2014) WSN sensors for precision agriculture.pdf N1 - Cited By (since 2014): 36 N2 - The application of technology in the field of agriculture has increased the effectiveness and efficiency of the farmers. The application of Wireless Sensor Network (WSN) in precision agriculture assists the farmers to know about their fields in statistical manner, which helps them in making better and accurate decisions. There are various type of sensors that can be used to calculate the statistical parameters of an agricultural fields, which convert the event or a phenomenon into an electrical or measurable quantity. This paper provides an elaboration of the basic principles of some of the sensors and their related specifications of few commercial products. ER - TY - Conference Paper T1 - IoT and data interoperability in agriculture: A case study on the gaiasenseTM smart farming solution A1 - Kalatzis, N Y1 - 2019/// KW - Internet of Things KW - gaiasense KW - interoperability KW - smart farming JF - Global IoT Summit, GIoTS 2019 - Proceedings DO - 10.1109/GIOTS.2019.8766423 UR - https://api.elsevier.com/content/abstract/scopus_id/85073891057 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) IoT and data interoperability in agriculture A case study on the gaiasenseTM smart farming solution.pdf N1 - Cited By (since 2019): 7 N2 - Among the most important challenges towards the digitisation of agriculture is the high cost of technical equipment and the lack of smart farming systems’ capability to interoperate. This paper presents the gaiasenseTM solution which follows an innovative approach in offering smart- farming services as an inexpensive service with zero technological related investment for farmers. In addition, the concept of the “Data Interoperability Zone” is introduced along with the “Information Management Adapter” aiming to facilitate data interoperability for smart-farming systems ER - TY - Article T1 - Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer A1 - Abdel-Basset, M A1 - Shawky, Laila A. A1 - Eldrandaly, Khalid Y1 - 2020/// KW - Algorithm KW - Area coverage KW - East Oweinat KW - Internet of things KW - Metaheuristic KW - Multi-verse optimizer KW - Smart agriculture KW - Wireless sensor networks JF - Neural Computing and Applications VL - 32 IS - 3 SP - 607 EP - 624 DO - 10.1007/s00521-018-3807-4 UR - https://api.elsevier.com/content/abstract/scopus_id/85055483671 N1 - Cited By (since 2020): 11 N2 - Wireless sensor networks (WSN) are the backbone in various modern Internet of Things (IoT) smart applications ranging from automated control, surveillance, forest fire detection, etc. One of the most important applications is the smart agriculture. The deployment of WSN in agricultural processes can predict crop yield, soil temperature, air quality, water level, crop price, and the appropriate time for market delivery which will help to increase productivity. In this paper, an enhanced metaheuristic algorithm called multi-verse optimizer with overlapping detection phase (DMVO) is introduced for optimizing the area coverage percentage of WSN. The proposed algorithm is tested on many datasets with different criterions and is compared with other algorithms including the original MVO, particle swarm optimization, and flower pollination algorithm. The experimental results are analyzed with one-way ANOVA test. In addition, DMVO is applied to IoT smart agriculture in East Oweinat area in Egypt and compared with Krill Herd algorithm. In addition, the experimental results are analyzed with Wilcoxon signed-rank test. The experimental results and the statistical analysis prove the prosperity and consistency of the proposed algorithm. ER - TY - Conference Paper T1 - IoT-based Measurement for Smart Agriculture A1 - Heideker, A Y1 - 2020/// KW - FIWARE KW - LoRaWAN KW - Smart Agriculture JF - 2020 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2020 - Proceedings SP - 68 EP - 72 DO - 10.1109/MetroAgriFor50201.2020.9277546 UR - https://api.elsevier.com/content/abstract/scopus_id/85099064250 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT-Based Smart Irrigation Systems An Overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture.pdf N1 - Cited By (since 2020): 4 N2 - Smart agriculture is increasingly seen as a solution to global sustainability problems such as global warming, waste of water resources, excessive use of pesticides, and low economic activity. The core of this technology is the acquisition of data from the soil, crop, and climate to act in the production. Several solutions exist, but many are proprietary, high cost, hard to install, maintain, and integrate with third-party solutions. This paper presents an IoT technology set applied to the acquisition of agricultural data using open source solutions such as FIWARE and LoRaWAN, which allow extensive customization and integration with advanced weather forecasting, Machine Learning, and real-time dashboard services. The results obtained by the combination of different tools and platforms in pilots located in Brazil and Europe reveal a high versatility of the IoT technology applied to smart agriculture ER - TY - Article T1 - Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study A1 - Kamath, R Y1 - 2019/// KW - Classifiers KW - Raspberry Pi KW - computer vision KW - precision agriculture KW - shape features KW - wireless visual sensor network JF - IEEE Access VL - 7 SP - 45110 EP - 45122 DO - 10.1109/ACCESS.2019.2908846 UR - https://api.elsevier.com/content/abstract/scopus_id/85064634879 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Raspberry Pi as Visual Sensor Nodes in Precision Agriculture A Study.pdf N1 - Cited By (since 2019): 33 N2 - Wireless sensor network applications in the agricultural sector are gaining popularity with the advancement of the Internet of Things technology. Predominantly, wireless sensor networks are used in agriculture to sense the important agricultural field parameters, such as temperature, humidity, soil moisture level, nitrite content in the soil, groundwater quality, and so on. These sensed parameters will be sent to a remote station, where it will be processed and analyzed to build a decision support system. This paper describes the implementation of a wireless visual sensor network for precision agriculture to monitor paddy crop for weeds using Raspberry Pi. Bluetooth 4.0 was used by visual sensor nodes to send the data to the base station. Base station forwarded the data to the remote station using IEEE 802.11 a/b/g/n standard. The solar cell battery was used to power up the sensor nodes and the base station. At the remote station, images were preprocessed to remove soil background and different shape features were extracted. Random forest and support vector machine classifiers were used to classify the paddy crop and weed based on the shape features. The results and observations obtained from the experimental setup of the system in a small paddy field are also reported. This system could be expected to enhance the crop production by giving timely advice to the crop producers about the presence of weeds so that steps can be taken to eradicate weeds. ER - TY - Review T1 - Nanostructured (Bio)sensors for smart agriculture A1 - Antonacci, A Y1 - 2018/// KW - (bio)sensors KW - Nanomaterials KW - Nanostructured KW - Smart agriculture KW - Soil physico-chemical parameters control JF - TrAC - Trends in Analytical Chemistry VL - 98 SP - 95 EP - 103 SN - 0165-9936 DO - 10.1016/j.trac.2017.10.022 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S016599361730273X UR - https://api.elsevier.com/content/abstract/scopus_id/85034755917 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Nanostructured (Bio)Sensors For Smart Agriculture.pdf N1 - Cited By (since 2018): 59 N2 - Intense farming represents one of the main sources causing detriments to vital resources as lands and water, due to unsustainable agricultural practices and the resulting environmental pollution. Furthermore, the increasing world population and the impact of climate change contribute to worsen these constraints. To these regards, several attempts have been completed to provide pioneering technologies for facing against these challenges, including nanostructured (bio)sensors. Indeed, nanotechnology-based (bio)sensors, thanks to the exploitation of fascinating properties of functional materials at the nanoscale, can support farmers in delivering fast, accurate, cost-effective, and in field analyses of i) soil humidity, ii) water and soil nutrients/pesticides, and iii) plant pathogens. Herein, we report a glance of the nano nanostructured (bio)sensors developed to support smart agriculture, reporting representative examples form the literature of the last 10 years. ER - TY - Conference Paper T1 - AgOnt: Ontology for agriculture internet of things A1 - Hu, S Y1 - 2011/// KW - Agriculture KW - Internet of Things KW - ontology KW - semantics JF - IFIP Advances in Information and Communication Technology VL - 344 SP - 131 EP - 137 SN - 1868-4238 DO - 10.1007/978-3-642-18333-1_18 UR - https://api.elsevier.com/content/abstract/scopus_id/79951620334 N1 - Cited By (since 2011): 32 N2 - Recent advances in networking and sensor technologies allow various physical world objects connected to form the Internet of Things (IOT). As more sensor networks are being deployed in agriculture today, there is a vision of integrating different agriculture IT system into the agriculture IOT. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of agriculture objects and provides a mechanism for sharing and reusing agriculture knowledge to solve the semantic interoperation problem. AgOnt, ontology for the agriculture IOT, is built from agriculture terminologies and the lifecycles including seeds, grains, transportation, storage and consumption. According to this unified meta-model, heterogeneous agriculture data sources can be integrated and accessed seamlessly. ER - TY - Conference Paper T1 - Smart Farming - IoT in Agriculture A1 - Dagar, R Y1 - 2018/// KW - Agriculture KW - Internet of things KW - Poly House KW - Sensors KW - Smart farming JF - Proceedings of the International Conference on Inventive Research in Computing Applications, ICIRCA 2018 SP - 1052 EP - 1056 DO - 10.1109/ICIRCA.2018.8597264 UR - https://api.elsevier.com/content/abstract/scopus_id/85061497918 N1 - Cited By (since 2018): 95 N2 - IoT is a revolutionary technology that represents the future of communication & computing. These days IoT is used in every field like smart homes, smart traffic control smart cities etc. The area of implementation of IoT is vast and can be implemented in every field. This paper is about the implementation of IoT in Agriculture. IoT helps in better crop management, better resource management, cost efficient agriculture, improved quality and quantity, crop monitoring and field monitoring etc. can be done. The IoT sensors used in proposed model are air temperature sensor, soil pH sensor, soil moisture sensor, humidity sensor, water volume sensor etc. In this paper I surveyed typical agriculture methods used by farmers these days and what are the problems they face, I visited poly houses for further more information about new technologies in farming. The proposed model is a simple architecture of IoT sensors that collect information and send it over the Wi-Fi network to the server, there server can take actions depending on the information. ER - TY - Conference Paper T1 - Applicability of IoT for Smart Agriculture: Challenges Future Research Direction A1 - Kaushik, I A1 - Prakash, Nupur Y1 - 2021/// KW - Disease Detection KW - Internet of Things KW - Precision Agriculture KW - Sensors KW - Smart Farming JF - 2021 IEEE World AI IoT Congress, AIIoT 2021 SP - 462 EP - 467 DO - 10.1109/AIIoT52608.2021.9454209 UR - https://api.elsevier.com/content/abstract/scopus_id/85113383776 N1 - Cited By (since 2021): 2 N2 - The ever-increasing population gave rise towards shift from traditional farming to modern farming methods. Limited availability of land, diminishing natural resources, unpredictable weather conditions resulting in food scarcity which is major concern which must be addressed. In order to increase the productivity and efficiency of agricultural land, Internet of things (IoT) offers many features in smart farming like monitoring of water, farm, soil, management of irrigation facilities, detection of pests and diseases, asset tracking, greenhouse farming, remotely control and diagnosis of farming related equipment etc. This paper presents different applications of IoT in smart agriculture and how productivity has increased which results in adding economic growth to the country using advanced technologies in agriculture industry. ER - TY - Article T1 - Reduction of water consumption in agriculture smart farms based on internet of things (IoT) A1 - Annapoorani, A A1 - Pandimeena R, B. Dhanalakshmi B A1 - S, Amutha Y1 - 2020/// KW - Internet of Things KW - Smart Agriculture KW - Water consumtion JF - International Journal of Control and Automation VL - 13 IS - 2 SP - 484 EP - 493 UR - https://api.elsevier.com/content/abstract/scopus_id/85084126232 N1 - Cited By (since 2020): 2 N2 - The proposed framework is centered around water the executives in rural land. Water is the essential and dominating beneficial for farming area and 0.69 of the universe crisp water is focused on rural needs. Along these lines, ample opportunity has already past to safeguard and use water assets proficiently through the help of sharp innovation similar to Internet of Things and Automation. Right now, have thought of an answer of preserving water by Internet of Things established Water reusing and Irrigation framework as "Shrewd Water the board in Agricultural property". Cultivating accept vital occupation in the improvement of country. In India about 70% of masses depends on developing and 0.33 of the nation's capital starts from developing. Issues concerning agribusiness have been ceaselessly destroying the headway of the country. The primary response for this issue is sagacious agribusiness by modernizing the current standard systems for cultivating. The Internet of things (IOT) is renovating the agribusiness engaging the agriculturists through the broad scope of systems, for instance, precision just as reasonable cultivating to manage difficulties in the field. IOT modernization helps in gathering data on conditions like atmosphere, clamminess, temperature and productivity of soil, Crop online assessment engages revelation of wild plant, level of water, bug area, animal break in to the field, trim advancement, agriculture. ER - TY - Conference Paper T1 - Internet of Things application for implementation of smart agriculture system A1 - Krishna, K Lokesh Y1 - 2017/// KW - Agriculture KW - Monitoring KW - Sensor systems KW - Thermal sensors KW - Wireless communication KW - Wireless sensor networks JF - Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017 SP - 54 EP - 59 DO - 10.1109/I-SMAC.2017.8058236 UR - https://api.elsevier.com/content/abstract/scopus_id/85034575635 N1 - Cited By (since 2017): 71 N2 - Over the past few years, there has been significant interest in designing smart agricultural systems. The use of smart farming techniques can enhance the crop yield, while simultaneously generating more output from the same amount of input. But still, most of the farmers are unaware of the latest technologies and practices. In this paper a novel wireless mobile robot based on Internet of Things (IoT) is designed and implemented for performing various operations on the field. This proposed wireless robot is equipped with various sensors for measuring different environmental parameters. It also includes Raspberry Pi 2 model B hardware for executing the whole process. The main features of this novel intelligent wireless robot is that it can execute tasks such as moisture sensing, scaring birds and animals, spraying pesticides, moving forward or backward and switching ON/OFF electric motor. The robot is fitted with a wireless camera to monitor the activities in real time. The proposed wireless mobile robot has been tested in the fields, readings have been monitored and satisfactory results have been observed, which indicate that this system is very much useful for smart agricultural systems. ER - TY - Article T1 - The role of IoT in agriculture fields A1 - Maram, B Y1 - 2019/// KW - Agriculture KW - Internet of things KW - Raspberri Pi KW - Sensors KW - Smart Farming JF - International Journal of Mechanical Engineering and Technology IS - 1 SP - 858 EP - 866 UR - https://api.elsevier.com/content/abstract/scopus_id/85060949620 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) THE ROLE OF IOT IN AGRICULTURE FIELDS.pdf N1 - Cited By (since 2019): 2 N2 - Today, majority of the farmers are dependent on agriculture for their survival. But majority of the agricultural tools and practices are outdated and it yields less crop products, because everything is depends on environment and Government support. The world population is becoming more comparatively cultivation land and crop yield. It is essential for the world to increase the yielding of the crop by adopting information technology and communication plays a vital role in smart farming. The objective of this research paper to present tools and best practices for understanding the role of information and communication technologies in agriculture sector, motivate and make the illiterate farmers to understand the best insights given by the big data analytics using machine learning. ER - TY - Conference Paper T1 - Modeling and Simulink of Smart Agriculture Using IoT Framework A1 - Krongthong, T Y1 - 2019/// KW - Internet of Things KW - agriculture KW - fuzzy control JF - 2019 1st International Conference on Cybernetics and Intelligent System, ICORIS 2019 SP - 185 EP - 188 DO - 10.1109/ICORIS.2019.8874914 UR - https://api.elsevier.com/content/abstract/scopus_id/85074429729 N1 - Cited By (since 2019): 5 N2 - The purpose of this research also support involved for three agri-food such as fruit, vegetable, and organic delivery using the internet of things (IoT) framework. In Thailand, the effects of the ecosystem are also a problem such as water, soil, and bad weather conditions. A model tuning scheme is a test and control for smart agriculture using IoT framework. The process can monitor the environment via personal computer (PC) and mobile phone. Once have implemented the Fuzzy Logic Controller (FLC) and resolving the modeling and the Simulink in the system. Overall, the IoT framework developed will monitor to check all conditions and performance results as well as help the framers improved various operations of smart agriculture is discussed. ER - TY - Conference Paper T1 - Cloud based Low-Power Long-Range IoT Network for Soil Moisture monitoring in Agriculture A1 - Bhattacherjee, S S Y1 - 2020/// KW - Internet of Things KW - LoRa KW - smart agriculture KW - soil moisture JF - 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings DO - 10.1109/SAS48726.2020.9220017 UR - https://api.elsevier.com/content/abstract/scopus_id/85095569359 N1 - Cited By (since 2020): 4 N2 - The intervention of sensors and wireless networks has transformed cliched agricultural practices. Internet of Things (IoT) has penetrated various verticals, with agriculture being one of them. The application of IoT in agriculture is primarily focused on field parameter monitoring and automation, which aims to help farmers increase crop yield. Long-range and low-power devices, convenient installation, and cost-efficiency are the primary factors to be considered for deploying an IoT network in real-time. In this paper, we proposed a low-power long-range IoT network for monitoring of soil moisture. We have selected LoRa as the communication interface, which uses 868 MHz ISM band for signal transmission. The soil-moisture sensor and the LoRa nodes are designed in-house. Accuracy of the sensor nodes is tested by placing two nodes in the same sector. All the data collected are stored in the server and are available online. ER - TY - Article T1 - Internet of things (IoT) in agriculture - Selected aspects A1 - Stočes, M Y1 - 2016/// KW - Internet of Things KW - Precision Agriculture KW - Sensors KW - Smart Agriculture KW - networks KW - protocols KW - standards JF - Agris On-line Papers in Economics and Informatics VL - 8 IS - 1 SP - 83 EP - 88 DO - 10.7160/aol.2016.080108 UR - https://api.elsevier.com/content/abstract/scopus_id/84963808522 N1 - Cited By (since 2016): 76 N2 - Article analyzes chosen aspects of Internet of Things (IoT) in general and in regards to its specific uses in agriculture, which is one of the areas where IoT is commonly implemented. It serves as a primary delve into the issues of IoT as part of the grant received from Internal Grant Agency of Faculty of Economics and Management at CULS Prague called “Potential use of the Internet of Things, with emphasis on rural development and agrarian sector”. Article overviews IoT equipment categorization, platforms, standards and network solutions. It focuses on network infrastructure, which is the foundation for IoT implementation. The specific environmental conditions of Czech Republic are also taken into account. Lastly, basic development trends of IoT are defined. ER - TY - Conference Paper T1 - Crop Selection and IoT Based Monitoring System for Precision Agriculture A1 - Bhojwani, Y A1 - Singh, Rishab A1 - Reddy, Rachana A1 - Perumal, Boominthan Y1 - 2020/// KW - Internet of things KW - KNN algorithm KW - Precision agriculture KW - Smart agriculture KW - Thingspeak KW - Weather monitoring JF - International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020 DO - 10.1109/ic-ETITE47903.2020.123 UR - https://api.elsevier.com/content/abstract/scopus_id/85085217570 N1 - Cited By (since 2020): 4 N2 - Internet of Things (IoT) is the future. IoT is the change that is required in every field. Being able to monitor and control things from a distance makes any task effortless. Agriculture is a very important field and hence every possible technological advancement should be made in this field. With the rise in population worldwide, the demand for agriculture has increased drastically and unfortunately, farmers are failing to fulfill the never-ending demand. Instead of increasing the scale of agriculture, a better way will be implementing smart or precision agriculture techniques using IoT. The yield of any crop can be maximized with the help of precision agriculture by reducing the wastage. The proposed work does so by monitoring the environmental factors like temperature, humidity, soil moisture, etc. that affect the growth of the crop as well as helps the farmers to decide the idle crop that will be suitable for them according to the data collected and environmental conditions. This model can be very effective than the traditional methods as the risk of crop failure, less yield, excessive water supply or excessive use of fertilizers and pesticides, etc. can be reduced to a great extent. The data collected by the sensor nodes deployed all over the field in sent to the cloud and there the data is analyzed and visualized for the ease of farmers. With the help of visualized data farmers can take precise and effective decisions affecting their crops. ER - TY - Conference Paper T1 - Designing a wireless sensor network for precision agriculture using zigbee A1 - Sahitya, G Y1 - 2017/// KW - Precision agriculture KW - Wireless sensor networks JF - Proceedings - 7th IEEE International Advanced Computing Conference, IACC 2017 SP - 287 EP - 291 DO - 10.1109/IACC.2017.0069 UR - https://api.elsevier.com/content/abstract/scopus_id/85027070670 N1 - Cited By (since 2017): 26 N2 - The newly emerging technology i.e. Wireless Sensor Networks spread rapidly into many field's like medical, habitat monitoring, bio-technology etc. The relevance of WSN are tremendous. The utility of WSN is for collecting the sensed data, storing or processing the sensed data and the transmitting data to the appropriate central station. Agriculture is one of the field which have recently averted their scrutiny to WSN. By taking help of WSN, one can transmit the real-time data quickly with in no time. The WSN system which is developed in this paper, is used for precision agriculture. Precision agriculture is nothing but applying right inputs at the right time to get more cultivation with less power and work. The real-time data is based on the several characteristics of weather like temperature, humidity etc. The architecture of the developed WSN system in this paper comprehend a set of sensors called sensor node, base station and central station. Base station sends the sensed data to the central station. ER - TY - Book Chapter T1 - Supply Chain Management in Agriculture Using Blockchain and IoT A1 - Borah, M D Y1 - 2020/// KW - BigchainDB KW - Blockchain KW - FARMAR KW - Internet of things KW - Supply chain management KW - Transaction JF - Studies in Big Data VL - 60 SP - 227 EP - 242 SN - 2197-6503 DO - 10.1007/978-981-13-8775-3_11 UR - https://api.elsevier.com/content/abstract/scopus_id/85132899048 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) Supply Chain Management in Agriculture Using Blockchain and IoT.pdf N1 - Cited By (since 2020): 18 N2 - Blockchains play a vital role in FARMAR to track and trace the origin of food products in food supply chain. Supply Chain Management (SCM) is an essential business process in all spheres of the economy. SCM uses specific processes to connect from producer to consumer requirement through a chain. In a BCT(Blockchain Technology) based system, “records are immutable and trusted, eliminating the need for third parties to be involved. Potential farmer-facing impacts include ensuring that farmers receive timely and complete payments through the use of smart contracts and helping farmers to capture real-time data to more effectively manage their crops and harvests (source: nextbillion.net)”. Another benefit of using BCT in FARMAR is security where hacking or tampering the existing data is impossible by any intermediary. As an add-on to this process, IoT devices (Mobile phone-based Android app) are used to update the real-time quality and transit time of the product in FARMAR. It is integrated for improved traceability and usability of the products in the supply chain. The FARMAR aims to achieve these goals by developing a web application where FARMAR creates a value chain of integrity from farm to fork by using BCT. ER - TY - Article T1 - Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform A1 - Boursianis, A D Y1 - 2021/// KW - Internet of Things KW - precision agriculture KW - radio frequency energy harvesting KW - smart irrigation JF - IEEE Sensors Journal VL - 21 IS - 16 SP - 17539 EP - 17547 DO - 10.1109/JSEN.2020.3033526 UR - https://api.elsevier.com/content/abstract/scopus_id/85101748949 N1 - Cited By (since 2021): 12 N2 - Agriculture 4.0, as the future of farming technology, includes several key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. In this paper, we present in detail the subsystems and the architecture of an intelligent irrigation system for precision agriculture, the AREThOU5A IoT platform. We describe the operation of the IoT node that is utilized in the platform. Moreover, we apply the radiofrequency energy harvesting technique to the presented IoT platform, as an alternative technique to deliver power to the IoT node of the platform. To this end, we fabricate and validate a rectenna module for radiofrequency energy harvesting. Experimental results of the fabricated rectenna exhibit a satisfactory performance as a harvester of ambient sources in an outdoor environment. ER - TY - Article T1 - A Creative IoT agriculture platform for cloud fog computing A1 - Hsu, T C Y1 - 2020/// KW - Agriculture KW - Communication model KW - Creative platform KW - Fog computing KW - internet of things JF - Sustainable Computing: Informatics and Systems VL - 28 DO - 10.1016/j.suscom.2018.10.006 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2210537918303275 UR - https://api.elsevier.com/content/abstract/scopus_id/85056759595 N1 - Cited By (since 2020): 35 N2 - The innovative service process is a process that uses newly developed technologies to improve the current service models. The study proposes a creative service process based on the cloud computing platform of the Internet of Things and it can be used to improve the integration of the current cloud-to-physical networking and to improve the computing speed of the Internet of Things. In the past, most of the computing technologies focused on high-speed computing in cloud computing or remote operations of a single object. If a service requires cloud or fog resources that can make device use of high-speed computing in the cloud and a single point of operation integration on the object side, it will be able to quickly increase in the process of collaboration, the required data will be moved back and forth between Cloud and Fog, speeding up the cloud computing integration schedule. This research uses innovative platform technology to be applied to the cloud agriculture platform. Through cloud integration, it can be applied to large-area data collection and analysis, allowing farmland with limited network information resources to be integrated and automated, including agricultural monitoring automation, pest management image analysis and monitoring, which can be used to solve the predicament of large-area automation construction. ER - TY - Conference Paper T1 - Smart agriculture based on IoT and cloud computing A1 - Namani, S A1 - Gonen, Bilal Y1 - 2020/// KW - Internet of things KW - cloud computing KW - smart agriculture KW - smart drone JF - Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020 SP - 553 EP - 556 DO - 10.1109/ICICT50521.2020.00094 UR - https://api.elsevier.com/content/abstract/scopus_id/85085563350 N1 - Cited By (since 2020): 16 N2 - The improvement in new technologies in this modern era has resulted to miniaturization of sensors and the attempts to utilize them in various areas are getting succeeded. Also, adoption of Internet of Things (IoT) and Cloud Computing in any area are leading them to a notion of "Smart" like Smart Health Care systems, Smart Cities, Smart Mobility, Smart Grid, Smart Home and Smart Metering etc. One such area of research that has also seen this adoption is agriculture and thus making it a Smart Agriculture. Agriculture is one of the major source for any of the largest population countries like India, China etc. to earn money and carry out the livelihood. Involvement of IoT and Cloud Computing in the agricultural sector would result in the better production of crops by controlling the cost, monitoring performance and maintenance, thereby benefiting the farmers and the overall nation. This paper focuses on introduction of a Smart Drone for crop management where the real-time Drone data coupled with IoT and Cloud Computing technologies help in building a sustainable Smart Agriculture. ER - TY - Conference Paper T1 - Monitoring of soil parameters and controlling of soil moisture through IoT based smart agriculture A1 - Srivastava, A Y1 - 2020/// KW - Internet of Things KW - Smart Agriculture KW - Smart Irrigation KW - Wireless sensor networks JF - 2020 IEEE Students' Conference on Engineering and Systems, SCES 2020 DO - 10.1109/SCES50439.2020.9236764 UR - https://api.elsevier.com/content/abstract/scopus_id/85096359165 N1 - Cited By (since 2020): 5 N2 - Agriculture plays a vital role in the economical growth and development of any nation. Changing climatic condition have badly affected the production of agriculture products. Therefore, to improve the quality and quantity of agriculture products, many new technologies are being developed to practice smart agriculture which can adapt to the changing climatic condition. In this paper, one such method is proposed. The developed method is a new and simple internet of thing based approach to practice smart agriculture. In this proposed approach, a hardware and software setup is used to monitor important soil parameters from a remote location and automatic control of soil moisture content. The proposed approach helps in remote monitoring and water conservation process. ER - TY - Conference Paper T1 - Sensor Based Waste Water Monitoring for Agriculture Using IoT A1 - Rekha, P Y1 - 2020/// KW - Internet of Things KW - agriculture KW - crops KW - geophysics computing KW - hydrological techniques KW - wastewater treatment KW - water quality JF - 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020 SP - 436 EP - 439 DO - 10.1109/ICACCS48705.2020.9074292 UR - https://api.elsevier.com/content/abstract/scopus_id/85084663017 N1 - Cited By (since 2020): 11 N2 - The monitoring of urban waste water for agriculture use provides a smart solution for testing the quality of water by using array of sensors and the measured value is displayed in LCD. The major objective of this paper includes the estimation of water quality parameters, for instance, pH, Turbidity, Temperature, BOD, TDS that helps to identified the deviations in the parameters and provides an alert messages when there is an abnormal level i.e., the value exceeds the predefined threshold or the standard value set in the Arduino Mega 2560 Controller. These extreme values indicated chemical spills, treatment plant issues or the problems in supply pipes which may causes severe problem in terms of the cultivation of crops and quality of the soil anomaly detection of water quality setup using a GSM module, the data is stored in a cloud and server is connected with an IoT to sent message to the government and provides a remedial measure to over come these problems and helps the farmers to improve the sales and business processes. ER - TY - Review T1 - A Comprehensive Review on the Application of Internet of Thing (IoT) in Smart Agriculture A1 - Srivastava, A Y1 - 2022/// KW - Internet of things KW - Smart agriculture KW - Smart farming KW - Wireless sensor networks JF - Wireless Personal Communications VL - 122 IS - 2 SP - 1807 EP - 1837 SN - 0929-6212 DO - 10.1007/s11277-021-08970-7 UR - https://api.elsevier.com/content/abstract/scopus_id/85113402909 N1 - Cited By (since 2022): 2 N2 - IoT-based smart farming techniques have come up as one of the solutions to tackle the effect of climate change, water scarcity, etc. which are the prime reason for the decline of agricultural products and increase in their price. In recent year, many works have presented innovative ideas and prototypes which can be used for IoT-based smart farming. This article presents a comprehensive review of the cutting-edge technologies and advancements in the field of IoT-based smart farming. This article also presents a discussion on the IoT-based commercial products developed for smart farming. Based on the review of these exiting works and commercial products, some key challenges and future scope of research in this domain are found and presented in the article. ER - TY - Article T1 - Smart water management platform: IoT-based precision irrigation for agriculture A1 - Kamienski, C A1 - Soininen, Juha-Pekka A1 - Taumberger, Markus A1 - Dantas, Ramide A1 - Toscano, Attilio A1 - Cinotti, Tullio Salmon A1 - Maia, Rodrigo Filev A1 - Neto, André Torre Y1 - 2019/// KW - FIWARE KW - Internet of Things KW - IoT platform KW - linked data KW - precision irrigation KW - smart agriculture KW - smart water management JF - Sensors (Switzerland) VL - 19 IS - 2 DO - 10.3390/s19020276 UR - https://api.elsevier.com/content/abstract/scopus_id/85060015568 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Smart water management platform IoT-based precision irrigation for agriculture.pdf N1 - Cited By (since 2019): 144 N2 - The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources. ER - TY - Conference Paper T1 - Study on precision agriculture monitoring framework based on WSN A1 - Li, X Y1 - 2008/// KW - Precision Agriculture KW - management KW - monitoring and control KW - wireless sensor networks JF - 2nd International Conference on Anti-counterfeiting, Security and Identification, ASID 2008 SP - 182 EP - 185 DO - 10.1109/IWASID.2008.4688381 UR - https://api.elsevier.com/content/abstract/scopus_id/58049161597 N1 - Cited By (since 2008): 45 N2 - The wireless sensor networks (WSN) is one of the most significant technologies in the 21st century. In recent years, achievements in micro-sensor technology and low-power electronics make WSN become into realities in applications. This paper describes a real-deployment of WSN based greenhouse management which is designed and implemented to realize modern precision agriculture. The proposed system can monitor the greenhouse environments, control greenhouse equipment, and provide various and convenient services to consumers with hand-held devices such as a PDA living a farming village. This paper discusses the advantages of using management strategy along wireless sensor-actor network technology for such cost-effective and environmental friendly greenhouse management. ER - TY - Conference Paper T1 - Design and implementation of the span greenhouse agriculture Internet of Things system A1 - Guo, T Y1 - 2015/// KW - Internet of Things KW - data acquisition KW - greenhouses KW - intelligent control KW - irrigation JF - Proceedings of 2015 International Conference on Fluid Power and Mechatronics, FPM 2015 SP - 398 EP - 401 DO - 10.1109/FPM.2015.7337148 UR - https://api.elsevier.com/content/abstract/scopus_id/84962480724 N1 - Cited By (since 2015): 27 N2 - This paper presents the new structure integrated data acquisition system and intelligent control system on agricultural facilities, in order to promote the information-based and intelligent level of comprehensive agricultural zone and to improve the production efficiency and effectiveness. The system included of agricultural intelligent frequency conversion irrigation function, and automatic control function of greenhouse combined with the sensor nodes, wireless transmission network and sensor configuration, data collection system. The system developed in greenhouse practical application has received the good effect in Tianjin. Due to realized the real-time data automatic acquisition of greenhouse environment parameters and biological information, the farmer achieved good economic and ecological benefits, and the great significance to the development of modern agricultural information-based and intelligent. ER - TY - Conference Paper T1 - The internet of things in agriculture for sustainable rural development A1 - Dlodlo, N Y1 - 2015/// KW - agriculture KW - internet of things KW - rural development JF - Proceedings of 2015 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2015 SP - 13 EP - 18 DO - 10.1109/ETNCC.2015.7184801 UR - https://api.elsevier.com/content/abstract/scopus_id/84957596858 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2015) The internet of things in agriculture for sustainable rural development.pdf N1 - Cited By (since 2015): 114 N2 - Rural areas in South Africa and Zambia face a number of similar issues in the domains of agriculture, connectivity, water, transport, health and education etc., which calls for potentially similar solutions to be directed towards solving these issues. The intention of this research is to investigate the potential contributions of internet of things technologies (IoT) towards poverty reduction in these rural areas, in line with the needs identified in these communities and with emphasis on agriculture. The paper identifies examples of IoTs to mitigate the agricultural needs of these communities for the domains of crop farming, weather forecasting, wildlife management, forestry, livestock farming, market identification and rural financing. ER - TY - Conference Paper T1 - An Internet of Things (IoT) Architecture for Smart Agriculture A1 - Verma, S Y1 - 2018/// KW - Internet of Things KW - smart agriculture JF - Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018 DO - 10.1109/ICCUBEA.2018.8697707 UR - https://api.elsevier.com/content/abstract/scopus_id/85065165793 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) An Internet of Things (IoT) Architecture for Smart Agriculture.pdf N1 - Cited By (since 2018): 6 refer N2 - Agriculture is one major and important sector for the growth of economy for any country. As per the current scenarios, various problems are present in agriculture like techniques which are used currently are not efficient, requirement of larger manpower and appropriate time for irrigation and spreading of fertilizer to yield. Internet of Things (IoT) is latest technology for smart farming to enhance efficiency, productivity and resolve various issues present in agriculture. IoT network consist of various sensor node which is used to monitor soil acidity level, temperature, and other variables. In this paper, the steps involved for agriculture are discussed and mainly focus on use of IoT in agriculture i.e. in proposed architecture which leads to growth of agriculture exponentially and the economy ER - TY - Conference Paper T1 - IOT in agriculture A1 - Shenoy, J Y1 - 2016/// KW - Internet of Things KW - agriculture KW - consumer behaviour KW - control engineering computing KW - data analysis KW - pricing KW - pumps JF - Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016 SP - 1456 EP - 1458 UR - https://api.elsevier.com/content/abstract/scopus_id/84997327517 UR - https://ieeexplore.ieee.org/document/7724508 N1 - Cited By (since 2016): 39 N2 - Major challenge in Agriculture is to cultivate produce in the farm and deliver it to the end consumers with the best possible price and best possible quality. Currently all over the world, it is found that around 50% for the farm produce never reach the end consumer due to wastage and suboptimal prices. This paper provides the solution to reduce the transport cost, predictability of prices on the past data analytics and the current market conditions, reduce number of middle hops and agents between the farmer and the end consumer using IOT based solution. ER - TY - Conference Paper T1 - IoT in Agriculture A1 - Parvez, B Y1 - 2020/// KW - Automation KW - ICT KW - Internet of things KW - Smart farming JF - 2020 International Conference on Computational Performance Evaluation, ComPE 2020 SP - 844 EP - 847 DO - 10.1109/ComPE49325.2020.9200035 UR - https://api.elsevier.com/content/abstract/scopus_id/85092722039 N1 - Cited By (since 2020): 5 N2 - IoT or Internet of Things is breakthrough advancement in technology that aids interconnectivity among intelligent devices and machines and helps reduce human intervention. IoT is revolutionizing the way we live in this world, from paying bills at a nearby provision store to booking a seat at a restaurant; it has found its way in almost every domain. A subset of information and technical communication (ICT), IoT may come in hand when our aim is to enhance the efficiency and productivity of any sort of industry or mass production, one such significant field is agriculture. this paper emphasises on the role of IoT in agriculture and the benefits that could be achieved by implementing them. ER - TY - Article T1 - Current trends and challenges in the deployment of IoT technologies for climate smart facility agriculture A1 - Symeonaki, E G A1 - Arvanitis, Konstantinos G. A1 - Piromalis, Dimitrios D. Y1 - 2019/// KW - automation KW - food safety KW - intelligent control KW - intelligent monitoring KW - internet of things KW - smart agriculture KW - sustainability KW - wireless sensor networks JF - International Journal of Sustainable Agricultural Management and Informatics VL - 5 IS - 2 SP - 181 EP - 200 DO - 10.1504/IJSAMI.2019.101673 UR - https://api.elsevier.com/content/abstract/scopus_id/85071243254 N1 - Cited By (since 2019): 6 N2 - Climate smart facility agriculture is considered to be a critical factor in terms of sustainability due to the predictions of the world population increase. Since the cutting edge technology of the internet of things (IoT) was introduced as the next internet revolution, enabling its continuously extending applications in facility agriculture is expected to become a major asset. In particular, the implementation of the IoT technologies in facility agriculture, through the intelligent monitoring and automated control of the entire agricultural production and food chain, is an innovative research field of essential importance for the global sustainable growth. In this paper, an attempt is made to survey the most significant approaches regarding the technologies and applications of the IoT in the sector of facility agriculture as well as to identify the trends and challenges regarding their efficient deployment in the context of climate smart philosophy for the benefit of sustainable development ER - TY - Conference Paper T1 - IOT in agriculture A1 - Shenoy, J Y1 - 2016/// KW - Internet of Things KW - agriculture KW - consumer behaviour KW - control engineering computing KW - data analysis KW - pricing KW - pumps JF - Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016 SP - 1456 EP - 1458 UR - https://api.elsevier.com/content/abstract/scopus_id/84997327517 N1 - Cited By (since 2016): 39 N2 - Major challenge in Agriculture is to cultivate produce in the farm and deliver it to the end consumers with the best possible price and best possible quality. Currently all over the world, it is found that around 50% for the farm produce never reach the end consumer due to wastage and suboptimal prices. This paper provides the solution to reduce the transport cost, predictability of prices on the past data analytics and the current market conditions, reduce number of middle hops and agents between the farmer and the end consumer using IOT based solution. ER - TY - Conference Paper T1 - IOT based wireless sensor network for precision agriculture A1 - Ahmad, N Y1 - 2019/// KW - Internet of Things KW - Precision Agriculture KW - Wireless Sensor Networks KW - XBee KW - sensors JF - iEECON 2019 - 7th International Electrical Engineering Congress, Proceedings DO - 10.1109/iEECON45304.2019.8938854 UR - https://api.elsevier.com/content/abstract/scopus_id/85077958577 N1 - Cited By (since 2019): 15 N2 - With the passage of time the trend of technology usage by farmers has been increasing to improve the quantity and quality of crop production. The multi parameter monitoring system is presented in this research where farmers/users will be updated with the help of internet. In this research two different technologies, Internet of things (IoT) and wireless sensor network are combined in an innovative way for smart remote monitoring system of crops. Sensor nodes are deployed in fields which gather data about different parameters. At transmission side these values are displayed and then transmitted to the base station using a network of XBee sensors. A database is established for maintaining sensor values which could be helpful for research and analysis of environmental factors such as soil moisture, UV index, rain, air pressure and humidity on crop production. ER - TY - Conference Paper T1 - Chatbot Application on Internet of Things (IoT) to Support Smart Urban Agriculture A1 - Gunawan, R Y1 - 2019/// KW - Internet of things KW - Line Chatbot KW - Natural Language Processing KW - REST API KW - Smart Urban Agriculture JF - Proceeding of 2019 5th International Conference on Wireless and Telematics, ICWT 2019 DO - 10.1109/ICWT47785.2019.8978223 UR - https://api.elsevier.com/content/abstract/scopus_id/85084645269 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Chatbot Application on Internet Of Things (IoT) to Support Smart Urban Agriculture.pdf N1 - Cited By (since 2019): 3 N2 - Information about the condition of the plant is certainly useful to do actions for plants to maintain the quality of these plants. The Internet of Things can help to inform the condition of the plant so that if there are circumstances that require further action such as watering can be informed immediately. In this study the tools used are wemos ESP8266 micro-controller that can be connected to Wi-Fi connected to the internet so that the data state from the sensor can be sent immediately via the internet. Sensors that are attached to plants are temperature, soil moisture, air humidity and light, the data sent to the web application uses the REST API service. The Web application sends the data to the mobile application and Line Chatbot. Natural Language Processing method in chatbot applications as message processing to approach Indonesian language. The result is the user's response in asking about the condition of the plant can be answered and notification of the condition of the plant can be sent. Data delivery must be received as fast as possible for the user to test it for how many seconds the sensor data is sent to the user, from 40 data samples there are delay about 1-3 seconds to be accepted by the user. For further research, sensors are needed which are more varied with the number and type of different plants and testing with sensor data compression. ER - TY - Conference Paper T1 - Real time analysis of weather parameters and smart agriculture using IoT A1 - Suciu, G A1 - Ijaz, Hussain A1 - Zatreanu, Ionel A1 - Drăgulinescu, Ana-Maria Y1 - 2019/// KW - Adcon KW - Crop monitoring KW - Environmental sensors KW - Libelium KW - Precision farming KW - Sensors KW - Smart agriculture JF - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST VL - 283 SP - 181 EP - 194 SN - 1867-8211 DO - 10.1007/978-3-030-23976-3_18 UR - https://api.elsevier.com/content/abstract/scopus_id/85075237372 N1 - Cited By (since 2019): 5 N2 - Modern day agriculture and civilization demand for increased production of food to feed fast increasing global population. New technologies and solutions are being adopted in agricultural sector to provide an optimal alternative to gather and process information while enhancing net productivity. At the same time, the alarming climate changes, increasing water crisis and natural disasters demand for an agricultural modernization with state-of-the-art technologies available in the market and improved methodologies for modern era agricultural and farming domains. Internet of things (IoT) has been broadly applied to every sector of agriculture and has become the most effective means & tools for booming agricultural productivity and for making use of full agricultural resources. The advent of Internet of Things (IoT) has shown a new way of innovative research in agricultural sector. The introduction of cloud computing and Internet of Things (IoT) into agricultural modernization will perhaps solve many issues. Based on significant characteristics of key techniques of IoT, visualization, Libelium and Adcon can build up data regarding agricultural production. It can accelerate fast development of agricultural modernization, integrate smart farming and efficiently solve the issues regarding agriculture. Our motive is to perform the research that would bring new solutions for the farmers to determine the most effective ways to manage and monitor the agricultural fields constantly. ER - TY - Article T1 - Agriculture IoT: Emerging Trends, Cooperation Networks, and Outlook A1 - Ruan, J Y1 - 2019/// KW - Agriculture KW - Internet of Things KW - Market research KW - Remote sensing KW - Sensors KW - Vegetation mapping KW - Wireless sensor networks JF - IEEE Wireless Communications VL - 26 IS - 6 SP - 56 EP - 63 DO - 10.1109/MWC.001.1900096 UR - https://api.elsevier.com/content/abstract/scopus_id/85077200273 N1 - Cited By (since 2019): 28 N2 - The arrival of the IoT era has been revolutionizing various fields of our current world. Precision agriculture is recognized as one sustainable, eco-friendly, and profitable mode to improve agriculture yields and quality, and will ultimately come true with the further implementation of IoT techniques in agriculture. To facilitate the implementation, we make a visualization review of the agriculture IoT literature in the last decade, using records of 3168 documents and their 100,205 references in Web of Science. The dynamics of research fronts and intellectual bases bring out emerging trends in both applied IoT techniques and topics of concern in agriculture. Based on the quantity of contributions in the cooperation networks, outstanding countries, institutions, and authors are detected. Moreover, influential studies and scholars are recognized from the citation networks, indicating hot research and trends in the agriculture IoT literature from 2009 to 2018. Through the review, we also propose future recommendations including construction of agriculture IoT infrastructures, data security and data sharing, sustainable energy solutions, economic analysis and operation management in agriculture IoT, and IoT-based agriculture financing and e-business modes. These results are helpful for scholars and practitioners to make further efforts on achieving IoT-based precision agriculture. ER - TY - Conference Paper T1 - Agriculture field monitoring and analysis using wireless sensor networks for improving crop production A1 - Bhanu, B B A1 - Rao, K. Raghava A1 - Ramesh, J.V.N. A1 - Hussain, Mohammed Ali Y1 - 2014/// KW - Agriculture KW - Wireless sensor networks KW - data acquisition KW - environmental variables KW - monitoring server JF - IFIP International Conference on Wireless and Optical Communications Networks, WOCN SN - 2151-7681 DO - 10.1109/WOCN.2014.6923043 UR - https://api.elsevier.com/content/abstract/scopus_id/84908238840 N1 - Cited By (since 2014): 32 N2 - The purpose of this is to design and develop an agricultural monitoring system using wireless sensor network to increase the productivity and quality of farming without observing it for all the time manually. Temperature, humidity and carbon dioxide levels are the most important factors for the productivity, growth, and quality of plants in agriculture. So this system periodically measures these parameters inside the fields, thus the farmers or the agriculture experts can observe the measurements from the web simultaneously. Moreover, when a critical change in one of the measurements occurs, then the farmer will be intimated via mobile text message and e-mail by an agriculture expert. With the continuous monitoring of many environmental parameters, the grower can analyze the optimal environmental conditions to achieve maximum crop productiveness, for the better productivity and to achieve remarkable energy savings. ER - TY - Article T1 - A miniature integrated multimodal sensor for measuring pH, EC and temperature for precision agriculture A1 - Futagawa, M A1 - Iwasaki, Taichi A1 - Murata, Hiroaki A1 - Ishida, Makoto A1 - Sawada, Kazuaki Y1 - 2012/// KW - Temperature sensors KW - agriculture KW - crosstalk KW - electrical conductivity sensor KW - multimodal sensor KW - pH sensor KW - real time measurement KW - rock wool KW - simultaneous measurement JF - Sensors (Switzerland) VL - 12 IS - 6 SP - 8338 EP - 8354 DO - 10.3390/s120608338 UR - https://api.elsevier.com/content/abstract/scopus_id/84863209258 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/sensors-12-08338.pdf N1 - Cited By (since 2012): 43 N2 - Making several simultaneous measurements with different kinds of sensors at the same location in a solution is difficult because of crosstalk between the sensors. In addition, because the conditions at different locations in plant beds differ, in situ measurements in agriculture need to be done in small localized areas. We have fabricated a multimodal sensor on a small Si chip in which a pH sensor was integrated with electrical conductivity (EC) and temperature sensors. An ISFET with a Si3N4 membrane was used for the pH sensor. For the EC sensor, the electrical conductivity between platinum electrodes was measured, and the temperature sensor was a p-n junction diode. These are some of the most important measurements required for controlling the conditions in plant beds. The multimodal sensor can be inserted into a plant bed for in situ monitoring. To confirm the absence of crosstalk between the sensors, we made simultaneous measurements of pH, EC, and temperature of a pH buffer solution in a plant bed. When the solution was diluted with hot or cold water, the real time measurements showed changes to the EC and temperature, but no change in pH. We also demonstrated that our sensor was capable of simultaneous in situ measurements in rock wool without being affected by crosstalk ER - TY - Conference Paper T1 - A smart aeroponic tailored for IoT vertical agriculture using network connected modular environmental chambers A1 - Belista, F Y1 - 2019/// KW - Internet of Things KW - aeroponics KW - precision agriculture KW - vertical farming JF - 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018 DO - 10.1109/HNICEM.2018.8666382 UR - https://api.elsevier.com/content/abstract/scopus_id/85064104904 N1 - Cited By (since 2019): 10 N2 - Precision farming, vertical farming, and Internet of things (IoT) are among the modern day agricultural advances that aims to help farmers across the globe. These measures are paramount to ensure that the supply of food and agricultural products can withstand the increasing amount of demand from the increasing population. As computers continue to improve, the advent of applying the powers of the IoT in farming is within grasp. The research revolves around the framework and design of an internet enabled modular farming system that addresses the need for people to tend heavily on their growing crops. A network controls different components and continuously gathers data while transceiving with users. The setup, ideally situated in non-agricultural lands such as residential and commercial areas enable the person in charge to accomplish other tasks and leave the system to tend to the crops. The system will control factors such as temperature, light, relative humidity, and nutrient concentration in the water for the crops to grow under ideal conditions. The group believes that this will bring the farming into a new level, allowing every consumer to have their own vertical farming set-up in their own households without worrying about it thus eliminating their need to rely on food manufacturers. In addition, growing edible plants in Metro Manila would make them more easily accessible and can be served fresh to consumers, more frequently, without the added cost of transportation and various chemicals. ER - TY - Conference Paper T1 - IOT Based Smart Agriculture System A1 - Sushanth, G Y1 - 2018/// KW - Internet of Things KW - agriculture KW - crops KW - irrigation KW - smart phones KW - wireless LAN KW - wireless sensor networks JF - 2018 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2018 DO - 10.1109/WiSPNET.2018.8538702 UR - https://api.elsevier.com/content/abstract/scopus_id/85059438120 N1 - Cited By (since 2018): 68 N2 - Smart agriculture is an emerging concept, because IOT sensors are capable of providing information about agriculture fields and then act upon based on the user input. In this Paper, it is proposed to develop a Smart agriculture System that uses advantages of cutting edge technologies such as Arduino, IOT and Wireless Sensor Network. The paper aims at making use of evolving technology i.e. IOT and smart agriculture using automation. Monitoring environmental conditions is the major factor to improve yield of the efficient crops. The feature of this paper includes development of a system which can monitor temperature, humidity, moisture and even the movement of animals which may destroy the crops in agricultural field through sensors using Arduino board and in case of any discrepancy send a SMS notification as well as a notification on the application developed for the same to the farmer's smartphone using Wi-Fi/3G/4G. The system has a duplex communication link based on a cellular-Internet interface that allows for data inspection and irrigation scheduling to be programmed through an android application. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas. ER - TY - Article T1 - Precision agriculture design method using a distributed computing architecture on internet of things context A1 - Ferrández-Pastor, F J Y1 - 2018/// KW - Internet of Things KW - fog and edge computing KW - precision agriculture JF - Sensors (Switzerland) VL - 18 IS - 6 DO - 10.3390/s18061731 UR - https://api.elsevier.com/content/abstract/scopus_id/85047853629 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Precision Agriculture Design Method Using.pdf N1 - Cited By (since 2018): 94 N2 - The Internet of Things (IoT) has opened productive ways to cultivate soil with the use of low-cost hardware (sensors/actuators) and communication (Internet) technologies. Remote equipment and crop monitoring, predictive analytic, weather forecasting for crops or smart logistics and warehousing are some examples of these new opportunities. Nevertheless, farmers are agriculture experts but, usually, do not have experience in IoT applications. Users who use IoT applications must participate in its design, improving the integration and use. In this work, different industrial agricultural facilities are analysed with farmers and growers to design new functionalities based on IoT paradigms deployment. User-centred design model is used to obtain knowledge and experience in the process of introducing technology in agricultural applications. Internet of things paradigms are used as resources to facilitate the decision making. IoT architecture, operating rules and smart processes are implemented using a distributed model based on edge and fog computing paradigms. A communication architecture is proposed using these technologies. The aim is to help farmers to develop smart systems both, in current and new facilities. Different decision trees to automate the installation, designed by the farmer, can be easily deployed using the method proposed in this document. ER - TY - Book Chapter T1 - The integration of blockchain and IoT edge devices for smart agriculture: The challenges and use cases A1 - Sugandh, U Y1 - 2022/// KW - Agriculture KW - Blockchain KW - Blockchain-Internet of things KW - Internet of Things JF - Advances in Computers SN - 0065-2458 DO - 10.1016/bs.adcom.2022.02.015 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0065245822000444 UR - https://api.elsevier.com/content/abstract/scopus_id/85127317923 N1 - Cited By (since 2022): 1 N2 - Growth of IoT (Internet of Things) has proved its importance in the various sector. But due to some limitations of security, privacy, etc. it is not possible to use IoT devices in the field of agriculture at its fullest. To overcome these limitations Blockchain is used as it provides security, privacy and it helps in monitoring, examining, to authenticate the agriculture data. With the help of Blockchain, traditional methods of collection, rearranging and distribution of Agri-products can be replaced by more trust-worthy, decentralized, vitreous, and immutable style. In agriculture sector, Blockchain and Internet of things can be amalgamated to have better results which leads us to one level up in the field of agriculture and may control or improve the supply chain in proper manner. The consequences of using blockchain and IoT in combination will result in better understanding to supervise and managing the agriculture effectively. This chapter will illustrate the importance of using blockchain and IoT collectively to develop smart agriculture from traditional agriculture. A model is also proposed to overcome the challenges encountered in agriculture sector, based on IoT applications with the help of blockchain. Also, a review is mentioned about the main characteristics and functions of blockchain used in agriculture sector such as livestock grazing, crops and food supply chain. Finally, some of the open issues Blockchain and security challenges are elaborate. ER - TY - Conference Paper T1 - CareBro (Personal Farm Assistant):An IoT based Smart Agriculture with Edge Computing A1 - Tendolkar, A A1 - Ramya, S Y1 - 2020/// KW - Arduino IDE KW - Edge Computing KW - Ethical KW - Internet of things KW - Off-grid KW - Sensorics KW - ThingSpeak cloud KW - autonomous KW - sustainability JF - MPCIT 2020 - Proceedings: IEEE 3rd International Conference on "Multimedia Processing, Communication and Information Technology" SP - 97 EP - 102 DO - 10.1109/MPCIT51588.2020.9350481 UR - https://api.elsevier.com/content/abstract/scopus_id/85101663760 N1 - Cited By (since 2020): 3 N2 - Post Covid-19 era redefines farming in terms of ensuring the maximum productivity and safety of the produce by leveraging technology. A contactless approach coupled with reliability and safety in the entre supply chain is the need of the hour. The proposed solution “CareBro”, plays a vital part in ensuring that the entire farm is managed autonomously and remotely without physical presence. The onboard edge computing capabilities interact with the smart farm sensorics in an IOT environment. This ensures seamless farming and allows for increased crop yield, ethical pest management and irrigation control. The CareBro is always in touch with the farmer through the cloud, with real time monitoring and decision making. Thereby ensuring the perfect farm management solution in urban, rural, largescale and small scale farmers throughout our country. ER - TY - Book Chapter T1 - Smart and Sustainable Agriculture Through IoT Interventions: Improvisation, Innovation and Implementation—An Exploratory Study A1 - Chakrabarty, A Y1 - 2020/// KW - Innovation KW - Internet of things KW - Sustainability agriculture KW - Technological advancement JF - Studies in Big Data VL - 63 SP - 229 EP - 240 SN - 2197-6503 DO - 10.1007/978-981-13-9177-4_11 UR - https://api.elsevier.com/content/abstract/scopus_id/85097611347 N1 - Cited By (since 2020): 4 N2 - From the dawn of civilization, the unending aspiration toward achieving excellence has been the paramount accelerator which is witnessed through different ages, i.e., Stone Age, Bronze Age, Iron Age, age of automation and information supremacy. The world is transforming into massive digital ecosystem. The comprehensive digital value system is being pioneered by developments in IT and ITES. Internet of things (IoT) is the culmination and assimilation of related instruments for sharing real-time data in a collaborative, harmonized and mutually exclusive manner to facilitate optimum decision-making process. In spite of technological advancement, the society survives on primary sector. So this is the high time to capitalize the threshold the technological knowledge into agriculture system so as to optimize resources, minimize losses and ensure achieving the spirit of sustainability. It is also interesting to see how the most advanced technology can be synergized in primitive farming techniques. The European and Latin American countries have been using IoT in agriculture in varied modes, dimensions and levels. These can be exemplified by glimpse of application like farming based on weather projection, real-life count of agriculture produces, real-life estimation for loss due to perishability or expiry, irrigation issues, controlling of infrastructure support for farming activities from distant location, census of cattle, etc. In fact, the concept of IoT is in still nascent stage in India. There are vast opportunities of IoT application in the country since India is primarily an agrarian society and around 60% population are engaged in this profession which contributes around 17% of share in GDP and feeding the elephantine population of the country. This paper would study various sparks of IoT system, its versatile application worldwide and possible intervention in India particularly in agricultural activities. The paper would explore innovative modeling for IoT integration in agriculture system and its ease of implementation globally with emphasizing on Indian subcontinent. ER - TY - Conference Paper T1 - Smart agriculture using iot A1 - Deepa, B A1 - Anusha, Chukka A1 - Devi, P. Chaya Y1 - 2021/// KW - Cloud computing KW - Farm automation KW - Monitoring KW - NodeMCU KW - Sensors KW - Smart agriculture JF - Advances in Intelligent Systems and Computing VL - 1171 SP - 11 EP - 19 SN - 2194-5357 DO - 10.1007/978-981-15-5400-1_2 UR - https://api.elsevier.com/content/abstract/scopus_id/85089720654 N1 - Cited By (since 2021): 6 N2 - An automated agriculture system is developed to monitor and maintain the important aspects of farming like temperature, humidity, soil moisture content and sunlight using IoT technology. The sensors must be placed at appropriate places and positions to sense and communicate the details using cloud computing to the mobile phones of farmers, to optimize the agriculture yield by automating the field maintenance system. Improved water supply process, brightness maintenance, temperature conditions adjustments can be achieved in the automated system using the proposed idea. Single board Node MCU microcontroller is used as the decision making and controlling device between various sensors and the farm maintenance equipment. The proposed system is expected to be helpful to the farmers in controlling an irrigation system in a better and accurate way. ER - TY - Article T1 - LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities A1 - Singh, D K Y1 - 2022/// JF - IET Communications VL - 16 IS - 5 SP - 604 EP - 618 DO - 10.1049/cmu2.12352 UR - https://api.elsevier.com/content/abstract/scopus_id/85124598774 N1 - Cited By (since 2022): 2 ER - TY - Conference Paper T1 - Smart agriculture using internet of things A1 - Mat, I Y1 - 2019/// KW - ICT and sensor technology KW - Internet of Things KW - agriculture KW - smart farming JF - 2018 IEEE Conference on Open Systems, ICOS 2018 SP - 54 EP - 59 DO - 10.1109/ICOS.2018.8632817 UR - https://api.elsevier.com/content/abstract/scopus_id/85062826894 N1 - Cited By (since 2019): 35 N2 - Recent researches hypothetically shown the potential of Internet of Things (IoT) to change major industries for a better world, which includes its impact towards the agriculture industry. Farming industry must grasp IoT to feed 9.6 billion of global population by 2050. Challenges such as extreme weather conditions and rising climate change shall be overcome to fulfil the demand for food. Smart farming based on IoT technologies will enable growers and farmers to reduce waste and enhance productivity ranging from the quantity of fertilizer utilized to the number of journeys the farm vehicles have made. So, what is smart farming? Smart farming is a capital-intensive and hi-tech system of growing food cleanly and sustainable for the masses. It is the application of modern ICT (Information and Communication Technologies) into agriculture. In this paper, the hardware and software of the IoT for smart farming will be presented besides sharing the successful results. ER - TY - Article T1 - Intelligent Agriculture and Its Key Technologies Based on Internet of Things Architecture A1 - Chen, J Y1 - 2019/// KW - Intelligent agriculture KW - Internet of Things KW - cluster analysis KW - data visualization analysis JF - IEEE Access VL - 7 SP - 77134 EP - 77141 DO - 10.1109/ACCESS.2019.2921391 UR - https://api.elsevier.com/content/abstract/scopus_id/85068256743 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Intelligent_Agriculture_and_Its_Key_Technologies_Based_on_Internet_of_Things_Architecture.pdf N1 - Cited By (since 2019): 26 N2 - In order to promote the efficient development of agriculture, the Internet of Things technology is applied to modern agricultural production, and a smart agricultural system is constructed in this paper. Moreover, the data visualization analysis and cluster analysis are used to find the key technologies in the development of the intelligent agriculture, which can effectively improve the production efficiency and ensure the quality of the agricultural products. Intellectualized products are gradually integrated into agricultural production, and the development of the Internet of Things also gives technical impetus to intelligent agriculture. Through the functions of sensing, identification, transmission, monitoring, and feedback of the Internet of Things, related agricultural activities can be accurately completed, which not only saves the time cost of farmers but also improves the crop yields and, ultimately, benefits the farmers. Therefore, the Internet of Things is used for agriculture induction, identification, monitoring, and feedback, and it is also applied to find the key technology in the application process to achieve intelligent, scientific, and efficient agriculture. It also has certain reference significance for the research of the front-end induction recognition and the intelligence of the Internet of Things in the aquaculture industry. ER - TY - Conference Paper T1 - A wireless sensor network for precision agriculture and its performance A1 - Sahota, H Y1 - 2011/// KW - energy efficiency KW - performance analysis KW - wireless application protocol KW - wireless sensor networks JF - Wireless Communications and Mobile Computing VL - 11 IS - 12 SP - 1628 EP - 1645 SN - 1530-8669 DO - 10.1002/wcm.1229 UR - https://api.elsevier.com/content/abstract/scopus_id/84555187658 N1 - Cited By (since 2011): 50 N2 - The use of wireless sensor networks is essential for implementation of information and control technologies in precision agriculture. We present our design of network stack for such an application where sensor nodes periodically collect data from fixed locations in a field. Our design of the physical layer consists of multiple power modes in both the receive and transmit operations for the purpose of achieving energy savings. In addition, we design our MAC layer to use these multiple power modes to improve the energy efficiency of wake-up synchronization phase. Our MAC protocol also organizes all the sender nodes to be synchronized with the receiver simultaneously and transmit their data in a time scheduled manner. Next, we design our energy aware routing strategy that balances the energy consumption over the nodes in the entire field and minimizes the number of wake-up synchronization overheads by scheduling the nodes for transmission in accordance with the structure of the routing tree. We develop analytical models and simulation studies to compare the energy consumption of our MAC protocol with that of the popular S-MAC protocol for a typical network topology used in our application under our routing strategy. Our MAC protocol is shown to have better energy efficiency as well as latency in a periodic data collection application. We also show the improvements resulting from the use of our routing strategy, in simulations, compared with the case when the next hop is chosen randomly from the set of neighbors that are closer to the sink node. ER - TY - Book Chapter T1 - Artificial and natural intelligence techniques as IoP-and IoT-based technologies for sustainable farming and smart agriculture A1 - Mkrttchian, V Y1 - 2021/// KW - internet of things KW - smart agriculture KW - suitable farming JF - Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture SP - 40 EP - 53 DO - 10.4018/978-1-7998-1722-2.ch003 UR - https://api.elsevier.com/content/abstract/scopus_id/85128022336 N1 - Cited By (since 2021): 1 N2 - In this chapter, the author describes the main new challenges and opportunities of blockchain technology for digital economy in Russia. The study in Russia showed that the Russian research community has not addressed a majority of these challenges, and he notes that blockchain developer communities actively discuss some of these challenges and suggest myriad potential solutions. Some of them can be addressed by using private or consortium blockchain instead of a fully open network. In general, the technological challenges are limited at this point, in terms of both developer support (lack of adequate tooling) and end-user support (hard to use and understand). The recent advances on developer support include efforts by of the towards model-driven development of blockchain applications sliding mode in intellectual control and communication and help the technological challenges and created tools. The chapter shows how avatars may communicate with each other by utilizing a variety of communications methods for sustainable farming and smart agriculture. ER - TY - Conference Paper T1 - Deep Learning and IoT for Smart Agriculture Using WSN A1 - Varman, S Aruul Mozhi Y1 - 2018/// KW - Agriculture KW - Internet of Things KW - Machine Learning KW - Smart Irrigation KW - Wireless sensor networks JF - 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017 DO - 10.1109/ICCIC.2017.8524140 UR - https://api.elsevier.com/content/abstract/scopus_id/85057990787 N1 - Cited By (since 2018): 32 N2 - Main objective of the smart agricultural system is to improve the yield of the field. In this paper, two main streams are adopted: (i) predicting the suitable crop for the next crop rotation (ii) improvising the irrigation system of the field by selective irrigation. The above goal is achieved by periodically monitoring the field. The monitoring process involves collecting information about the soil parameters of the field. A wireless sensor network (WSN) is established to collect these data and have a hindsight of it by sporadically uploading it to cloud. This uploaded data forms the basis for analytics. Through experimentation, Long Short Term Memory (LSTM) networks is found to be the suitable algorithm. The inferred results are compared with the optimal values and the best-suited crop is intimated to the user through SMS service. ER - TY - Conference Paper T1 - Smart agriculture system using IoT A1 - Mishra, D Y1 - 2019/// KW - NodeMCU KW - blynk application KW - internet of things KW - smartphone KW - soil moisture sensor JF - ACM International Conference Proceeding Series DO - 10.1145/3339311.3339350 UR - https://api.elsevier.com/content/abstract/scopus_id/85071622128 N1 - Cited By (since 2019): 5 N2 - Agriculture plays a vital role in the growth of a country, it has been found in recent studies that we need to double our food production. As the growth in the agriculture sector has been stagnant over the past few years thus it is required to implement new technologies in this sector to improve food production. This system proposes a smart farming method in a limited area by using sensor nodes like temperature & humidity sensor and soil moisture sensor. This system is developed in such a way to keep the cost minimized and provide a simple platform to monitor the parameters for growth of cops through the internet over IoT. ER - TY - Conference Paper T1 - An Integrated Low Cost IoT Node based on Discrete Components for Customized Smart Applications; Use case on Precision Agriculture A1 - Gialelis, J Y1 - 2019/// KW - Customized Smart Applications KW - disrete electronic components KW - internet of things JF - 2019 8th Mediterranean Conference on Embedded Computing, MECO 2019 - Proceedings DO - 10.1109/MECO.2019.8760147 UR - https://api.elsevier.com/content/abstract/scopus_id/85073889679 N1 - Cited By (since 2019): 3 N2 - This work depicts the concept and methodology as well as the architecture and physical implementation of a low-cost Internet of Things (IoT) node for customized smart-applications. The presented node efficiently integrates state-of-the-art discrete electronic components able to support a variety of smart applications. The node comprises an ARM CortexM based microcontroller designed for low power wireless applications, a power management unit and several sensors for which special adapters have been implemented. The proposed IoT node is being validated in vineyard fields, in the framework of precision agriculture practice, with the aim of collecting critical environmental parameters, to be used as input to algorithmic models for the detection and subsequently prevention of related agricultural diseases. ER - TY - Conference Paper T1 - Field Monitoring and Automation Using IOT in Agriculture Domain A1 - Mohanraj, I Y1 - 2016/// KW - Knowledge Base KW - Monitoring KW - Ontology KW - e-Agriculture JF - Procedia Computer Science VL - 93 SP - 931 EP - 939 SN - 1877-0509 DO - 10.1016/j.procs.2016.07.275 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1877050916315216 UR - https://api.elsevier.com/content/abstract/scopus_id/84985905914 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Field Monitoring and Automation using IOT in Agriculture Domain.pdf N1 - Cited By (since 2016): 119 N2 - Agriculture sector in India is diminishing day by day which affects the production capacity of ecosystem. There is an exigent need to solve the problem in the domain to restore vibrancy and put it back on higher growth. The paper proposes an e-Agriculture Application based on the framework consisting of KM-Knowledge base and Monitoring modules. To make profitable decisions, farmers need information throughout the entire farming cycle. The required information is scattered in various places which includes real time information such as market prices and current production level stats along with the available primary crop knowledge. A knowledge dataflow model is constructed connecting various scattered sources to the crop structures. The world around is getting automated replacing manual procedures with the advancement of technology, since it is energy efficient and engross minimal man power. The paper proposes the advantages of having ICT in Indian agricultural sector, which shows the path for rural farmers to replace some of the conventional techniques. Monitoring modules are demonstrated using various sensors for which the inputs are fed from Knowledge base. A prototype of the mechanism is carried out using TI CC3200 Launchpad interconnected sensors modules with other necessary electronic devices. A comparative study is made between the developed system and the existing systems. The system overcomes limitations of traditional agricultural procedures by utilizing water resource efficiently and also reducing labour cost. ER - TY - Review T1 - Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges A1 - Ojha, T A1 - Misra, Sudip A1 - Raghuwanshi, Narendra Singh Y1 - 2015/// KW - Actuators KW - Agriculture KW - Agriculture in India KW - Automation KW - Sensors KW - Wireless sensor networks JF - Computers and Electronics in Agriculture VL - 118 SP - 66 EP - 84 SN - 0168-1699 DO - 10.1016/j.compag.2015.08.011 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169915002379 UR - https://api.elsevier.com/content/abstract/scopus_id/84941123409 N1 - Cited By (since 2015): 517 N2 - The advent of Wireless Sensor Networks (WSNs) spurred a new direction of research in agricultural and farming domain. In recent times, WSNs are widely applied in various agricultural applications. In this paper, we review the potential WSN applications, and the specific issues and challenges associated with deploying WSNs for improved farming. To focus on the specific requirements, the devices, sensors and communication techniques associated with WSNs in agricultural applications are analyzed comprehensively. We present various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters. In this regard, the WSN deployments for various farming applications in the Indian as well as global scenario are surveyed. We highlight the prospects and problems of these solutions, while identifying the factors for improvement and future directions of work using the new age technologies. ER - TY - Article T1 - Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland: Evaluation from a Data User’s Perspective A1 - Kotamäki, N Y1 - 2009/// KW - Sensor networks KW - agriculture KW - data quality KW - environmental monitoring KW - network maintenance JF - Sensors VL - 9 IS - 4 SP - 2862 EP - 2883 DO - 10.3390/s90402862 UR - https://api.elsevier.com/content/abstract/scopus_id/70350018507 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2009) Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland Evaluation from a Data User Perspective.pdf N1 - Cited By (since 2009): 67 N2 - Sensor networks are increasingly being implemented for environmental monitoring and agriculture to provide spatially accurate and continuous environmental information and (near) real-time applications. These networks provide a large amount of data which poses challenges for ensuring data quality and extracting relevant information. In the present paper we describe a river basin scale wireless sensor network for agriculture and water monitoring. The network, called SoilWeather, is unique and the first of this type in Finland. The performance of the network is assessed from the user and maintainer perspectives, concentrating on data quality, network maintenance and applications. The results showed that the SoilWeather network has been functioning in a relatively reliable way, but also that the maintenance and data quality assurance by automatic algorithms and calibration samples requires a lot of effort, especially in continuous water monitoring over large areas. We see great benefits on sensor networks enabling continuous, real-time monitoring, while data quality control and maintenance efforts highlight the need for tight collaboration between sensor and sensor network owners to decrease costs and increase the quality of the sensor data in large scale applications. ER - TY - Conference Paper T1 - A framework of optimizing the deployment of IoT for precision agriculture industry A1 - Chehri, A Y1 - 2020/// KW - Agriculture Industry KW - Efficient deployment KW - Internet of things KW - Optimization KW - Wireless Sensor Networks JF - Procedia Computer Science VL - 176 SP - 2414 EP - 2422 SN - 1877-0509 DO - 10.1016/j.procs.2020.09.312 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1877050920322237 UR - https://api.elsevier.com/content/abstract/scopus_id/85093359705 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) A framework of optimizing the deployment of IoT for precision agriculture industry.pdf N1 - Cited By (since 2020): 10 N2 - The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer’s practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer’s experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment. ER - TY - Article T1 - A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0 A1 - Symeonaki, E Y1 - 2020/// KW - actuators KW - agriculture KW - cloud KW - context awareness KW - farm management KW - internet of things KW - middleware KW - precision agriculture KW - sustainability KW - wireless sensor networks JF - Applied Sciences (Switzerland) VL - 10 IS - 3 DO - 10.3390/app10030813 UR - https://api.elsevier.com/content/abstract/scopus_id/85081217626 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0.pdf N1 - Cited By (since 2020): 33 N2 - The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted. ER - TY - Conference Paper T1 - IOT application system with crop growth models in facility agriculture A1 - Hu, X Y1 - 2011/// KW - Internet of things KW - agricultural engineering KW - agriculture KW - crops KW - greenhouses KW - wireless sensor networks JF - Proceedings - 6th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2011 SP - 129 EP - 133 UR - https://api.elsevier.com/content/abstract/scopus_id/84869460959 N1 - Cited By (since 2011): 26 N2 - The concept of Internet of Things (IOT) have been put into practice widely. This paper puts forward the idea of embedding crop growth models (CGMs) into the IOT application system in facility agriculture to make the system more intelligent and adaptive. Besides, this paper shares our practical experience and proposes engineering challenges in further practical deployment. ER - TY - Conference Paper T1 - An IoT service-oriented system for agriculture monitoring A1 - Cambra, C Y1 - 2017/// KW - Internet of Things KW - cloud computing KW - computerised monitoring KW - irrigation KW - telecommunication network topology KW - vegetation KW - wireless sensor networks JF - IEEE International Conference on Communications SN - 1550-3607 DO - 10.1109/ICC.2017.7996640 UR - https://api.elsevier.com/content/abstract/scopus_id/85028358075 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) An IoT service-oriented system for agriculture monitoring.pdf N1 - Cited By (since 2017): 82 N2 - Wireless Sensor Networks (WSNs), Internet of things (IoT) and aerial mapping are nowadays being used very much in agriculture. The challenge of joining those technologies requires a new and smart wireless network topology for devices communication. Problems like scalability and manageability are important challenges when there are many devices. This paper presents the design of a smart IoT communication system manager used as a low cost irrigation controller. The proposal is a powerful irrigation tool that uses real time data such as the variable rate irrigation and some parameters taken from the field. The field parameters, the index vegetation (estimated using aerial images) and the irrigation events, such as flow level, pressure level or wind speed, are periodically sampled. Data is processed in a smart cloud service based on the Drools Guvnor (a Business Rules Manager). The developed multimedia platform can be controlled remotely by a mobile phone. Finally, we measured the bandwidth consumed when the system is sending different kinds of commands and data. ER - TY - Article T1 - Innovative IoT sensing and communication unit in agriculture A1 - Krčmařík, D Y1 - 2019/// KW - GSM KW - Internet of things KW - big data KW - precision agriculture KW - smart agriculture KW - tensiometer JF - European Journal of Electrical Engineering VL - 21 IS - 3 SP - 273 EP - 278 DO - 10.18280/ejee.210302 UR - https://api.elsevier.com/content/abstract/scopus_id/85072248481 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Innovative IoT Sensing and Communication Unit in Agriculture.pdf N1 - Cited By (since 2019): 2 refer N2 - This paper aims to propose an innovative reconfigurable sensing unit, suited to be used in harsh agriculture environment. The heart of the unit is a PCB with Linux based processor which is established such a way that the unit communicates via a backend, SQL based database with a user-friendly web interface. It can aggregate several data sources: tensometers, accelerometer, temperature sensor, CAN, digital and analog inputs. It communicates via GSM and It implements GPS information to provide precise real-time position. The unit is designed to be powered with different power sources. Collection the units which are used, can be easily reached using standard private network. The backend enables the users to configure desired actions. One can choose certain data to be sensed. It is possible to configure also the data acquisition rate and time interval between two packages sent to backend. Data are available through the web interface and can be downloaded to universal format of CSV for further processing. It has been shown that the whole system is capable to monitor user defined data limits and if such a limit is reached a notification (e.g. SMS) can be sent to a predefined destination. ER - TY - Conference Paper T1 - IoT based Smart Agriculture using Machine Learning A1 - Reddy, K S Pratyush Y1 - 2020/// KW - Decision Tree Algorithm KW - Humidity KW - Internet of Things KW - Irrigation System KW - Mail alert KW - Soil Moisture KW - Temperature JF - Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020 SP - 130 EP - 134 DO - 10.1109/ICIRCA48905.2020.9183373 UR - https://api.elsevier.com/content/abstract/scopus_id/85092021607 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT based Smart Agriculture using Machine Learning.pdf N1 - Cited By (since 2020): 13 N2 - Agriculture balances both food requirement for mankind and supplies indispensable raw materials for many industries, and it is the most significant and fundamental occupation in India. The advancement in inventive farming techniques is gradually enhancing the crop yield making it more profitable and reduce irrigation wastages. The proposed model is a smart irrigation system which predicts the water requirement for a crop, using machine learning algorithm. Moisture, temperature and humidity are the three most essential parameters to determine the quantity of water required in any agriculture field. This system comprises of temperature, humidity and moisture sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. Decision tree algorithm, an efficient machine learning algorithm is applied on the data sensed from the field in to predict results efficiently. The results obtained through decision tree algorithm is sent through a mail alert to the farmers, which helps in decision making regarding water supply in advance. ER - TY - Article T1 - CLAY-MIST: IoT-cloud enabled CMM index for smart agriculture monitoring system A1 - Mekala, M Y1 - 2019/// KW - Agriculture monitoring system KW - CLAY-MIST measurement index KW - Cloud computing KW - Internet of Things JF - Measurement: Journal of the International Measurement Confederation VL - 134 SP - 236 EP - 244 DO - 10.1016/j.measurement.2018.10.072 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0263224118310091 UR - https://api.elsevier.com/content/abstract/scopus_id/85055689364 N1 - Cited By (since 2019): 49 N2 - The temperature and soil moisture factors affect the growth of agriculture such as productivity, diseases, and yield production. Most of the existing techniques were used to assess comfort level based on dew-point-humidity data which gives a false decision with time and energy consumption. To comprehend these issues, we proposed a cloud-enabled CLAY-MIST measurement (CMM) index based on temperature and relative humidity to assess the comfort levels of a crop. In this research, temperature quotient is evaluated based on the amount of water vapour and pressure in the air which appraises plant growth. The relative humidity is subtracted with the standard constant optimal temperature to extract the comfort level. Therefore, the CMM index experiments with real-time data show an accurate decision and the detailed report sent to farmers. The results are 94% accurate with less execution time when compared with the existing thermal comfort techniques. ER - TY - Article T1 - On-the-go soil sensors for precision agriculture A1 - Adamchuk, V I A1 - Hummel, J.W A1 - Morgan, M.T A1 - Upadhyaya, S.K Y1 - 2004/// KW - Mapping soil properties KW - Precision agriculture KW - Soil sensors JF - Computers and Electronics in Agriculture VL - 44 IS - 1 SP - 71 EP - 91 DO - 10.1016/j.compag.2004.03.002 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169904000444 UR - https://api.elsevier.com/content/abstract/scopus_id/2942719066 N1 - Cited By (since 2004): 490 N2 - The basic objectives of site-specific management of agricultural inputs are to increase profitability of crop production, improve product quality, and protect the environment. Information about the variability of different soil attributes within a field is essential for the decision-making process. The inability to obtain soil characteristics rapidly and inexpensively remains one of the biggest limitations of precision agriculture. Numerous researchers and manufacturers have attempted to develop on-the-go soil sensors to measure mechanical, physical and chemical soil properties. The sensors have been based on electrical and electromagnetic, optical and radiometric, mechanical, acoustic, pneumatic, and electrochemical measurement concepts. While only electric and electromagnetic sensors are widely used at this time, other technologies presented in this review may also be suitable to improve the quality of soil-related information in the near future. ER - TY - Conference Paper T1 - The realization of precision agriculture monitoring system based on wireless sensor network A1 - Xiao, L Y1 - 2010/// KW - agriculture KW - computerised monitoring KW - protocols KW - telecommunication network topology KW - wireless sensor networks JF - CCTAE 2010 - 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering VL - 3 SP - 89 EP - 92 DO - 10.1109/CCTAE.2010.5544354 UR - https://api.elsevier.com/content/abstract/scopus_id/77956480772 N1 - Cited By (since 2010): 33 N2 - Based on the analysis of the development of agricultural mechanization, the trend of agricultural service system reform, agricultural environment protection and the development of information technology, it is possible to realize the precision agriculture. This paper designs the agricultural environmental monitoring system based on the wireless sensor network (WSN). The system can real-timely monitor agriculture environmental information, such as the temperature, humidity, and light intensity. This paper introduces the theory of the monitoring system, and discusses the aspect of hardware and software design of the composed modules, network topology, network communication protocol and the present challenges. Experiments show that the node can achieve agricultural environmental information collection and transmission. The system has the feature of compact in frame, light in weight, steady in performance and facilitated in operation. It greatly improves the agricultural production efficiency and automatic level drastically. ER - TY - Article T1 - Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications A1 - Li, Y Y1 - 2012/// KW - Automated guidance systems KW - GPS/INS KW - Kalman Filter KW - Loose-coupled integration JF - GPS Solutions VL - 16 IS - 1 SP - 41 EP - 52 DO - 10.1007/s10291-011-0207-y UR - https://api.elsevier.com/content/abstract/scopus_id/84855533762 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2011) Attitude determination by integration of MEMS inertial sensors.pdf N1 - Cited By (since 2012): 65 N2 - Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies, which has widespread usage in industry, is also regarded as an ideal solution for automated agriculture because it fulfils the accuracy, reliability and availability requirements of industrial and agricultural applications. Agriculture applications use position, velocity and heading information for automated vehicle guidance and control to enhance the yield and quality of the crop, and in order to vary the application of fertilizer and herbicides according to soil heterogeneity at sub-field level. A loosely coupled GPS/ INS integration algorithm known as ‘‘AhrsKf’’ is introduced for automated agriculture vehicle guidance and control utilizing MEMS inertial sensors and GPS. The AhrsKf can produce high-frequency attitude solutions for the vehicle’s guidance and control system, by using inputs from a single survey grade L1/L2 antenna, eliminating the need for the previous two antenna solutions. Given its agricultural application, the AhrsKf has been implemented with some specific design features to improve the accuracy of the attitude solution including, temperature compensation of the inertial sensors, and the aid of plough lines of farm lands. To evaluate the AhrsKf solution, two benchmarking tests have been conducted by using a threeantenna GPS system and NovAtel’s SPAN-CPT. The results have demonstrated that the AhrsKf solution is stable and can correctly track the movement of the farming vehicle ER - TY - Article T1 - From wireless sensors to field mapping: Anatomy of an application for precision agriculture A1 - Camilli, A Y1 - 2007/// KW - Indicator kriging KW - Precision agriculture KW - Wireless sensor networks JF - Computers and Electronics in Agriculture VL - 58 IS - 1 SP - 25 EP - 36 DO - 10.1016/j.compag.2007.01.019 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169907000610 UR - https://api.elsevier.com/content/abstract/scopus_id/34248550476 N1 - Cited By (since 2007): 136 N2 - Precision agriculture demands intensive field data acquisition, which is usually done as machines perform field operations. However, more frequent data acquisition and interpretation can be the key to understanding productivity variability. Wireless sensor networks are a new technology that can provide processed real-time field data from sensors physically distributed in the field. This paper describes a simulated application for precision agriculture in which a network of wireless sensors report their measurements to a collector point, where an estimate for the field properties is calculated. Estimation is obtained using the sensor network for processing and transport of the measured data. Centralized and distributed implementations for on-the-go kriging and inverse distance weight procedures are compared considering the influence of noise in the measurements and the in-network coding simplifications. We show that a wireless sensor network application can validate a field estimate constructed only upon local data with less than a 3% loss in precision compared to a centralized approach. We also show how to utilize the communication capacities and processing of a wireless sensor network to create new paradigms for precision agriculture applications, elucidating some of the benefits and drawbacks that arise from this distributed coding approach. Finally, we demonstrate the need to simultaneously engineer the application and the technology knowledge and we show how choices in these two domains can influence the results of the application. ER - TY - Conference Paper T1 - Application of IoT-Enabled Smart Agriculture in Vertical Farming A1 - Bhowmick, S A1 - Biswas, Bikram A1 - Biswas, Mandira A1 - Dey, Anup A1 - Roy, Subhashis A1 - Kumar, Subir Y1 - 2019/// KW - Intel Edison KW - Sensor arrays KW - ThingSpeak cloud KW - Vertical farming JF - Lecture Notes in Electrical Engineering VL - 537 SP - 521 EP - 528 SN - 1876-1100 DO - 10.1007/978-981-13-3450-4_56 UR - https://api.elsevier.com/content/abstract/scopus_id/85063466096 N1 - Cited By (since 2019): 12 N2 - Vertical farming is an unconventional farming technique that has gained relevance in recent years, as existing agricultural lands fail to meet the needs of the growing population. Smart monitoring of the ambient parameters in vertical farming can improve the productivity and quality of the crops. A system has been proposed to develop sensor arrays that can measure the ambient parameters and upload the data onto the ThingSpeak Cloud, using the Intel Edison wireless module. The web-based application can be used to analyze and monitor the light, temperature, humidity, and soil moisture of the vertical farming stacks. Using the Virtuino app, a SMS can be sent if the parameters fall below a threshold value. ER - TY - Article T1 - Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications A1 - Ullo, S L Y1 - 2021/// KW - AI KW - Internet of Things KW - agriculture applications KW - remote sensing KW - sensors KW - smart sensors JF - Remote Sensing VL - 13 IS - 13 DO - 10.3390/rs13132585 UR - https://api.elsevier.com/content/abstract/scopus_id/85110024039 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications.pdf N1 - Cited By (since 2021): 10 N2 - Modern sensors find their wide usage in a variety of applications such as robotics, navigation, automation, remote sensing, underwater imaging, etc. and in recent years the sensors with advanced techniques such as the artificial intelligence (AI) play a significant role in the field of remote sensing and smart agriculture. The AI enabled sensors work as smart sensors and additionally the advent of the Internet of Things (IoT) has resulted into very useful tools in the field of agriculture by making available different types of sensor-based equipment and devices. In this paper, we have focused on an extensive study of the advances in smart sensors and IoT, employed in remote sensing and agriculture applications such as the assessment of weather conditions and soil quality; the crop monitoring; the use of robots for harvesting and weeding; the employment of drones. The emphasis has been given to specific types of sensors and sensor technologies by presenting an extensive study, review, comparison and recommendation for advancements in IoT that would help researchers, agriculturists, remote sensing scientists and policy makers in their research and implementations. ER - TY - Conference Paper T1 - IoT Based Power Efficient Agro Field Monitoring and Irrigation Control System : An Empirical Implementation in Precision Agriculture A1 - Islam, A Y1 - 2018/// KW - Internet of things KW - LoRa KW - Wireless sensor networks KW - precision agriculture KW - smart agriculture JF - 2018 International Conference on Innovations in Science, Engineering and Technology, ICISET 2018 SP - 372 EP - 377 DO - 10.1109/ICISET.2018.8745605 UR - https://api.elsevier.com/content/abstract/scopus_id/85069201724 N1 - Cited By (since 2018): 11 N2 - Internet of Things (IoT) has been flourishing the communication and networking system in recent years, while experiencing a growing phase in agricultural field. Precision agriculture requires sensor integration, automatic control, and networking and data processing capabilities. With the implementation of rapidly growing IoT, challenges of scalability and data management can be overcome. In this research, we have developed an IoT based smart monitoring system for agricultural practice including client application- web application and android application; and designed a controlling system for power efficient irrigation. We have used LoRa, wireless RF transceiver to overcome the limitations of area coverage and have enabled to established real time data communication. While our android application makes this system feasible for users. Real time data were collected deploying the system and empirical data has been included in the result section. ER - TY - Conference Paper T1 - A software model for precision agriculture framework based on smart farming system and application of IoT gateway A1 - Donzia, S Y1 - 2019/// KW - Agriculture KW - Cloud KW - Farming KW - Gateway KW - Internet of things KW - Wi-Fi JF - Studies in Computational Intelligence VL - 787 SP - 49 EP - 58 SN - 1860-949X DO - 10.1007/978-3-319-96806-3_4 UR - https://api.elsevier.com/content/abstract/scopus_id/85051800853 N1 - Cited By (since 2019): 4 N2 - Contemporary society is seriously threatened with food as part of the world due to the continuous increase in world population, the degradation and decline of agricultural lands due to high industrialization, climate change and the aging of the population. Therefore, modern society is studying different solutions to solve human food. In this paper, a framework for precision agriculture using IoT Gateway is proposed for solving human food, and the productivity of crops must be increased first. IoT solution through architecture, platforms and IoT standards, or the use of interoperable IoT technologies beyond the adopters in particular, simplifying existing proposals. Connecting different sensors, connected devices, developing intelligent breeding systems as much as possible. One of our aims is to manage and challenges. We provide a techniques and technologies applications during our work. The result shows that the advantages of various types of sensors for agriculture services in their decision making. And a proposed architecture for Agriculture Mobile services based on Sensor Cloud substructure that helps farms and IoT applications are effective in intelligent farming system. ER - TY - Article T1 - AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture A1 - Anand, T Y1 - 2021/// KW - Deep learning KW - Internet of things KW - agriculture KW - agriculture-vision KW - semantic segmentation KW - sensors JF - IEEE Sensors Journal VL - 21 IS - 16 SP - 17581 EP - 17590 DO - 10.1109/JSEN.2021.3071290 UR - https://api.elsevier.com/content/abstract/scopus_id/85103874486 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) AgriSegNet Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture.pdf N1 - Cited By (since 2021): 16 N2 - Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques. ER - TY - Conference Paper T1 - Internet of Things (IoT) for Precision Agriculture Application A1 - Dholu, M Y1 - 2018/// KW - ESP8266 KW - Internet of things KW - Mobile Application KW - Node KW - Precision Agriculture KW - Smart Farming KW - Wi-Fi KW - sensors JF - Proceedings of the 2nd International Conference on Trends in Electronics and Informatics, ICOEI 2018 SP - 339 EP - 342 DO - 10.1109/ICOEI.2018.8553720 UR - https://api.elsevier.com/content/abstract/scopus_id/85059975146 N1 - Cited By (since 2018): 70 N2 - Internet is experiencing a very explosive growth nowadays with the amount of the devices connecting to it. Earlier we had only personal computers (pCs) and Mobile handset connected to internet but now with Internet of Things i.e. IoT concept of connecting things with internet, millions of device are connecting with it. This development of IoT leads to the idea of machine to machine communication which means that two machines can communicate to each other and also all the data which was previously with private server can now is available on internet so the user can access it remotely. Application of IoT is feasible in almost all industries particularly where speed of communication is not an issue. This paper proposes the application of cloud based IoT in the agriculture domain. Precision agriculture is basically a concept which insists to provide right amount of resources at and for exact duration of time. These resources can be any things such as water, light, pesticides etc. To implement precision agriculture the benefits of IOT has been utilized in the proposed paper. The fundamental idea is to sense all the required parameter from the agriculture field and take required decision to control the actuator. These agriculture parameters are Soil Moisture, Temperature & Relative Humidity around plant, Light intensity. Based on the reading sensed by the sensor suitable action is taken i.e. irrigation valve is actuated based on soil moisture readings, valve for fogger (for spraying water droplet) is actuated based on the Relative humidity(RH) readings etc. This paper proposed the development of the sensor node capable of measuring all these parameter and creating the actuation signal for all the actuator. On top of that sensor nodes are also capable of sending this data to cloud. An Android application is also developed in order to access all these agricultural parameter. ER - TY - Book Chapter T1 - Enabling IoT Wireless Technologies in Sustainable Livestock Farming Toward Agriculture 4.0 A1 - Symeonaki, E Y1 - 2021/// KW - Ecological Engineering KW - Internet of things KW - agriculture JF - Lecture Notes on Data Engineering and Communications Technologies VL - 67 SP - 213 EP - 232 SN - 2367-4512 DO - 10.1007/978-3-030-71172-6_9 UR - https://api.elsevier.com/content/abstract/scopus_id/85107341387 N1 - Cited By (since 2021): 3 N2 - As part of the latterly introduced approach of Agriculture 4.0, practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production tend to be adopted. This fact is considered to be critical in addressing the challenge of securing adequate food supplies for the constantly increasing world population, taking also into regard the imperative necessity of exploiting natural resources according to policies related to sustainable growth. Moreover it has already been recognized that enabling the Internet of Things (IoT) wireless technologies into livestock farming systems for the benefit of sustainable growth is of high significance. To this end, potential solutions should be provided for developing responsive and adaptive IoT integrated systems which will deliver a wide variety of qualitative low-cost services in accordance with the objectives of modern sustainable livestock farming. This work presents and critically analyzes the existence, functionality and interoperability of various approaches in this area, as well as their maturity to be integrated toward the concept of Agriculture 4.0. In addition to this, some key challenges are identified regarding the management, process and exchange of the large amounts of heterogeneous sensory raw data that are acquired remotely in precision livestock farming environments. ER - TY - Article T1 - MyGreen: An IoT-Enabled Smart Greenhouse for Sustainable Agriculture A1 - Tripathy, P K Y1 - 2021/// KW - Internet of Things KW - agriculture KW - agrochemicals KW - decision support systems KW - fault diagnosis KW - greenhouses KW - sustainable development JF - IEEE Consumer Electronics Magazine VL - 10 IS - 4 SP - 57 EP - 62 DO - 10.1109/MCE.2021.3055930 UR - https://api.elsevier.com/content/abstract/scopus_id/85100748039 N1 - Cited By (since 2021): 15 N2 - This article presents the potential of Internet-of-Things (IoT) in the area of greenhouse farming and leading to the smart agriculture. The different parameters, such as humidity, water nutrients solution level, pH and electrical conductivity (EC) value, temperature, UV light intensity, CO2 level, mist, and amount of insecticides or pesticides, are monitored through various sensors so that significant knowledge can be captured and early fault detection and diagnosis can be done. A decision support system (DSS) acts as the central operating system that governs and coordinates all the activities. Furthermore, this work also accounts for the different challenges of greenhouse rose farming and highlights a new IoT-based solution, which is smart and sustainable. The model presented in this work is well adapted to the changing environment, thereby redefining the terms of sustainability. ER - TY - Article T1 - An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture A1 - Banđur, Đ Y1 - 2019/// KW - Energy efficiency KW - Smart agriculture KW - Wireless Sensor Networks JF - Computers and Electronics in Agriculture VL - 156 SP - 500 EP - 507 DO - 10.1016/j.compag.2018.12.016 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169918316570 UR - https://api.elsevier.com/content/abstract/scopus_id/85058173997 N1 - Cited By (since 2019): 44 N2 - In this paper the usage of Wireless Sensor Networks (WSN) in smart agriculture applications was analyzed. The main focus of the paper is on the power consumption of the various WSN components, on the both levels, physical and functional. The analysis, from the energy efficiency aspect, includes a comparative review and discussion of the most commonly used protocols on the physical, data link and network layers. The analysis outcome provides a precise identification of the main power consumers, the magnitude of their consumption, and a deep understanding of the key mechanisms that should be applied in order to improve the energy efficiency in a WSN. The analysis also includes simulation of a WSN operation in a smart agriculture application. The simulation scenario and the measured values of the average power consumption and the average time of activity of the radio component of each network node provide a confirmation of the key points of the previously performed analysis and detailed insights into the possible directions of the strategy for energy efficiency improvement. Additionally, the simulation results reveal the magnitude of the energy savings that can be accomplished by deploying the duty cycle mechanisms within the WSNs. Finally, the paper includes a discussion about various factors and the way they impact the level of energy efficiency, which have to be addressed within the requirements gathering, comprehensive analysis and the design phases of a WSN life cycle implementation. ER - TY - Article T1 - Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry A1 - Adão, T Y1 - 2017/// KW - UAS KW - agriculture KW - agroforestry KW - forestry KW - hyperspectral KW - hyperspectral data processing KW - hyperspectral sensors KW - unmanned aerial vehicles JF - Remote Sensing VL - 9 IS - 11 DO - 10.3390/rs9111110 UR - https://api.elsevier.com/content/abstract/scopus_id/85034756154 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Hyperspectral Imaging A Review on UAV-Based.pdf N1 - Cited By (since 2017): 485 N2 - Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors’ ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectral sensors plays a center role—is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing—by isolating the common user from the processes’ mathematical complexity—several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper’s conclusions. ER - TY - Article T1 - IoT adoption in agriculture: the role of trust, perceived value and risk A1 - Jayashankar, P Y1 - 2018/// KW - IT KW - Trust KW - agriculture KW - business to business marketing KW - united stated of america KW - value JF - Journal of Business and Industrial Marketing VL - 33 IS - 6 SP - 804 EP - 821 DO - 10.1108/JBIM-01-2018-0023 UR - https://api.elsevier.com/content/abstract/scopus_id/85053674927 N1 - Cited By (since 2018): 79 N2 - Purpose This paper aims to study the antecedents of Internet of Things (IoT) adoption among farmers and determine how trust in the technology influences its adoption when mediated by perceived value and risk. Through the conceptualization of trust and perceived risk, the authors factor in farmers’ perceptions of agricultural technology providers and discuss different forms of perceived value, spanning economic, green and epistemic value. Design/methodology/approach This paper develops a distinctive research design, drawing on elements of the value-based adoption and technology acceptance models. By linking different elements of perceived value with IoT technology, the authors also apply the service-dominant logic to this study. They study how trust affects perceived value and risk and then determine how perceived value and risk, in turn, affect IoT adoption. The authors test the hypotheses by developing a structural equation model to analyze the results of a survey, wherein 492 farmers from Iowa, the USA, participated. Findings The results show a positive relationship between trust and perceived value and a negative relationship between trust and perceived risk. Perceived value had a positive impact on IoT adoption, whereas perceived risk had a negative impact on IoT adoption. Practical implications The research findings on trust and perceived value and risk are timely and relevant for business-to-business (B2B) marketing practitioners and agricultural stakeholders, especially in an era where farmers are expressing growing concerns about data handling risk posed by IoT technology adoption. Originality/value The research findings signal a transition in focus from the goods-dominant logic to the service-dominant logic in agriculture, whereby farmers are drawn to IoT technology because of perceived economic, green and epistemic value and as a result, can differentiate themselves on how well they deploy operant resources. This paper not only provides a unique conceptualization of perceived value but also pave the way for a richer conceptualization of IoT core functions that enable farmers to fulfill green and epistemic goals. This is the first B2B marketing paper discussing the antecedents of IoT adoption in agriculture, such as farmers’ perceptions of both monetary and non-monetary forms of value and perceived data handling risk. ER - TY - JOUR T1 - Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices A1 - Alyahya, S A1 - Khan, W U A1 - Ahmed, S A1 - Marwat, S N K A1 - Habib, S Y1 - 2022/// KW - Internet of Things KW - agriculture KW - authentication KW - security PB - mdpi.com JF - Electronics UR - https://www.mdpi.com/1551372 UR - https://www.mdpi.com/2079-9292/11/6/963/pdf L1 - file:///C:/Users/sonsu/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Alyahya et al. - 2022 - Cyber Secure Framework for Smart Agriculture Robust and Tamper-Resistant Authentication Scheme for IoT Devices.pdf N1 - Cited By (since 2022): 2 Q2 N2 - Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences such as logistics, healthcare, traffic, oil and gas industries and agriculture. In agriculture field, the farmer still used conventional agriculture methods resulting in low crop and fruit yields. The integration of IoT in conventional agriculture methods has led to significant developments in agriculture field. Different sensors and IoT devices are providing services to automate agriculture precision and to monitor crop conditions. These IoT devices are deployed in agriculture environment to increase yields production by making smart farming decisions and to collect data regarding crops temperature, humidity and irrigation systems. However, the integration of IoT and smart communication technologies in agriculture environment introduces cyber security attacks and vulnerabilities. Such cyber attacks have the capability to adversely affect the countries’ economies that are heavily reliant on agriculture. On the other hand, these IoT devices are resource constrained having limited memory and power capabilities and cannot be secured using conventional cyber security protocols. Therefore, designing robust and efficient secure framework for smart agriculture are required. In this paper, a Cyber Secured Framework for Smart Agriculture (CSFSA) is proposed. The proposed CSFSA presents a robust and tamper resistant authentication scheme for IoT devices using Constrained Application Protocol (CoAP) to ensure the data integrity and authenticity. The proposed CSFSA is demonstrated in Contiki NG simulation tool and greatly reduces packet size, communication overhead and power consumption. The performance of proposed CSFSA is computationally efficient and is resilient against various cyber security attacks i.e., replay attacks, Denial of Service (DoS) attacks, resource exhaustion. ER - TY - Article T1 - Design and development of an IoT-enabled portable phosphate detection system in water for smart agriculture A1 - Akhter, F Y1 - 2021/// KW - Internet of Things KW - MWCNT KW - PDMS KW - Phosphate KW - Water quality JF - Sensors and Actuators, A: Physical VL - 330 DO - 10.1016/j.sna.2021.112861 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0924424721003241 UR - https://api.elsevier.com/content/abstract/scopus_id/85108060357 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) Design and development of an IoT-enabled portable phosphate detection system in water for smart agriculture.docx N1 - Cited By (since 2021): 12 N2 - This research proposes a novel low-cost, low-power planar interdigital phosphate sensor for smart agriculture. A 3D printed mould is used for sensor fabrication. The electrodes and substrate of the sensor are formed using Multi-Walled Carbon Nanotubes (MWCNTs) and Polydimethylsiloxane (PDMS), respectively. Electrochemical Impedance Spectroscopy (EIS) is applied to characterize the sensor for a wide range of temperature and phosphate detection. The proposed sensor can differentiate differently concentrated phosphate solutions from 0.01 ppm ∼ 40 ppm. Validation of the experimental outcomes using the standard UV–vis Spectrometry promotes the reliability of the sensor. An IoT-enabled portable smart phosphate detection system is also designed and developed. The Arduino-based system is trained with a machine learning model trains to predict phosphate concentration in actual water samples. This enables surveilling water quality from any place and getting experts opinion from any remote location. The portable phosphate detection system will be highly beneficial for continuous water quality monitoring and significantly impact smart agriculture. ER - TY - Review T1 - State-of-the-art Review for Internet of Things in Agriculture A1 - Li, D Y1 - 2018/// KW - Wireless sensor networks KW - agricultural big data KW - agricultural information perception KW - agricultural internet of things KW - artificial intelligence KW - integrated application JF - Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery VL - 49 IS - 1 SP - 1 EP - 20 SN - 1000-1298 DO - 10.6041/j.issn.1000-1298.2018.01.001 UR - https://api.elsevier.com/content/abstract/scopus_id/85047731474 N1 - Cited By (since 2018): 53 N2 - n China, agricultural production still mainly based on manpower, while agricultural labor population continuously declining, low agricultural resource efficiency, average agricultural resource shortage, low average age of new generation farmers and population aging in practical agricultural labor are the main troubles in developing agriculture. Research of intelligent devices and models to realize precise agriculture is a criticle approach to solve the problems and realize modernlization agriculture in China. IoT (Internet of things) in agriculture is an advanced technology aims at digging agricultural productivity potential, intelligentize agricultural equipment and realizing intelligent production, it includes agricultural information perception, agricultural information transmission and intelligent information processing technologies, IoT in agriculture focusing on applying in field planting, facility horticulture, livestock feeding, aquaculture and agricultural products logistics according to individual requirements. The progress of IoT in agriculture was concluded in last three years, which mainly focused on important breakthroughs of agricultural individual identification technologies, agricultural sensing theories and crafts, low-power wide-area network (LPWAN) technologies, agricultural big data technologies and artificial intelligent technologies. A framework of IoT in agriculture was proposed that agricultural operational control was based on agricultural operational models and self managed device network, according to the framework, the role of human was consumer of real-time data and valuable information, the labor output was based on IoT drived intelligent equipment. Furthermore, restriction factors were summarized by contrasting IoT applications in China and in advanced agricultural countries, the development strategy was raised for the development of IoT in Chinese agriculture, and finally the perspectives and main research issues of IoT were concluded in Chinese agriculture. © 2018, Chinese Society of Agricultural Machinery. All right reserved. ER - TY - Article T1 - Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms A1 - Keswani, B Y1 - 2019/// KW - Fuzzy logic KW - Gradient descent KW - Internet of things KW - Interpolation KW - Soil moisture KW - Structural similarity index KW - Variable learning rate gradient descent KW - Wireless sensor networks JF - Neural Computing and Applications VL - 31 SP - 277 EP - 292 DO - 10.1007/s00521-018-3737-1 UR - https://api.elsevier.com/content/abstract/scopus_id/85053894721 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Adapting weather conditions based IoT enabled smart irrigation in technique in precision agriculture mechanisms.pdf N1 - Cited By (since 2019): 81 N2 - Precision agriculture is the mechanism which controls the land productivity and maximizes the revinue and minimizes the impact on sorroundings by automating the complete agriculture processes. This projected work relies on independent internet of things (IoT) enabled wireless sensor network (WSN) framework consisting of soil moisture (MC) probe, soil temperature measuring device, environmental temperature sensor, environmental humidity sensing device, CO2 sensor, daylight intensity device (light dependent resistor) to acquire real-time farm information through multi-point measurement. The projected observance technique consists of all standalone IoT-enabled WSN nodes used for timely data acquisitions and storage of agriculture information. The farm history is additionally stored for generating necessary action throughout the whole course of farming. The work summarizes the optimum usage of irrigation by the precise management of water valve using neural network-based prediction of soil water requirement in 1 h ahead. Our proposed irrigation control scheme utilizes structural similarity (SSIM)-based water valve management mechanism which is used to locate farm regions having water deficiency. Moreover, a close comparative study of optimization techniques, like variable learning rate gradient descent, gradient descent for feedforward neural network-based pattern classification, is performed and the best practice is employed to forecast soil MC on hourly basis together with interpolation method for generating soil moisture content (MC) distribution map. Finally, SSIM index-based soil MC deficiency is calculated to manipulate the specified valves for maintaining uniform water requirement through the entire farm area. The valve control commands are again processed using fuzzy logic-based weather condition modeling system to manipulate control commands by considering different weather conditions. ER - TY - Conference Paper T1 - Agriculture Blockchain Service Platform for Farm-to-Fork Traceability with IoT Sensors A1 - Chun-Ting, P Y1 - 2020/// KW - Agriculture KW - Blockchain KW - Information security KW - Internet of Things KW - Traceability KW - smart contracts JF - International Conference on Information Networking VL - 2020 SP - 158 EP - 163 SN - 1976-7684 DO - 10.1109/ICOIN48656.2020.9016535 UR - https://api.elsevier.com/content/abstract/scopus_id/85082117057 N1 - Cited By (since 2020): 15 N2 - The traceability of agricultural products has always been one of the most important things that customers care about. For the past decades, the trend of applying Internet of Things (IoT) technology to agriculture has been on an exponential rise and has also shown great success in different aspects. Not only crops can be irrigated and taken care of more precisely, but more reliable production history can be created based on the environmental data collected by IoT sensors. To achieve a trustable traceability for agricultural production process, data security becomes a major factor after the collection of massive data via IoT. By using blockchain technology for data storing, integrity and reliability can be ensured due to its immutability. Furthermore, when dealing with several components (producer, processor, distributor...etc.) on a food production chain, blockchain also has the ability to connect untrusted nodes and restrict them in following certain protocols. This study designs and implements an agriculture blockchain service platform for farm-to-fork traceability with IoT sensors. The Ethereum blockchain technology is employed and smart contracts are also designed to support tampering-proof data storing and provide reliable financial transactions which may happen throughout the entire food production chain. ER - TY - Review T1 - A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends A1 - Ruiz-Garcia, L Y1 - 2009/// KW - Radio frequency identification KW - Wireless sensor networks KW - agriculture KW - food JF - Sensors (Switzerland) VL - 9 IS - 6 SP - 4728 EP - 4750 SN - 1424-8220 DO - 10.3390/s90604728 UR - https://api.elsevier.com/content/abstract/scopus_id/70349645089 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/sensors-09-04728.pdf N1 - Cited By (since 2009): 506 N2 - The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed. ER - TY - JOUR T1 - IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture A1 - García, L A1 - Parra, L A1 - Jimenez, J M A1 - Lloret, J A1 - Lorenz, P Y1 - 2020/// KW - Internet of things KW - irrigation KW - precision agriculture KW - sensors PB - mdpi.com JF - Sensors UR - https://www.mdpi.com/641794 UR - https://www.mdpi.com/1424-8220/20/4/1042/pdf L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT-Based Smart Irrigation Systems An Overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture.pdf N1 - Cited By (since 2020): 209 Q1 N2 - ...Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems... ER - TY - Conference Paper T1 - Sensor drone for aerial odor mapping for agriculture and security services A1 - Pobkrut, T Y1 - 2016/// KW - Drone KW - Electronic nose KW - flying E-nose drone JF - 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2016 DO - 10.1109/ECTICon.2016.7561340 UR - https://api.elsevier.com/content/abstract/scopus_id/84988835611 N1 - Cited By (since 2016): 34 N2 - In this work, an electronics nose (E-nose) based on six polymers and functionalized single walled carbon nanotube (SWCNT) nanocomposite gas sensors was developed and installed on a small unmanned aerial vehicle (UAV or drone) platform for detection of volatile compounds in the air. The efficiency of each gas sensor was tested in a static gas measurement chamber with presence of volatiles. The gas sensors were observed to increase response with increasing concentration of ammonia and toluene. Polyvinyl pyrolidon (PVP)/SWCNT-COOH shows the highest sensor response to both ammonia and toluene. The E-nose drone has then been demonstrated under two situations, i.e., in a closed clean room with presence of ammonia evaporation, and in open air with low wind environment. It was found that the pattern of sensor data obtained from flying the E-nose drone under different situations can be clearly distinguished. It is hoped that the E-nose drone can be a very useful technology for military usage; such as to detect explosives, as well as for farmers; such as to map the malodor emission from their cattle farms or to search for ethylene for fruit ripeness detection, etc. ER - TY - Article T1 - T-MQM: Testbed-based multi-metric quality measurement of sensor deployment for precision agriculture - A case study A1 - Kaiwartya, O Y1 - 2016/// KW - Precision agriculture KW - Wireless sensor networks KW - deployment KW - testbed JF - IEEE Sensors Journal VL - 16 IS - 23 SP - 8649 EP - 8664 DO - 10.1109/JSEN.2016.2614748 UR - https://api.elsevier.com/content/abstract/scopus_id/84997831586 N1 - Cited By (since 2016): 41 N2 - Efficient sensor deployment is one of the primary requirements of the precision agriculture use case of wireless sensor networks (WSNs) to provide qualitative and optimal coverage and connectivity. The application-based performance variations of the geometrical-model-based sensor deployment patterns restrict the generalization of a specific deployment pattern for all applications. Furthermore, single or double metrics-based evaluation of the deployment patterns focusing on theoretical or simulation aspects can be attributed to the difference in performance of real applications and the reported performance in the literature. In this context, this paper proposes a testbed-based multi-metric quality measurement of sensor deployment for the precision agriculture use case of WSNs. Specifically, seven metrics are derived for the qualitative measurement of sensor deployment patterns for precision agriculture. The seven metrics are quantified for four sensor deployment patterns to measure the quality of coverage and connectivity. Analytical- and simulation-based evaluations of the measurements are validated through testbed experiment-based evaluations which are carried out in “INDRIYA” WSNs testbed. Toward realistic research impact, the investigative evaluation of the geometrical-model-based deployment patterns presented in this paper could be useful for practitioners and researchers in developing performance guaranteed applications for precision agriculture and novel coverage and connectivity models for deployment patterns. ER - TY - Conference Paper T1 - Implementation of IoT (Internet of Things) and Image processing in smart agriculture A1 - Kapoor, A A1 - Bhat, S.I A1 - Shidnal, S A1 - Mehra, A Y1 - 2016/// KW - Image Processing KW - Internet of Things KW - MATLAB KW - Sensing network JF - 2016 International Conference on Computation System and Information Technology for Sustainable Solutions, CSITSS 2016 SP - 21 EP - 26 DO - 10.1109/CSITSS.2016.7779434 UR - https://api.elsevier.com/content/abstract/scopus_id/85010420402 N1 - Cited By (since 2016): 53 N2 - Internet of Things and Image processing have been so far been applied for various applications independently. Their individual application in the field of agriculture exists and has achieved certain degree of success, however the combination of both these technology so far is non-existent. This paper describes an approach to combine IoT and image processing in order to determine the environmental factor or man-made factor (pesticides/fertilizers) which is specifically hindering the growth of the plant. Using an IoT sensing network which takes the readings of the crucial environmental factors and the image of the leaf lattice, it is processed under MATLAB software by the help of histogram analysis to arrive at conclusive results. ER - TY - JOUR T1 - Towards Industry 4.0 A1 - Ardito, Lorenzo A1 - Petruzzelli, Antonio Messeni A1 - Panniello, Umberto A1 - Garavelli, Achille Claudio Y1 - 2019/01// KW - bibliographic coupling KW - bibliometrics KW - Co-citation KW - R package KW - Science mapping KW - Workflow PB - Emerald Publishing Limited JF - Business Process Management Journal VL - 25 IS - 2 SP - 323 EP - 346 DO - 10.1108/BPMJ-04-2017-0088 UR - https://doi.org/10.1108/BPMJ-04-2017-0088 N2 - Purpose The purpose of this paper is to present a comprehensive picture of the innovative efforts undertaken over time to develop the digital technologies for managing the interface between supply chain management and marketing processes and the role they play in sustaining supply chain management-marketing (SCM-M) integration from an information processing point of view. Design/methodology/approach Patent analysis and actual examples are used to carry out this study. In detail, first, the authors identify the subset of enabling technologies pertaining to the fourth industrial revolution (Industry 4.0) that can be considered the most relevant for effective SCM-M integration (i.e. Industrial Internet of Things, Cloud computing, Big Data analytics and customer profiling, Cyber security). Second, the authors carry out a patent analysis aimed at providing a comprehensive overview of the patenting activity trends characterizing the set of digital technologies under investigation, hence highlighting their innovation dynamics and applications. Findings This research provides insightful information about which digital technologies may enable the SCM-M integration. Specifically, the authors highlight the role those solutions play in terms of information acquisition, storage and elaboration for SCM-M integration by relying on illustrative actual examples. Moreover, the authors present the organisations more involved in the development of digital technologies for SCM-M integration over time and offer an examination of their technological impact in terms of influence on subsequent technological developments. Originality/value So far, much has been said about why marketing and supply chain management functions should be integrated. However, a clear picture of the digital technologies that might be adopted to achieve this objective has yet to be revealed. Thus, the paper contributes to the literature on SCM-M integration and Industry 4.0 by highlighting the enabling technologies for the Industry 4.0 that may particularly serve for managing the SCM-M interface from an information processing perspective. ER - TY - Article T1 - Smart agriculture using IoT multi-sensors: A novel watering management system A1 - Khoa, T A A1 - M, Man A1 - T, Nguyen Y1 - 2019/// KW - Internet of Things KW - LPWAN KW - LoRa KW - sensors KW - smart agriculture KW - water management JF - Journal of Sensor and Actuator Networks VL - 8 IS - 3 DO - 10.3390/jsan8030045 UR - https://api.elsevier.com/content/abstract/scopus_id/85074171006 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Smart Agriculture Using IoT Multi-Sensors A Novel Watering Management System.pdf L1 - file:///C:/Users/sonsu/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Khoa - 2019 - Smart agriculture using IoT multi-sensors A novel watering management system.pdf N1 - Cited By (since 2019): 44 N2 - Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign. ER - TY - Conference Paper T1 - A precision agriculture management system based on Internet of Things and WebGIS A1 - Ye, J Y1 - 2013/// KW - Agriculture KW - Geographic information systems KW - Internet KW - Mobile communication KW - Monitoring KW - Production KW - Spatial databases JF - International Conference on Geoinformatics SN - 2161-024X DO - 10.1109/Geoinformatics.2013.6626173 UR - https://api.elsevier.com/content/abstract/scopus_id/84893290616 N1 - Cited By (since 2013): 36 N2 - This thesis is based on the application of Internet of Things (IoT) and WebGIS in precision agriculture. Through analyzing the current development of precision agriculture in China and considering its advantages and shortcomings, we choose an ecology farm as an example to conduct a new precision agriculture management system (PAMS) based on the above two techniques. We designed the four architectures of PAMS: the spatial information infrastructure platform, the IoT infrastructure platform, the agriculture management platform and the mobile client. Users can monitor and manage the agriculture production by PAMS. What's more, module integration method and open source software can help us to reduce the development cost and to improve the system efficiency. ER - TY - Article T1 - A smart agriculture IoT system based on deep reinforcement learning A1 - Bu, F A1 - Wang, Xin Y1 - 2019/// KW - Cloud computing KW - Deep reinforcement learning KW - Edge computing KW - Internet of things KW - Smart agriculture JF - Future Generation Computer Systems VL - 99 SP - 500 EP - 507 DO - 10.1016/j.future.2019.04.041 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0167739X19307277 UR - https://api.elsevier.com/content/abstract/scopus_id/85065541268 N1 - Cited By (since 2019): 104 N2 - Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricultural data collection layer, edge computing layer, agricultural data transmission layer, and cloud computing layer. The presented system integrates some advanced information techniques, especially artificial intelligence and cloud computing, with agricultural production to increase food production. Specially, the most advanced artificial intelligence model, deep reinforcement learning is combined in the cloud layer to make immediate smart decisions such as determining the amount of water needed to be irrigated for improving crop growth environment. We present several representative deep reinforcement learning models with their broad applications. Finally, we talk about the open challenges and the potential applications of deep reinforcement learning in smart agriculture IoT systems. ER - TY - Article T1 - An investigation of IOT based smart agriculture A1 - Vennila, G Y1 - 2020/// KW - Green House KW - Internet of Things KW - Smart Farming JF - International Journal of Scientific and Technology Research VL - 9 IS - 1 SP - 2054 EP - 2056 UR - https://api.elsevier.com/content/abstract/scopus_id/85078765498 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) An-Investigation-Of-Iot-Based-Smart-Agriculture.pdf N1 - Cited By (since 2020): 4 N2 - In antique days, the agriculturalists used to measure the improvement of soil and slanted reservations to make which to kind of yield. Less concentration about the tenacity, level of water and especially air condition which horrendous a farmer continuously The Internet of things (IOT) is restructuring the agri-business empowering the agriculturists through the wide extent of methods, for example, exactness similarly as common sense developing to oversee troubles in the field. IOT helps in social event information on conditions like climate, clamminess, temperature and profitability of soil, Crop electronic evaluation draws in disclosure of wild plant, level of water, bug territory, creature break in to the field, trim improvement, development. IOT strategies use farmers to get related with his home from wherever and at whatever point. Remote sensor structures are utilized for viewing the domain conditions are utilized to control and mechanize the home shapes. This paper presented the analysis of IOT Smart Agriculture and its applications for further elevations ER - TY - Conference Paper T1 - IOT Based Monitoring System in Smart Agriculture A1 - Prathibha, S R Y1 - 2017/// KW - Internet of Things KW - agriculture KW - crops KW - environmental factors KW - farming KW - wireless LAN JF - Proceedings - 2017 International Conference on Recent Advances in Electronics and Communication Technology, ICRAECT 2017 SP - 81 EP - 84 DO - 10.1109/ICRAECT.2017.52 UR - https://api.elsevier.com/content/abstract/scopus_id/85040006611 N1 - Cited By (since 2017): 199 N2 - Internet of Things (IoT) plays a crucial role in smart agriculture. Smart farming is an emerging concept, because IoT sensors capable of providing information about their agriculture fields. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. Monitoring environmental factors is the major factor to improve the yield of the efficient crops. The feature of this paper includes monitoring temperature and humidity in agricultural field through sensors using CC3200 single chip. Camera is interfaced with CC3200 to capture images and send that pictures through MMS to farmers mobile using Wi-Fi. ER - TY - Conference Paper T1 - Agro-sense: Precision agriculture using sensor-based wireless mesh networks A1 - Roy, A D Siuli Y1 - 2008/// KW - IEEE 802.15.4 KW - pecision agriculture KW - routing algorithm KW - wireless mesh networks JF - International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN SP - 383 EP - 388 DO - 10.1109/KINGN.2008.4542291 UR - https://api.elsevier.com/content/abstract/scopus_id/50849116823 N1 - Cited By (since 2008): 63 N2 - Advances in wireless personal area networks have made the practical deployment of various services possible, which until a few years ago was considered extremely costly or labor intensive. We build such a wireless sensor network for precision agriculture where real time data of the climatological and other environmental properties are sensed and relayed to a central repository. The architecture comprises of three distinct sections - (a) the sensor-nodes (b) the wireless mesh network and (c) the actuation components. The sensors are selected based on the properties suited for the most common crops and we identify four such attributes. The sensor network is based on the IEEE-802.15.4 standard and we develop a new static routing algorithm suited for the sensing application. The algorithm overrides the deficiency of the Hierarchical Routing scheme inherent in the ZigBee specification where the C skip addressing algorithm limits the possible depth of the network topology due to address wastage. The new algorithm maintains the hierarchical network topology and thus ensures routing at its optimal best. The algorithms for both addressing and routing are provided. The actuation components are also a part of mesh network and are activated wirelessly for controlling irrigation and fertigation. ER - TY - Article T1 - Application of Non-Orthogonal Multiple Access in Wireless Sensor Networks for Smart Agriculture A1 - Hu, Z Y1 - 2019/// KW - smart agriculture KW - non-orthogonal multiple access. KW - relay KW - wireless sensor networks JF - IEEE Access VL - 7 SP - 87582 EP - 87592 DO - 10.1109/ACCESS.2019.2924917 UR - https://api.elsevier.com/content/abstract/scopus_id/85069768199 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Application of Non-Orthogonal Multiple Access in.pdf N1 - Cited By (since 2019): 26 N2 - Agriculture is one of the main economic industries of a country. Application of information technologies in agriculture, smart agriculture, aims to realize precision control of irrigation, fertilizer, diseases, and insect pests prevention in the growing of crops. For the sake of obtaining the interest data, wireless sensor networks (WSNs) are used to collect the interest data in the farm field and send the obtained data to the servers via wireless communication. Since the WSNs usually operate in the unlicensed spectrum, the available resource elements (REs) are scarce especially when a large number of sensor nodes are deployed in the farm field. To accommodate more sensor nodes and prolong the lifetime of the WSNs in agriculture, relay-aided non-orthogonal multiple access is introduced into the uplink transmission stage of the direct transmission from the sensor nodes to the sink node. Non-orthogonal multiple access (NOMA) can transmit multiple symbols simultaneously on the same RE by splitting them in the power domain and distinguish them according to diverse power levels of different symbols. The average sum data rate and outage probability of the relay-aided NOMA in uplink transmission are theoretically analyzed. The numerical simulation results show that the WSNs with relay-aided NOMA outperforms the traditional OMA scheme in uplink transmission in WSNs in agriculture. ER - TY - Article T1 - Green IoT Agriculture and Healthcare Application (GAHA) A1 - Nandyala, C Y1 - 2016/// KW - Agriculture application KW - Cloud KW - Cloud Computing KW - Green KW - Healthcare application KW - Internet of Things KW - Sensors JF - International Journal of Smart Home VL - 10 IS - 4 SP - 289 EP - 300 DO - 10.14257/ijsh.2016.10.4.26 UR - https://api.elsevier.com/content/abstract/scopus_id/84970005238 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2016) Green IoT Agriculture and Healthcare Application (GAHA).pdf N1 - Cited By (since 2016): 42 N2 - The application of the two trending and popular technologies, Cloud Computing (CC) and the Internet of Things (IoT) are current hot discussions in the field of agriculture and healthcare applications. Motivated by achieving a sustainable world, this paper discusses various technologies and issues regarding green cloud computing and green Internet of Things, further improves the discussion with the reduction in energy consumption of the two techniques (CC and IoT) combination in agriculture and healthcare systems. The history and concept of the hot green information and communications technologies (ICT’s) which are enabling green IoT will be discussed. Green computing introduction first and later focuses on the recent works done regarding the two emerging technologies in both agriculture and healthcare cases. Furthermore, this paper contributes by presenting Green IoT Agriculture and Healthcare Application (GAHA) using sensor-cloud integration model. Finally, lists out the advantages, challenges, and future research directions related to green application design. Our research aims to make green area broad and contribution to sustainable application world ER - TY - Article T1 - Energy-Efficient Edge-Fog-Cloud Architecture for IoT-Based Smart Agriculture Environment A1 - Alharbi, H A A1 - Aldossary, Mohammad Y1 - 2021/// KW - Internet of Things KW - Smart agriculture KW - carbon emission KW - edge-fog-cloud computing KW - energy-efficiency JF - IEEE Access VL - 9 SP - 110480 EP - 110492 DO - 10.1109/ACCESS.2021.3101397 UR - https://api.elsevier.com/content/abstract/scopus_id/85111613745 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(2019) Energy-Efficient_Edge-Fog-Cloud_Architecture_for_IoT-Based_Smart_Agriculture_Environment.pdf N1 - Cited By (since 2021): 4 N2 - The current agriculture systems compete to take advantage of industry advanced technologies, including the internet of things (IoT), cloud/fog/edge computing, artificial intelligence, and agricultural robots to monitor, track, analyze and process various functions and services in real-time. Additionally, these technologies can make the agricultural processes smarter and more cost-efficient by using automated systems and eliminating any human interventions, hence enhancing agricultural production to meet future expectations. Although the current agriculture systems that adopt the traditional cloud-based architecture have provided powerful computing infrastructure to distributed IoT sensors. However, the cost of energy consumption associated with transferring heterogeneous data over the multiple network tiers to process, analyze and store the sensor's information in the cloud has created a huge load on information and communication infrastructure. Besides, the energy consumed by cloud data centers has an environmental impact associated with using non-clean fuels, which usually release carbon emissions (CO 2 ) to produce electricity. Thus, to tackle these issues, we propose a new integrated edge-fog-cloud architectural paradigm that promises to enhance the energy-efficient of smart agriculture systems and corresponding carbon emissions. This architecture allows data collection from several sensors to process and analyze the agriculture data that require real-time operation (e.g., weather temperature, soil moisture, soil acidity, irrigation, etc.) in several layers (edge, fog, and cloud). Thus, the real-time processing could be held by the edge and fog layers to reduce the load on the cloud layer, which will help to enhance the overall energy consumption and process the agriculture applications/services efficiently. Mathematical modeling is conducted using mixed-integer linear programming (MILP) for a smart agriculture environment, where the proposed architecture is implemented, and results are analyzed and compared to the traditional implementation. According to the results of thousands of agriculture sensors, the proposed architecture outperforms the traditional cloud-based architecture in terms of reducing the overall energy consumption by 36% and the carbon emissions by 43%. In addition to these achievements, the results show that our proposed architecture can reduce network traffic by up to 86%, which can reduce network congestion. Finally, we develop a heuristic algorithm to validate and mimic the presented approach, and it shows comparable results to the MILP model. ER - TY - Conference Paper T1 - Smartphone accessible agriculture IoT node based on NFC and BLE A1 - Xue-fen, W A1 - Xing-jing, Du A1 - Wen-qiang, Bao A1 - Le-han, Li A1 - Jian, Zhang A1 - Chang, Zu A1 - Ling-xuan, Zhang A1 - Yu-xiao, Yang Pan A1 - Yi, Yang Y1 - 2018/// KW - Bluetooth Low Energy KW - distributed agriculture service KW - near field communication KW - smart agriculture KW - smartphone JF - Proceedings of the International Symposium on Consumer Electronics, ISCE SP - 78 EP - 79 UR - https://api.elsevier.com/content/abstract/scopus_id/85064108147 N1 - Cited By (since 2018): 4 N2 - The smartphones play an Important part In agriculture service today. A smartphone accessible agriculture IoT node is presented in this paper. This node is designed for sightseeing agriculture and education agriculture services. Nodes can meet the needs of in-field farm services as well as the demand of centralized management. The hardware structure of the node and the design of the Android App are discussed. This node is expected to provide new technologies and business supports for smart agriculture. ER - TY - Conference Paper T1 - Wireless sensor network in precision agriculture application A1 - Kassim, M R Mohd Y1 - 2014/// KW - Green products KW - Irrigation KW - Monitoring KW - Sensors KW - Soil KW - Wireless sensor networks JF - 2014 International Conference on Computer, Information and Telecommunication Systems, CITS 2014 DO - 10.1109/CITS.2014.6878963 UR - https://api.elsevier.com/content/abstract/scopus_id/84906761871 N1 - Cited By (since 2014): 94 N2 - The Wireless Sensors Network (WSN) is nowadays widely used to build decision support systems to overcome many problems in the real-world. One of the most interesting fields having an increasing need of decision support systems is precision agriculture (PA). This paper presents WSN as the best way to solve the agricultural problems related to farming resources optimization, decision making support, and land monitoring. This approach provides real-time information about the lands and crops that will help farmers make right decisions. Using the basic principles of Internet and WSN technology, precision agriculture systems based on the internet of things (IOT) technology is explained in detail especially on the hardware architecture, network architecture and software process control of the precision irrigation system. The software monitors data from the sensors in a feedback loop which activates the control devices based on threshold value. Implementation of WSN in PA will optimize the usage of water fertilizer and also maximized the yield of the crops. ER - TY - Article T1 - (t,n): Sensor Stipulation with THAM Index for Smart Agriculture Decision-Making IoT System A1 - Mekala, M S Y1 - 2020/// KW - (t n) sensor selection model KW - Agronomy function KW - Cloud computing KW - Field monitoring KW - IoT sensor network KW - Measurement analysis KW - Node stipulation JF - Wireless Personal Communications VL - 111 IS - 3 SP - 1909 EP - 1940 DO - 10.1007/s11277-019-06964-0 UR - https://api.elsevier.com/content/abstract/scopus_id/85076147729 N1 - Cited By (since 2020): 18 N2 - The rapid growth of industrial infrastructure creates ecological issues such as climate change. Field indecisiveness affects agricultural yields due to improper measurement, field assessment, selection of sensors and deployment of sensors. The accurate prediction of changes in weather parameters, field assessment and soil parameters, has become an outstanding challenge for the agricultural IoT. To solve this problem, we propose a (t, n) sensor selection mechanism and a soil temperature, humidity, air- and water-quality measurement (THAM) index for node stipulation, based on a smart decision-making system for the agricultural domain that considers the temperature quotient, an NPK fertilizer regulatory model and the agronomy function. The (t, n) node stipulation index defines an optimal number of sensors to monitor the field. The temperature quotient considers soil temperature and moisture to assess the growth rate. The agronomy function, based on water pH level and SO2 concentration level in air, assesses the production yield rate of the field. This framework improves the prediction performance for detecting abnormal conditions by 75%, with a reduction in the creation of unimportant data and the resources loss rate. It increases the agricultural production yield compared to existing systems. ER - TY - Article T1 - An energy efficient and secure IoT-based WSN framework: An application to smart agriculture A1 - Haseeb, K A1 - Din, Ikram Ud A1 - Almogren, Ahmad A1 - Islam, Naveed Y1 - 2020/// KW - cluster heads KW - data security KW - energy efficiency KW - signal strength KW - smart agriculture JF - Sensors (Switzerland) VL - 20 IS - 7 DO - 10.3390/s20072081 UR - https://api.elsevier.com/content/abstract/scopus_id/85083191172 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) An energy efficient and secure IoT-based WSN framework An application to smart agriculture.pdf N1 - Cited By (since 2020): 69 N2 - Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions. ER - TY - Conference Paper T1 - A multi-actor approach to promote the employment of IoT in Agriculture A1 - Roussaki, I Y1 - 2019/// KW - Internet of Things KW - Multi-Actor Approach KW - precision agriculture KW - smart farming JF - Global IoT Summit, GIoTS 2019 - Proceedings DO - 10.1109/GIOTS.2019.8766416 UR - https://api.elsevier.com/content/abstract/scopus_id/85073887133 N1 - Cited By (since 2019): 5 N2 - The evolution of smart farming and precision agriculture during the last decades has led to an increase of the available solutions that can be used by farmers. However, these two paradigms have not yet achieved high acceptance by end user farmers due to various reasons. In this respect, this paper elaborates on an innovative Multi-Actor Approach architecture that assists farmers to take better decisions and enables them to harness the full value of their own data and knowledge aiming to promote the adoption of the Internet of Things in the agrifood sector. ER - TY - Article T1 - Successful deployment of a wireless sensor network for precision agriculture in Malawi A1 - Mafuta, M Y1 - 2013/// KW - Sensors KW - Wireless sensor networks KW - irrigation KW - precision agriculture JF - International Journal of Distributed Sensor Networks VL - 2013 DO - 10.1155/2013/150703 UR - https://api.elsevier.com/content/abstract/scopus_id/84878661531 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2013) Successful Deployment of a Wireless Sensor Network for Pecision Agriculture in Malawi.pdf N1 - Cited By (since 2013): 49 N2 - This paper demonstrates how an irrigation management system (IMS) can practically be implemented by deploying a wireless sensor network (WSN). Specifically, the paper describes an IMS which was set up in Manja township, city of Blantyre. Deployment of IMS in rural areas of developing countries like Malawi is a challenge as grid power is scarce. For the system to be self-sustained in terms of power, the study used solar photovoltaic and rechargeable batteries to power all electrical devices. The system incorporated a remote monitoring mechanism through a General Packet Radio Service modem to report soil temperature, soil moisture, WSN link performance, and photovoltaic power levels. Irrigation valves were activated to water the field. Preliminary results in this study have revealed a number of engineering weaknesses of deploying such a system. Nevertheless, the paper has highlighted areas of improvement to develop a robust, fully automated, solar-powered, and low-cost IMS to suit the socioeconomic conditions of small scale farmers in developing countries. ER - TY - Conference Paper T1 - IoT based Smart System for Enhanced Irrigation in Agriculture A1 - Bhanu, K N Y1 - 2020/// KW - Aurdino Uno KW - Cloud Computing KW - Internet of Things KW - Wi-Fi KW - Wireless Sensor Networks JF - Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020 SP - 760 EP - 765 DO - 10.1109/ICESC48915.2020.9156026 UR - https://api.elsevier.com/content/abstract/scopus_id/85090826915 N1 - Cited By (since 2020): 11 N2 - Internet of Things (IoT) is an interconnection of devices that can transfer information over the internet and to control operations without human interference. Agriculture provides a rich source of parameters for data analysis which helps in better yielding of crops. The usage of IoT devices in agriculture helps in the modernizing of information and communication in smart farming. The key parameters that can be considered for better growth of crops are soil types, soil moisture), mineral nutrients, temperature, light, oxygen and so on. Various sensors have been used to sense these parameters and communicate the same to the cloud. This paper considers a few of these parameters for data analysis that helps in proposing the users to take better agricultural decisions using IoT. The proposed system performed better and is implemented atThingS peak IoT cloud platform. ER - TY - Review T1 - Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies A1 - Friha, O Y1 - 2021/// KW - Agricultural internet of things KW - Sustainability agriculture KW - internet of things KW - smart agriculture KW - smart farming JF - IEEE/CAA Journal of Automatica Sinica VL - 8 IS - 4 SP - 718 EP - 752 SN - 2329-9266 DO - 10.1109/JAS.2021.1003925 UR - https://api.elsevier.com/content/abstract/scopus_id/85102749457 N1 - Cited By (since 2021): 66 N2 - This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs. ER - TY - Article T1 - Internet of things monitoring system of modern eco-agriculture based on cloud computing A1 - Liu, S Y1 - 2019/// KW - Internet of Things KW - Management system KW - big data KW - cloud computing KW - modern agriculture JF - IEEE Access VL - 7 SP - 37050 EP - 37058 DO - 10.1109/ACCESS.2019.2903720 UR - https://api.elsevier.com/content/abstract/scopus_id/85065123468 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Internet of Things Monitoring System of Modern eco agriculture based on cloud computing.pdf N1 - Cited By (since 2019): 45 N2 - In order to enhance the efficiency and safety of production and management of modern agriculture in China, problems, such as the quality and safety of agricultural products and the pollution of the environment from agricultural activities, should be unraveled. Based on the new generation of information technology (IT), an integrated framework system platform incorporating the Internet of Things (IoT), cloud computing, data mining, and other technologies is investigated and a new proposal for its application in the field of modern agriculture is offered. The experimental framework and simulation design suggest that the basic functions of the monitoring system of the IoT for agriculture can be realized. In addition, the innovation derived from integrating different technologies plays an important role in reducing the cost of system development and ensuring its reliability as well as security. ER - TY - Article T1 - A Smart Insect Pest Detection Technique with Qualified Underground Wireless Sensor Nodes for Precision Agriculture A1 - Bayrakdar, M Y1 - 2019/// KW - Agriculture KW - Communication system security KW - Intelligent sensors KW - Monitoring KW - Wireless communication KW - Wireless sensor networks JF - IEEE Sensors Journal VL - 19 IS - 22 SP - 10892 EP - 10897 DO - 10.1109/JSEN.2019.2931816 UR - https://api.elsevier.com/content/abstract/scopus_id/85073877772 N1 - Cited By (since 2019): 28 N2 - Wireless underground sensor networks are the new area of research. It is widely used in many engineering applications, from smart irrigation to security and precision agriculture. Some of the application areas of wireless underground sensor networks are underground with space, such as tunnel, cave, and so on, while some consist of no spaced underground solid areas as well. In this context, wireless underground sensor networks have recently become very important for precision agriculture purposes. In this paper, a smart insect pest detection technique with qualified underground wireless sensor nodes for precision agriculture has been investigated with a mathematical simulation model. In a simulated smart technique, insect pest detection is assumed to be carried out with a qualified acoustic sensor. In order to evaluate the performance of the underground network structure, the received signal strength and path loss parameters are examined. As the depth distance increases, the increase in path loss of communication has been revealed. The obtained performance evaluation result reveals the need for signal transmission with different transmitter power for depth-based communication in wireless underground sensor networks. ER - TY - Conference Paper T1 - Farmbeats: An IoT platform for data-driven agriculture A1 - Vasisht, D Y1 - 2017/// KW - agriculture KW - data driven KW - farmbeats KW - internet of things JF - Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017 SP - 515 EP - 529 UR - https://api.elsevier.com/content/abstract/scopus_id/85075122567 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Farmbeats An IoT platform for data-driven agriculture.pdf N1 - Cited By (since 2017): 211 N2 - Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this paper, we present FarmBeats, an end-to-end IoT platform for agriculture that enables seamless data collection from various sensors, cameras and drones. FarmBeats’s system design that explicitly accounts for weather-related power and Internet outages has enabled six month long deployments in two US farms. ER - TY - Conference Paper T1 - Survey on IoT and its applications in agriculture A1 - Kanupuru, P Y1 - 2018/// KW - Internet of Things KW - Wireless Sensor Networks JF - 2018 International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2018 - Proceedings DO - 10.1109/ICNEWS.2018.8903969 UR - https://api.elsevier.com/content/abstract/scopus_id/85075914469 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2018) Survey on IoT and its applications in agriculture.pdf N1 - Cited By (since 2018): 3 N2 - Internet of Things has a major part in utilization of advanced technologies for better management of agricultural requirements. Man power and changing environmental conditions are considered to be the major issues in present day agriculture. The traditional methods employed in the cultivation has to be modified in order to meet the present day demand for agricultural products. Therefore the agricultural automation is required, which can be achieved using Wireless Sensor Network and Internet of Things. This paper summarizes the existing smart systems with Wireless Sensor Network based sensor monitoring techniques by considering environmental parameters such as temperature, moisture, PH, humidity, light intensity which are very useful in efficient decision making for yielding high productivity. This survey also helps in understanding the recent technological developments in Internet of Things for building an efficient smart agricultural system. ER - TY - Conference Paper T1 - A model for smart agriculture using IoT A1 - Patil, K A Y1 - 2017/// KW - Internet of things KW - Sensor technology KW - Smart agriculture JF - Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016 SP - 543 EP - 545 DO - 10.1109/ICGTSPICC.2016.7955360 UR - https://api.elsevier.com/content/abstract/scopus_id/85025129925 N1 - Cited By (since 2017): 121 N2 - Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Messaging Service (SMS) and advices on weather pattern, crops etc. ER - TY - Conference Paper T1 - IoT Applications in Smart Agriculture: Issues and Challenges A1 - Kassim, M R M Y1 - 2020/// KW - Internet of Things KW - agriculture KW - architecture KW - network KW - open systems KW - smart farming KW - wireless sensor networks JF - 2020 IEEE Conference on Open Systems, ICOS 2020 SP - 19 EP - 24 DO - 10.1109/ICOS50156.2020.9293672 UR - https://api.elsevier.com/content/abstract/scopus_id/85099184724 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2020) IoT Applications in Smart Agriculture Issue and Challenges.pdf N1 - Cited By (since 2020): 18 N2 - The rapid development of Internet of Things (IoT) technologies created tsunamis almost in every industry across the world and particularly in agriculture. This massive changes are shaking the existing agriculture methods and creating new wave of opportunities. Due to the increase of world population by 30%, agriculture products will have a very high demand by 2050. Human resources for agriculture development is becoming less due to migration of young people to big cities and land use for agriculture cultivation is being used for rapid development. As a result, most of the agriculture activities need to be automated to fulfill the food demand. IoT and related technologies will be the potential solution to solve the above agricultural and food demand issues. This paper will explore the latest trends in IoT agriculture applications and highlight the issues and challenges particularly in network and open source software for smart agriculture ER - TY - Article T1 - Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification A1 - El-Magd, I Y1 - 2003/// KW - agriculture KW - irrigation KW - mapping land KW - sensors JF - International Journal of Remote Sensing VL - 24 IS - 21 SP - 4197 EP - 4206 DO - 10.1080/0143116031000139791 UR - https://api.elsevier.com/content/abstract/scopus_id/0344961367 N1 - Cited By (since 2003): 65 N2 - The accuracy of conventional land use classification of irrigated agriculture from optical satellite images using maximum likelihood supervised classification was compared with a classification based on multistage maximum likelihood supervised classification. In the multistage maximum likelihood classification series of sub-classifications were carried out which included masking and/or omitting certain crops from the classifications. These series of classifications improved the identification of individual crops/land use types. The output from the optimum sub-classifications were stacked to give an overall crop types/land use map. When the multistage classification was tested against a single stage classification on a large irrigation scheme in Central Asia the final accuracy of crop/land use classification increased from 85% to 94%. Field verification confirmed the accuracy at 93.5%. These results were achieved with a single Landsat 7 Enhanced Thematic Mapper (ETM+) sensor dataset as of 2 August 1999 over an area of 38.5 km2. ER - TY - Conference Paper T1 - Cloud service oriented architecture (CSoA) for agriculture through internet of things (IoT) and big data A1 - Srinivasulu, P Y1 - 2017/// KW - Agriculture KW - Big Data KW - Cloud Computing KW - Cloud Services KW - Internet of Things KW - Smart Farming JF - Proceedings - 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering, ICEICE 2017 VL - 2017 SP - 1 EP - 4 DO - 10.1109/ICEICE.2017.8191906 UR - https://api.elsevier.com/content/abstract/scopus_id/85047181671 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2017) Cloud service oriented architecture (CSoA) for agriculture through internet of things (IoT) and big data.pdf N1 - Cited By (since 2017): 23 N2 - In the present backdrop of agriculture scenario the fruits of farming are not being enjoyed by the producer due to various obstacles that come up in the process. Hence in order to get rid off these obstacles and to see that farming becomes smart and friendly, by using the technological advancements, the present work proposed has been prepared. The proposed work which makes use of various technologies like Big Data, Internet of Things (IoT), Cloud Computing, etc is going to be a big boon to the farmer who otherwise is made to undergo a tough time in view of lack of the technology that he/she should have been adapted by this time. The proposed one will provide a number of services to the farmers that include crop management, marketing, finance management, e-commerce, web services through cloud etc. which also will reduce the unemployment problem in the youth. It also makes agriculture not only a profession for living but also a profitable sector in the globe which further enhances the GDP. ER - TY - Conference Paper T1 - Murphy loves potatoes experiences from a pilot sensor network deployment in precision agriculture A1 - Langendoen, K Y1 - 2006/// KW - agriculture KW - digital simulation KW - wireless sensor networks JF - 20th International Parallel and Distributed Processing Symposium, IPDPS 2006 VL - 2006 DO - 10.1109/IPDPS.2006.1639412 UR - https://api.elsevier.com/content/abstract/scopus_id/33847162489 N1 - Cited By (since 2006): 282 N2 - We report on preliminary experiences with deploying a large-scale sensor network (about 100 nodes) for a pilot in precision agriculture. The pilot did not answer the initial research questions, but instead revealed many engineering problems typically overlooked by (computer) scientists evaluating their work by means of simulation. The deployment prompted us to rethink our development process and includes important lessons for the WSN research community as a whole ER - TY - Review T1 - Soil Sensors and Plant Wearables for Smart and Precision Agriculture A1 - Yin, H Y1 - 2021/// KW - Wireless sensor networks KW - plant wearables KW - precision agriculture KW - smart agriculture KW - soil sensors JF - Advanced Materials VL - 33 IS - 20 SN - 0935-9648 DO - 10.1002/adma.202007764 UR - https://api.elsevier.com/content/abstract/scopus_id/85103661459 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2021) Soil Sensors and Plant Wearables for Smart and Precision.pdf N1 - Cited By (since 2021): 29 N2 - Soil sensors and plant wearables play a critical role in smart and precision agriculture via monitoring real-time physical and chemical signals in the soil, such as temperature, moisture, pH, and pollutants and providing key information to optimize crop growth circumstances, fight against biotic and abiotic stresses, and enhance crop yields. Herein, the recent advances of the important soil sensors in agricultural applications, including temperature sensors, moisture sensors, organic matter compounds sensors, pH sensors, insect/pest sensors, and soil pollutant sensors are reviewed. Major sensing technologies, designs, performance, and pros and cons of each sensor category are highlighted. Emerging technologies such as plant wearables and wireless sensor networks are also discussed in terms of their applications in precision agriculture. The research directions and challenges of soil sensors and intelligent agriculture are finally presented. ER - TY - Article T1 - Pervasive Agriculture: IoT-Enabled Greenhouse for Plant Growth Control A1 - Somov, A Y1 - 2018/// KW - Internet of Things KW - agriculture KW - artificial intelligence KW - cloud computing KW - greenhouses KW - wireless sensor networks JF - IEEE Pervasive Computing VL - 17 IS - 4 SP - 65 EP - 75 DO - 10.1109/MPRV.2018.2873849 UR - https://api.elsevier.com/content/abstract/scopus_id/85061193015 N1 - Cited By (since 2018): 42 N2 - We present Internet of Things (IoT) deployment in a tomato greenhouse in Russia. The IoT enabling technologies applied for this deployment comprise a wireless sensor network, cloud computing, and artificial intelligence. They are to help in monitoring and controlling of both plants and greenhouse conditions as well as predicting the growth rate of tomatoes. ER - TY - Conference Paper T1 - An Architectural Framework Proposal for IoT Driven Agriculture A1 - Kuaban, G S Y1 - 2019/// KW - FIWARE KW - Internet of Things KW - LoRaWAN JF - Communications in Computer and Information Science VL - 1039 SP - 18 EP - 33 SN - 1865-0929 DO - 10.1007/978-3-030-21952-9_2 UR - https://api.elsevier.com/content/abstract/scopus_id/85068164806 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Architectural_framework_for_IoT_driven_agriculture.pdf N1 - Cited By (since 2019): 3 N2 - The Internet of Things is paving the way for the transition into the fourth industrial revolution with the mad rush of connecting physical devices and systems to the internet. IoT is a promising technology to drive the agricultural industry, which is the backbone for sustainable development especially in developing countries like those in Africa that are experiencing rapid population growth, stressed natural resources, reduced agricultural productivity due to climate change, and massive food wastage. In this paper, we assessed challenges in the adoption of IoT in developing countries in agriculture. We propose a cost effective, energy efficient, secure, reliable and heterogeneous (independent of the IoT protocol) three layer architecture for IoT driven agriculture. The first layer consists of IoT devices and it is made up of IoT driven agriculture systems such as smart poultry, smart irrigation, theft detection, pest detection, crop monitoring, food preservation, and food supply chain systems. The IoT devices are connected to the gateways by low power LoRaWAN network. The gateways and local processing servers co-located with the gateways create the second layer. The cloud layer is the third layer, which exploits the open source FIWARE platform to provide a set of public and free-to-use API specifications that come along with open source reference implementations. ER - TY - Article T1 - Automation in agriculture using IoT and machine learning A1 - Abhishek, L Y1 - 2019/// KW - Internet of Things KW - Machine Learning KW - Multi-hop KW - Smart Agriculture KW - TDMA KW - Wireless Sensor Networks KW - ZigBee KW - communication JF - International Journal of Innovative Technology and Exploring Engineering VL - 8 IS - 8 SP - 1520 EP - 1524 UR - https://api.elsevier.com/content/abstract/scopus_id/85067876710 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) Automation in Agriculture Using IOT and Machine Learning.pdf N1 - Cited By (since 2019): 4 N2 - The purpose of this project is to improve the efficiency of the agriculture sector. In India, agriculture plays a vital role for development in food production. Internet of Things (IoT) is a milestone in evolution of technology. IoT helps us in many fields among which agriculture is one of the primary ones. With the help of IoT along with Machine Learning in the field of agriculture, we can increase the efficiency of crop production.Different weather parameters are taken into consideration with which the best suitable crop to be grown are predicted with the help of supervised learning like Decision Tree Classifier, Regression. With help of different sensors, the soil and atmospheric conditions are determined and transferred through multi-hop communication to the server in which monitoring of crops’ health and control of irrigation system takes place. TDMA is used for the above purpose ER - TY - Article T1 - Urban planning and agriculture. Methodology for assessing rooftop greenhouse potential of non-residential areas using airborne sensors A1 - Nadal, A Y1 - 2017/// KW - Cities sustainability KW - Food security KW - Industrial parks KW - Smart cities KW - Urban agriculture KW - Vertical farming JF - Science of the Total Environment VL - 601 SP - 493 EP - 507 DO - 10.1016/j.scitotenv.2017.03.214 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0048969717307350 UR - https://api.elsevier.com/content/abstract/scopus_id/85019972565 N1 - Cited By (since 2017): 29 N2 - The integration of rooftop greenhouses (RTGs) in urban buildings is a practice that is becoming increasingly important in the world for their contribution to food security and sustainable development. However, the supply of tools and procedures to facilitate their implementation at the city scale is limited and laborious. This work aims to develop a specific and automated methodology for identifying the feasibility of implementation of rooftop greenhouses in non-residential urban areas, using airborne sensors. The use of Light Detection and Ranging (LIDAR) and Long Wave Infrared (LWIR) data and the Leica ALS50-II and TASI-600 sensors allow for the identification of some building roof parameters (area, slope, materials, and solar radiation) to determine the potential for constructing a RTG. This development represents an improvement in time and accuracy with respect to previous methodology, where all the relevant information must be acquired manually. The methodology has been applied and validated in a case study corresponding to a non-residential urban area in the industrial municipality of Rubí, Barcelona (Spain). Based on this practical application, an area of 36,312 m2 out of a total area of 1,243,540 m2 of roofs with ideal characteristics for the construction of RTGs was identified. This area can produce approximately 600 tons of tomatoes per year, which represents the average yearly consumption for about 50% of Rubí total population. The use of this methodology also facilitates the decision making process in urban agriculture, allowing a quick identification of optimal surfaces for the future implementation of urban agriculture in housing. It also opens new avenues for the use of airborne technology in environmental topics in cities ER - TY - Conference Paper T1 - Sustainable Water Resource Management Using IOT Solution for Agriculture A1 - Kadar, H H Y1 - 2019/// KW - Farming KW - Internet of things KW - Smart agriculture KW - water management JF - Proceedings - 9th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2019 SP - 121 EP - 125 DO - 10.1109/ICCSCE47578.2019.9068592 UR - https://api.elsevier.com/content/abstract/scopus_id/85084301822 N1 - Cited By (since 2019): 8 N2 - Internet of Things (IoT) as an emergent technology, are set to progress the agriculture industry. Agriculture, as one of the sector, embracing IoT, to set the changes, deploying IoT for smart farming, creating what is now called as Smart Agriculture. Agriculture is the leading consumer of water around the globe, which sums to up to 70% of the total usage. Thus, making the ultimatum for smart water management as an assurance for water and food security as well as agricultural products. Water resources management includes planning, developing, distributing and managing the optimum use of water resources, which is vital for the proliferation of crop yields despite contributing to water sustainability. This article predominantly periodicals the engagement of a smart water management system prototype, the AGRI2L system, proposed as part of the IoT solution. The system architecture and a detailed description of the physical scenario on how AGRI2L system works for data management as part of IoT platforms. AGRI2L system allows being manageable and interoperable in the specific context of water resource management processes. This prototype aims at proposing a design for an implementation detail of smart water level and leakage monitoring system by engaging the real-time data to facilitate the analyst focuses more on analysis and actions in short period with low cost. Overall, data and IoT-based smart agriculture enable the future of agriculture. ER - TY - Review T1 - A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming A1 - Farooq, M S A1 - RIAZ, SHAMYLA A1 - ABID, ADNAN A1 - ABID, KAMRAN A1 - NAEEM, MUHAMMAD AZHAR Y1 - 2019/// KW - Internet of things KW - applications KW - architecture KW - challenges KW - industries KW - network KW - platforms KW - policies KW - protocols KW - security KW - smart farming KW - technologies JF - IEEE Access VL - 7 SP - 156237 EP - 156271 SN - 2169-3536 DO - 10.1109/ACCESS.2019.2949703 UR - https://api.elsevier.com/content/abstract/scopus_id/85074718442 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/referensi/new2/(2019) A Survey on the Role of IoT in Agriculture for the implementation of smart farming.pdf N1 - Cited By (since 2019): 200 N2 - Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented. ER - TY - Article T1 - A Cluster-Tree-Based Secure Routing Protocol Using Dragonfly Algorithm (DA) in the Internet of Things (IoT) for Smart Agriculture A1 - Hosseinzadeh, M Y1 - 2023/// KW - internet of things KW - smart agriculture KW - routing KW - dragonfly algorithm KW - wireless sensors networks JF - Mathematics VL - 11 IS - 1 DO - 10.3390/math11010080 UR - https://api.elsevier.com/content/abstract/scopus_id/85146036606 L1 - file:///C:/Users/sonsu/Downloads/mathematics-11-00080-v3.pdf N1 - Cited By (since 2023): 1 N2 - The Internet of Things defines a global and comprehensive network whose task is to monitor and control the physical world by collecting, processing, and analyzing data sensed by IoT devices. This network has succeeded in various areas, and one of its most important applications is in smart agriculture because there are many demands for producing high-quality foodstuff in the world. These demands need new production schemes in the agriculture area. In IoT, communication security is essential due to the extensive heterogeneity of IoT devices. In this paper, a cluster-tree-based secure routing approach using the dragonfly algorithm (CTSRD) is proposed for IoT. The proposed scheme presents a distributed and lightweight trust mechanism called weighted trust (W-Trust). W-Trust reduces the trust value corresponding to malicious nodes based on a penalty coefficient to isolate this node in the network. Furthermore, it improves the trust value of honest IoT devices based on a reward coefficient. Additionally, CTSRD introduces a trust-based clustering process called T-Clustering. In this clustering process, cluster head nodes (CHs) are selected among honest IoT nodes. Finally, CTSRD establishes a routing tree based on the dragonfly algorithm (DA) between CHs. This tree is called DA-Tree. To evaluate the quality of the routing tree, a new fitness function is provided in CTSRD. DA-Tree finds a secure, stable, and optimal routing tree to balance the consumed energy and boost the network lifetime. CTSRD is compared with EEMSR and E-BEENISH with regard to the network lifetime, consumed energy, and packet delivery rate. This comparison shows that our scheme can uniformly distribute the consumed energy in IoT and improves the energy consumption and network lifetime. However, it has a slightly lower packet delivery rate than EEMSR. ER - TY - Conference Paper T1 - A Comprehensive Study of Using Internet of Things (IOT) in Monitoring System for Smart Agriculture A1 - Kumhar, S H Y1 - 2022/// KW - internet of things KW - smart agriculture KW - sensors KW - hardware KW - soil condition JF - 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022 SP - 255 EP - 258 DO - 10.1109/ICACITE53722.2022.9823934 UR - https://api.elsevier.com/content/abstract/scopus_id/85135459027 N1 - Cited By (since 2022): 1 N2 - Well-designed IoT (Internet of Things) technologies are a major part of real-time system management. The IoT technologies considered wireless sensors to manage power consumption through power management. The sensor networks based on the sensing nodes provide efficient power management tools for the smart connection. The wireless system measures the power voltage and power consumption. Implementation of IoT and other technologies are effective to promote a smart agricultural system over the traditional methods. Secondary qualitative method is used to gather all the data regarding the implementation and the effectiveness of IoT. Findings and analyses suggested that the Basic IoT system is affordable for agricultural farms; however, the inclusion of special features in the smart sensors increases the cost significantly. Moreover, farms and villages have a poor internet connection which results in poor data transfer between farmers and agricultural experts ER - TY - Conference Paper T1 - A Cost Effective Agriculture System based on IoT using Sustainable Energy A1 - Vijayaraja, L Y1 - 2022/// KW - ADAFRUIT KW - internet of things KW - Message Queuing Measure Transport KW - Node MCU KW - sensors KW - solar energy JF - 2022 6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 - Proceedings SP - 546 EP - 549 DO - 10.1109/ICOEI53556.2022.9776726 UR - https://api.elsevier.com/content/abstract/scopus_id/85131955925 N1 - Cited By (since 2022): 1 N2 - In this generation, automation has become popular in all the sectors. Even in all hard environments, the latest sensors can read accurate results. Now the deployment of Internet of Things (IoT) has made remote control operations to perform effectively with ease. The sensors have been reduced in size and can be installed easily at required space. The IoT implementation helps out bridging the control between the devices and end users. The proposed model uses Node MCU with Wi-Fi enabled ESP8266 as a microcontroller which can handle the sensory components with ease. The model features soil dampness, water level and motor control along with some rich features like smart drainage system. The control interface is simple through a web application coupled with ADAFRUIT server which is optimized for better usage. This can be accessed by farmers through their smart phone with any Wi-Fi/2G/3G/4G internet connectivity for remote usage and monitoring. By using renewable energy source named solar energy is used to supply power to entire automated agriculture system, so as to reduce the power consumption and the cost incurred by the power tariff. This model aims for the development of farmers with low cost and energy efficient to readily accommodate the fields. ER - TY - Article T1 - A Cost Effective Identity-Based Authentication Scheme for Internet of Things-Enabled Agriculture A1 - Hassan, B Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/4275243 UR - https://api.elsevier.com/content/abstract/scopus_id/85129924994 L1 - file:///C:/Users/sonsu/Downloads/A Cost Effective Identity-Based Authentication Scheme for.pdf N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) has revolutionized practically every industry, including agriculture, due to its fast expansion and integration into other industries. The application of IoT in agriculture motivates farmers to use their resources wisely and allows for better field monitoring and decision-making, resulting in increased agricultural productivity. Because IoT-enabled agriculture systems need the use of various types of sensors that collect data (such as soil moisture and humidity) and then transmit it over the network. IoT-based agriculture systems, on the other hand, are always vulnerable to security threats. Authentication is one of the assured options for addressing the security concern, since it only enables an authorized party to access the data. Existing authentication schemes typically use the Rivest-Shamir-Adleman (RSA) algorithm and elliptic curve cryptography (ECC), which has a greater computational and communication cost. Furthermore, the security of the majority of existing authentication schemes is not verified using any security tool. As a result, we propose an identity-based authentication scheme for IoT-enabled agriculture in this article. To ensure that our scheme is cost-effective, we employ hyperelliptic curve cryptography (HECC). Our scheme surpasses existing authentication schemes in terms of computational cost and communication overhead while providing better security, according to a thorough investigation of performance and security. ER - TY - Article T1 - A Deep Learning and Social IoT Approach for Plants Disease Prediction Toward a Sustainable Agriculture A1 - Delnevo, G Y1 - 2022/// KW - deep learning KW - plant disease detection KW - plant disease prediction KW - internet of things JF - IEEE Internet of Things Journal VL - 9 IS - 10 SP - 7243 EP - 7250 DO - 10.1109/JIOT.2021.3097379 UR - https://api.elsevier.com/content/abstract/scopus_id/85110929716 N1 - Cited By (since 2022): 21 N2 - As the world becomes increasingly interconnected, emerging and innovative sensing technologies are shaping the future of agriculture, with a special focus on sustainability-related issues. In this context, we envision the possibility to exploit Social Internet of Things for sensing of environmental conditions (solar radiation, humidity, air temperature, and soil moisture) and communications, deep learning for plant disease detection, and crowdsourcing for images collection and classification, engaging farmers and community garden owners and experts. Through, data fusion and deep learning, the designed system can exploit the collected data and predict when a plant would (or not) get a disease, with a specific degree of precision, with the final purpose to render agriculture more sustainable. We here present the architecture, the deep learning model, and the responsive Web app. Finally, some experimental evaluations and usability/engagement tests are reported and discussed, together with final remarks, limitations, and future work. ER - TY - Article T1 - A Heterogeneous Access Metamodel for Efficient IoT Remote Sensing Observation Management: Taking Precision Agriculture as an Example A1 - Zhou, L Y1 - 2022/// KW - Geographic information science (GIS) KW - heterogeneity KW - internet of things KW - remote sensing observation (RSO) KW - management KW - metaobject facility KW - RSO metadata representation model KW - sensor Web JF - IEEE Internet of Things Journal VL - 9 IS - 11 SP - 8616 EP - 8632 DO - 10.1109/JIOT.2021.3118024 UR - https://api.elsevier.com/content/abstract/scopus_id/85119594171 N1 - Cited By (since 2022): 3 N2 - Standard remote sensing observation (RSO) access and formulization is essential to Internet of Things (IoT) data management, such as in precision agriculture (PA). Because of the heterogeneous characteristics and the petabyte data size of RSO, massive remote sensing processing in RSO management has been hampered. Here, we present a heterogeneous access metamodel for efficient RSO management (HAMERM) and verify it in PA. The structure of basic metadata components is defined. A five-tuple metadata structure based on the metaobject facility is designed. HAMERM consists of identification, platform, observation, product, and access, which represent the five aspects of RSO metadata information. In addition, the flatMap/reduceByKey algorithms and the table structure have been proposed under Sensor Web and Geographic Information Science (GIS) techniques. Intensive experiments in Guangdong Province, China are conducted to test the proposed method. Two RSO metadata formulization instances were conducted to examine the ability of sheltering the differences of multisource and heterogeneous RSO. Experiments containing data storage and data soil moisture (SM) mapping were performed. The results suggest that the HAMERM method achieved a performance 30.1 times higher than that of Hadoop and three times higher than that of Spark (stand-alone). Consequently, the proposed HAMERM can be applied to achieve efficient SM mapping within PA, which is helpful for efficient RSO management for the IoT. ER - TY - Article T1 - A Lightweight Huffman-based Differential Encoding Lossless Compression Technique in IoT for Smart Agriculture A1 - Al-Qurabat, A K M Y1 - 2022/// JF - International Journal of Computing and Digital Systems VL - 11 IS - 1 SP - 117 EP - 127 DO - 10.12785/ijcds/110109 UR - https://api.elsevier.com/content/abstract/scopus_id/85123546584 N1 - Cited By (since 2022): 11 ER - TY - Article T1 - A LoRaWAN IoT System for Smart Agriculture for Vine Water Status Determination A1 - Valente, A Y1 - 2022/// KW - smart agriculture KW - IoT KW - LoRaWAN KW - WSN KW - water status JF - Agriculture (Switzerland) VL - 12 IS - 10 DO - 10.3390/agriculture12101695 UR - https://api.elsevier.com/content/abstract/scopus_id/85141866947 L1 - file:///C:/Users/sonsu/Downloads/agriculture-12-01695.pdf N1 - Cited By (since 2022): 1 N2 - In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources. Keywords: ER - TY - Article T1 - A Novel Model for Optimization of Resource Utilization in Smart Agriculture System Using IoT (SMAIoT) A1 - Jani, K A Y1 - 2022/// KW - IoT KW - agriculture KW - sensors KW - pest KW - insect KW - drone KW - irrigation KW - soil KW - moisture KW - fertigation JF - IEEE Internet of Things Journal VL - 9 IS - 13 SP - 11275 EP - 11282 DO - 10.1109/JIOT.2021.3128161 UR - https://api.elsevier.com/content/abstract/scopus_id/85133270018 L1 - file:///C:/Users/sonsu/Downloads/IEEEIoTJournal-A_Novel_Model_for_Optimization_of_Resource_Utilization_in_Smart_Agriculture_System_Using_IoT_SMAIoT.pdf N1 - Cited By (since 2022): 5 N2 - Many Countries have rich resources of land, rivers, groundwater, environment and fertilizers availability. Agriculture is the main source of income for several country’s people. Since the last few decades, there are few resource shortages like groundwater, river water. People are unaware of proper utilization of available valuable resources, which leads to use more resources for less crop production. One of the solutions of this problem is to design and implement an IoT based smart framework for agriculture. In this paper, we have proposed a smart agriculture framework to monitor different types of low cost IoT sensors-devices, which collects data from soil, air, water, insects and make appropriate decisions based on analysis of sensors data. Novel contribution of our proposed approach is to automate tasks of irrigation, fertigation, pest detection, pesticide spray in a scientific way with minimal farmer’s intervention in one framework. This paper contains detailed implementation steps and result of smart irrigation module of our framework ER - TY - Conference Paper T1 - A Review on Agriculture Monitoring Systems using Internet of Things (IoT) A1 - Osupile, K Y1 - 2022/// KW - Wireless Sensor Networks KW - Internet of Things KW - Machine Learning KW - Smart Farming KW - Crop Monitoring KW - Agriculture JF - Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022 SP - 1565 EP - 1572 DO - 10.1109/ICAAIC53929.2022.9792979 UR - https://api.elsevier.com/content/abstract/scopus_id/85133450473 N1 - Cited By (since 2022): 2 N2 - As technological advancements have been introduced; there is a need to make strides in agriculture. Th; it has become possible to implement precision agri; there is a need for crop growth to be also monitor; atmosphere conditions; moisture; temperature; and humidity. ER - TY - Review T1 - A Revisit of Internet of Things Technologies for Monitoring and Control Strategies in Smart Agriculture A1 - Rehman, A Y1 - 2022/// KW - agriculture KW - land monitoring KW - control strategies KW - IoT KW - sensors KW - economic growth KW - water management and water resources JF - Agronomy VL - 12 IS - 1 SN - 2073-4395 DO - 10.3390/agronomy12010127 UR - https://api.elsevier.com/content/abstract/scopus_id/85123681522 L1 - file:///C:/Users/sonsu/Downloads/agronomy-12-00127-v5.pdf N1 - Cited By (since 2022): 40 N2 - With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management. ER - TY - Article T1 - A Self-Powered, Real-Time, NRF24L01 IoT-Based Cloud-Enabled Service for Smart Agriculture Decision-Making System A1 - Raju, K Lova Y1 - 2022/// JF - Wireless Personal Communications VL - 124 IS - 1 SP - 207 EP - 236 DO - 10.1007/s11277-021-09462-4 UR - https://api.elsevier.com/content/abstract/scopus_id/85123092535 N1 - Cited By (since 2022): 3 N2 - Agriculture has been benefited by advanced research and development due to Internet of Things (IoT)-based automation. Environmental and deployment sensors such as DHT11, soil moisture, soil temperature and others are used in agriculture field production and IoT technology is being employed to assess field environment in smart agriculture. Most of the existing systems work only on the air temperature and humidity sensing, for agriculture health monitoring. These systems have limitations to send sensing data from long distances. The approximate range of data communication for these systems is below 100 m which is quite less for agriculture field coverage, in general. As a result of this, agricultural crop production is not up to the mark. We propose an architecture framework to address the above-mentioned shortcomings. This proposed architecture can be applied for long range communications with no data loss and no interference in the information. The system employs an NRF24L01 transceiver module, which works at 2.4 GHz for long-distance communications towards monitoring of agriculture parameters. This research is aimed into a suitable, feasible, and integrated Internet of Things (IoT) technique for smart agriculture. The proposed system saves energy and boosts productivity. This method reduces human effort while evaluating heat index measurement parameters in order to monitor the environment for optimal agriculture growth. The current consumption and life expectancy of the Agriculture Wireless Monitoring Unit (AWMU) are 0.02819 Amperes and 3 days 20 hours 13 minutes and 47 seconds, respectively, according to the experimental analysis. In an open environmental area, the maximum transmission distance for AWMU is up to 200 meters from the wireless access point. ER - TY - Book Chapter T1 - A Survey on Wireless Sensor Networks and Instrumentation Techniques for Smart Agriculture A1 - Madhumathi, R Y1 - 2022/// KW - Smart agriculture KW - Sensors KW - Wireless sensor networks KW - Instrumentation techniques KW - Soil properties JF - Lecture Notes on Data Engineering and Communications Technologies VL - 68 SP - 453 EP - 467 SN - 2367-4512 DO - 10.1007/978-981-16-1866-6_33 UR - https://api.elsevier.com/content/abstract/scopus_id/85111957256 N1 - Cited By (since 2022): 4 N2 - Agriculture is the primary source for the development of our country’s economy. It involves production, processing, marketing, and distribution of agricultural produce. Smart agriculture is one of the modern farming practices that increase the agricultural production in a most efficient manner. Smart agriculture involves wireless sensor networks (WSN) for various operations. Wide variety of sensors are used in WSN that assists farmers to know the statistical details of their field which helps them to take accurate decisions and provides instantaneous feedback for various agricultural parameters. Numerous researchers and manufacturers aim to develop real-time sensors based on different approaches, namely electromagnetic, electric, optical, mechanistic, acoustic, or electrochemical, which calculates the physical and chemical properties of soil in agricultural fields. The main objective of this study is to review the application of wireless sensors used in agriculture, the principle and various instrumentation techniques in measuring different parameters in agriculture. ER - TY - Short Survey T1 - A comparative study of deep learning and Internet of Things for precision agriculture A1 - Saranya, T Y1 - 2023/// JF - Engineering Applications of Artificial Intelligence VL - 122 SN - 0952-1976 DO - 10.1016/j.engappai.2023.106034 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S095219762300218X UR - https://api.elsevier.com/content/abstract/scopus_id/85150799703 N1 - Cited By (since 2023): 2 N2 - Precision farming is made possible by rapid advances in deep learning (DL) and the internet of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to sustainably fulfill the future food supply. This paper presents a comprehensive overview of recent research contributions in DL and IoT for precision agriculture. This paper surveys the diverse research on DL applications in agriculture, such as detecting pests, disease, yield, weeds, and soil, including fundamental DL techniques. Also, the work describes the IoT architecture and analyzes sensor categorization, agriculture sensors, and unmanned arial vehicles (UAVs) used in recent research. Besides that, data acquisition, annotation, and augmentation for agriculture datasets were covered, and a few widely used datasets were listed. This work also discusses some challenges and issues that DL and IoT face. Furthermore, the research proposed a bootstrapping approach of Transfer learning where fine-tuned VGG16 is fused with optimized and improved newly built fully connected layers for pest detection. The performance of the proposed model is evaluated and compared with other models, such as custom VGG16 as a classifier; fine-tuned VGG16 is optimized with other optimizers like SGD, RMSProp, and Adam. The results show that the proposed model for pest detection outperforms all other models with an accuracy of 96.58 % and a loss of 0.15%. The review and the proposed work presented in this paper will significantly direct researchers toward DL and IoT for intelligent farming. ER - TY - Article T1 - A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing A1 - Ting, L Y1 - 2022/// KW - Internet of Things KW - sensors KW - data privacy and security KW - fuzzy logic KW - decision support JF - Journal of Intelligent Systems VL - 31 IS - 1 SP - 221 EP - 236 DO - 10.1515/jisys-2022-0012 UR - https://api.elsevier.com/content/abstract/scopus_id/85124456861 L1 - file:///C:/Users/sonsu/Downloads/10.1515_jisys-2022-0012.pdf N1 - Cited By (since 2022): 21 N2 - An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants. ER - TY - Article T1 - A smart agriculture framework for IoT based plant decay detection using smart croft algorithm A1 - Gupta, B Y1 - 2022/// JF - Materials Today: Proceedings VL - 62 SP - 4758 EP - 4763 DO - 10.1016/j.matpr.2022.03.314 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2214785322016546 UR - https://api.elsevier.com/content/abstract/scopus_id/85127921747 N1 - Cited By (since 2022): 9 N2 - Internet of things (IOT) is an assuring technology which provides systematic and logical solutions towards the revolution of various realms. Various researches and analysis have been conducted and numerous methods have been incorporated to apply IoT technology on agronomical fields. IoT can play a vital role in timely detection of declining plant health so that appropriate measures can be taken. It is a huge step towards smart agriculture. In this paper we propose a model to build up an automated framework which will recognize the crop decay in the initial phase which is imperceptible to naked human eyes. This model helps in prevention of huge losses and also save a lot of time and labor. The proposed model builds a recognition framework using sensors like humidity, moisture, temperature and color of the plant leaf. The data from the sensors is sent to Arduino to Cloud which then analyzes the data and helps in identifying the plant decay. In the upcoming years, the internet of things will be a crucial bit in the smart farming system. ER - TY - Conference Paper T1 - A survey on IoT-based smart agriculture to reduce vegetable and fruit waste A1 - Pal, H Y1 - 2022/// KW - Drone in agriculture KW - IoT KW - reduce waste in agriculture KW - smart agriculture JF - Journal of Physics: Conference Series VL - 2273 IS - 1 SN - 1742-6588 DO - 10.1088/1742-6596/2273/1/012009 UR - https://api.elsevier.com/content/abstract/scopus_id/85132754873 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/Pal_2022_J._Phys.__Conf._Ser._2273_012009.pdf N1 - Cited By (since 2022): 2 N2 - Agriculture automation is a top focus and developing area for a number of countries right now. We are seeing a surge in demand for Internet of Things (IoT) in various industries these days. One of the most essential applications of IoT is agriculture. Today, we notice that the world's population is quickly rising, and an agro product plays a critical part in this population’s existence. We are conscious of the fact that resources are limited. If we continue to farm in the traditional manner, it will be extremely difficult for the rising population to survive. Due to improper fertilizers, quantity of water, chemicals and huge amount of pesticides decreases the fertility of land. There is a need for smart agriculture to monitor all these factors which affect the fertility of soil. When the fertility is decreasing it will impact on the growth of fruits and vegetables. In this paper we are going to study how we can manage our resources through Internet of Things, multispectral camera, hyper spectral camera, and thermal camera and RGB camera. Plant diseases, pesticide control, weed control, proper irrigation, and water management are all problems in agriculture that can be readily solved with the various automated and control approaches stated above. ER - TY - Article T1 - A tutorial on data mining for Bayesian networks, with a specific focus on IoT for agriculture A1 - Krause, P J Y1 - 2023/// JF - Internet of Things (Netherlands) VL - 22 DO - 10.1016/j.iot.2023.100738 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2542660523000616 UR - https://api.elsevier.com/content/abstract/scopus_id/85150263417 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Krause 2023) A tutorial on data mining for Bayesian networks, with a specific.pdf N1 - Cited By (since 2023): 1 N2 - We are seeing a steady build up in momentum of two trends that will lead to a significant, and necessary, transformation of agriculture in the 21st Century. Firstly, the move to digital with IoT facilitating the use of sensor networks to support precise decision making. Secondly, the move to “ecological intensification”; working with natural processes to lower the carbon footprint of agricultural processes and increase the biodiversity of agricultural units without sacrificing yield. Clearly data mining and machine learning have an important role to play in supporting this transformation. However, given the range of biotic, abiotic and social contexts that need to inform the development of models for decision support in agriculture, we need data mining techniques that support the use of qualitative and unstructured data as well as hard numerical data. In this tutorial we show how Bayesian Networks can be built from a wide range of sources of expert knowledge and data. Whist we use digital agriculture as a key beneficiary of these techniques, this tutorial will also be of interest to all those with an interest in building IoT systems that require combinations of social, technical and environmental understanding ER - TY - Conference Paper T1 - Adaptive IoT System for Precision Agriculture A1 - Lekshmy, V Geetha Y1 - 2022/// KW - Adaptive agriculture system KW - Adaptive IoT systems KW - Automated irrigation KW - Pest detection KW - YOLO KW - LSTM KW - Random forest JF - Lecture Notes in Networks and Systems VL - 336 SP - 39 EP - 49 SN - 2367-3370 DO - 10.1007/978-981-16-6723-7_4 UR - https://api.elsevier.com/content/abstract/scopus_id/85124165131 N1 - Cited By (since 2022): 3 N2 - Precision agriculture refers to the application of modern tools and techniques to increase crop productivity in an environment-friendly manner. In the proposed work, a model of self-adaptive system for precision agriculture is developed. This Internet of Things (IoT)-based agriculture system mainly incorporates two functions, automated irrigation and pest detection and is augmented with machine learning models to make it self-adaptive. It handles the sensor failure events automatically by predicting the possible sensor values and keeps the system running without interruption. The system notifies the user about the failure so that it can be replaced later, thus avoiding abrupt termination or malfunctioning of the system. Another adaptive aspect of the proposed system is that it can adjust the system parameters based on prediction of stochastic environmental parameters like rain and temperature. Occurrence of rain is predicted by a machine learning model, and based on this, the system parameters like frequency of getting moisture sensor values are adjusted. This adaptation is fruitful during occurrence of continuous rain when the soil is wet and the moisture content information can be collected less frequently, thus saving the power consumption involved in data collection. The learning models long short-term memory (LSTM) and random forest are used in implementing adaptive functions. The automated irrigation becomes active on fixed times, and the amount of water dispensed is based on the values obtained from soil moisture sensors deployed. The pest detection module captures the images of field and detects mainly the bird pests attacking the crop. The object detection technique, Yolo4, is used to spot the pest. ER - TY - Conference Paper T1 - AgriBot: Smart Autonomous Agriculture Robot for Multipurpose Farming Application Using IOT A1 - Rai, H M Y1 - 2022/// KW - Agriculture robot KW - Seeding KW - Ploughing KW - Autonomous KW - Farming KW - Sensors JF - Lecture Notes in Electrical Engineering VL - 875 SP - 491 EP - 503 SN - 1876-1100 DO - 10.1007/978-981-19-0284-0_36 UR - https://api.elsevier.com/content/abstract/scopus_id/85128910834 N1 - Cited By (since 2022): 1 N2 - Internet of Things (IoT) is used all around the globe for connecting things with each other. IoT is a term broadly used for devices that are connected to each other via embedded sensors or with the use of wireless networks may be cellular or Wi-Fi. The proposed system is basically an Agricultural Robot or “AgriBots” used for increasing the productivity and quality of the crop and also to reduce the time and labor cost. The system explains about the network of sensors and the applications of different sensors in the agricultural fields. There are number of Agricultural Robots that already exist at present but they are used at small scale only. In the existing system, the monitoring of the parameters such as the soil moisture and temperature are done by using the manual method. In the proposed system, the IoT is integrated with the Arduino UNO to improve the efficiency of the agricultural fields. The details collected by the Robot from the agricultural field will be stored on cloud and can be monitored without any human interaction ER - TY - Article T1 - Agriculture monitoring system based on internet of things by deep learning feature fusion with classification A1 - Kumari, K S Y1 - 2022/// JF - Computers and Electrical Engineering VL - 102 DO - 10.1016/j.compeleceng.2022.108197 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0045790622004384 UR - https://api.elsevier.com/content/abstract/scopus_id/85134418731 N1 - Cited By (since 2022): 2 N2 - This research proposed novel technique in crop monitoring system using machine learning-based classification using UAV. To monitor and operate activities from remote locations, UAVs extended their freedom of operation. For smart farming, it's significant to use UAV prospects. On the other hand, the cost and convenience of using UAVs for smart-farming may be a major factor in farmers’ decisions to use UAVs in farming. The IoT-based module is used to update the database with monitored data. Using this method, live data should be updated soon, and it can help in crop cultivation identification. Research also monitor climatic conditions using live satellite data. The data is collected as well as classified for detecting crop abnormality based on climatic conditions and pre-historic data based on cultivation for the field also this monitoring system will differentiate weeds and crops. Simulation results show accuracy, precision, specificity for trained data by detecting the crop abnormality. ER - TY - Review T1 - Agriculture-Food Supply Chain Management Based on Blockchain and IoT: A Narrative on Enterprise Blockchain Interoperability A1 - Bhat, S A Y1 - 2022/// KW - precision agriculture KW - supply chain KW - blockchain KW - internet of things KW - traceability KW - smart contracts JF - Agriculture (Switzerland) VL - 12 IS - 1 SN - 2077-0472 DO - 10.3390/agriculture12010040 UR - https://api.elsevier.com/content/abstract/scopus_id/85122799004 L1 - file:///C:/Users/sonsu/Downloads/agriculture-12-00040-v2.pdf N1 - Cited By (since 2022): 46 N2 - Modern-day agriculture supply chains have evolved from sovereign and autonomous local stakeholders to a worldwide interconnected system of multiple participants linked by complicated interactions, impacting the production, processing, transportation, and delivery of food to end consumers. Regular instances of fraudulent acts reveal a lack of openness in agriculture supply chains, raising worries about financial losses, eroding customer trust, and lowering corporate brand value. To develop an efficient and reliable trading environment, several fundamental modifications in the present supply chain architecture are required. There is broad consensus that blockchain can improve transparency in agriculture-food supply chains (agri-food SCs). Consumers now demand safe, sustainable, and equitable food production processes, and businesses are using blockchains and the internet of things to meet these needs. For enhanced responsiveness in agri-food SCs, new concepts have evolved that combine blockchains with various Industry 5.0 technologies (e.g., blockchain technology, big data, internet of things (IoT), radio frequency identification (RFID), near field communication (NFC), etc.). It is critical to cut through the hype and examine the technology’s limits, which might stymie its acceptance, implementation, and scalability in agri-food supply chains. This study presents Agri-SCM-BIoT (Agriculture Supply Chain Management using Blockchain and Internet of things) architecture to address the storage and scalability optimization, interoperability, security and privacy issues security, and privacy of personal data along with storage concerns with present single-chain agriculture supply chain systems. We also discussed the classification of security threats with IoT infrastructure and possible available blockchain-based defense mechanisms. Finally, we discussed the features of the proposed supply chain architecture, followed by a conclusion and future work. ER - TY - Article T1 - An Advanced and Efficient Cluster Key Management Scheme for Agriculture Precision IoT Based Systems A1 - Anand, S Y1 - 2022/// KW - internet of things KW - Clustering key management KW - precision agricultureision KW - Wireless sensor networks KW - key management JF - International Journal of Electrical and Electronics Research VL - 10 IS - 2 SP - 264 EP - 269 DO - 10.37391/IJEER.100235 UR - https://api.elsevier.com/content/abstract/scopus_id/85134324750 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(anand 2022) An Advanced and Efficient Cluster Key Management scheme for precision agriclulture.pdf N1 - Cited By (since 2022): 2 N2 - Things that connect to other devices & systems via Internet or communication networks are called IoT. It can also be said as a network of wireless sensors connected to a cloud and controlled by embedded devices. Considering the large framework of IoT, it becomes a little difficult to maintain security at each sensor node especially with limited information regarding hardware and deployment capabilities. Therefore, management of keys has become a point of concern peculiarly taking account of node capturing attack. This paper proposes an advanced cluster key management scheme for agriculture precision which involves EBS constructor and Chinese remainder theorem together. Once the data is collected from the nodes and a list is created, it is sent from the Cluster Head to the Backend Server, which filters it for hostile IDs and ignore the unauthentic sensor, returning filtered list with preloaded keys, & an authentication code to Cluster Head for use. To ensure added security, in this scheme encryption of data is done twice. Upon comparing the proposed scheme with others, it has been observed that we have achieved higher delivery ratio and reduced the energy consumption and packet drop rate to a great extent. ER - TY - Article T1 - An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks A1 - Singh, K U Y1 - 2022/// KW - Wireless sensor networks KW - pest detection JF - Journal of Food Quality VL - 2022 DO - 10.1155/2022/5801206 UR - https://api.elsevier.com/content/abstract/scopus_id/85131191188 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Singh 2022) An Artificial Neural Network-Based Pest Identification and control in smart agriculture using WSN.pdf N1 - Cited By (since 2022): 2 N2 - Despite living in a rural country, farmers in India face several challenges. Every year, they suffer significant losses due to agricultural insect infestation. These losses are primarily the result of inadequate field surveillance, crop diseases, and ineffective pesticide management. We need cutting-edge technology that is constantly evolving to maintain control over such major concerns responsible for output reductions year after year. Wireless sensor networks address all of these issues; in fact, wireless sensor network technology is quickly becoming the backbone of modern precision agriculture. We propose a strategy for pest monitoring using wireless sensor networks in this study by simply recognizing insect behaviour using various sensors. We proposed a rapid and accurate insect detection and categorization approach based on five important crops and associated insect pests. This method examines insect behaviour by collecting data from sensors placed in the field. The results show that the proposed work improves the accuracy of the existing work by 3.9 percent. ER - TY - Conference Paper T1 - An Edge-IoT Architecture and Regression Techniques Applied to an Agriculture Industry Scenario A1 - Pérez-Pons, M E Y1 - 2022/// KW - Smart Farming KW - Data transfer KW - Eco-efficiency KW - Linear regression KW - Internet of Things KW - Edge computing JF - Lecture Notes in Networks and Systems VL - 253 SP - 92 EP - 102 SN - 2367-3370 DO - 10.1007/978-3-030-78901-5_9 UR - https://api.elsevier.com/content/abstract/scopus_id/85113508279 N1 - Cited By (since 2022): 2 N2 - The agricultural industry must adapt to todays market by using resources efficiently and respecting the environment. This paper presents the analysis of data and the application of the Internet of Things (IoT) and advanced computing technologies in a real-world scenario. The proposed model monitors environmental conditions on a farm through a series of deployed sensors and the most outstanding feature of this model is the robust data transmission it offers. The analysis of information collected by the sensors is measured using state-of-the-art computing technology that helps reduce data traffic between the IoT layers and the cloud. The designed methodology integrates sensors and a state-of-the-art computing platform for data mining. This small study forms the basis for a future test with several operations at the same time. ER - TY - Conference Paper T1 - An Energy Efficient LoRa-based Multi-Sensor IoT Network for Smart Sensor Agriculture System A1 - Mishra, S Y1 - 2023/// KW - smart agriculture KW - edge and fog computing KW - LoRa KW - low-cost sensors KW - performance JF - 2023 IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT 2023 SP - 28 EP - 31 DO - 10.1109/WiSNeT56959.2023.10046242 UR - https://api.elsevier.com/content/abstract/scopus_id/85149400388 N1 - Cited By (since 2023): 1 N2 - With the advancement and usage of Internet of Things (IoT), smart agriculture is evolving rapidly. Smart solutions based on IoT can not only help the farmers in maximizing the profits but also help them in reducing the manual supervision of agriculture land. In a smart agriculture system, inexpensive, resource-constrained sensors are installed near the crops as well as at some strategic locations in an agriculture field to collect relevant crop and environment data in real time. This data is then used for both critical, latency-sensitive decision making as well as long-term planning. The key challenges in building a smart agriculture system include high communication latency and bandwidth consumption incurred with computing on the cloud, frequent Internet disconnections in rural areas, and a need for keeping the overall cost low for the end users (farmers). In this paper, we discuss the design and implementation of our on-going, LoRa-based three-tier smart agriculture system comprised of (i) Sensing layer, (ii) Fog layer, and (iii) Cloud layer. In particular, we focus on the how the low-power, low-bandwidth and long-range features of LoRa are utilized to transform traditional agriculture land of rural areas in India into smart agriculture system. We present the performance of our current prototype and compare with the existing, state-of-the-arts framework for smart agriculture system in terms of cost, latency and distance ER - TY - Article T1 - An Experimental study of IoT-Based Topologies on MQTT protocol for Agriculture Intrusion Detection A1 - A, J S Y1 - 2022/// KW - Star topology KW - Bus topology KW - P2P Topology KW - Mesh topology KW - Noise ratio Power KW - Latency and data loss JF - Measurement: Sensors VL - 24 DO - 10.1016/j.measen.2022.100470 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2665917422001040 UR - https://api.elsevier.com/content/abstract/scopus_id/85138142981 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/((Simla 2022) An Experimental study of IoT-Based Topologies on MQTT protocol for agriculture intrusioj detection.pdf N1 - Cited By (since 2022): 3 N2 - griculture is the backbone of the country. Half of the world population depends on agriculture and agriculture based products. Growth of the agriculture is mainly affected by the intruders. Detecting intruders in agriculture is a challenging task. Effective intruder detection module requires performance analysis and improvisation. Agriculture intrusion detection model starts with the selection of the suitable topology. Internet of Things (IoT) module mainly relies on the data transmission and reception from sensors which are logically arranged in the farm field. Agriculture Intrusion Detection System can be used with wireless sensors in order to detect the intruders in the agriculture field and sends an alert message to the user which exchange information among internet connected devices based on the Message Queue Telemetry Transport (MQTT) protocol. Main objective is to identify the suitable topology for the transmission of Data from various sensor nodes. Nodes are placed in the field at various topologies like star topology, Bus topology, P2P Topology, Mesh Topology. Performance of the various topologies is measured and compared by analysing metrics like Bandwidth, Latency, Throughput, Noise Ratio, Power Factor and Packet Loss. Result shows that the Mesh topology outperforms other topologies. ER - TY - Conference Paper T1 - An Improved Agriculture Plant Disease Detection and Monitoring Using IOT A1 - Janani, K Y1 - 2022/// KW - Agrobots KW - Robots KW - Chemical management KW - internet of things JF - 8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022 SP - 621 EP - 624 DO - 10.1109/ICACCS54159.2022.9785109 UR - https://api.elsevier.com/content/abstract/scopus_id/85133179373 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/((Simla 2022) An Experimental study of IoT-Based Topologies on MQTT protocol for agriculture intrusioj detection.pdf N1 - Cited By (since 2022): 1 N2 - This project will investigate the development and testing of a robot capable of spraying pesticides, fertilisers, and water for agricultural lands. Precision spraying operations and perfect operation logging were made possible by the robot system. Based on the precise actions and records, optimal chemical management could be projected, with the required amount of chemicals sprayed only when necessary, and this robot may contribute to the lowest input maximum output production system by building a traceability system in production. The robot module is designed to spray pesticides, hydrate plants, and supply nutrients. The introduction of the Agrobots would result in the abolition of annual labour. In this context, a demonstration model of such equipment capable of carrying out the approach effectively may be provided. Work hours and expenditures would be decreased as a result of the production of these Agrobots. It captivates people's interest in agriculture now and in the future, because implementing an embedded automation project in agriculture has a significant influence ER - TY - Conference Paper T1 - An In-Depth Study of Smart Agriculture Based on Internet of Things and Wireless Sensor Networks A1 - Prasath, S T Y1 - 2022/// JF - ECS Transactions VL - 107 IS - 1 SP - 1363 EP - 1374 SN - 1938-6737 DO - 10.1149/10701.1363ecst UR - https://api.elsevier.com/content/abstract/scopus_id/85130534933 N1 - Cited By (since 2022): 1 N2 - Most governments monitor and prioritize agriculture. Subsidies and loans to farmers to promote food production are common in many countries. They are pushing farmers to employ modern agrarian technology to boost grain quality and farming. Smart farming relies on Wireless Sensor Networks and the Internet of Things. Using the above technology, farmers may monitor their property remotely and manage their farms at any time sensors nodes installed on fields capture agricultural growth data using GPS. Sensor nodes collect data on soil nutrient levels, temperature, and humidity. The sensor delivers data to a wireless gateway, which sends it to a remote server. Researchers and scientists have been working hard to improve farmers' productivity, quantity, and quality. This study examines the impact of new technologies and sensors used in smart farming on food security and how such devices are transforming agricultural sectors to meet rising population demands. ER - TY - Conference Paper T1 - An IoT & AI-assisted Framework for Agriculture Automation A1 - Anwarul, S Y1 - 2022/// KW - Internet of Things KW - Artificial Intelligence KW - Automation KW - Smart Agriculture JF - 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022 DO - 10.1109/ICRITO56286.2022.9964567 UR - https://api.elsevier.com/content/abstract/scopus_id/85144592338 N1 - Cited By (since 2022): 1 N2 - Virtually every business in the world, especially agriculture, experienced tsunamis because of the quick and huge expansion of Artificial Intelligence (AI) and the Internet of Things (IoT). The current agricultural practices are being drastically altered, which is opening up new prospects. By 2050, there will be a huge demand for agricultural goods due to the 30% rise in the global population. Farmers have recently demonstrated a strong interest in smart agricultural approaches. Several things, including the growing availability of inexpensive, low-powered wireless Internet of Things (IoT) sensors that may be used to remotely monitor and report crop, weather, and field conditions, cause this. This enables effective resource management, such as reducing the number of hazardous pesticides used and the amount of water needed for irrigation. In addition, farmers may be able to use autonomous farming equipment and make better future forecasts based on current and previous conditions, thanks to the recent development in artificial intelligence, which would help farmers to reduce crop illnesses and insect infestation. These two enabling technologies have transformed traditional agricultural methods when used together. This paper proposes an IoT and AI-assisted framework to resolve the food and agricultural problems for smart and sustainable agriculture. Further, there is a comparison of the proposed method with other existing approaches for Agriculture Automation Systems that shows the supremacy of the proposed framework. ER - TY - Article T1 - An IoT and Blockchain-based approach for the smart water management system in agriculture A1 - Zeng, H Y1 - 2023/// JF - Expert Systems VL - 40 IS - 4 DO - 10.1111/exsy.12892 UR - https://api.elsevier.com/content/abstract/scopus_id/85119871897 N1 - Cited By (since 2023): 17 N2 - Agriculture in rural areas facing critical issues such as irrigation with the increase in water crises followed by some other issues line seed quality, poor fertilizers and many others. The recent advances suggest that IoT and Blockchain Technology along with artificial intelligence will be most dominant technologies in near future. In this article, the integration of Internet of Things (IoT) with Blockchain technology is implemented for monitoring agricultural fields efficiently. An efficient seed quality monitoring and smart water management system is design using IoT and Blockchain Technology for managing and coordinating the use of good quality seeds and water resources among communities. The Blockchain network is implemented for securing the information and supporting trust among the members of community. The Blockchain network is also implemented for sporting trust among commercial resource constrained systems, which are communicating with the Blockchain network consisting of a hardware platform. The design of a prototype and its performance evaluation based on implementation is also presented ER - TY - Article T1 - An IoT-based agriculture maintenance using pervasive computing with machine learning technique A1 - Kailasam, S Y1 - 2022/// KW - crop harvesting KW - detection of plant disease KW - RFO KW - Classification KW - treshold segmentation JF - International Journal of Intelligent Computing and Cybernetics VL - 15 IS - 2 SP - 184 EP - 197 DO - 10.1108/IJICC-06-2021-0101 UR - https://api.elsevier.com/content/abstract/scopus_id/85119492141 N1 - Cited By (since 2022): 12 N2 - Purpose In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. ER - TY - Article T1 - An Ultra-wideband Dielectric Resonator Antenna for WSN based IoT Applications in Agriculture A1 - Khosla, D Y1 - 2022/// KW - Electrical and Electronic Engineering KW - Control and Optimization KW - Computer Networks and Communications KW - Computer Science Applications JF - International Journal of Sensors, Wireless Communications and Control VL - 12 IS - 4 SP - 281 EP - 291 DO - 10.2174/2210327910666201228155145 UR - https://api.elsevier.com/content/abstract/scopus_id/85125007137 N1 - Cited By (since 2022): 2 N2 - Background: Agriculture sector is one of the prime and widely spread sectors. So to make it autonomous and increase its yield, we require a major technological improvement. The only solution to make advancement is with the use of wireless sensor networks. Internet of Things in this field is used to provide connectivity to all real-time sensors and to collect that data in computer-based systems without human involvement. Objective: IoT based system is used to monitor physical and environmental conditions of the agriculture field through a network of wireless sensor. Here, a novel ultra-wideband Dielectric Resonator antenna is designed that is used in Wi-Fi for transmission of data received from sensors. The antenna designed should be easy to fabricate and compact in size and should provide high data rates. The complete designed system should be reliable and cost effective one. Method: A proposed IoT based system monitors physical and environmental conditions using a wireless sensor network consisting of power supply, soil moisture sensor (FC-28), humidity sensor (LM-35), temperature sensor (HR-202), water level sensor, ARM 7 processor, Liquid Crystal Display (LCD), Relay, motor and Wi-Fi module that is installed at remote locations and connected to the main system comprises of a novel ultra-wideband Dielectric Resonator antenna. Results: The designed WSN based IoT system for agriculture application monitors temperature, humidity, soil moisture, and water level in the field. For Wi-Fi module implementation ultra-wideband inverted sigmoid shaped DRA is designed that provides an impedance bandwidth of 36.46 % at 6.226 GHz (5.51 - 7.78 GHz). The designed antenna provides a peak gain of 5.44 dB at a resonant frequency of 6.226 GHz. Conclusion: The proposed IoT based system is used to monitors physical and environmental conditions like soil moisture, humidity, temperature and water level and sends the data through Wi-Fi module comprising of an ultra-wideband Dielectric Resonator antenna. The designed antenna is compact and can be easily fabricated using printed circuit board technology. The complete system is cost-effective and can be easily implemented. ER - TY - Article T1 - An efficient LoRa-based smart agriculture management and monitoring system using wireless sensor networks A1 - Prasad, S J Suji Y1 - 2022/// KW - LoRa KW - smart agriculture KW - Wi-fi KW - wireless sensor networks JF - International Journal of Ambient Energy VL - 43 IS - 1 SP - 5447 EP - 5450 DO - 10.1080/01430750.2021.1953591 UR - https://api.elsevier.com/content/abstract/scopus_id/85111673801 N1 - Cited By (since 2022): 20 N2 - The objective of this paper is to build up a LoRa-based smart agricultural management and monitoring system using Wireless Sensor Networks (WSNs) in rural areas, in order to replace the current technology of the agricultural monitoring system. A private network server is created and interfaced with a gateway that collects data or signals from end nodes and transmits the data to the cloud without the use of routers. The data can be used for end user application. The network interface is fulfilled by LoRa by solving communication failure problems and energy saving data transmission. This intelligent agriculture platform improves the efficiency of agricultural techniques. ER - TY - Article T1 - An efficient cluster head selection for wireless sensor network-based smart agriculture systems A1 - Gheisari, M Y1 - 2022/// JF - Computers and Electronics in Agriculture VL - 198 DO - 10.1016/j.compag.2022.107105 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169922004227 UR - https://api.elsevier.com/content/abstract/scopus_id/85131931933 N1 - Cited By (since 2022): 8 N2 - With the increasing availability of high-resolution satellite and drone images and the Internet of Things (IoT) has begun transforming remote sensing of agriculture by improving accessibility and frequency of updates. Modern IoT-based smart agriculture systems use Wireless Sensor Networks (WSNs) to gather information from an ecosystem that regulates the quantity of water in agricultural fields could be one of these activities. The WSNs remained a challenge to transfer data to drones for analysis purposes. These are composed of tiny sensory architectures organized together to bring efficiency and scalability features to a network. WSN nodes are controlled and managed by a cluster. It is quite difficult to design an efficient leader election protocol. The computation power, storage space, and energy supply of sensor nodes make them unable to frequently switch to a different cluster. The WSN cluster-head election process requires a lot of energy (evaluation and computational process to select the most appropriate node with the least impact on network fragmentation in energy consumption of selected node). Then it is necessary to formulate a mechanism where WSNs utilize the least energy to coordinate with the remote sensing sources. This study presents a cluster election algorithm using the fuzzy logic inference system. It uses a coordinates system to map network nodes and map them based on prioritized scheduling. Lifetime augmentation in wireless sensor networks has always been of great interest. During data transmission from normal sensor nodes to the base station (sink), excess energy is dissipated. Optimizing the energy dissipation of WSNs through the selection of cluster heads is a powerful way to increase the lifespan. By electing more efficient nodes as cluster heads, the proposed method extends the network's lifetime by reducing the number of unimportant communications between nodes. With the utilization of network resources efficiently, the network's lifetime is extended. The proposed algorithm is evaluated with the LEACH (Low Energy Adaptive Clustering Structure) algorithm and FCA method based on the remaining energy and the number of active nodes. The simulation results show that the proposed algorithm utilizes less energy for communication with remote sensory equipment for intelligent agriculture. The performance of the method improved for remaining energy by 9%, the number of active nodes rate by 24%, and indirectly network resource utilization than other states of the art solutions. ER - TY - Article T1 - An intelligent IOT sensor coupled precision irrigation model for agriculture A1 - Lakshmi, G S Prasanna Y1 - 2023/// KW - Irrigation KW - LSTM and precision JF - Measurement: Sensors VL - 25 DO - 10.1016/j.measen.2022.100608 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2665917422002422 UR - https://api.elsevier.com/content/abstract/scopus_id/85145721533 L1 - file:///C:/Users/sonsu/Downloads/IntelligentIoT.pdf N1 - Cited By (since 2023): 1 N2 - Sustainable agricultural practices are required, especially for irrigation, to provide for a growing population. About 85% of the freshwater resources in the world are used for irrigation. "Us," current irrigation procedures must be either updated or replaced with cutting-edge, intelligent systems that utilize the ML, IoT (Internet of Things), and sensor networks which are wireless. In the proposed paper, a brainy system for tracking and scheduling accuracy irrigation using IoT, a LoRa-based machine learning (ML) is being introduced. We developed an independent irrigation technique that would provide the tomato and eggplant with the precise amount of water they required was set up using the information obtained from the soil moisture sensors. In comparison to traditional watering, which required 7541 mL for the banana plant and 8755 mL for the rice plant, the irrigation system irrigated the plants with an overall quantity of 5010.745 mL & 4421.09 mL in one month. Overall, this led to a 46% reduction in water usage, and the plants looked better than they would have with conventional wa- tering. The simulation results clearly show that the suggested approach uses water more sensibly than cutting- edge models in the experiment farming region ER - TY - Article T1 - An intelligent WSN-UAV-based IoT framework for precision agriculture application A1 - Singh, P K Y1 - 2022/// KW - wireless sensor networks JF - Computers and Electrical Engineering VL - 100 DO - 10.1016/j.compeleceng.2022.107912 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0045790622001951 UR - https://api.elsevier.com/content/abstract/scopus_id/85126871488 N1 - Cited By (since 2022): 27 N2 - Climate change has introduced many challenges, which affect various sectors including agriculture. The major challenge lies in increasing food productivity while confronting climatic changes. The recent advent of Information and Communication Technology (ICT) provides a solution to increase food productivity and address climate change issues in the agricultural domain. The growth of Wireless Sensor Networks (WSNs), Internet of Things (IoT) technologies, and particularly the introduction of Unmanned Aerial Vehicles (UAVs) leads to the development of economical precision agriculture applications. This article presents a platform for managing agricultural crop information collected through multi-rotor UAV. The Django framework is utilized to design the information service system to collect the crop information and position information from UAV in real-time. An extensive performance evaluation is conducted for sensing and monitoring crops exploiting the proposed architecture. The proposed architecture presents a coverage efficiency of 96.3% and has high potential in agricultural applications such as crop health monitoring, spraying fertilizers, and pesticides. ER - TY - Article T1 - An online machine learning-based sensors clustering system for efficient and cost-effective environmental monitoring in controlled environment agriculture A1 - Uyeh, D Dooyum Y1 - 2022/// KW - machine learning KW - sensors KW - internet of things JF - Computers and Electronics in Agriculture VL - 199 DO - 10.1016/j.compag.2022.107139 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169922004562 UR - https://api.elsevier.com/content/abstract/scopus_id/85133278013 N1 - Cited By (since 2022): 1 N2 - Sensors are vital in controlled environment agriculture for measuring parameters for effective decision-making. Currently, most growers randomly install a limited number of sensors due to economic implications and data management issues. The microclimate within a protected cultivation system is continuously affected by the macroclimate (ambient), which further complicates decision-making around optimal sensor placement. The ambient weather's effect on the indoor microclimate makes it challenging to predict or acquire the ideal condition of the systems through using sensors. This study proposed and implemented a machine learning (K-Means++) algorithm to select optimal sensor locations through clustering. Temperature and relative humidity data were collected from 56 different locations within the greenhouse for over a year covering and these covered four major seasons (spring, summer, autumn, and winter). The data was processed to remove outliers or noise interference using interquartile. The original temperature and relative humidity data were transformed to other air properties (dew point temperature, enthalpy, humid ratio, and specific volume) and used in simulations. The results obtained showed that the number of optimal sensor locations ranged between 3 and 5, and there were similar sensor locations among the air properties. An online machine learning web-based system was developed to systematically determine the optimal number of sensors and location. ER - TY - Article T1 - An optimization clustering and classification based on artificial intelligence approach for internet of things in agriculture A1 - Tangwannawit, S Y1 - 2022/// KW - Agriculture KW - Artificial intelligence KW - Classification KW - Internet of things KW - Optimize clustering JF - IAES International Journal of Artificial Intelligence VL - 11 IS - 1 SP - 201 EP - 209 DO - 10.11591/ijai.v11.i1.pp201-209 UR - https://api.elsevier.com/content/abstract/scopus_id/85131796777 L1 - file:///C:/Users/sonsu/Downloads/20 21417 1570751564.pdf N1 - Cited By (since 2022): 7 N2 - This research focused on testing with maize, economical crop grown in Phetchabun province, Thailand, by installing a total of 20 sets of internet of things (IoT) devices which consist of soil moisture sensors and temperature and humidity sensors (DHT11). Data science tools such as rapidminer studio was used for data cleansing, data imputation, clustering, and prediction. Next, these data would undergo data cleansing in order to group them to obtain optimization clustering to identify the optimum condition and amount of water required to grow the maize through k-mean technique. From the analysis, the optimization result showed 3 classes and these data were further analyzed through prediction to identify precision. By comparing several algorithms including artificial neural network (ANN), decision tree, naïve bayes, and deep learning, it was found that deep learning algorithm can provide the most accurate result at 99.6% with root mean square error (RMSE)=0.0039. The algorithm obtained was used to write function to control the automated watering system to make sure that the temperature and humidity for growing maize is at appropriate condition. By using the improved watering system, it improved the efficacy of watering system which saves more water by 13.89%. ER - TY - Book Chapter T1 - Analysis of Agriculture Production and Impacts of Climate Change in South Asian Region: A Concern Related with Healthcare 4.0 Using ML and Sensors A1 - Rastogi, R Y1 - 2022/// KW - Agriculture KW - Squared error KW - Machine learning KW - Re-projection KW - Masking KW - Modis KW - Test cases JF - Intelligent Systems Reference Library VL - 210 SP - 41 EP - 65 SN - 1868-4394 DO - 10.1007/978-3-030-76653-5_3 UR - https://api.elsevier.com/content/abstract/scopus_id/85114107359 N1 - Cited By (since 2022): 2 N2 - The Effect of Global Warming and rapid changing climate in an indefinite manner is a major concern and all domain of science are trying to address it in their ways. It is not only creating challenges to food production, yet to the human health. The presented research work is all about the prediction of the yield of agriculture of the land without involving any activity of humans and this makes our procedure superfast and quite easy and reliable for humans and hence the name of the project “Predicting Agricultural Productivity”. Main purpose of the research work includes the implementation and training of machine learning algorithms for the prediction of the yield of agriculture so that the error can get minimized and accuracy gets maximized. For training of the model, a collection of features from actual yield and pictures of satellite is extracted by us. After This phase, a suitable algorithm like Naive Bayes, NN and its variant are chosen and used as the mathematical way to learn the parameters that are based on the features of yield. Then during study, harvest of agriculture is prognoses for a separate set of data. Data that is prognosticated is compared in contrast to the actual land yield. The manuscript also focuses the different data sets which are obtained by satellite imaging and using remote sensing, the clear mapping of current condition is obtained which helps to predict the yield in better way. ER - TY - Article T1 - Analysis of Agriculture and Food Supply Chain through Blockchain and IoT with Light Weight Cluster Head A1 - Adow, A H Y1 - 2022/// JF - Computational Intelligence and Neuroscience VL - 2022 DO - 10.1155/2022/1296993 UR - https://api.elsevier.com/content/abstract/scopus_id/85136554109 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Adow 2022) Analysis of Agriculture and Food Supply Chain through blockchain and iot.pdf N1 - Cited By (since 2022): 2 N2 - By 2050, the world’s population will have increased by 34%, to more than 9 billion people, needing a 70% increase in food production. Prepare more dishes with fewer ingredients. Therefore, the critical goal of manufacturers is to increase production while being ecologically benign. Supply chain systems that do not enable direct farmer-to-consumer connection and rising input costs influence data collection, security, and sharing. Constraints on data security, manipulation, and single-point failure are unfulfilled due to a lack of centralized IoT agricultural infrastructure. To address these issues, the article proposes a blockchain-based IoT model. This study also shows one-of-a-kind energy savings. The decentralization of data storage improves the supply chain’s transparency and quality through blockchain technology, thus farmers can engage more efficiently. Blockchain technology improves supply chain traceability and security. This article provides a transparent, decentralized blockchain tracking solution and proposes an intelligent model protocol for several Internet of Things (IoT) devices that monitor crop development and the agricultural environment. A new approach has resolved the bulk of the supply chain difficulties. Smart contracts were utilized to organize all transactions in decentralized supply networks. The use of blockchain technology improves transaction quality, and customers may verify the legitimacy of an item’s authenticity and legality by using the system. A total of 100 IoT nodes were distributed randomly to each 500 m2 cluster farm. The Internet of Things nodes were used to assess soil moisture, temperature, and crop disease. Network stability period and network life of the proposed method show 90.4% accuracy. The food supply chain will be more efficient and trustworthy with an intelligent model. The immutability of ledger technology and smart contract support further increases supply chain security, privacy, transparency, and trust among all stakeholders in the multi-party system. By 2050, the world’s population will need a 70% increase in food production. The food supply chain will be more efficient and trustworthy with an intelligent model. This article provides a transparent, decentralized, and intelligent model protocol for several Internet of Things (IoT) devices. ER - TY - Conference Paper T1 - Analysis of Hydroponic System Crop Yield Prediction and Crop IoT-based monitoring system for precision agriculture A1 - Sathanapriya, M Y1 - 2022/// KW - Internet of things KW - Hybrid Crops KW - Agriculture KW - Hydroponics KW - wireless sensor networks JF - International Conference on Edge Computing and Applications, ICECAA 2022 - Proceedings SP - 575 EP - 578 DO - 10.1109/ICECAA55415.2022.9936473 UR - https://api.elsevier.com/content/abstract/scopus_id/85142769678 N1 - Cited By (since 2022): 1 N2 - IoT is the necessary revolution in every industry. Any task is made simple when it can be observed and managed remotely. Every technical advancement should be undertaken in agriculture because it is such an important subject. The demand for agriculture has dramatically expanded due to the expansion in global population, but sadly, farmers are unable to meet the unending demand. IoT will be a better option than expanding the scale of agriculture.By cutting down on waste, precision agriculture can increase the output of any crop. The proposed work accomplishes this by keeping an eye on environmental factors that influence crop growth, such as temperature, humidity, soil moisture, etc. It also assists farmers in selecting an idle crop that will work best for them based on the information gathered and the environmental circumstances. The danger of crop failure, low yield, excessive water consumption, excessive fertiliser and pesticide use, etc. can be greatly decreased with this model, making it more effective than previous methods.The information gathered by the network nodes placed across the field is transmitted to the cloud, where it is evaluated and displayed for farmers' use. Farmers may make accurate and effective decisions that will affect their crops with the use of displayed data. ER - TY - Conference Paper T1 - Analysis of MQTT-SN and LWM2M communication protocols for precision agriculture IoT devices A1 - Santos, R P dos Y1 - 2022/// KW - Internet of Things KW - Protocols KW - MQTT-SN KW - LWM2M JF - Iberian Conference on Information Systems and Technologies, CISTI VL - 2022 SN - 2166-0727 DO - 10.23919/CISTI54924.2022.9820048 UR - https://api.elsevier.com/content/abstract/scopus_id/85134833786 N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) has become an integral part of the lifestyle of modern society and an important tool in many areas of business. In recent years, there has been a great need to connect new IoT devices to precision agriculture. Known as connected objects, it has been gaining more and more strength. As well as the adoption of IoT for agriculture, homes, smart cities, logistics, healthcare, manufacturing and others. There are also numerous concerns regarding the communication of these devices. With the ability to collect data, IoT technology becomes a valuable resource and care must be taken in the search for effective communication mechanisms. In this sense, this work aims to present an analysis between the MQTT-SN and LWM2M communication protocols, comparing their performance in the transmission of messages. The model was developed with the help of the Node-RED tool, which consists of flow-based programming in the evaluation and performance implemented at runtime. At the end of the simulations, it was possible to evaluate that the MQTTSN protocol presented better results in the tests performed. ER - TY - Article T1 - Anomaly Detection for Internet of Things Time Series Data Using Generative Adversarial Networks With Attention Mechanism in Smart Agriculture A1 - Cheng, W Y1 - 2022/// KW - anomaly detection KW - smart agriculture KW - time series data KW - deep learning KW - generative adversarial network KW - attention mechanism JF - Frontiers in Plant Science VL - 13 DO - 10.3389/fpls.2022.890563 UR - https://api.elsevier.com/content/abstract/scopus_id/85133392601 L1 - file:///C:/Users/sonsu/Downloads/fpls-13-890563.pdf N1 - Cited By (since 2022): 1 N2 - More recently, smart agriculture has received widespread attention, which is a deep combination of modern agriculture and the Internet of Things (IoT) technology. To achieve the aim of scientific cultivation and precise control, the agricultural environments are monitored in real time by using various types of sensors. As a result, smart agricultural IoT generated a large amount of multidimensional time series data. However, due to the limitation of applied scenarios, smart agricultural IoT often suffers from data loss and misrepresentation. Moreover, some intelligent decision-makings for agricultural management also require the detailed analysis of data. To address the above problems, this article proposes a new anomaly detection model based on generative adversarial networks (GAN), which can process the multidimensional time series data generated by smart agricultural IoT. GAN is a deep learning model to learn the distribution patterns of normal data and capture the temporal dependence of time series and the potential correlations between features through learning. For the problem of generator inversion, an encoder–decoder structure incorporating the attention mechanism is designed to improve the performance of the model in learning normal data. In addition, we also present a new reconstruction error calculation method that measures the error in terms of both point-wise difference and curve similarity to improve the detection effect. Finally, based on three smart agriculture-related datasets, experimental results show that our proposed model can accurately achieve anomaly detection. The experimental precision, recall, and F1 score exceeded the counterpart models by reaching 0.9351, 0.9625, and 0.9482, respectively. ER - TY - Book Chapter T1 - Application Possibilities of IoT-based Management Systems in Agriculture A1 - Tóth, M Y1 - 2022/// KW - Data acquisition KW - internet of things KW - Sensor networks KW - Decision support KW - Agriculture JF - Springer Optimization and Its Applications VL - 183 SP - 77 EP - 102 SN - 1931-6828 DO - 10.1007/978-3-030-84148-5_4 UR - https://api.elsevier.com/content/abstract/scopus_id/85127190317 N1 - Cited By (since 2022): 2 N2 - The optimization of agricultural production and business processes is a crucial task in order to fulfill the demand of the increasing population, to meet quality requirements, to reduce the environmental impact as well as to improve economic efficiency. The Industry 4.0 concept provides various methods in this regard, including data acquisition based on IoT (Internet of Things), or data analytics based on Big Data, to support the decision-making process of the management and the data requirement of process control methods. During preliminary research, several modular data acquisition systems, as well as management applications have been developed based on a production system to measure various environmental factors at multiple spatial points. Considering the experience gained from the testing sessions, there was a need for further development regarding the end-user perspective in order to substantiate the practical application. A comparative research was required, considering previous experience and the literature of data acquisition systems, used in agriculture. The comparison concerned an own iteration of a production system and other systems, developed by researchers of the field, to examine different options and directions. Considering three important factors, the focus was on the data acquisition systems, data management, and data utilization methods. The comparison begins with a quantitative bibliometric analysis, determining the field and characteristic connections using network and cluster analysis, considering the IoT concept as the central element. Subsequently, the progression of a system and its evaluation is presented, performed in a greenhouse. This iteration highly focuses on data management with the modification of the existing infrastructure by integrating the Hadoop ecosystem to achieve a standardized interface. ER - TY - Article T1 - Application of IoT and Cloud Computing in Automation of Agriculture Irrigation A1 - Phasinam, K Y1 - 2022/// KW - internet of things KW - cloud KW - agriculture irrigation JF - Journal of Food Quality VL - 2022 DO - 10.1155/2022/8285969 UR - https://api.elsevier.com/content/abstract/scopus_id/85124018450 N1 - Cited By (since 2022): 20 N2 - All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world’s fresh water. People who work in agriculture are not only the world’s biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation. ER - TY - Article T1 - Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture A1 - Riaz, A R Y1 - 2022/// KW - internet of things KW - smart agriculture KW - adaptive agro security KW - risk assessment JF - Sustainability (Switzerland) VL - 14 IS - 17 DO - 10.3390/su141710964 UR - https://api.elsevier.com/content/abstract/scopus_id/85138054513 L1 - file:///C:/Users/sonsu/Downloads/sustainability-14-10964.pdf N1 - Cited By (since 2022): 2 N2 - In modern times, the Internet of Things (IoT) is having a major impact on agriculture. Risk and security parameters are always linked when researching, developing, implementing and deploying IoT-based devices. It is a myth that security does not play a major role in IoT applications in agriculture. Data accuracy and availability is a high-priority requirement for farmers who help achieve high yields. A secure IoT network requires a situational approach, often referred to as dynamic security. An advanced security approach to improving IoT security is adaptive security, a cybersecurity-based approach. The lack of security in a smart farming environment is a very important factor for agricultural growth. In this study, we introduce IoT together with adaptive security operations and integrate it into a smart farming environment. We propose an evaluation framework that can be applied to diverse smart farming environments. Several scenarios of an agricultural environment with smart devices and sensors are described for execution. Storylines with real-time environments are then derived from these scenarios to extend and incorporate adaptive security frameworks and scenarios in IoT-based agriculture. ER - TY - Article T1 - Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture A1 - Wongchai, A Y1 - 2022/// JF - Computers and Electrical Engineering VL - 102 DO - 10.1016/j.compeleceng.2022.108128 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0045790622003780 UR - https://api.elsevier.com/content/abstract/scopus_id/85134618985 N1 - Cited By (since 2022): 6 N2 - internet IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in agriculture.AI-enabled sensors function as smart sensors and IoT has made various types of sensor-based equipment in the field of agriculture. This research proposes novel techniques in AI technique based soft sensor integrated with remote sensing model using deep learning architectures. The input has been pre-processed to recognize the missing value, data cleaning and noise removal from the image which is collected from the agricultural land. The feature representation has been carried out usingweight-optimized neural network with maximum likelihood (WONN_ML). after representing the features, classification process has been carried out using ensemble architecture of stacked auto-encoder and kernel-based convolution network (SAE_KCN). The experimental results have been done for various crops in terms of computational time of 56%, accuracy 98%, precision of 85.5%, recall of 89.9% and F-1 score of 86% by proposed technique. ER - TY - Article T1 - Artificial intelligence and machine learning for the green development of agriculture in the emerging manufacturing industry in the IoT platform A1 - Zhou, Y Y1 - 2022/// KW - Artificial Intelligence KW - Agriculture KW - Machine Learning KW - Internet of Things KW - Remote Sensing JF - Acta Agriculturae Scandinavica Section B: Soil and Plant Science VL - 72 IS - 1 SP - 284 EP - 299 DO - 10.1080/09064710.2021.2008482 UR - https://api.elsevier.com/content/abstract/scopus_id/85121118406 L1 - file:///C:/Users/sonsu/Downloads/Artificial intelligence and machine learning for the green development of agriculture in the emerging manufacturing industry in the IoT platform.pdf N1 - Cited By (since 2022): 6 N2 - In recent years, greenhouse development has been innovative in agriculture based on information systems guidance with accelerated growth. The IoT provides an intelligent system and remote access technologies such as green infrastructure. The usability of information systems for effective training and producing intelligent systems and predictive models in organizational real-time based on machine learning and artificial intelligence (AI). Therefore, a Remote Sensing Assisted Control System (RSCS) has been proposed for improving greenhouse agriculture requirements. This proposed method utilizes artificial intelligence and machine learning technology for the green development potential industry’s ability to manage economic resources and increase innovative agriculture product development patterns. Thus, the key preconditions for increasing healthy food choices and promoting local and global organic farmers’ potential development are straightforward suggestions for developing an effective marketing strategy. The experimental results RSCS the highest precision ratio of 95.1%, the performance ratio of 96.35%, a data transmission rate of 92.3%, agriculture production ratio of 94.2%, irrigation control ratio of 94.7%, the lowest moisture content ratio of 18.7%, and CO2 emission ratio of 21.5%, compared to other methods. ER - TY - Article T1 - Autonomous Vehicles Management in Agriculture with Bluetooth Low Energy (BLE) and Passive Radio Frequency Identification (RFID) for Obstacle Avoidance A1 - Monarca, D Y1 - 2022/// KW - agriculture KW - smart farming KW - work safety KW - BLE KW - radio frequency identification KW - remote control KW - tractor JF - Sustainability (Switzerland) VL - 14 IS - 15 DO - 10.3390/su14159393 UR - https://api.elsevier.com/content/abstract/scopus_id/85137014755 L1 - file:///C:/Users/sonsu/Downloads/Autonomous_Vehicles_Management_in_Agriculture_with.pdf N1 - Cited By (since 2022): 1 N2 - Obstacle avoidance is a key aspect for any autonomous vehicles, and their usage in agricul- ture must overcome additional challenges such as handling interactions with agricultural workers and other tractors in order to avoid severe accidents. The simultaneous presence of autonomous vehicles and workers on foot definitely calls for safer designs, vehicle management systems and major developments in personal protective equipment (PPE). To cope with these present and future challenges, the “SMARTGRID” project described in this paper deploys an integrated wireless safety network infrastructure based on the integration of Bluetooth Low Energy (BLE) devices and passive radio frequency identification (RFID) tags designed to identify obstacles, workers, nearby vehicles and check if the right PPE is in use. With the aim of detecting workers at risk by scanning for passive RFID-integrated into PPE in danger areas, transmitting alerts to workers who wear them, tracking of near-misses and activating emergency stops, a deep analysis of the safety requirements of the obstacle detection system is shown in this study. Test programs have also been carried out on an experimental farm with detection ranging from 8 to 12 meters, proving that the system might represent a good solution for collision avoidance between autonomous vehicles and workers on foot ER - TY - Conference Paper T1 - Blockchain in IoT Networks for Precision Agriculture A1 - Tanwar, R Y1 - 2023/// KW - Internet of Things KW - Blockchain KW - Ledger KW - Precision agriculture KW - Smart contract JF - Lecture Notes in Networks and Systems VL - 471 SP - 137 EP - 147 SN - 2367-3370 DO - 10.1007/978-981-19-2535-1_10 UR - https://api.elsevier.com/content/abstract/scopus_id/85140464141 N1 - Cited By (since 2023): 1 N2 - With the increase in research and development in communication technology; it is predicted that more and more number of sensing devices will be added in various sectors by application of IoT. Therefore; there is an immediate need of replacing the traditional methods of storing; sorting and sharing of data that has been collected from various sensing devices (Chiang and Zhang in IEEE Internet Things J 3:854–864; 2016); (Lee et al. in Comput Electron Agric 74:2–33; 2010). This will help in making data more transparent; reliable; decentralised and immutable. This has led to the integration of blockchain into IoT systems. The upcoming section gives a vivid picture about the basic concept and feature of blockchain technology and thereby detecting various advantages of integration of blockchain into IoT. ER - TY - Article T1 - Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture A1 - Chaganti, R Y1 - 2022/// KW - smart contract KW - AWS cloud KW - blockchain KW - IoT security KW - smart agriculture KW - security KW - sensor monitoring JF - Future Internet VL - 14 IS - 9 DO - 10.3390/fi14090250 UR - https://api.elsevier.com/content/abstract/scopus_id/85138671391 L1 - file:///C:/Users/sonsu/Downloads/futureinternet-14-00250-v2.pdf N1 - Cited By (since 2022): 9 N2 - The Internet of Things (IoT) has rapidly progressed in recent years and immensely influ- enced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation. ER - TY - Article T1 - Border-Square net: a robust multi-grade fruit classification in IoT smart agriculture using feature extraction based Deep Maxout network A1 - Meshram, V Y1 - 2022/// KW - Deep Maxout network KW - Fruit quality classification KW - Border collie optimization (BCO) KW - Least mean square (LMS) algorithm KW - Agriculture JF - Multimedia Tools and Applications VL - 81 IS - 28 SP - 40709 EP - 40735 DO - 10.1007/s11042-022-12855-7 UR - https://api.elsevier.com/content/abstract/scopus_id/85132598066 N1 - Cited By (since 2022): 1 N2 - Internet of Things (IoT) is a distributed system of interconnected tools, such as people, animals, wireless devices, and agents, called nodes. In IoT, clustering is a data collection process that reduces energy consumption by forming IoT nodes into clusters. In the clustering, all nodes are arranged into virtual clusters, while one node acts as the Cluster Head (CH). The correct selection of the cluster head reduces the energy consumption. Now a day, IoT is being distributed in environments, such as the smart agriculture sector or forests. Fruit quality classification is a significant task in the supermarket, factories, as well as other industrial applications. Accordingly, fruit classification mechanism helps cashier of supermarket to find the species and prices of fruits. Various fruit quality classification approaches are developed to find quality of fruit. Accordingly, an efficient fruit quality classification method is modeled by Border Square Optimization-based Deep Maxout network (BSO-based Deep Maxout network) classifier. The proposed Border Square Optimization (BSO) approach is designed by the incorporation of Border Collie Optimization (BCO) with Least Mean Square (LMS) algorithm. It is necessary to select the energy-efficient node as CH, as the process of routing the fruit image to the sink node is done through CH. With the features acquired from fruit image, the multi grade classification of fruit quality is done by the Deep Maxout network model in such a way that training practice of deep learning classifier is accomplished by BSO model. The proposed approach achieved superior performance in terms of throughput, energy, delay, and accuracy with the values of 0.6759, 0.6753 J, 0.3659 s, and 0.9467. ER - TY - Article T1 - Classification and yield prediction in smart agriculture system using IoT A1 - Gupta, A Y1 - 2022/// KW - internet of things KW - sensors KW - Agriculture KW - Machine learning KW - Classification KW - Crop yield prediction JF - Journal of Ambient Intelligence and Humanized Computing DO - 10.1007/s12652-021-03685-w UR - https://api.elsevier.com/content/abstract/scopus_id/85122406341 N1 - Cited By (since 2022): 4 N2 - The modern agriculture industry is data-centred, precise and smarter than ever. Advanced development of Internet-of-Things (IoT) based systems redesigned “smart agriculture”. This emergence in innovative farming systems gradually increases crop yields, reduces irrigation wastages and making it more profitable. Machine learning (ML) methods achieve the requirement of scaling the learning performance of the model. This paper introduces a hybrid ML model with IoT for yield prediction. This work involves three phases: pre-processing, feature selection (FS) and classification. Initially, the dataset is pre-processed and FS is done on the basis of Correlation based FS (CBFS) and the Variance Inflation Factor algorithm (VIF). Finally, a two-tier ML model for an IoT based smart agriculture system is proposed. In the first tier, the Adaptive k-Nearest Centroid Neighbour Classifier (aKNCN) model is proposed to estimate the soil quality and to classify the soil samples into different classes based on the input soil properties. In the second tier, the crop yield is predicted using the Extreme Learning Machine algorithm (ELM). In the optimized strategy, the weights are updated using a modified Butterfly Optimization Algorithm (mBOA) to improve the performance accuracy of ELM with minimum error values. PYTHON is the implementation tool for evaluating the proposed system. Soil dataset is utilized for performance evaluation of the proposed prediction model. Various metrics such as accuracy, RMSE, R2, MSE, MedAE, MAE, MSLE, MAPE and Explained Variance Score (EVS) are considered for the performance evaluation. ER - TY - Article T1 - Climate-smart agriculture using intelligent techniques, blockchain and Internet of Things: Concepts, challenges, and opportunities A1 - Ahmed, R A Y1 - 2022/// JF - Transactions on Emerging Telecommunications Technologies VL - 33 IS - 11 DO - 10.1002/ett.4607 UR - https://api.elsevier.com/content/abstract/scopus_id/85137579608 L1 - file:///C:/Users/sonsu/Downloads/ETT_RaniaMordy.pdf N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) is an important technology that provides efficient and dependable solutions in a variety of domains, such as smart agriculture and climatic change. It integrates billions of smart devices that can communicate with one another and gives solutions to automatically maintain and monitor smart agricultural and environmental fields. The combination of IoT, Artificial Intelligence (AI), and blockchain technology will allow us to transform smart agriculture into the Internet of smart agriculture, providing greater control, management, and security in supply-chain networks. This paper presents an overview of the technologies in the domains of IoT, Climate-Smart Agriculture (CSA), AI, Machine Learning (ML), and blockchain. In addition, the paper presents several approaches for integrating IoT with CSA data analysis. Both AI and blockchain are adopted for efficient CSA systems. The paper is concerned with the combination of three recent technologies: IoT, ML, and blockchain to serve the CSA applications. The challenges and opportunities of combining these technologies to serve CSA are also discussed in the paper. ER - TY - Conference Paper T1 - Comparative Analysis of Grid and Tree Topologies in Agriculture WSN with RPL Routing A1 - Pangestu, F A Y1 - 2022/// KW - wireless sensor networks KW - RPL KW - Cooja simulator KW - Grid topology KW - Tree topology KW - Power consumption KW - Routing metric KW - ETX JF - Lecture Notes in Networks and Systems VL - 236 SP - 459 EP - 467 SN - 2367-3370 DO - 10.1007/978-981-16-2380-6_40 UR - https://api.elsevier.com/content/abstract/scopus_id/85115994397 N1 - Cited By (since 2022): 3 N2 - Agricultural Internet of Things is very dependent on its Wireless Sensor Network (WSN) performance. The Routing Protocol for Low Power and Lossy Network (RPL) is an IPv6-based routing protocol that was developed to provide more addresses and lower power for sensor nodes on WSN. This research compares the performance of grid and tree topologies with RPL routing protocol on Cooja simulator. The parameters evaluated in this study are power consumption, routing metrics, Expected Transmission Count (ETX), throughput, and delay. The result is the performance of the RPL routing protocol with the grid topology has better values than the tree topology in the various parameters tested. In throughput parameters, the grid topology values with 20, 30, and 42 nodes are 901 bps, 722 bps, and 678 bps, better than the tree topology which are 812 bps, 697 bps, and 531 bps. ER - TY - Article T1 - Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture A1 - Kour, K Y1 - 2022/// KW - internet of things KW - saffron KW - agronomical variables KW - precision agriculture JF - Sustainability (Switzerland) VL - 14 IS - 9 DO - 10.3390/su14095607 UR - https://api.elsevier.com/content/abstract/scopus_id/85130172217 L1 - file:///C:/Users/sonsu/Downloads/sustainability-14-05607-v4.pdf N1 - Cited By (since 2022): 6 N2 - Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation. ER - TY - Article T1 - Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture A1 - Junior, F M Ribeiro Y1 - 2022/// JF - Biosystems Engineering VL - 223 SP - 142 EP - 158 DO - 10.1016/j.biosystemseng.2021.12.021 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1537511021003299 UR - https://api.elsevier.com/content/abstract/scopus_id/85123707420 N1 - Cited By (since 2022): 13 N2 - Smart agriculture applications that analyse and manage agricultural yield using IoT systems may suffer from intermittent operation due to cloud disconnections commonly occurring in rural areas. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, the fog needs to send a high volume of data to the cloud and this can cause link congestion with unusable data traffic. Here we propose an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods. Sixteen techniques for data reduction are investigated; eight machine learning (ML) methods combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors. ER - TY - Review T1 - Deep Insight into IoT-Enabled Agriculture and Network Protocols A1 - Hasan, M Z Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 SN - 1530-8669 DO - 10.1155/2022/5617903 UR - https://api.elsevier.com/content/abstract/scopus_id/85140141816 N1 - Cited By (since 2022): 3 N2 - In recent years, research has combined the connection of agricultural equipment to increase crop growth rates and lower planting costs by refining the entire planting process. IT-enabled agriculture has beneficial effects on this industry and is yet a source of debate in academic circles. Trending network technologies like WSN and IoT have never been easy to develop and use in agriculture. The growth rate was not increased using outdated, conventional methods and technologies. Additionally, the rapid population expansion cannot meet human demands and expectations. Survey Methodology. The existence of IoT in agriculture was investigated and reported in this review. The paper describes the different IoT-agriculture network protocols. This study clarifies how the Internet affects agriculture and its underlying mechanisms. It also discusses how the growth rate is boosted when both sectors work together. This study intends to explore a platform that offers an infrastructure to link devices using the network protocol used in agriculture. In this study, several contemporary network difficulties relating to agriculture are also covered. Conclusion. The results of this study can be used as a guide for creating particular network protocols for the agriculture industry ER - TY - Article T1 - Deep Learning and Smart Contract-Assisted Secure Data Sharing for IoT-Based Intelligent Agriculture A1 - Kumar, R Y1 - 2022/// KW - Blockchain KW - Deep Learning KW - Internet of Things KW - Smart Contracts KW - Intelligent Agriculture JF - IEEE Intelligent Systems DO - 10.1109/MIS.2022.3201553 UR - https://api.elsevier.com/content/abstract/scopus_id/85137871657 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Kumar 2022) Deep_Learning_and_Smart_Contract-Assisted_Secure_Data_Sharing_for_IoT-Based_Intelligent_Agriculture.pdf N1 - Cited By (since 2022): 8 N2 - The recent development of Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) has revolutionized traditional agriculture with intelligence and automation. In a typical Intelligent Agriculture (IA) ecosystem, massive and real-time data is generated, analyzed, and sent to the Cloud Server (CS) for the purpose of addressing complex agricultural issues like yield prediction, water feed calculation, and so on. This helps farmer and associated stakeholders to take correct decision that improves the yield and quality of agricultural product. However, the distributed nature of IA entities and the usage of insecure wireless communication open various challenges related to data sharing, monitoring, storage and further makes the entire IA ecosystem vulnerable to various potential attacks. In this paper, we exploit deep learning and smart contract to propose a new IoT-enabled IA framework for enabling secure data sharing among its various entities. Specifically, first we develop new authentication and key management scheme to ensure secure data transmission in IoT-enabled IA. The encrypted transactions are then used by the CS to analyze and further detect intrusions by a novel deep learning architecture. In CS, the smart contract-based consensus mechanism is executed on legitimate transactions that verifies and adds the formed blocks into blockchain by a peerto-peer (P2P) CSs network. In comparison to existing competing security solutions, a rigorous comparative research demonstrates that the proposed approach provides greater security and more utility characteristics. ER - TY - Article T1 - Deep malware detection framework for IoT-based smart agriculture A1 - Smmarwar, S K Y1 - 2022/// JF - Computers and Electrical Engineering VL - 104 DO - 10.1016/j.compeleceng.2022.108410 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0045790622006279 UR - https://api.elsevier.com/content/abstract/scopus_id/85139185354 N1 - Cited By (since 2022): 4 N2 - The advancement in smart agriculture through the Internet of Things (IoT) devices has increased the risk of cyber-attacks. Most of the existing malware detection techniques are unable to detect new variants of malware and suffer from poor accuracy. To overcome the challenges of new malware, this research work proposes a novel three-phase Deep Malware Detection (DMD) framework based on the fusion of Discrete Wavelet Transform (DWT) and Generative Adversarial Network (GAN) named as DMD-DWT-GAN, for IoT-based Smart Agriculture (IoT-SA). This work applied DWT for multiresolution analysis by decomposing the image into Approximation coefficients (Ac) and Detail coefficients (Dc). Finally, a lightweight Convolutional Neural Network (CNN) is employed for in-depth analysis of the malware family. The performance of the proposed framework is evaluated using two benchmark datasets such as IoT malware and Malimg. The proposed framework has achieved 99.99% accuracy on both datasets and is better than the state-of-art models. ER - TY - Article T1 - Design and Implementation of a Low-cost IoT Node for Data Processing, Case Study: Smart Agriculture A1 - Amr, M E Y1 - 2022/// KW - Internet of Things KW - system design KW - ThingSpeak KW - smart agriculture KW - data analysis KW - Softmax JF - Journal of Communications VL - 17 IS - 2 SP - 99 EP - 109 DO - 10.12720/jcm.17.2.99-109 UR - https://api.elsevier.com/content/abstract/scopus_id/85123095876 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Amr 2022) Design and Implementation of a Low-cost IoT Node for data processing case study smart agriculture.pdf N1 - Cited By (since 2022): 3 N2 - The majority of IoT nodes work within specific scenarios and can be configured in different ways. This paper seeks to design and implement a low-cost Internet of Things node for research applications to make it suitable for a wider variety of scenarios. The design is divided into hardware board and mobile application. With Bluetooth, the mobile application can connect to the node, and the node can collect data, store this data in the ThingSpeak database, control some connected devices, and check if the connected devices are on or off. The node was designed and tested for research purposes using smart agriculture as the case study. The system detects temperature, humidity, and soil moisture using node sensors, enabling data collection and interpretation by smartphone and web application. There are many challenges associated with the collected data preparation, analysis, visualization, and prediction using the Softmax function for optimal future management. Python was utilized to apply necessary data analysis techniques. The system saves time and makes farming more convenient as it uses few resources in terms of hardware and cost. ER - TY - Article T1 - Development of Algorithms for an IoT-Based Smart Agriculture Monitoring System A1 - Siddiquee, K.N.E.A. Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/7372053 UR - https://api.elsevier.com/content/abstract/scopus_id/85130574725 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Shidiquee 2022) Development of Algorithms for an IoT-Based Smart Agriculture monitoring system.pdf N1 - Cited By (since 2022): 8 N2 - Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. ER - TY - Conference Paper T1 - Development of an IoT Based Data Acquisition and Automatic Irrigation System for Precision Agriculture A1 - Adetiba, E Y1 - 2022/// KW - Data acquisition KW - Internet of Things KW - Irrigation KW - Precision Agriculture JF - Proceedings of the 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development, NIGERCON 2022 DO - 10.1109/NIGERCON54645.2022.9803132 UR - https://api.elsevier.com/content/abstract/scopus_id/85133964124 N1 - Cited By (since 2022): 1 N2 - Agriculture has benefited greatly from improvements in Internet of Things based technology. Farm data can be sent to farmers in real-time through the advent of Internet of Things based technology which integrates data collection, transmission, storage and other essential components that provide for great user experience. This work involves the development of a system that enable the transmission of sensor field data to the Internet, via a microcontroller, a transceiver and a Wi-Fi module. In this work, an Internet of Things based data acquisition and automatic irrigation system for precision agriculture was designed and implemented using Arduino Uno, Soil Moisture and Temperature sensors, Proteus design suite, and the Arduino integrated development environment software. The significance of this work is evident as it, enables farmers perform specified functionalities at the comfort of their home, minimize wastage of water during irrigation and most importantly reduce the maintainability cost of the farm through minimal physical supervision. This work also elicits requirements for better improvements on the IoT-based data acquisition and automatic irrigation system. ER - TY - Conference Paper T1 - Effective Contribution of Internet of Things (IoT) in Smart Agriculture: State of Art A1 - Bikoro, D M Andeme Y1 - 2022/// KW - Internet of Things KW - Smart farm KW - ICT’s KW - Review JF - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST VL - 443 SP - 219 EP - 233 SN - 1867-8211 DO - 10.1007/978-3-031-06374-9_14 UR - https://api.elsevier.com/content/abstract/scopus_id/85131953781 N1 - Cited By (since 2022): 1 N2 - The popularization of the Internet of Things (IoT) (IoT) has made it possible to optimize and significantly improve the agricultural system. Thanks to this technology, farmers and ranchers are more confident in running their farms, which are now smart farms. By using New Information and Communication Technologies as a catalyst and essential facilitator for the development of this new style of agriculture. Several devices and mechanisms are used in intelligent systems related to agriculture. In this article, we review 30 scientific works produced and published by certain researchers and which sufficiently review the different dof management of smart farms without being exhaustive. This study aims to present a state of the art of integrating Internet of Things (IoT) into the agricultural environment to understand the improvements it brings and detect future smart agriculture issues. It turns out that smart agriculture is really at the heart of researchers, farmers, producers, and even states. The results of this state of the art show us that various devices and technologies are deployed in smart farms to ensure better performance. Depending on requirements and realities, each intelligent system should be adapted. This reflection helps to present directions for the development of intelligent farms that would be applicable and adaptable locally for the rest of our work. ER - TY - Article T1 - Effective monitoring and protecting system for agriculture farming using IoT and raspberry pi A1 - Billa, P Y1 - 2023/// JF - Materials Today: Proceedings VL - 80 SP - 2917 EP - 2920 DO - 10.1016/j.matpr.2021.07.065 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2214785321049178 UR - https://api.elsevier.com/content/abstract/scopus_id/85126824959 N1 - Cited By (since 2023): 3 N2 - Among the various occupations available in India, agriculture plays predominant role in India and almost 20 percent of Indian economy is obtaining from this sector. But now a days, due to many problems facing by the farmers in agriculture the graph is getting down year by year and leads to increase of suicides by leaving their farmlands. The reason may be due to lack of awareness on different soil and crop conditions during the seed cultivation time and results in crop damage. Another main problem is protection of yielded crop from damaging due to crop diseases, pests and animal attacks. By considering the above problems faced by farmers, here a method is proposed for monitoring the conditions like weather and soil and also for providing the suggestion regarding the crop type based on climatic and soil conditions. This method provides the necessary steps to be taken for better yield of crop by providing the images of plant. The image of plants gives the information regarding any animal attacks, diseases or pests so on. This system provides the farmer to get better crop yield. ER - TY - Article T1 - Efficient sensor node localization in precision agriculture: an ANN based framework A1 - Mohanty, M K Y1 - 2023/// KW - Wireless sensor network KW - Artificial neural network KW - Precision agriculture KW - Mean absolute error KW - Received signal strength indicator JF - OPSEARCH DO - 10.1007/s12597-023-00625-4 UR - https://api.elsevier.com/content/abstract/scopus_id/85147662187 N1 - Cited By (since 2023): 1 N2 - An efficient feed-forward artificial neural network (ANN) model based on received signal strength indicator (RSSI) is proposed to improve localization accuracy for finding the sensor nodes in an agricultural field. Furthermore, a fertigation management system is proposed by utilizing the predicted locations of the sensor nodes. The input dataset for the proposed ANN model includes RSSI signal values from beacons and the coordinates of all the sensor nodes. The proposed ANN model is trained using four different training algorithms and the best one amongst those is determined. The efficiency of the proposed model is shown through various simulation results with respect to several performance metrics, such as R-Squared (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) etc. The reduced amount of localization errors, in terms of MAE and RMSE, obtained by the proposed work over other existing approaches are shown. Finally, the proposed ANN based localization model renders an effective tool for the end users to obtain a support in an efficient fertigation in an agricultural field. ER - TY - Article T1 - Electrochemical sensor based on Bimetallic phosphosulfide Zn– Ni–P–S Nanocomposite -reduced graphene oxide for determination of Paraoxon Ethyl in agriculture wastewater A1 - Xiao, B A Y1 - 2022/// KW - Electrochemical Sensor KW - Bimetallic phosphosulfide Zn–Ni–P–S KW - Nanocomposite KW - hydrothermal KW - Silver Nanoparticles KW - Graphene Oxide KW - Paraoxon Ethyl KW - agricultural wastewaters JF - International Journal of Electrochemical Science VL - 17 DO - 10.20964/2022.06.72 UR - https://api.elsevier.com/content/abstract/scopus_id/85130370293 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Xiao 2022) Electrochemical sensor based on Bimetallic phosphosulfide.pdf N1 - Cited By (since 2022): 3 N2 - This research focused on the development of an electrochemical sensor for the detection of paraoxon ethyl (POE) in agricultural wastewater using a bimetallic phosphosulfide Zn–Ni–P–S nanocompositereduced graphene oxide modified glassy carbon electrode (Zn–Ni–P–S/GO/GCE). The hydrothermal process was used to make the nanocomposite. SEM and XRD investigations revealed that the Zn–Ni– P–S/GO nanocomposites were successfully prepared. The limit of detection and sensitivity were estimated to be 35 nM and 0.06369 μA/μM, respectively, in electrochemical studies using DPV and amperometry analyses, and a wide linear range response to POE (1 to 200 µM) was observed on the Zn– Ni–P–S/GO/GCE surface compared to other reported paraoxon sensors in the literature, which was associated with the incorporation of high conductive Zn–Ni bimetallic phosphosulf Bimetallic-based nanoparticles acted as a bridge between the GO and GCE surfaces, facilitating charge transfer. The proposed sensor was successfully applied to the determination of POE pesticide in agricultural wastewaters, with results indicating that the obtained recovery (90.00 to 98.00%) and RSD (2.32 to 4.37%) values by the standard addition method indicate that the proposed method for determining POE in agricultural wastewaters has good accuracy and precision. ER - TY - Conference Paper T1 - Empirical Study on Energy-Efficient IoT-Based WSN Routing Protocols for Smart Agriculture System A1 - Rao, A K Y1 - 2022/// KW - internet of things KW - Smart agriculture KW - Routing protocols KW - wireless sensor networks JF - Lecture Notes in Networks and Systems VL - 392 SP - 259 EP - 271 SN - 2367-3370 DO - 10.1007/978-981-19-0619-0_23 UR - https://api.elsevier.com/content/abstract/scopus_id/85130391557 N1 - Cited By (since 2022): 1 N2 - In agriculture, Internet of Thing (IoT) system can be used to provide information to the farmers which will very helpful to increase the farming efficiency. Modern agriculture requires the use of new technology to increase manufacturing process, supply, and consistency. Indeed, recent developments in computational modelling, segments and subsystems, software, and smart sensors have allowed the development of compact and inexpensive detectors. The smart systems are allowing the implementation process in varying circumstances considerably simpler. In this paper, various smart agriculture existing techniques are reviewed. The basic architecture of the wireless IoT-based agriculture system is represented with its nodes. There are various techniques used in wireless IoT-based agriculture system that are also mentioned in the paper. In wireless sensor networks, routing protocols play a very essential role. The routing protocols that are helpful in wireless IoT-based agriculture system are discussed in the paper. The existing techniques are compared with different parameters. ER - TY - Article T1 - Energy Efficient Resource Allocation Algorithm for Agriculture IoT A1 - Dhaya, R Y1 - 2022/// KW - Energy efficient KW - Agriculture KW - internet of things KW - Resource allocation KW - Estimation JF - Wireless Personal Communications VL - 125 IS - 2 SP - 1361 EP - 1383 DO - 10.1007/s11277-022-09607-z UR - https://api.elsevier.com/content/abstract/scopus_id/85125661764 N1 - Cited By (since 2022): 5 N2 - Agriculture Productivity is a numerical representation, whereas agriculture efficiency is a qualitative assessment. Further efficiency can be used in a variety of agro-climatic situations and crops, as efficiency refers to the most optimization of resources. On the other hand, in reduction of energy consumption, energy efficiency is an important aspect of sustainable energy conservation. As a result, increasing agricultural energy efficiency is critical for lowering energy demand and, as a result, prices. Improvements in agricultural energy efficiency are defined as a reduction in primary energy consumption for the manufacturing of a unit of agricultural commodity within farm bounds. Energy allocation of agricultural production expenses varies greatly by activity, production practice, and location, and growing energy import dependency for lubricants and nutrients has raised worries about the impact on agriculture. In order to achieve increased agricultural productivity, resource and energy allocation in production planning is critical. The integration of multiple data sources to create reliable, precise, and valuable information is a challenging task in agricultural resource management. In order to overcome the resource allocation problem and enhance efficiency, While collecting the data for computing in terms of processing agriculture resources such as temperature data, soil data, crop growth data, humidity data, and water level data, the traditional data fusion algorithms lack computational complexities. That results in the attainment of poor energy efficiency. To overcome the above problem, our proposed algorithm, called the naive multi-phase resource allocation algorithm, guarantees the effective utilization of agricultural resources in a dynamic agriculture environment that ensures energy efficiency. ER - TY - Conference Paper T1 - Energy Harvesting Sensors based Internet of Things System for Precision Agriculture A1 - Gill, R Y1 - 2022/// KW - Sensor node KW - NodeMCU KW - Lithium Ion Batteries KW - Solar panel JF - Proceedings of 2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022 SP - 270 EP - 273 DO - 10.1109/ICIPTM54933.2022.9754203 UR - https://api.elsevier.com/content/abstract/scopus_id/85129452681 N1 - Cited By (since 2022): 2 N2 - This paper proposes a design of wireless sensor node based on Internet of Things mainly its power unit which is integrated to solar energy for agriculture. The sensor node is developed using NodeMCu with four different sensors namely relative humidity and temperature sensor, soil moisture sensor, soil temperature sensor and luminosity sensor to monitor key parameters related to soil and environment. A power unit of sensor node is designed with Lithium ion batteries and charging of the batteries is maintained by solar panel. The developed node is tested for 24 hours for the battery voltage which proves that the power consumption during daytime is completely maintained while it drops during night. ER - TY - Article T1 - Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture A1 - Yao, Y D Y1 - 2022/// KW - %\boldsymbol Clustering routing protocol KW - cluster head selection KW - multi-threshold segmentation KW - network energy consumption KW - wireless sensor networks JF - IEEE Sensors Journal VL - 22 IS - 7 SP - 6216 EP - 6231 DO - 10.1109/JSEN.2022.3150770 UR - https://api.elsevier.com/content/abstract/scopus_id/85124747074 N1 - Cited By (since 2022): 8 N2 - Wireless sensor networks (WSNs), one of the fundamental technologies of the Internet of Things (IoT), can provide sensing and communication services efficiently for IoT-based applications, especially energy-limited applications. Clustering routing protocol plays an important role in reducing energy consumption and prolonging network lifetime. The cluster formation and cluster head selection are the key to improving the performance of the clustering routing protocol. An energy-efficient routing protocol based on multi-threshold segmentation (EERPMS) was proposed in this paper to improve the rationality of the cluster formation and cluster heads selection. In the stage of cluster formation, inspired by multi-threshold image segmentation, an innovative node clustering algorithm was developed. In the stage of cluster heads selection, aiming at minimizing the network energy consumption, a calculation theory of the optimal number and location of cluster heads was established. Furthermore, a novel cluster head selection algorithm was constructed based on the residual energy and optimal location of cluster heads. Simulation results show that EERPMS can improve the distribution uniformity of cluster heads, prolong the network lifetime and save up to 64.50%, 58.60% and 56.15% network energy as compared to RLEACH, CRPFCM and FIGWO protocols respectively. ER - TY - Conference Paper T1 - Enhanced Time-to-Time Monitoring and Surveillance in Agriculture Warehouse for Diverse Harvest Crop Yields through IoT Gadget A1 - Rajesh, G Y1 - 2022/// KW - Cold Storage KW - internet of things KW - Multifarious Harvest KW - Raspberry-Pi JF - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings SP - 172 EP - 175 DO - 10.1109/DISCOVER55800.2022.9974778 UR - https://api.elsevier.com/content/abstract/scopus_id/85145350042 N1 - Cited By (since 2022): 1 N2 - The losses in agriculture especially during the post-harvest sector is estimated to be from 10 to 25 per cent in fruits and vegetables. The most important entity for farmer is to master the agriculture warehouse conditions in real time, and then to take effective measures. The cold storages are built in domestic and suburban areas which are detached to agricultural fields which causes great management inconvenience. Therefore, it has great realistic meaning to improve the management efficiency using real time monitoring system. The large harvest yield storage always occupies large area and must be adapt to different crop in different seasons. This requires a flexible deployment, wide coverage and low-cost agricultural warehouse for value addition and efficient cold storage monitoring for variant crop preservation. The main objective of this paper is to avoid harvest losses (short and long term) for farmers during harvest cache and monitor agriculture warehouse atmosphere remotely through an IoT Gadget. The designed architectural framework of the cold storage environment monitoring system creates an enhanced time-to-time environmental monitoring and surveillance in agricultural warehouse for diverse harvest crop yields. ER - TY - Conference Paper T1 - Enhancement of Smart Agriculture using Internet of Things A1 - Arora, P Y1 - 2022/// JF - ECS Transactions VL - 107 IS - 1 SP - 7047 EP - 7058 SN - 1938-6737 DO - 10.1149/10701.7047ecst UR - https://api.elsevier.com/content/abstract/scopus_id/85130533600 N1 - Cited By (since 2022): 3 ER - TY - Article T1 - Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation A1 - Strong, R Y1 - 2022/// KW - diffusion barriers KW - sustainability KW - Industry 4.0 technologies KW - agricultural innovation KW - systems KW - knowledge transfer JF - Sensors VL - 22 IS - 18 DO - 10.3390/s22186833 UR - https://api.elsevier.com/content/abstract/scopus_id/85138354794 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Strong 2022) Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture.pdf N1 - Cited By (since 2022): 9 N2 - The study sought to: (1) evaluate agriculturalists’ characteristics as adopters of IoT smart agriculture technologies, (2) evaluate traits fostering innovation adoption, (3) evaluate the cycle of IoT smart agriculture adoption, and, lastly, (4) discern attributes and barriers of information communication. Researchers utilized a survey design to develop an instrument composed of eight adoption constructs and one personal characteristic construct and distributed it to agriculturalists at an agricultural exposition in Rio Grande do Sul. Three-hundred-forty-four (n = 344) agriculturalists responded to the data collection instrument. Adopter characteristics of agriculturalists were educated, higher consciousness of social status, larger understanding of technology use, and more likely identified as opinion leaders in communities. Innovation traits advantageous to IoT adoption regarding smart agriculture innovations were: (a) simplistic, (b) easily communicated to a targeted audience, (c) socially accepted, and (d) larger degrees of functionality. Smart agriculture innovation’s elevated levels of observability and compatibility coupled with the innovation’s low complexity were the diffusion elements predicting agriculturalists’ adoption. Agriculturalists’ beliefs in barriers to adopting IoT innovations were excessive complexity and minimal compatibility. Practitioners or change agents should promote IoT smart agriculture technologies to opinion leaders, reduce the innovation’s complexity, and amplify educational opportunities for technologies. The existing sum of IoT smart agriculture adoption literature with stakeholders and actors is descriptive and limited, which constitutes this inquiry as unique. ER - TY - Article T1 - Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications A1 - Fizza, K Y1 - 2022/// KW - Internet of Things KW - Data integrity KW - Robot sensing systems KW - Data models KW - Measurement KW - Dairy products KW - Temperature measurement JF - IEEE Micro VL - 42 IS - 1 SP - 51 EP - 60 DO - 10.1109/MM.2021.3137401 UR - https://api.elsevier.com/content/abstract/scopus_id/85122086795 N1 - Cited By (since 2022): 3 N2 - The unprecedented growth of Internet of Things (IoT) underpinned by machine-to-machine communication, analytics, and actuation is spearheading the development of IoT-based smart agriculture applications (IoTSAs). Various factors (e.g., location and weather) impact data availability for such IoT applications, which impacts the decision making and actuation process of IoTSA. This article proposes a conceptual framework and a novel model to compute sensor data quality model that can be used by IoTSA in their decision/actuation process to adapt to uncertainty in IoT sensor data. To demonstrate the efficacy of the proposed data quality metrics and model, we apply them to an IoTSA for monitoring milk condition in dairy farms (an instance of IoTSA). Our real-world experimental evaluations conclude that the proposed model 1) can be employed by IoTSAs to adapt to different factors that may impact the quality of decision making (actuation) and 2) aids in developing data quality-aware IoTSA and beyond. ER - TY - Book Chapter T1 - Evaluation of vulnerabilities in IoT-based intelligent agriculture systems A1 - Phasinam, K Y1 - 2022/// JF - Autonomous Vehicles: Smart Vehicles for Communication VL - 2 SP - 237 EP - 258 UR - https://api.elsevier.com/content/abstract/scopus_id/85148195628 N1 - Cited By (since 2022): 1 N2 - Humans can't survive without agriculture. Farming supplies a significant section of the world's population with their primary source of income. Additionally, it offers several employment possibilities for the local population. Low yields are a common side effect of ancient agricultural methods, which are still widely used by many farmers. The long-term prosperity of the economy depends on the long-term growth of agriculture and allied businesses. Key issues in agricultural production include crop selection, support systems, and decision-making. Temperature, soil fertility, water volume, water quality, seasons, crop prices, and other environmental factors affect agricultural predictions. With the rapid development of agricultural automation tools and apps, it's now easier than ever to get the information you need. Most Internet of Things (IoT) applications and devices are also known to be vulnerable to various types of attacks because of their inherent insecurities. Aside from their impact on the device's availability, various threats have differing implications on its security or quality. There is a stumbling block in enterprises determining what risks they face with their information assets and how to handle them. This article establishes a taxonomy based on the application domain and the architectural design to better detect IoT security problems. In this research, IoT development boards and sensors, as well as cloud subscriptions, are utilized to construct an experimental setup. Using network host scanning and vulnerability scanning technologies, raw data on IoT apps and devices is obtained. Additionally, the Shodan scanning tool is used to successfully uncover vulnerabilities in IoT devices as well as to do penetration testing on such devices. This article provides an in-depth study of attacks and vulnerabilities to Internet of Things related to agriculture fields. ER - TY - Article T1 - Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors A1 - Barrile, V Y1 - 2022/// KW - vineyards KW - unmanned aerial vehicles KW - satellite imagery KW - agriculture 4.0 KW - sensor networks JF - Sensors (Basel, Switzerland) VL - 22 IS - 20 DO - 10.3390/s22207910 UR - https://api.elsevier.com/content/abstract/scopus_id/85140933059 L1 - file:///C:/Users/sonsu/Downloads/sensors-22-07910.pdf N1 - Cited By (since 2022): 3 N2 - Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0. ER - TY - Review T1 - Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture A1 - Ramachandran, V Y1 - 2022/// KW - Internet of Things KW - agriculture KW - irrigation KW - cloud platforms KW - sensors KW - controllers KW - machine learning KW - neural networks JF - Water (Switzerland) VL - 14 IS - 5 SN - 2073-4441 DO - 10.3390/w14050719 UR - https://api.elsevier.com/content/abstract/scopus_id/85125639485 L1 - file:///C:/Users/sonsu/Downloads/water-14-00719-v3.pdf N1 - Cited By (since 2022): 25 N2 - The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications. ER - TY - Article T1 - FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications A1 - Kamal, S Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/2770706 UR - https://api.elsevier.com/content/abstract/scopus_id/85130547780 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(kamal 2022) FCN Network-Based Weed and Crop Segmentation for IoT-Aided agriculture application.pdf N1 - Cited By (since 2022): 2 N2 - The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real-time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: the fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset. ER - TY - Article T1 - Flexible IoT Agriculture Systems for Irrigation Control Based on Software Services A1 - Palomar-Cosín, E Y1 - 2022/// KW - IoT software services KW - flexible sofware design KW - agriculture irrigation software KW - agriculture KW - software framework KW - alarm detection in IoT software JF - Sensors VL - 22 IS - 24 DO - 10.3390/s22249999 UR - https://api.elsevier.com/content/abstract/scopus_id/85144541512 L1 - file:///C:/Users/sonsu/Downloads/sensors-22-09999.pdf N1 - Cited By (since 2022): 2 N2 - IoT technology applied to agriculture has produced a number of contributions in the recent years. Such solutions are, most of the time, fully tailored to a particular functional target and focus extensively on sensor-hardware development and customization. As a result, software-centered solutions for IoT system development are infrequent. This is not suitable, as the software is the bottleneck in modern computer systems, being the main source of performance loss, errors, and even cyber attacks. This paper takes a software-centric perspective to model and design IoT systems in a flexible manner. We contribute a software framework that supports the design of the IoT systems’ software based on software services in a client–server model with REST interactions; and it is exemplified on the domain of efficient irrigation in agriculture. We decompose the services’ design into the set of constituent functions and operations both at client and server sides. As a result, we provide a simple and novel view on the design of IoT systems in agriculture from a sofware perspective: we contribute simple design structure based on the identification of the front-end software services, their internal software functions and operations, and their interconnections as software services. We have implemented the software framework on an IoT irrigation use case that monitors the conditions of the field and processes the sampled data, detecting alarms when needed. We demonstrate that the temporal overhead of our solution is bounded and suitable for the target domain, reaching a response time of roughly 11 s for bursts of 3000 requests. ER - TY - Article T1 - Framing the response to IoT in agriculture: A discourse analysis A1 - McCaig, M Y1 - 2023/// JF - Agricultural Systems VL - 204 DO - 10.1016/j.agsy.2022.103557 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0308521X22001937 UR - https://api.elsevier.com/content/abstract/scopus_id/85143541866 N1 - Cited By (since 2023): 1 N2 - CONTEXT A technology in smart farming, the Internet of Things (IoT), is predicted to continue altering farm life by introducing opportunities and obstacles. However, there are limited studies on how farmers' views of IoT influence their decision-making regarding technology adoption. OBJECTIVE To understand what characterises farmers' experiences with IoT, we conducted a discourse analysis of 32 interviews with farmers in Ontario. METHODS Discourse analysis was used to understand the range of meanings associated with IoT by farmers. RESULTS AND CONCLUSIONS We find that two main discourses are present (1) the extent to which IoT was viewed as useful/helpful vs not useful/unhelpful and (2) the extent to which IoT was viewed as being their choice. The results indicate that farmers respond to IoT in four categories: embrace, accept, ignore, and caution. SIGNIFICANCE This paper contributes to the literature by categorising the farmers' responses to IoT implementation and highlighting why farmers adopt these categories. Current literature recognizes that diagnosing the current readiness and use of innovations is a proxy for their readiness to scale. Understanding how farmers view opportunities enabled by IoT and how they experience the diffusion of IoT is a foundation for suggesting recommendations for technology improvement and development in agriculture. ER - TY - Article T1 - Futuristic IoT based Smart Precision Agriculture: Brief Analysis A1 - Swamidason, I T J Y1 - 2022/// KW - Precision agriculture KW - Internet of Things KW - sensors KW - irrigation KW - drones JF - Journal of Mobile Multimedia VL - 18 IS - 3 SP - 935 EP - 956 DO - 10.13052/jmm1550-4646.18323 UR - https://api.elsevier.com/content/abstract/scopus_id/85125854714 L1 - file:///C:/Users/sonsu/Downloads/document8.pdf N1 - Cited By (since 2022): 2 N2 - Agriculture is considered as the backbone of any nation across the globe. With the advent of modern technologies, smart tools and techniques are used in the agriculture/farming to build on the quantity as well as quality of the agriculture production to feed the basic necessity of the humans. Smart technology such as Internet of Things play a vital role in monitor- ing and analyzing various environmental parameters such as water level, humidity, soil moisture, air quality, UV level, rain etc. which are highly essential to ensure the fruitful yield of any nutritious crops. In this research article, precision agriculture concepts are investigated widely with the focus of improving the productivity level and also the effective utilization of resources with the minimal cost while compared with the conventional methodologies. ER - TY - Book Chapter T1 - Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS) A1 - Wanare, A L Y1 - 2022/// JF - Green Internet of Things and Machine Learning: Towards a Smart Sustainable World SP - 239 EP - 268 DO - 10.1002/9781119793144.ch9 UR - https://api.elsevier.com/content/abstract/scopus_id/85135195862 N1 - Cited By (since 2022): 1 N2 - In the last couple of years, there are applications relevant to Green Internet of Things (GIoT) and the major focus on two development trending and admired technologies is upcoming: Green Cloud Computing Application (GCCA) and GIoT are current buzz discussions in the field of crop growing (agriculture) and medical related things, i.e., healthcare industry–based applications. Motivated by achieving a sustainable globe, this chapter discusses a variety of technology and issue concerning GCCA and GIoT and, additionally, further improves the conversation with the suppression of energy utilization of the combination of these two techniques (CCA and GIoT) in farming industry, i.e., one is agriculture-based and the other is healthcare industry–based system. The past and perception of the hot green information and communication technologies (GICTs) which enabled GIoT have been discussed rigorously. Green mathematical computational calculations opens first and, furthermore, or we can say, afterward focuses on the modern significant works completed concerning of these two upcoming emerging technologies in both agriculture and healthcare cases. In addition, this chapter has contributed significant information by presenting GIoT farming and healthcare applications linear time-invariant system (GIoT-AHAS) using digital wireless sensor cloud discrete integration or digital summation modelling. Finally, we have summarized the limitations, advantages, challenges, and prospects of the research guidelines associated to emerging and advanced green-based application oriented development in relevant field. The aim of our chapter is to research and create broad green area and also to make contribution to sustainable application around the globe ER - TY - Conference Paper T1 - GreenFarm: An IoT-Based Sustainable Agriculture with Automated Lighting System A1 - Dey, D Y1 - 2023/// KW - Internet of Things KW - Weather detector sensor KW - Motion detector sensor KW - Solar power system KW - Automated lighting system JF - Lecture Notes in Networks and Systems VL - 492 SP - 517 EP - 528 SN - 2367-3370 DO - 10.1007/978-981-19-3679-1_43 UR - https://api.elsevier.com/content/abstract/scopus_id/85142708241 N1 - Cited By (since 2023): 1 N2 - The population of the world in 2021 was approximate 7.9 billion which will be increased about 10 billion by 2050. In this symphony, the necessity of food and pure water will await about double. On the other hand, the space of free land for agriculture is decreasing day by day. So, it is a very hard challenge for everyone to manage a huge amount of food which is a courtly right for us. Always this challenge is might be a footprint for fulfilling the great demand. For solving such types of problems, we have to connect with the modern technological systems. Nowadays, the Internet of Things (IoT) is an optimal way to prevent such types of challenges. In this paper, we proposed a model by which a farmer can control lighting system, water pump, soil condition, and crops condition with the help of IoT. By implementing such type of model, the farmer will be able to monitor an auto lighting system, an auto water irrigation system, prevent external objects, save the electric power and analyze real-time data which are collected from different types of sensors by using a Wi-Fi system. All the hardware of the proposed model is directly connected with NodeMCU ESP8266. The essential energy of the whole system depends on the solar panel which reduces the cost, saves electricity and makes the total system eco-friendly and cost-effective. By using our proposed model, the farmers can detect the condition of the weather which makes a good impact on agriculture. For the current demand, the proposed model will make a good platform to complete our civil rights in upcoming future. ER - TY - Article T1 - Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System A1 - Subramanian, M Y1 - 2022/// JF - Complexity VL - 2022 DO - 10.1155/2022/4525220 UR - https://api.elsevier.com/content/abstract/scopus_id/85140024237 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(subramanian 2022) Hybrid Electro Search with Ant Colony Optimization.pdf N1 - Cited By (since 2022): 2 N2 - Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters. ER - TY - Article T1 - Hybrid Sensing Platform for IoT-Based Precision Agriculture A1 - Bagha, H Y1 - 2022/// KW - internet of things KW - precision agriculture KW - smart farming KW - remote sensing KW - hybrid sensing JF - Future Internet VL - 14 IS - 8 DO - 10.3390/fi14080233 UR - https://api.elsevier.com/content/abstract/scopus_id/85136570325 L1 - file:///C:/Users/sonsu/Downloads/futureinternet-14-00233-v3.pdf N1 - Cited By (since 2022): 3 N2 - Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA—an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs. ER - TY - Article T1 - Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment A1 - Alrowais, F Y1 - 2022/// KW - Applied computing KW - Computing methodologies KW - Artificial intelligence KW - Computer vision KW - Machine learning KW - Information systems JF - Computers and Electrical Engineering VL - 104 DO - 10.1016/j.compeleceng.2022.108411 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0045790622006280 UR - https://api.elsevier.com/content/abstract/scopus_id/85139419650 N1 - Cited By (since 2022): 3 N2 - Recent technological advancements of Cloud Computing (CC), Internet of Things (IoT), Artificial Intelligence (AI), computer vision, etc. enable the transformation of traditional agricultural practices into smart agricultural practices. In this background, the current article introduces a novel Hybrid Leader-based Optimization with DL-driven Weed Detection in IoT-enabled Smart Agriculture (HLBODL-WDSA) model. The prime aim of the proposed HLBODL-WDSA model is to collect the images using IoT devices and recognize the weeds automatically. Initially, the HLBODL-WDSA model enables the IoT devices to capture the farm images and transmits the images to the cloud server for examination. Next, the HLBODL-WDSA model applies YOLO-v5-based weed detection process in which HLBO algorithm is exploited as a hyperparameter optimizer. Finally, the Kernel Extreme Learning Machine (KELM) model is applied for effective classification of the weeds. The proposed HLBODL-WDSA model was experimentally validated and the outcomes established the better performance of the proposed HLBODL-WDSA model over recent approaches. ER - TY - Article T1 - INTERNET OF THINGS (IOT) FOR SMART CITY, AGRICULTURE AND HEALTHCARE A1 - Elhattab, K Y1 - 2022/// KW - Internet of Things KW - Smart City KW - Smart Parking KW - Smart agriculture KW - Smart Healthcare JF - Journal of Theoretical and Applied Information Technology VL - 100 IS - 4 SP - 1104 EP - 1112 UR - https://api.elsevier.com/content/abstract/scopus_id/85126143458 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(elhabtab 2022) INTERNET OF THINGS (IOT) FOR SMART CITY agriculture and healthcare.pdf N1 - Cited By (since 2022): 4 N2 - The Internet of Things (IoT) technology has revolutionized all areas of human life, making it more comfortable. IoT refers to the current trend of The Internet of Things (IoT) technology that has revolutionized all areas of human life, making it more comfortable. IoT refers to the current trend of connecting all kinds of physical objects to the Internet, even the most unexpected ones, without human intervention, which constitutes a self-configurable network. The Internet of Things (IoT) enables organizations to automate the process and improve service delivery via Internet technology and data transfer to the cloud. Nowadays, the Internet of Things (IoT) is becoming a widely discussed topic among researchers, specialists, and experts. It is seen as the next step in the evolution of the Internet. This paper covers the application of (IoT) technology in three different areas: smart cities, health, and agriculture ER - TY - Article T1 - IOT Automation with Segmentation Techniques for Detection of Plant Seedlings in Agriculture A1 - Kamal, S Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/6466555 UR - https://api.elsevier.com/content/abstract/scopus_id/85129926109 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(kamal 2022) IOT Automation with Segmentation Techniques for Detection of plant seedling in agriculture.pdf N1 - Cited By (since 2022): 1 N2 - The present work proposes to evaluate, compare, and determine software alternatives that present good detection performance and low computational cost for the plant segmentation operation in computer vision systems. In practical aspects, it aims to enable low-cost and accessible hardware to be used efficiently in real-time embedded systems for detecting seedlings in the agricultural environment. The analyses carried out in the study show that the process of separating and classifying plant seedlings is complex and depends on the capture scene, which becomes a real challenge when exposed to unstable conditions of the external environment without the use of light control or more specific hardware. These restrictions are driven by functionality and market perspective, aimed at low-cost and access to technology, resulting in limitations in processing, hardware, operating practices, and consequently possible solutions. Despite the difficulties and precautions, the experiments showed the most promising solutions for separation, even in situations such as noise and lack of visibility. ER - TY - Book Chapter T1 - IOT Sensor-Based Smart Agriculture Using Agro-robot A1 - Patil, D D Y1 - 2023/// KW - Arduino KW - Farming robot KW - LCD KW - ATmega KW - Agriculture innovations KW - PPM (parts per million) KW - Farming technology JF - EAI/Springer Innovations in Communication and Computing SP - 345 EP - 361 SN - 2522-8595 DO - 10.1007/978-3-031-04524-0_20 UR - https://api.elsevier.com/content/abstract/scopus_id/85139525191 N1 - Cited By (since 2023): 16 N2 - The current investigation and research innovation of agro-robot is to define the technical work in the form of project and prototype which illustrates robots are capable and useable in the field of farming and work to solve the challengers of agriculture workings by using robotics. The work is developed by integrating sensors based on various types such as moisture detector module, temperature sensor, motor controllers, LCD, wireless communication modules, ultrasonic sensor, and motors with microcontroller board like Arduino; ATmega328 is more versatile in handling more functions than any other conventional microcontroller. The moisture sensor is used to detect actual moisture of soil in farming sectors same respectively other sensors used with mixing signals to provide expected output which will require to execute tasks and solve them using Agrobot. The use of robots in agriculture has reduced operating costs and response times in agriculture. ER - TY - Conference Paper T1 - IOT based Smart Agriculture Monitoring Using Node MCU and BLYNK App A1 - Rajput, A Y1 - 2022/// JF - 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022 SP - 448 EP - 451 DO - 10.1109/COM-IT-CON54601.2022.9850847 UR - https://api.elsevier.com/content/abstract/scopus_id/85137521737 N1 - Cited By (since 2022): 1 ER - TY - Article T1 - Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture A1 - Wang, Y Y1 - 2022/// JF - Journal of Sensors VL - 2022 DO - 10.1155/2022/8953914 UR - https://api.elsevier.com/content/abstract/scopus_id/85134892038 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Wang 2022) Image Detection System Based on Smart Sensor Network and ecology economy in the context of fine agriculture.pdf N1 - Cited By (since 2022): 1 N2 - In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture. ER - TY - Article T1 - Image Driven Multi Feature Plant Management with FDE Based Smart Agriculture with Improved Security in Wireless Sensor Networks A1 - Santhosh, J Y1 - 2022/// JF - Wireless Personal Communications VL - 127 IS - 2 SP - 1647 EP - 1663 DO - 10.1007/s11277-021-08710-x UR - https://api.elsevier.com/content/abstract/scopus_id/85120543441 N1 - Cited By (since 2022): 3 N2 - Technology development in satellite, sensor network and image processing has been applied for various issues. Such developments are well adapted towards smart agriculture in recent times. It has been adapted to monitor the plants for their growth and yield being achieved. There exist different approaches towards monitoring the plants in agriculture sector, but the issue is with the performance metrics which are not arrived up to expected level. To improve the performance in plant monitoring with security concerns, an efficient multi feature multi feature plant management framework is proposed in this paper. The proposed model utilizes different images of statellite over the agriculture region and local images obtained with regional data sets. The images obtained are extracted for the features of texture, color to classify the images against different diseases and deficiencies. Also, with the color features and temperature, fluid, rainfall features, the method computes yield aggregation weight, Growth Aggregation Weight towards regulation of fluid. Using such weight measures estimated, the approach would perform sanitization as well as fertilizer manaagement. Also, the transmission of control messages are adapted to control the fertilizer injectors, water regulators and so on. The model enables controlling each device on the field with the support of sensor nodes. The entire data placed in the channel are encrypted using Feature level Data Encryption which uses different encryption keys and standards for different attributes. This restricts the sensor nodes in decrypting only allowed features to perform controlling the devices under the node and to perform required actions. The proposed method improves the security in smart agriculture and improves the performance. ER - TY - Conference Paper T1 - Impact of Internet of Things and Machine Learning in Smart Agriculture A1 - Kassanuk, T Y1 - 2022/// JF - ECS Transactions VL - 107 IS - 1 SP - 3215 EP - 3222 SN - 1938-6737 DO - 10.1149/10701.3215ecst UR - https://api.elsevier.com/content/abstract/scopus_id/85130531315 N1 - Cited By (since 2022): 1 N2 - Using irrigation, water is delivered to the roots at the proper moment. Plants use evapo-transpiration (ET) to pull water from moist soil and release water into the atmosphere at the same time as absorbing nutrients from the soil with water for root zone growth. There is a critical threshold beyond which plants cannot get the nutrients and water they need for growth. The root zone must be supplied with high-quality water before the limit is reached, as a consequence. Species, soil, and climate all influence this limit. The threshold cap differs by plant kind. The application of the proper amount of water at the right time and place inside the facility is a requirement of scientific scheduling. Monitoring the soil moisture content at the root zone needs a predetermined irrigation schedule based on the plant's nature, its growth, the kind of soil, and its climatic conditions. As a consequence, sensors near the soil's root zone are required to acquire a representative moisture condition for scientific irrigation scheduling. Deep learning and machine learning are two of the most popular techniques to artificial intelligence. People, companies, and governments all use these models to predict and learn from data. Complex and diverse data sets in the food business need the development of machine learning algorithms. The irrigation system shown here is based on machine learning and the Internet of Things. The data owner may then take necessary action depending on the results provided. ER - TY - Conference Paper T1 - Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation A1 - Viswanatha, V Y1 - 2022/// KW - Smart irrigation KW - IoT Cloud KW - Moisture sensor KW - Temperature sensor KW - MQTT KW - Cloud Computing JF - MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference DO - 10.1109/MysuruCon55714.2022.9972734 UR - https://api.elsevier.com/content/abstract/scopus_id/85145353823 L1 - file:///C:/Users/sonsu/Downloads/Implementation_of_IoT_in_Agriculture_A_Scientific_Approach_for_Smart_Irrigation.pdf N1 - Cited By (since 2022): 1 N2 - Digital technologies empower the transformation into data-driven, intelligent, agile, and autonomous farm operations and are typically considered a key to addressing the grand challenges in agriculture. To avoid unscientific water supply for plantation as well as to save the water and also yield the better crop, therefore, to increase production efficiency out of smart irrigation and to send the status of irrigation at standard environmental conditions, The Internet of Things (IoT) based prototype is designed and implemented. The prototype automatically turns ON /OFF the motor pump based on the moisture level of the soil by taking the temperature and humidity of the environment near the plantation into consideration (In India, the standard parameters for watering the vegetable plantation are Humidity>60%, Temperature < 25°C and Humidity<40% ). The prototype is designed with an ESP32S microcontroller with DHT 11 and a moisture sensor. Arduino IDE development tool is used for programming ESP32S using embedded C programming language. The prototype is configured, programmed, and connected to the Arduino IoT cloud. The data of temperature, humidity, and moisture are received via message queuing telemetry transport (MQTT) protocol on IoT cloud through public IP therefore the data can be accessed worldwide. The authorized person can access the data and control the motor pump from anywhere across the world. The test data obtained out of the prototype over the cloud and at the system are presented in the result section. ER - TY - Article T1 - Improving stability of aerial videos acquired through vision sensors onboard UAVs for applications in precision agriculture A1 - Latif, M A Y1 - 2022/// JF - Signal, Image and Video Processing VL - 16 IS - 5 SP - 1263 EP - 1270 DO - 10.1007/s11760-021-02077-z UR - https://api.elsevier.com/content/abstract/scopus_id/85123117300 N1 - Cited By (since 2022): 2 N2 - Certain nonlinear dynamic factors come into play to perturb the stability of UAVs during aerial data acquisition process. For instance, the vibration(s) caused by the motor(s), gust of winds in different directions and additional payload for custom applications are the major reasons for in-flight instability that may lead to alter the pitch, roll and yaw of the UAV thus affecting the quality of aerial data. The perturbations are generally managed autonomously using different mechanical and electrical sensors installed within the UAVs leading to capture smooth aerial data. Nevertheless, additional vision sensors mounted directly on UAVs remain prone to these perturbations and as a result instability is created in the video frames being captured producing a shaky video. In this paper, a novel algorithm is proposed to stabilize the instable videos captured through vision sensors attached directly to the frame of a UAV for applications in precision agriculture. The scope for the proposed approach covers a wide range of applications, e.g., generation of orthomosaic maps, vegetation maps, image registration, etc. The proposed approach eliminates geometric distortions in video frames using estimations and corrections based on affine and projective transformations. The results show significant improvement in video stability. ER - TY - Conference Paper T1 - Industrial Internet of Things (IoT) and 3D Reconstruction Empowered Smart Agriculture System A1 - Ma, Z Y1 - 2022/// KW - smart agriculture KW - crop monitoring KW - IoT KW - 3D reconstruction JF - Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 SP - 311 EP - 316 DO - 10.1109/IoTaIS56727.2022.9975929 UR - https://api.elsevier.com/content/abstract/scopus_id/85145974279 N1 - Cited By (since 2022): 1 N2 - Smart agriculture is a new agricultural production mode and is considered a potential solution for food supply issues under current limited land space conditions. The application of the Internet of Things (IoT) in smart agriculture can effectively increase food production with relatively low labor costs by deploying various wireless communication sensors in the field to collect plant information during the agricultural process. This paper developed an extendable IoT based sensor system for smart agriculture applications. The proposed sensing system can acquire real-time plant information through its plant environment and plant phenotyping monitoring process. The plant environment monitoring process can collect real-time plant environmental data through multiple wireless environment measuring sensors. At the same time, the plant phenotyping monitoring process can achieve plant height monitoring with the root-mean-square error (RMSE) of 0.051 m and the mean absolute error (MAE) of 0.049 m through remote RGB-D (red, green, blue plus depth data) cameras and 3D reconstruction method. This study shows that the proposed system can provide valuable real-time plant information for farmers’ decision-making. ER - TY - Conference Paper T1 - Integrated IoT Blockchain-Based Smart Agriculture System A1 - Priya, M Deva Y1 - 2022/// KW - Internet of things KW - Smart agriculture KW - Arduino KW - Cloud KW - Linear regression JF - Lecture Notes in Networks and Systems VL - 341 SP - 237 EP - 249 SN - 2367-3370 DO - 10.1007/978-981-16-7118-0_21 UR - https://api.elsevier.com/content/abstract/scopus_id/85124030185 N1 - Cited By (since 2022): 2 N2 - Smart irrigation and automated crop field cultivation are innovative applications in the field of IoT. In this paper, an IoT-based agricultural information monitoring system involving IoT devices is designed. The controller unit collects data related to crops by connecting with temperature, humidity and soil dampness sensors. It is customized with limit estimations of temperature and moisture content to improve the agricultural yields. Challenges faced in the design of such architectures are addressed in this paper. The data collected from the sensors are transferred to the ThingSpeak cloud platform, where it is processed. The network is trained using Linear Regression (LR) model to guide the farmers for increased yield in the upcoming years. ER - TY - Conference Paper T1 - Intelligent IoT-Based Monitoring Rover for Smart Agriculture Farming in Rural Areas A1 - Menon, A G Y1 - 2023/// KW - Internet of Things KW - Agriculture KW - Real-time monitoring JF - Lecture Notes in Networks and Systems VL - 401 SP - 619 EP - 630 SN - 2367-3370 DO - 10.1007/978-981-19-0098-3_60 UR - https://api.elsevier.com/content/abstract/scopus_id/85133019000 N1 - Cited By (since 2023): 1 N2 - For generations, since the first agricultural revolution in prehistoric times, human beings have depended on agricultural produces for their sustenance and survival. In the present era, man-made and natural disasters, global warming, heavy industrialization and deforestation have severely affected the quantity and quality of agricultural crop produces. As the population increases every year by millions, stable crop produce is essential for maintaining the very livelihood of these people. To improve agricultural production, a real-time monitoring system that continuously monitors crop health till the harvest season is necessary. The Arduino system proposed in this paper performs three major functions that include an area monitoring function where the system detects the presence of pests, trespassers and fire using sensors and wards off the pests or extinguishes the fire, a weather monitoring function that collects the climatic conditions during the analysis interval and updates the farmer via SMS and, a soil monitoring function that collects information regarding soil parameters and controls any soil parameter fluctuation using a microcontroller. It also provides a sprinkler system that sprays the required amount of water, insecticides, pesticides or other nutrients required to maintain crop health. The system is self-sustainable and self-maintainable as it runs on solar energy and the crop locations are pre-programmed into the system microcontroller using a method called geotagging. The system also guarantees controlled irrigation thus saving water. ER - TY - Conference Paper T1 - Intelligent Irrigation System for Agriculture using IoT and Machine Learning A1 - Gujar, K A Y1 - 2022/// KW - Internet of Things KW - Machine learning KW - Microcontroller KW - Irrigation KW - Agriculture KW - K-Nearest Neighbor KW - Decision Tree KW - Support Vector Machine JF - 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022 SP - 86 EP - 90 DO - 10.1109/ICACITE53722.2022.9823522 UR - https://api.elsevier.com/content/abstract/scopus_id/85135495076 N1 - Cited By (since 2022): 1 N2 - Agriculture plays vital role in development of social and economic condition of the all countries in the world. Water is the source of life and also a precious source for agriculture. Emerging trends in agriculture aims at ensuring more productivity, less damage to the land and helpful for the concept of Sustainable Agriculture (12). Crop productivity is mainly dependent on soil moisture level, less moisture result in loss of yield and plants dying. Excess water causes water wastage and root diseases. To maximize crop production the adequate water quantity at right time is required. In the proposed study IoT and Machine Learning based automated irrigation system developed. The system is divided in to three stages, first is circuit building and sensors installation, the second stage is of reading sensor data and check the present soil moisture level and in third stage motor ON and OFF decisions is taken by considering the crop type. On server the sensor data is get stored, system gives real time data from the sensors. IoT (Internet of Thing) helps to implement Wireless Sensor Network and Machine Learning can manage optimum water utilization for precious farming by analyzing available data. ER - TY - Article T1 - Intelligent agriculture technology based on internet of things A1 - Sun, L Y1 - 2022/// KW - Intelligent agriculture KW - internet of things KW - traceability of agricultural products JF - Intelligent Automation and Soft Computing VL - 32 IS - 1 SP - 429 EP - 439 DO - 10.32604/iasc.2022.021526 UR - https://api.elsevier.com/content/abstract/scopus_id/85118116975 L1 - file:///C:/Users/sonsu/Downloads/TSP_IASC_21526.pdf N1 - Cited By (since 2022): 3 N2 - Although the application of agricultural product traceability technology is a key point to realize Modern Agricultural IoT, it has still encountered various food safety problems. For example, immature environmental monitoring technol- ogy of agricultural products, weak product traceability and imperfect product monitoring equipment. For this reason, this paper studies and compares several emerging technologies of the things Internet, then it analyzes the functional diver- sity and practicability of the Modern Agricultural IoT. It builds the experimental environment based on the agricultural product traceability technology, so as to realize the monitoring of crop growth environment and traceability. The result plays a positive role in popularizing the traceability technology of agricultural products and has a certain impact on the development of intelligent agriculture. It has designed and developed a set of crop growth monitoring terminal equip- ment through experimental debugging. Then it could obtain temperature and humidity, illumination, CO2 concentration, soil data, and other crop growth envir- onment parameters in real time. The relevant data has provided strong support for the traceability model to realize the multi-point monitoring, intelligent control, and automatic operation of crop growing environment. Then it can be sorted and analyzed through the application layer. This system can promote the intelli- gent processing, improve the utilization efficiency of agricultural resources, pro- mote the development of modern agriculture, save agricultural production costs, and increase the production and marketing. It is an innovative application of the Internet of things technology applied to agricultural production. ER - TY - Conference Paper T1 - Internet of Things (IOT) Based Technologies in Smart Agriculture A1 - Konde, S Y1 - 2022/// KW - Internet of things KW - Sensors KW - Cloud KW - Irrigation and Farming KW - Arduino JF - Lecture Notes in Electrical Engineering VL - 828 SP - 249 EP - 262 SN - 1876-1100 DO - 10.1007/978-981-16-7985-8_26 UR - https://api.elsevier.com/content/abstract/scopus_id/85130875656 N1 - Cited By (since 2022): 2 N2 - Over decades, agriculture has seen tremendous revolution. The development of farm machineries such as tractors, harvesters, improved irrigation systems have contributed immensely to food security and sustainable economy. The development of Computers has contributed significantly in improving our daily activities, and agriculture is not an exception. New technology called Internet of Things (IoT) is bringing to light amazing developments in process automation, process control, data collection, and real-time response to events. It has helped in home automation, self-driving cars, Unmanned Aerial vehicles and many more. IoT can be applied in agriculture by means of sensors aimed at field monitoring, disease detection; temperature, humidity and soil moisture monitoring, automatic irrigation system, IoT based drones, farm animals monitoring etc. The change of climate in the Sahel, caused largely by global warming and desertification, has affected so much of production and farm output, we aim in this research to apply IoT technology in climate monitoring such as the temperature and humidity, soil moisture monitoring, crop management, precision farming practice, , farm management using End-to-End Farm Management System. The result shows an interesting output of the variations in temperature, humidity, soil moisture, sunlight levels and probable rain drops in the experiment site. The result were shown directly on the LCD screen attached to the Arduino microcontroller and at the same time transmitted over the internet to the IoT cloud and displayed on the Arduino IoT remote application. This research was implemented on a small part on an irrigation site and can thus be expanded to accommodate larger components for use on large farm site ER - TY - Conference Paper T1 - Internet of Things (IoT) in agriculture: An exploratory study on the production of growth tomato (industrial) in the south of Goiás, Brazil A1 - Dias, K C Y1 - 2022/// KW - Industry 4.0 KW - Internet of Things KW - Agribusiness KW - Industrial tomato KW - Agriculture 4.0 JF - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 DO - 10.1109/ICECCME55909.2022.9988362 UR - https://api.elsevier.com/content/abstract/scopus_id/85146426396 N1 - Cited By (since 2022): 1 N2 - Disruptive technologies have changed the way people relate to everyday objects, work, and other individuals. In this context, the Internet of Things (loT) concept represents the interconnection of physical and virtual obj ects. Through exploratory and qualitative research by investigating the regional producers, the present work aims to understand the loT adoption process in producing industrial tomatoes in the south of the State of Golas, Brazil. Given the data gathered in this research, several efforts were identified as part of all the value chain partners' efforts to increase product competitiveness and quality based on Goias's tomato industry. The method was a non-probabilistic study, and the technique used was the semi-structured interview. The results show that the technologies adopted increased 8% on average in the annual revenue of tomato productions in the south of Colas. Therefore, comparing the increment provided by technologies already applied, producers reported that loT technologies would also increase by about 10% in revenue. In addition, several factors prevent producers from expanding their technology portfolios, such as lack of internet coverage, government incentives to purchase machines, and specialized labor. ER - TY - Conference Paper T1 - Internet of Things Application: E-health data acquisition system and Smart agriculture A1 - Shukla, R Y1 - 2022/// KW - E-health data acquisition system KW - Internet of Things KW - Humidity KW - Irrigation KW - Raspberry pi KW - Sensors KW - Smart farming KW - Smart healthcare KW - Temperature JF - International Conference on Emerging Trends in Engineering and Technology, ICETET VL - 2022 SN - 2157-0477 DO - 10.1109/ICETET-SIP-2254415.2022.9791834 UR - https://api.elsevier.com/content/abstract/scopus_id/85132292361 N1 - Cited By (since 2022): 1 N2 - IoT has emerged as one of the most advanced techniques of the 21 century in recent years. With the help of low cost computation, big data, cloud technology, embedded system and mobile technology, physical objects are able to communicate and collect information with minimal human interaction. This major motive of the paper is to demonstrate how the IoT is adaptable to variety of fields, for instance healthcare, agriculture, manufacturing, logistics, and transportation. For smart healthcare application, this paper discusses an e-health data acquisition, transmission, and monitoring system for cardiac patient in which the health parameters of patients are monitored by a wireless sensor network and relayed to the far end through GPRS. For smart agriculture application this paper discusses an autonomous irrigation system, in which a sensor analyses soil humidity and waters the plant accordingly, saving time and water and requiring less human work. ER - TY - Review T1 - Internet of Things Applications in Precision Agriculture: A Review A1 - Abu, N S Y1 - 2022/// KW - Internet of Things KW - Precision KW - agriculture KW - Data management KW - Crop monitoring KW - Smart farming JF - Journal of Robotics and Control (JRC) VL - 3 IS - 3 SP - 338 EP - 347 SN - 2715-5056 DO - 10.18196/jrc.v3i3.14159 UR - https://api.elsevier.com/content/abstract/scopus_id/85136487548 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Abu 2022) Internet of Things Applications in Precision Agriculture A Literature Review.pdf N1 - Cited By (since 2022): 4 N2 - The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases. ER - TY - Article T1 - Internet of Things application in Indian agriculture, challenges and effect on the extension advisory services – a review A1 - Jarial, S Y1 - 2022/// KW - internet of things KW - extention advisory services KW - agriculture extention agent KW - india JF - Journal of Agribusiness in Developing and Emerging Economies DO - 10.1108/JADEE-05-2021-0121 UR - https://api.elsevier.com/content/abstract/scopus_id/85122914302 N1 - Cited By (since 2022): 9 N2 - Purpose The emerging technologies of the Fourth Industrial Revolution are transforming various industries, including agriculture. Unaware, young male and female farmers leave the agriculture profession as they perform unsustainable practices. Precision agriculture using the Internet of Things (IoT) is a solution to sustainable agriculture. Extension professionals are at the heart of disseminating agricultural advisory agricultural services in India. The discourse on the IoT is entering the space of extension advisory services (EASs) and social sciences. Thus, the present paper seeks to review the application of IoT in Indian agriculture, its challenges and its effect on EASs. The conceptual framework is drawn from disruptive and surveillance capitalist theories. Design/methodology/approach Online literature review was conducted on electronic e-book Ebsco, Google scholar, PubMed, Jane, j gate, research4life, springer journal and Mendeley databases for full-text repositories, textbook, thesis, web articles, newspaper articles, reports, blogs for the year 1990 to May 2021 using keywords “IoT application in agriculture,” “emerging technologies in agriculture,” “challenges in IoT application,” “extension advisory services sources of information,” “big data and extension advisory, “IoT and extension advisory in India.” Only publications in the English language were included. Findings IoT aids progressive farmers and small farmers alike. Drones, robotics, precision irrigation, livestock tracking and crop disease surveillance are examples of IoT applications in agriculture. Only large corporations and governments access IoT, and for them, big data storage is an issue. Privacy and security concerns demand upgrades in IoT systems. Solutions to the convergence of IoT with the cloud will leverage agricultural EASs, resulting in fast computing, precise and proactive up-to-date problem solving. Hence, the need for communication between firms and clients has ceased. Thus, the jobs of extension agents are replaced. Research limitations/implications The competence of future human extension agents lies in reskilling as a “knowledge broker” of relationships and expertise, as s/he cannot have all multidisciplinary knowledge. Originality/value Although IoT applications in agriculture are available from a technological standpoint, there remains an awareness gap regarding the impact of IoT applications in agricultural EASs. This study will aid in a better comprehension of IoT applications from current and prospective EASs. ER - TY - Article T1 - Internet of Things in Greenhouse Agriculture: A Survey on Enabling Technologies, Applications, and Protocols A1 - Farooq, M S Y1 - 2022/// KW - internet of things KW - smart greenhouse KW - hydroponics KW - vertical farm KW - security issues KW - network technologies KW - communication protocols KW - IoT sensors KW - mobile app JF - IEEE Access VL - 10 SP - 53374 EP - 53397 DO - 10.1109/ACCESS.2022.3166634 UR - https://api.elsevier.com/content/abstract/scopus_id/85128272006 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Farooq 2022) Internet of Things in Greenhouse Agriculture a survey on enabling technologies applications and protocols.pdf N1 - Cited By (since 2022): 5 N2 - The greenhouse is one of the sustainable forms of smart agricultural farming. It is considered as an alternate method to overcome the food crisis which is generated due to high population growth, climate change, and environmental pollution. Although this method supports off-the-season crops within the enclosed area even in severe climatic zones. It has required to efficiently control and manage the crop parameters at a greenhouse in a more precise and secure way. The advancement of the Internet of Things (IoT) has introduced smart solutions to automate the greenhouse farming parameters such as plant monitoring, internal atmosphere control, and irrigation control. The survey presents a hierarchy on the major components of IoT-based greenhouse farming. A rigorous discussion on greenhouse farming techniques, IoT-based greenhouse categories, network technologies (cloud/edge computing, IoT protocols, data analytics, sensors) has been presented. Furthermore, a detailed discussion on mobile-based greenhouse applications and IoT applications has been presented to manage the greenhouse farm. Moreover, the success stories and statistical analysis of some agricultural countries have been presented to standardize IoT-based greenhouse farming. Lastly, the open issues and research challenges related to IoT-enabled greenhouse farming has been presented with state-of-the-art future research directions. ER - TY - Conference Paper T1 - Internet of Things in Precision Agriculture: A Survey on Sensing Mechanisms, Potential Applications, and Challenges A1 - Madhumathi, R Y1 - 2022/// KW - Internet of Things KW - Sensors KW - Wireless sensor networks KW - Agricultural applications KW - IoT challenges KW - Smart agriculture KW - Automation in agriculture JF - Lecture Notes in Networks and Systems VL - 213 SP - 539 EP - 553 SN - 2367-3370 DO - 10.1007/978-981-16-2422-3_42 UR - https://api.elsevier.com/content/abstract/scopus_id/85115145394 N1 - Cited By (since 2022): 7 N2 - Precision agriculture is one of the modern farming practices that gathers, processes, and analyzes data with the goal of increasing the agricultural production and reducing the usage of resources. Agriculture contributes an important share in the Gross Domestic Product (GDP) of our country. Precision agriculture involves the usage of Internet of Things (IoT) along with Wireless Sensor Networks (WSN) which provides an intelligent farm management system. IoT in agriculture provides decision support systems that help farmers to know the real-time information of their field. The applications of IoT becomes a game changer in agriculture as it monitors and transfers information without human intervention. Agricultural IoT systems are implemented with the help of sensors and actuators that senses and responds to different inputs and provides instant feedbacks. The key sensors involved in precision agriculture are used in both small and large-scale farmlands for effective production. The main objective of this study is to review the potential application of sensors used in agriculture, to describe the layers of IoT in agriculture, discuss the existing sensing approaches for monitoring the agricultural parameters effectively, and deliver the general challenges encountered while implementing IoT systems ER - TY - Conference Paper T1 - Internet of Things, Big Data Analytics, and Deep Learning for Sustainable Precision Agriculture A1 - Micheni, E Y1 - 2022/// KW - internet of things KW - Big Data Analytics KW - Deep Learning KW - Precision Agriculture KW - Sustainability JF - 2022 IST-Africa Conference, IST-Africa 2022 DO - 10.23919/IST-Africa56635.2022.9845510 UR - https://api.elsevier.com/content/abstract/scopus_id/85137529817 N1 - Cited By (since 2022): 1 N2 - Agriculture is undergoing a digital transformation because of population growth, climate change, and food security concerns. Agriculture is influenced by information technology in terms of cost reduction, efficiency, and sustainability. Precision agriculture employs IoT, deep learning, predictive analytics, and AI-based technologies to aid in the detection of plant diseases, pests, and poor plant nutrition in the field. The study’s objectives are as follows: 1) evaluate the role of smart technologies and their impact on precision agriculture sustainability; 2) assess the typical application of IoT data analytics and deep learning in precision agriculture; and 3) investigate the barriers to the adoption of sustainable precision farming. IoT technologies collect data and relay it to data analytics and deep learning for in-depth analysis. The findings indicate that data assists farmers in managing crop variety, phenotypes and selection, crop performance, soil quality, pH level, irrigation, and fertilizer application quantity. The study looks at typical application areas and critical success factors for precision agriculture. Technological issues, safety, privacy, cost, and legal issues influence the adoption of these technologies. Individual farmers, government, academics, and agricultural authorities will all benefit from the research. The study recommends the adoption and optimization of innovations and technologies e.g. mobile devices, access to better internet speed, low-cost and dependable satellites for positioning and imagery, and precision agriculture-optimized agricultural machinery. Future research should focus on the application of appropriate decision-support systems for implementing precision decisions. ER - TY - Book Chapter T1 - Internet of Things-Based Devices/Robots in Agriculture 4.0 A1 - Singh, G Y1 - 2022/// KW - Internet of Things KW - Internet of Things-based devices KW - Sensors KW - Communication technology KW - Agriculture 4.0 KW - UAVs JF - Lecture Notes on Data Engineering and Communications Technologies VL - 93 SP - 87 EP - 102 SN - 2367-4512 DO - 10.1007/978-981-16-6605-6_6 UR - https://api.elsevier.com/content/abstract/scopus_id/85123366649 N1 - Cited By (since 2022): 6 N2 - Agriculture 4.0 focuses majorly on precision agriculture. Precision agriculture can be achieved in several ways such as refinement of cultivation practices, choices of crops, reduction of risk and volatility, water management, optimized use of pesticides, land/crop monitoring with minimal environmental impact. The best way to achieve precision agriculture through the Internet of Things-based devices in agriculture. The rapid developments on the Internet of Things-based devices have impacted every industry including “Agriculture.” This revolutionary change in agriculture is changing the present agricultural methods, and creating new opportunities, and challenges. The Internet of Things-based devices and communication techniques along with wireless sensors are analyzed in this chapter in detail. The specific sensors available for precision agricultural applications like the preparation of soil, checking the status of the crop, pest, and insect identification, and detection, irrigation, spraying of fertilizers are explained. The use of Internet of Things-based devices helps the farmers through the crop stages i.e., sowing to harvesting is explained. At last, this chapter concludes and provides the challenges faced while implementing Internet of Things-based devices in agriculture. ER - TY - Conference Paper T1 - IoT Based Smart Agriculture A1 - Jeyaselvi, M Y1 - 2022/// KW - internet of things KW - Deep Learning KW - Machine Learning KW - PDDS KW - Agriculture JF - Proceedings of the 2022 8th International Conference on Applied System Innovation, ICASI 2022 SP - 123 EP - 126 DO - 10.1109/ICASI55125.2022.9774472 UR - https://api.elsevier.com/content/abstract/scopus_id/85130919524 N1 - Cited By (since 2022): 1 N2 - Today, IOT is connected to all aspects of life from home automation, automatic, and even in health, fitness, and logistics. In the past, farmers used to check the ripeness of the soil and factors that influenced the growth of the better kind of product. But they are unable to consider the dampness climate conditions and water level, etc. The IoT plays a very vital role in the remodeling of agriculture by the facility in the wide range of new strategies to address challenges in the field. IOT modernization helps to get information on a situation such as the weather, climate, temperature, and soil fertility. There are many technological transformations in the last decades that have become technology-driven. Smart farming is a new technology in agriculture that makes agriculture more effective and more efficient. The farmer has achieved better results on the process of growing crops, making it smarter agriculture. The rapid development of IoT-based technology is redesigning every industry, including agriculture. The main focus of this study is to explore the benefits of using IoT in agricultural applications. ER - TY - Review T1 - IoT Based Smart Greenhouse Framework and Control Strategies for Sustainable Agriculture A1 - Farooq, M S Y1 - 2022/// KW - Internet of Things KW - greenhouse KW - applications KW - sensors KW - communication protocols KW - cloud computing KW - big data analytics KW - security attacks JF - IEEE Access VL - 10 SP - 99394 EP - 99420 SN - 2169-3536 DO - 10.1109/ACCESS.2022.3204066 UR - https://api.elsevier.com/content/abstract/scopus_id/85137902526 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Farooq 2022) Internet of Things in Greenhouse Agriculture a survey on enabling technologies applications and protocols.pdf N1 - Cited By (since 2022): 1 N2 - In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. In perspective to the current standing of IoT in agriculture, identification of the most prominent application of IoT-based smart farming i.e. greenhouse has been highlighted and presented a systematic analysis and investigated the high quality research work for the implementation of greenhouse farming. The primary objective of this study is to propose an IoT-based network framework for a sustainable greenhouse environment and implement control strategies for efficient resources management. A rigorous discussion on IoT-based greenhouse applications, sensors/devices, and communication protocols have been presented. Furthermore, this research also presents an inclusive review of IoT-based greenhouse sensors/devices and communication protocols. Moreover, we have also presented a rigorous discussion on smart greenhouse farming challenges and security issues as well as identified future research directions to overcome these challenges. This research has explained many aspects of the technologies involved in IoT-based greenhouse and proposed network architecture, topology, and platforms. In the end, research results have been summarized by developing an IoT-based greenhouse farm management taxonomy and attacks taxonomy ER - TY - Article T1 - IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms A1 - Bakthavatchalam, K Y1 - 2022/// KW - precision agriculture KW - WEKA KW - machine learning KW - multilayer perceptron KW - JRip KW - decision table JF - Technologies VL - 10 IS - 1 DO - 10.3390/technologies10010013 UR - https://api.elsevier.com/content/abstract/scopus_id/85131328489 L1 - file:///C:/Users/sonsu/Downloads/technologies-10-00013.pdf N1 - Cited By (since 2022): 7 N2 - IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendations are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different crops are recommended for a different combination of 8 attributes. Using the supervised learning method, the optimum model is attained using selected machine learning algorithms in WEKA. The Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip, and decision table classifier. The main objective of this case study is to end up with a model which predicts the high yield crop and precision agriculture. The proposed system modeling incorporates the trending technology, IoT, and Agriculture needy measurements. The performance assessed by the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with the maximum time taken to build the model being 8.05 s. ER - TY - Article T1 - IoT Smart Agriculture and Agricultural Product Income Insurance Participant Behavior Based on Fuzzy Neural Network A1 - Tian, J Y1 - 2022/// JF - Computational Intelligence and Neuroscience VL - 2022 DO - 10.1155/2022/4778975 UR - https://api.elsevier.com/content/abstract/scopus_id/85131652327 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(tian 2022) IoT Smart Agriculture andAgricultural Product Income Insurance participant behavior based on fuzzy neural network.pdf N1 - Cited By (since 2022): 1 N2 - With the continuous development of modern science, more and more attention is paid to the application of science and technology for agricultural production. First of all, this article analyzes the development trend of modern agriculture, and designs the system through an overall plan of four parts: automatic acquisition, terminal control, network transmission, and cloud platform and terminal application. Taking automatic irrigation system and temperature automatic control system as examples, design an automatic control algorithm based on fuzzy neural network, and obtain the best control strategy through fuzzy inference and neural network training. Then, this paper studies the reliability of “price setting” in agricultural income insurance. This article takes the main crops of a certain province as an example to verify and analyze the risks of farmers’ income levels from the viewpoint of corn yield risks and price risks, and advocate the necessity and feasibility of agricultural income insurance. Finally, from the perspective of different participants, this article analyzes the impact of insurance premium subsidies on the enthusiasm of agricultural producers to participate in insurance, the impact of insurance companies on the improvement of insurance operation efficiency in the planting industry, and the stability of the government’s promotion of agricultural production. It also influences to design a set of IoT automatic control system for intelligent agriculture. The system realizes the acquisition and remote control of agricultural data by establishing an Internet of Things cloud platform, and uses automatic control algorithms based on fuzzy neural networks to realize automatic water-saving irrigation, automatic temperature control, and other functions. Based on this research, the main actions of joining agricultural income insurance and the participation model based on the perspective of tripartite participants. ER - TY - Conference Paper T1 - IoT Technologies for Precision Agriculture: A Survey A1 - Pyingkodi, M Y1 - 2022/// KW - IoT Sensors KW - Smart farming KW - Agriculture Sensors KW - Precision Agriculture KW - Remote Sensing KW - Remote Sensor JF - Proceedings - 6th International Conference on Computing Methodologies and Communication, ICCMC 2022 SP - 372 EP - 376 DO - 10.1109/ICCMC53470.2022.9753823 UR - https://api.elsevier.com/content/abstract/scopus_id/85129189012 N1 - Cited By (since 2022): 3 N2 - Precision agriculture is gaining popularity in both commercial and research and development applications recent days. Plant management systems are constantly evolving, taking into account real-time or offline data collecting and processing in order to fully account for the geographical and temporal fluctuations of plant and soil components. This step can be achieved by lowering energy use, reducing the use of chemicals, fertilizers, and greenhouse gases (GHG), while simultaneously increasing output. To address the food and fibre needs of a rapidly rising population, sustainability is critical. With this in mind, the goal of this study is to not only present a complete picture, but also to elucidate the context by highlighting major scientific research and applications related to optoelectronics proximate sensors. Is to attempt to create a 360-degree vision. Agriculture. The reviews are divided into categories based on the most common sensor types and applications (grayscale / RGB imaging, Visible Near Infrared (VisNIR) and NIR, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index (NDRE) sensors, stereo). That's precisely the Point that the Vision, thermography, composite sensors, phenotypic sensors, and other technologies are being developed. Two distinct chapters on the use and development of specialised analytical methodologies, as well as term map analysis to visually identify major clusters based on certain subject areas are studied. ER - TY - Conference Paper T1 - IoT and Blockchain based Smart Agriculture Monitoring and Intelligence Security System A1 - Ali, M A Y1 - 2022/// KW - internet of things KW - Blockchain KW - Decentralized Database KW - Smart Agriculture KW - Data Analytics JF - Proceedings - 2022 3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022 DO - 10.1109/ICCAKM54721.2022.9990243 UR - https://api.elsevier.com/content/abstract/scopus_id/85146261733 N1 - Cited By (since 2022): 2 N2 - Food security seems to be a more prevalent concern for all countries throughout the world due to global population growth, dwindling natural resources, agricultural land, and an increase in unfavorable environmental circumstances. These problems are the driving force behind agriculture industry’s migration towards modern agriculture through the use of IoT and Blockchain technology for improving operations, productivity and maintaining, monitoring agricultural farms and creating fewer people involvement. We have demonstrated how IoT and Blockchain systems may be linked with agriculture’s intelligent component to maximize benefits for farmers. Security essential not only for the resources but also essential for agricultural products need to be protected and protected in the first instance, as protection from rodents and pests in the large agricultural field or grain shops. As a result, these problems need to be addressed. So, we designed an IoT and Blockchain based Smart Agriculture monitoring and Blockchain oriented Intelligence Security systems for Smart Agriculture Infrastructure. ER - TY - Conference Paper T1 - IoT in Agriculture and Healthcare: Applications and Challenges A1 - Hafeez, P Abdul Y1 - 2022/// KW - IoT Sensors KW - IoT in Agriculture KW - IoT in Healt-hcare KW - IoT Applications JF - 3rd International Conference on Smart Electronics and Communication, ICOSEC 2022 - Proceedings SP - 446 EP - 450 DO - 10.1109/ICOSEC54921.2022.9952061 UR - https://api.elsevier.com/content/abstract/scopus_id/85143690602 N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) is the present and future of every field, influencing everyone’s lives by making everything smart. Almost every industry, including agriculture and healthcare, has been rebuilt as a result of the quick rise of Internet-of-Things-based technologies. Agriculture’s significant developments with the deployment of IoT are changing the landscape of traditional farming methods by making them not only more productive but also more financially viable and sustainable for farmers.Health is the most pressing concern for the bulk of the population, irrespective of age. IoT has shown potential in connecting a variety of medical devices, sensors, and healthcare experts in order to provide high-quality medical treatment. The flow of this current study gives a complete collection of information on the IoT environment,including most commonly utilized IoT sensors. The significance of integrating IoT technologies into the agriculture and healthcare domains has also been presented herein in the tabular form. Incorporating Agriculture- IoT and Health-IoT systems into a real- time scenario can provide ample scope for research in predicting, processing, and analyzing circumstances, as well as boosting timely actions. ER - TY - Article T1 - IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry A1 - Misra, N N Y1 - 2022/// KW - precision agriculture KW - social media KW - gene KW - sequencing KW - blockchain KW - sensors KW - internet KW - digital KW - robotics JF - IEEE Internet of Things Journal VL - 9 IS - 9 SP - 6305 EP - 6324 DO - 10.1109/JIOT.2020.2998584 UR - https://api.elsevier.com/content/abstract/scopus_id/85129568445 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(misra 2022) IoT, big data and artificial intelligence in agriculture and food industry.pdf N1 - Cited By (since 2022): 122 N2 - Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supplychain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes. ER - TY - Book Chapter T1 - IoT-Based Smart Agriculture and Poultry Farms for Environmental Sustainability and Development A1 - Vinueza-Naranjo, P G Y1 - 2022/// KW - Internet of Things KW - Poultry Farm KW - Agriculture KW - Smart Tech- nologies KW - Machine Learning JF - EAI/Springer Innovations in Communication and Computing SP - 379 EP - 406 SN - 2522-8595 DO - 10.1007/978-3-030-75123-4_17 UR - https://api.elsevier.com/content/abstract/scopus_id/85117714245 L1 - file:///C:/Users/sonsu/Downloads/IoT_based_Smart_Agriculture_and_Poultry_Farms_for_Environmental_Sustainability_and_Development.pdf N1 - Cited By (since 2022): 2 N2 - Latin America is seen as a pivotal supplier of agricultural and poultry commodities to an ever-growing world population. How- ever, serious pressure has been put on the rural agriculture communities as a consequence of the urbanization. In addition, the lack of using ef- ficient processes have hindered the development of both the agriculture and poultry sector. One possible solution to address this issue is the Internet of Things (IoT)-based smart management system in order to introduce modernization in traditional methods. This chapter will begin with the examination of the challenges faced by the conventional agri- culture and poultry farm followed by possible contributions of IoT-based technologies and solutions to improve quality, quantity, sustainability, and cost-effectiveness of agricultural and poultry sectors. Then, the en- ablement of intelligent control and real-time decision making by applying state-of-the-art methodologies comprising of sensors for monitoring, and drones for maintaining surveillance and detecting irregularities through image analysis is discussed. The major contribution of this chapter is to elucidate the system design for monitoring various environmental pa- rameters regarding smart agriculture and poultry farms by using wireless sensor networks and artificial intelligence. ER - TY - Review T1 - IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges A1 - Quy, V K Y1 - 2022/// KW - sustainable agriculture KW - food security KW - green technologies KW - Internet of Things KW - natural resources KW - sustainable environment KW - IoT ecosystem JF - Applied Sciences (Switzerland) VL - 12 IS - 7 SN - 2076-3417 DO - 10.3390/app12073396 UR - https://api.elsevier.com/content/abstract/scopus_id/85127581801 L1 - file:///C:/Users/sonsu/Downloads/applsci-12-03396.pdf N1 - Cited By (since 2022): 58 N2 - The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach. ER - TY - Review T1 - IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends A1 - Qazi, S Y1 - 2022/// KW - Smart agriculture KW - Internet of Things KW - smart irrigation KW - organic farming KW - artificial intelligence KW - big data JF - IEEE Access VL - 10 SP - 21219 EP - 21235 SN - 2169-3536 DO - 10.1109/ACCESS.2022.3152544 UR - https://api.elsevier.com/content/abstract/scopus_id/85125359360 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Qazi 2022)IoT-Equipped_and_AI-Enabled_Next_Generation_Smart_Agriculture_A_Critical_Review_Current_Challenges_and_Future_Trends.pdf N1 - Cited By (since 2022): 32 N2 - Smart agriculture techniques have recently seen widespread interest by farmers. This is driven by several factors, which include the widespread availability of economically-priced, low-powered Internet of Things (IoT) based wireless sensors to remotely monitor and report conditions of the field, climate, and crops. This enables efficient management of resources like minimizing water requirements for irrigation and minimizing the use of toxic pesticides. Furthermore, the recent boom in Artificial Intelligence can enable farmers to deploy autonomous farming machinery and make better predictions of the future based on present and past conditions to minimize crop diseases and pest infestation. Together these two enabling technologies have revolutionized conventional agriculture practices. This survey paper provides: (a) A detailed tutorial on the available advancements in the field of smart agriculture systems through IoT technologies and AI techniques; (b) A critical review of these two available technologies and challenges in their widespread deployment; and (c) An in-depth discussion about the future trends including both technological and social, when smart agriculture systems will be widely adopted by the farmers globally. ER - TY - Article T1 - Leaf Disease Classification in Smart Agriculture using Deep Neural Network Architecture and IoT A1 - Ramana, K Y1 - 2022/// KW - Smart agriculture KW - IOT in smart farming KW - disease classification KW - convolutional neural network KW - sensor data for agriculture JF - Journal of Circuits, Systems and Computers DO - 10.1142/S0218126622400047 UR - https://api.elsevier.com/content/abstract/scopus_id/85133140042 N1 - Cited By (since 2022): 3 N2 - The Internet of Things (IoT) is bringing a new dimension to the smart farming market. This helps the user to collect the data from the agricultural fields in real time and move it to remote areas for processing. With the available sensor data and the image taken from the fields, automated disease prediction is possible. Deep neural network is used for classification of disease using the leaf images. Agriculture is the backbone of our country, but our output is poor when compared to the global standards due to lack of using technologies in the fields. In this work, various sensors like humidity sensor, pH level monitoring sensor, Temperature sensor, and Soil moisture sensor are used in the agricultural fields for collecting the real-time data. Multiple Sensors are installed in various locations of farms with one common controller Raspberry PI 3 module (RPI3), which was used to control all these sensors. Camera interfacing with RPI can be observed on leaf disease. Convolutional neural network architecture is used for leaf disease detection and classification. The accuracy of the disease classification system using convolutional neural network is 96% when the system is iterated for 50 epochs. ER - TY - Article T1 - Learning Aided System for Agriculture Monitoring Designed Using Image Processing and IoT-CNN A1 - Sarma, K K Y1 - 2022/// KW - Artificial intelligence KW - near-infrared images KW - CNN KW - image processing KW - leaf disease KW - smart agriculture JF - IEEE Access VL - 10 SP - 41525 EP - 41536 DO - 10.1109/ACCESS.2022.3167061 UR - https://api.elsevier.com/content/abstract/scopus_id/85128311810 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(sharma 2022) Learning_Aided_System_for_Agriculture_Monitoring_Designed_Using_Image_Processing_and_IoT-CNN.pdf N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) and artificial intelligence (AI) based methods for monitoring, control, and decision support are combined to design of a smart agriculture assistance system. The proposed system has a sensor pack that provides continuous data capture of temperature records, air and soil moisture and a camera for obtaining near-infrared (NIR) images of the plant leaves for use with an AI decision support system. We identify twelve types of vegetation for the study, out of which five disease classes of the tomato leaves are categorized using a Convolutional Neural Network (CNN). The work also includes experiments conducted with multiple clustering-based segmentation methods and some features namely Gray level co-occurrence matrix (GLCM), Local binary pattern (LBP), Local Binary Gray Level Co-occurrence Matrix (LBGLCM), Gray Level Run Length Matrix (GLRLM), and Segmentation-based Fractal Texture Analysis (SFTA). Out of several AI tools, CNN proves to be effective in providing automated decision support for classifying the plant leaf disease types through a cloud server that can be accessed using an app. Extensive on-field trials show that the system (VGG16 CNN, GLCM and a fuzzy based clustering) is effective in hot and humid conditions and proves to be reliable in identifying disease classes of certain vegetable types, certain usable vegetation cover of farmland and regulation of watering mechanism of crops. ER - TY - Conference Paper T1 - Leveraging IoT solutions as a base for development of the agriculture advisory services A1 - Mueller, S Y1 - 2022/// KW - decision support system KW - internet of things KW - integrated plant protection KW - apiary management KW - smart agriculture KW - interoperability JF - 2022 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2022 DO - 10.1109/COINS54846.2022.9854950 UR - https://api.elsevier.com/content/abstract/scopus_id/85137990478 N1 - Cited By (since 2022): 1 N2 - Plant protection is one of the key factors determining the quality of agricultural production. In this paper, we present the results regarding the implementation and deployment of the Polish decision support system (DSS) for integrated plant protection, as part of the large scale national project called eDwin. At the core of the DSS is an IoT network of agro-meteorological stations deployed across the whole Poland. Additionally, the DSS leverages a set of automatic phenological observation stations that are deployed in selected experimental fields. After the deployment and validation of several pest prediction models, the DSS currently allows Polish farmers and advisors to access and use 20 models related to the main cultivations in Poland. Moreover, a digital platform for the farmers and advisors has been designed and developed, providing four e-services, complemented with interoperable mechanisms and APIs. ER - TY - Conference Paper T1 - LoRa System with IOT Technology for Smart Agriculture System A1 - Edwin, L Y1 - 2022/// KW - energy KW - communication KW - long distance KW - security KW - agriculture KW - multiple nodes JF - 2022 IEEE 20th Student Conference on Research and Development, SCOReD 2022 SP - 39 EP - 44 DO - 10.1109/SCOReD57082.2022.9974084 UR - https://api.elsevier.com/content/abstract/scopus_id/85145436129 N1 - Cited By (since 2022): 1 N2 - LoRa (short for long-range) refers to the RF modulation under the low-power wide area networks (LPWANs) that offers the potential of smart solution under various environment conditions using the long-range communication capability. Nevertheless, the performance of this technology varies depending on the surrounding conditions as well as the presence of obstructions between the end-to-end transceivers. This paper describes the LoRa project that was developed to foster the local rural agriculture business by implementing smart agriculture applications based on LoRa. The Reyax RYLR890 LoRa transceiver is used to allow bi-directional communications between multiple nodes to operate two different agriculture systems remotely without using the intenet. The communication performance was investigated by studying the changes in the receiving signal strength indicator (RSSI) and signal-to-noise ratio (SNR) against distance in the urban and rural areas. The initial hypotheses stated that LoRa communicates better in rural areas. However, it is highly dependent on the placement of the transceiver. ER - TY - Book Chapter T1 - Low-Cost IoT Framework for Indian Agriculture Sector: A Compressive Review to Meet Future Expectation A1 - Verma, A Y1 - 2022/// KW - internet of things KW - LoRaWAN KW - Image processing KW - Insect and pest KW - Irrigation KW - Machine learning KW - Deep learning JF - Lecture Notes on Data Engineering and Communications Technologies VL - 90 SP - 241 EP - 258 SN - 2367-4512 DO - 10.1007/978-981-16-6289-8_21 UR - https://api.elsevier.com/content/abstract/scopus_id/85122484960 N1 - Cited By (since 2022): 2 N2 - This paper reviews the complete framework of IoT-based agriculture systems with a specific emphasis agricultural sector of India. Agriculture is a major contributor to the economies of developing countries, and it assists with satisfying the fundamental necessities of food, pay, and work for the population. Minimal effort with low cost is a significant factor in making any IoT network valuable and satisfactory to farmers. LoRa is a fewer power consuming, long-range remote systems administration innovation, reasonable for small-rate large area of applications in the Internet of Things. An IoT-based agriculture framework provides better resource management, crop management, improved production quality and quantity, cost-effective farming, crop monitoring, and field tracking. The IoT equipment and networking methods related to wireless sensors work in agricultural applications are thoroughly investigated. Advance image processing methods and the Internet of Things together produce a new way of smart agriculture systems. IoT-based framework system includes monitoring of crop status, field irrigation, insect and pest detection on field, weather monitoring, actuator intervention, expert suggestions and warning system for farmers, and automations. This study aims to determine the better method for a low-cost IoT-based agriculture system. ER - TY - Conference Paper T1 - Low-Power IoT Environmental Monitoring and Smart Agriculture for Unconnected Rural Areas A1 - Andreadis, A Y1 - 2022/// KW - internet of things KW - UAV KW - LoRa/LoRaWAN KW - Opportunistic Connectivity JF - 2022 20th Mediterranean Communication and Computer Networking Conference, MedComNet 2022 SP - 31 EP - 38 DO - 10.1109/MedComNet55087.2022.9810376 UR - https://api.elsevier.com/content/abstract/scopus_id/85135069130 N1 - Cited By (since 2022): 4 N2 - This paper considers massive IoT devices deployed in remote areas without terrestrial Internet connectivity. IoT devices use sensors for continuously monitoring ground data for smart agriculture and environmental purposes. We connect these sensors to the Internet via an aerial system based on Unmanned Aerial Vehicle (UAV), High-Altitude Platform Station (HAPS), or cubesat. Then, our particular focus is on using a UAV flying over the remote area that receives data from distributed IoT devices on the ground. An opportunistic protocol has been introduced that is based on the synchronization of sensor transmission to be consistent with the UAV pass close to them. A model has allowed validating the applicability of this scheme depending on a set of parameters, including the distance to be covered. Finally, a preliminary experimental testbed has been built to verify a path loss model in a benchmark configuration with the gateway on the ground. This experiment will be replicated using the gateway onboard the UAV. ER - TY - Article T1 - MHADBOR: AI-Enabled Administrative-Distance-Based Opportunistic Load Balancing Scheme for an Agriculture Internet of Things Network A1 - Adil, M Y1 - 2022/// KW - Load balancing KW - Industrial Internet of Things KW - MHADBOR Protocol KW - micro base station and base station JF - IEEE Micro VL - 42 IS - 1 SP - 41 EP - 50 DO - 10.1109/MM.2021.3112264 UR - https://api.elsevier.com/content/abstract/scopus_id/85115162210 N1 - Cited By (since 2022): 28 N2 - In this article, we present a supervised machine learning multipath and administrative-distance-based load balancing algorithm for an Agriculture Internet of Things (AG-IoT) network. The proposed algorithm is known as an artificial intelligence or simply AI-enabled multihop and administrative-distance-based opportunistic routing (MHADBOR) algorithm, which processes the collected information from source to the destination by means of multihop count and administrative-distance-based communication infrastructure in the network. Beside that, we used cluster heads (CH), microbase stations (RBSℜBS), and macrobase stations (ℵBSℵBS) in the network with a frequent rate to effectively utilize the administrative distance while managing the deployed network traffic in a congestionless communication environment. In addition, the MHADBOR algorithm empowers the participating devices to practice the administrative distance rather than hop count communication when they are in the vicinity of network special components, e.g., CH and RBSℜBS outcome statistics of the MHADBOR algorithm in the simulation environment exhibit an extraordinary improvement in contention, congestion, communication, and computing costs, accompanied by throughput and end-to-end (E2E) delay and packet loss ratio in the deployed AG-IoT network. ER - TY - Article T1 - Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things A1 - Murugamani, C Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/6534238 UR - https://api.elsevier.com/content/abstract/scopus_id/85132502782 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(murugamani 2022) Machine Learning Technique for Precision Agriculture application in 5G based in IoT.pdf N1 - Cited By (since 2022): 3 N2 - Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers. ER - TY - Conference Paper T1 - Machine Learning Techniques for Precision Agriculture Using Wireless Sensor Networks A1 - Goel, S Y1 - 2022/// JF - ECS Transactions VL - 107 IS - 1 SP - 9229 EP - 9238 SN - 1938-6737 DO - 10.1149/10701.9229ecst UR - https://api.elsevier.com/content/abstract/scopus_id/85130570806 N1 - Cited By (since 2022): 6 N2 - In India, approximately 70% of the total population is dependent on agriculture for their livelihood. Hence, it is essential to pay attention to agriculture to increase crop quality and quantity, thus increasing the overall cultivation yield. The traditional methods used require a lot of farmer's effort and hard work, which results in delayed crop cultivation. Moreover, it's challenging to predict the environmental conditions and detect the particular area where there are weeds, insects, etc., which requires immediate treatments, thus affecting overall crop production. So, there is a need to make it automated, and this can be done by adopting advanced techniques of precision agriculture (PA) or intelligent agriculture. Precision agriculture is one of the fields in which wireless sensor networks (WSNs) are widely adopted, which consists of a large number of sensors placed in the field to monitor and measure the various environmental parameters, such as humidity, temperature, soil moisture, soil PH value, precipitation, water level, etc., for enhancing the productivity, profitability, quantity, and quality of crops. The machine learning techniques can be applied to precision agriculture to increase crop growth, manage the process of crop cultivation, and create a perfect environment for the crops to increase productivity with less human effort. This paper provides an insight into various machine learning techniques used for precision agriculture using wireless sensor networks. ER - TY - Editorial T1 - Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture A1 - Fuentes, S Y1 - 2022/// JF - Sensors VL - 22 IS - 20 SN - 1424-8220 DO - 10.3390/s22207898 UR - https://api.elsevier.com/content/abstract/scopus_id/85140634258 L1 - file:///C:/Users/sonsu/Downloads/sensors-22-07898-v2.pdf N1 - Cited By (since 2022): 1 N2 - When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multi- spectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth and water status of crops, to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In ER - TY - Article T1 - Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation A1 - Kour, K Y1 - 2022/// KW - internet of things KW - saffron KW - sensors KW - precision agriculture KW - smart farming KW - hydroponics KW - NFT JF - Sensors VL - 22 IS - 22 DO - 10.3390/s22228905 UR - https://api.elsevier.com/content/abstract/scopus_id/85142704070 L1 - file:///C:/Users/sonsu/Downloads/sensors-22-08905.pdf N1 - Cited By (since 2022): 3 N2 - The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. ER - TY - Conference Paper T1 - New page of agriculture: On the view of 5G generation and GPS A1 - Wu, Y Y1 - 2022/// KW - smart agriculture KW - precision agriculture KW - 5G KW - GPS KW - sustainability KW - food production JF - ICCC 2022 - IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, Proceedings SP - 105 EP - 110 DO - 10.1109/ICCC202255925.2022.9922891 UR - https://api.elsevier.com/content/abstract/scopus_id/85141702926 N1 - Cited By (since 2022): 1 N2 - The coronavirus outbreak emphasizes the potential crisis of traditional agriculture, weak resilience to natural disasters and natural resources, and highly relies on the human workforce. 5G is the fifth generation of the mobile network with higher speed and bigger capacity. GPS is the position and timing operation system. Based on secondary research and content analysis, this review paper concluded that it is possible for a sustainable agriculture era by implementing 5G and GPS and other related sophisticated technologies. The smart agriculture scheme can maximize the economic benefits of agriculture, maximize agricultural production, and minimize the cost and environmental effects. However, the threats and security risks of 5G and GPS performing in agriculture are also important topics left for future researchers. ER - TY - Article T1 - Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility A1 - Symeonaki, E Y1 - 2022/// KW - agriculture 4.0 KW - integrated farm management KW - smart livestock farming KW - internet of things KW - middleware KW - context-awareness KW - context modeling KW - context reasoning JF - Agronomy VL - 12 IS - 3 DO - 10.3390/agronomy12030750 UR - https://api.elsevier.com/content/abstract/scopus_id/85129874233 L1 - file:///C:/Users/sonsu/Downloads/agronomy-12-00750-v2.pdf N1 - Cited By (since 2022): 5 N2 - Integrated farm management (IFM) is promoted as a whole farm approach toward Agriculture 4.0, incorporating smart farming technologies for attempting to limit livestock production’s negative impacts on the environment while increasing productivity with regard to the economic viability of rural communities. The Internet of Things (IoT) may serve as an enabler to ensure key properties—such as interconnectivity, scalability, agility, and interoperability—in IFM systems so that they could provide object-based services while adapting to dynamic changes. This paper focuses on the problem of facilitating the management, processing, and sharing of the vast and heterogeneous data points generated in livestock facilities by introducing distributed IoT middleware as the core of a responsive and adaptive service-oriented IFM system, specifically targeted to enable smart livestock farming in view of its unique requirements. The proposed IoT middleware encompasses the context-awareness approach via the integration of a flexible ontology-based structure for modeling and reasoning. The IoT middleware was assessed in actual conditions on the grounds of a case study for smart control of the thermal environment in a medium-sized pig farming facility. As derived from the obtained evaluation results, the system appears to perform quite satisfactorily in terms of computational performance as well as ontology coherence, consistency, and efficiency ER - TY - Article T1 - Performance Evaluation of Communication Systems Used for Internet of Things in Agriculture A1 - Yascaribay, G Y1 - 2022/// KW - internet of things KW - LPWAN KW - LoRaWAN KW - Omnet++ KW - FLoRa KW - agriculture KW - rural applications JF - Agriculture (Switzerland) VL - 12 IS - 6 DO - 10.3390/agriculture12060786 UR - https://api.elsevier.com/content/abstract/scopus_id/85131661390 L1 - file:///C:/Users/sonsu/Downloads/agriculture-12-00786-v2.pdf N1 - Cited By (since 2022): 2 N2 - The rapid development of Internet of Things (IoT) technology has provided ample opportunity for the implementation of intelligent agricultural production. Such technology can be used to connect various types of agricultural devices, which can collect and send data to servers for analysis. These tools can help farmers optimize the production of their crops. However, one of the main problems that arises in agricultural areas is a lack of connectivity or poor connection quality. For these reasons, in this paper, we present a method that can be used for the performance evaluation of communication systems used in IoT for agriculture, considering metrics such as the packet delivery ratio, energy consumption, and packet collisions. To achieve this aim, we carry out an analysis of the main Low-Power Wide-Area Networks (LPWAN) protocols and their applicability, from which we conclude that those most suited to this context are Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN). After that, we analyze various simulation tools and select Omnet++ together with the Framework for LoRa (FLoRa) library as the best option. In the first stage of the simulations, the performances of LoRa and LoRaWAN are evaluated by comparing the average propagation under ideal conditions against moderate propagation losses, emulating a rural environment in the coastal region of Ecuador. In the second phase, metrics such as the package delivery ratio and energy consumption are evaluated by simulating communication between an increasing number of nodes and one or two gateways. The results show that using two gateways with the Adaptive Data Rate technique can actively increase the delivery ratio of the network while consuming the same amount of energy per node. Finally, a comparison is made between the results of the simulation scenario considered in this project and those of other research works, allowing for the validation of our analytical and simulation results. ER - TY - Conference Paper T1 - Positioning and Attitude determination for Precision Agriculture Robots based on IMU and Two RTK GPSs Sensor Fusion A1 - Vieira, D Y1 - 2022/// KW - Sensor fusion KW - Global Navigation Satellite Systems (GNSS) KW - Real-Time Kinematic (RTK) KW - Inertial Measurement Unit (IMU) KW - Inertial Navigation System (INS) KW - Kalman Filter (KF) KW - agricultural vehicle's localizationprecision agric JF - IFAC-PapersOnLine VL - 55 IS - 32 SP - 60 EP - 65 SN - 2405-8963 DO - 10.1016/j.ifacol.2022.11.115 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2405896322027483 UR - https://api.elsevier.com/content/abstract/scopus_id/85144823584 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(viera 2022) Positioning and Attitude determination for PA robots.pdf N1 - Cited By (since 2022): 1 N2 - This paper focuses on the development of a data fusion architecture for positioning and attitude estimation of an autonomous agricultural vehicle combining two RTK-GNSS and an Inertial Measurement Unit (IMU) system. The main algorithm steps are presented giving a generic approach for real-time vehicle guidance applications or localization using data fusion. Important features such as sensor error modeling based on the Allan variance method as well as compensation phenomena related to terrestrial navigation using IMU mechanization are presented. A loosely coupled fusion architecture is proposed allowing low complexity for realtime algorithm integration. Finally, results based on real data from a real prototype are exploited to show the efficiency of the proposed algorithm. ER - TY - Review T1 - Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks A1 - Arrubla-Hoyos, W Y1 - 2022/// KW - 5G KW - Colombia KW - agriculture KW - smart farm KW - spectrum KW - sustainability JF - Sensors VL - 22 IS - 19 SN - 1424-8220 DO - 10.3390/s22197295 UR - https://api.elsevier.com/content/abstract/scopus_id/85139961138 N1 - Cited By (since 2022): 6 N2 - The growing global demand for food and the environmental impact caused by agriculture have made this activity increasingly dependent on electronics, information technology, and telecommunications technologies. In Colombia, agriculture is of great importance not only as a commercial activity, but also as a source of food and employment. However, the concept of smart agriculture has not been widely applied in this country, resulting in the high production of various types of crops due to the planting of large areas of land, rather than optimization of the processes involved in the activity. Due to its technical characteristics and the radio spectrum considered in its deployment, 5G can be seen as one of the technologies that could generate the greatest benefits for the Colombian agricultural sector, especially in the most remote rural areas, which currently lack mobile network coverage. This article provides an overview of the current 5G technology landscape in Colombia and presents examples of possible 5G/IoT applications that could be developed in Colombian fields. The results show that 5G could facilitate the implementation of the smart farm in Colombia, improving current production and efficiency. It is useful when designing 5G implementation plans and strategies, since it categorizes crops by regions and products. This is based on budget availability, population density, and regional development plans, among others. ER - TY - Review T1 - Precision agriculture using IoT data analytics and machine learning A1 - Akhter, R Y1 - 2022/// KW - Internet of Things KW - Data Analytics KW - Machine Learning JF - Journal of King Saud University - Computer and Information Sciences VL - 34 IS - 8 SP - 5602 EP - 5618 SN - 1319-1578 DO - 10.1016/j.jksuci.2021.05.013 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S1319157821001282 UR - https://api.elsevier.com/content/abstract/scopus_id/85110103193 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(akhter 2022) PRECISION AGRICULTURE USING IOT DATA ANALYTICS AND MACHINE LEARNING.pdf N1 - Cited By (since 2022): 38 N2 - In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT)has reformed nearly each industry like smart city, smart health, smart grid, smart home, including ”smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches. ER - TY - Conference Paper T1 - Quality Assessment Framework for IoT Based Systems for Agriculture Industry 4.0 A1 - Naqvi, S A H Y1 - 2022/// KW - Industry 4.0 KW - Smart agriculture KW - Internet of things KW - Cloud based IoT KW - Quality assessment framework KW - Reliability analysis JF - Communications in Computer and Information Science VL - 1615 SP - 134 EP - 142 SN - 1865-0929 DO - 10.1007/978-3-031-19968-4_14 UR - https://api.elsevier.com/content/abstract/scopus_id/85142706777 N1 - Cited By (since 2022): 1 N2 - As world is transforming to digital era so agriculture tends towards the IoT based smart systems with smarts objects. Smart object helps in monitoring and performing smart actions according to situations without human intervention. Real time monitoring of agriculture fields requires efficient and reliable services for cost reduction, efficient management and making smart decisions. IoT, as a critical Industry 4.0 enabler emerges smart agriculture technologies for cost reduction, increase production and advanced Big data analytics for smart decisions for further improvements. However, the agriculture with limited resources is facing challenges to change the longstanding production and meet current requirements. This study aims to fulfil the gaps related to quality of system by transforming conventional agriculture to IoT-enabled smart systems. An industry-led study demonstrates how to make the reliable smart systems based on IoT technologies with emerging Industry 4.0 and improve the production rate to fulfill the current needs of era. ER - TY - Article T1 - Ranking of performance indicators in an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC) A1 - Yadav, S Y1 - 2022/// KW - internet of things KW - agriculture supply chain KW - additive ratio assesment KW - covid-19 KW - efficient traceability mechanism JF - International Journal of Quality and Reliability Management VL - 39 IS - 3 SP - 777 EP - 803 DO - 10.1108/IJQRM-03-2021-0085 UR - https://api.elsevier.com/content/abstract/scopus_id/85116493366 N1 - Cited By (since 2022): 3 N2 - Purpose The prime aim of this paper is the identification and prioritization of performance indicators, which motivate the development of an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC). Also, this research aims for checking the robustness of obtained results. Design/methodology/approach Ten performance indicators have been identified based on the five “criteria in the IoT-based traceable system”. Further, based on five criteria, performance indicators were ranked by using grey-based “Additive Ratio Assessment”. Findings Sustainable practices obtained first rank, and certification of agri-products obtained worst ranking. Further, based on sensitivity analysis, tracking of agri-products and stakeholders' behavior have found high sensitivity. Also, information sharing and global distribution networks have found the least sensitive performance indicators. Research limitations/implications This research has some limitations of taking only a few criteria and alternatives. This study may also contribute as a practical insight to the practitioners and managers in decision-making in the adoption of an IoT-based traceable system within the ASC. Originality/value This research may motivate the implementation of an IoT-based efficient traceability mechanism that improved the sustainability and consumer's trust in the ASC during different types of hazardous activities and other outbreaks (COVID-19). Also, this research has provided a theoretical insight based on the dynamic capability theory (DCT). ER - TY - Review T1 - Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture A1 - Shaikh, F K Y1 - 2022/// KW - Communication protocols KW - computing infrastructure KW - Internet of Things KW - sensing technologies KW - sensors KW - smart agriculture KW - smart farming JF - IEEE Internet of Things Journal VL - 9 IS - 23 SP - 23583 EP - 23598 SN - 2327-4662 DO - 10.1109/JIOT.2022.3210154 UR - https://api.elsevier.com/content/abstract/scopus_id/85139402709 N1 - Cited By (since 2022): 2 N2 - Smart agriculture integrates key information communication technologies with sensing technologies to provide effective and cost-efficient agricultural services. Smart agriculture leverages a wide range of advanced technologies, such as wireless sensor networks, Internet of Things, robotics, agricultural bots, drones, artificial intelligence, and cloud computing. The adoption of these technologies in smart agriculture enables all stakeholders in the agricultural sector to develop better managerial decisions to get more yield. We differentiate between traditional agriculture and smart agriculture based on the deployment architectures along with a focus on the various processing stages in smart agriculture. We present a comprehensive review of various types of sensors that are playing a vital role in enabling smart agriculture. We also review the integration of various sensing technologies with emerging technologies and computing infrastructures to make agriculture smarter. Finally, we discuss open research challenges that must be addressed to improve the adoption and deployment of smart agriculture in the future ER - TY - Review T1 - Recent advancements and challenges of Internet of Things in smart agriculture: A survey A1 - Sinha, B B Y1 - 2022/// KW - Applied computing KW - Computer systems organization KW - Operations research KW - Embedded and cyber-physical systems JF - Future Generation Computer Systems VL - 126 SP - 169 EP - 184 SN - 0167-739X DO - 10.1016/j.future.2021.08.006 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0167739X21003113 UR - https://api.elsevier.com/content/abstract/scopus_id/85113348010 N1 - Cited By (since 2022): 119 N2 - The Internet of Things (IoT) is an evolving paradigm that seeks to connect different smart physical components for multi-domain modernization. To automatically manage and track agricultural lands with minimal human intervention, numerous IoT-based frameworks have been introduced. This paper presents a rigorous discussion on the major components, new technologies, security issues, challenges and future trends involved in the agriculture domain. An in-depth report on recent advancements has been covered in this paper. The goal of this survey is to help potential researchers detect relevant IoT problems and, based on the application requirements, adopt suitable technologies. Furthermore, the significance of IoT and Data Analytics for smart agriculture has been highlighted. ER - TY - Conference Paper T1 - Regulated Energy Harvesting Scheme for Self-Sustaining WSN in Precision Agriculture A1 - Goel, K Y1 - 2022/// KW - Wireless sensor networks KW - energy harvesting KW - LEACH KW - TEH KW - NEH and REH JF - Lecture Notes on Data Engineering and Communications Technologies VL - 91 SP - 367 EP - 385 SN - 2367-4512 DO - 10.1007/978-981-16-6285-0_30 UR - https://api.elsevier.com/content/abstract/scopus_id/85119658759 N1 - Cited By (since 2022): 2 N2 - In case of precision agriculture-based WSN, energy consumption may vary due to different parameters (i.e., dynamic computational overload/sensor density variations) and traditional energy harvesting scheme does not consider these conditions during harvesting and thus may reduce the overall lifespan of the network. In order to meet the current energy requirements of WSN, an energy harvesting scheme can regulate itself as per the current energy requirements of the WSN. In this paper, a regulated energy harvester will be introduced to overcome the above-discussed constraint and its performance will be analyzed using various performance parameters (throughput/residual energy/harvested energy, etc.). ER - TY - Article T1 - Remote Monitoring and Management System of Intelligent Agriculture under the Internet of Things and Deep Learning A1 - Zhu, M Y1 - 2022/// JF - Wireless Communications and Mobile Computing VL - 2022 DO - 10.1155/2022/1206677 UR - https://api.elsevier.com/content/abstract/scopus_id/85131399777 L1 - file:///D:/Penelitian/Simlitabmas/PDP 2021/Garapan/Data dan analisa/Artikel 1/referensi/(Zhu 2022) Remote Monitoring and Management System of Intelligent agriculture under IoT and DL.pdf N1 - Cited By (since 2022): 1 N2 - Based on the Internet of Things (IoT) technology and deep learning algorithm, a greenhouse intelligent agriculture management system was established to analyse the application value of the intelligent agriculture remote monitoring management system in the greenhouse planting industry. Based on the analysis of greenhouse planting demand and environmental factors, the intelligent agriculture monitoring system is established based on the IoT, and the greenhouse system controller is designed based on the adaptive proportion integration differentiation (PID) algorithm. The noise data removal method is established based on the furthest priority strategy -means (FPKM) algorithm, and the greenhouse data management system is established mainly by the business platform and management platform. The data set of air temperature during the cultivation of Flammulina velutifolia in a factory from October 2020 to January 2021 was selected as the research data to analyse the ability of the IoT-based IARMM system to collect greenhouse temperature, carbon dioxide, and light data. In addition, the application of the greenhouse data management system in greenhouse data monitoring and control is analysed. The processing capability of agricultural environment monitoring data based on the FPKM algorithm is analysed. The results show that the intelligent agriculture monitoring system based on IoT and machine learning can effectively monitor the data on greenhouse temperature, carbon dioxide, light, and other environmental factors, and the greenhouse data management system can effectively ensure the normal operation of equipment and data storage. After being processed by the FPKM algorithm, outliers are identified and effectively removed. Under random seeds, the iteration times of the FPKM algorithm and the -means algorithm are significantly different. The iteration number of the FPKM algorithm is basically stable at approximately 2 times, while the iteration number of the -means algorithm obviously fluctuates. Based on the IoT and FPKM algorithm, the intelligent agriculture monitoring system covering the user monitoring center, data center module, and mobile phone client module is established. This work establishes a practical remote monitoring and management system for intelligent agriculture based on the IoT and machine learning algorithm, which provides a new idea for intelligent agricultural management. ER - TY - Article T1 - Risk monitoring model of intelligent agriculture Internet of Things based on big data A1 - Wang, Q Y1 - 2022/// KW - Big data KW - Smart agriculture KW - Risk monitoring KW - internet of things JF - Sustainable Energy Technologies and Assessments VL - 53 DO - 10.1016/j.seta.2022.102654 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2213138822007032 UR - https://api.elsevier.com/content/abstract/scopus_id/85138456606 N1 - Cited By (since 2022): 2 N2 - With the development of the times, there is a huge amount of data in every industry. Big data technology is to collect, analyze, process and information from these huge data and apply it to all aspects of our life to improve people's production life. The proposal and development of smart agriculture will play a significant role in the further implementation of the country's rural revitalization strategy. However, the research on risk monitoring in smart agriculture is not yet systematic enough. The purpose of this article is to strengthen the development of smart agriculture under big data, focusing on risk monitoring. To this end, this article studies big data monitoring through data analysis methods and Internet of Things technologies, and discusses the principles of big data and key technology principles of the Internet of Things. Explained, and proposed a modern agricultural technology platform based on the Internet of Things and big data. This platform is established on the basis of precision agriculture and wireless sensor network work, and analyzes the accuracy of various types of light wavelengths for determining wheat rust. The analysis results show that the accuracy of light with multiple wavelengths is not necessarily better than the light with a single wavelength. The accuracy of 600 nm wavelength can reach 100 %, and the accuracy of monitoring with 4 wavelengths together is rather low. However, less consideration is given to the factors in this article, and wheat does not necessarily cause only one disease. Comprehensive analysis is required. With the help of spectral research and aerial photography of drones, the severity of the disease and the outbreak area can be speculated. ER - TY - Conference Paper T1 - Role of Internet-of-Things (IoT) and Sensor Devices in Smart Agriculture: A Survey A1 - Viswanathan, S Y1 - 2022/// KW - Internet of Things KW - Smart Agriculture KW - Sensor Device KW - Nutrient Level Measurement KW - Soil Moisture KW - Pesticide KW - Crop Prediction JF - Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022 SP - 421 EP - 424 DO - 10.1109/ICICCS53718.2022.9788280 UR - https://api.elsevier.com/content/abstract/scopus_id/85133205001 N1 - Cited By (since 2022): 3 N2 - Agriculture is affected by many factors such as environmental changes, natural disasters, soil erosion, water irrigation, and pesticide control. All these factors will affect the yield of the crop. The Internet of Things (IoT) is a unique technology that will bind the physical components to modernize various domains. Agriculture can be carried out from anywhere, at any time, through IoT. The IoT - based framework is designed to automate the control and monitoring processes in agriculture without requiring human intervention. IoT sensors are used in specialized agricultural applications such as soil moisture measurement, nutrient levels, crop forecasting, pesticide management, and water irrigation. The difficulty faced by the researchers is identifying suitable sensors based on the need of the application. This difficulty was overcome by discussing the importance of IoT in agriculture and even the author's thoughts on various sensor devices used in smart agriculture. This survey stands to aid the prospective investigators in identifying appropriate IoT sensors, the importance of IoT in agriculture, and the technologies based on application requirements ER - TY - Book Chapter T1 - Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming A1 - Kumar, P Y1 - 2022/// KW - Big data KW - modern farming KW - smart farming KW - artificial intelligence KW - sensors JF - Bioinformatics in Agriculture: Next Generation Sequencing Era SP - 625 EP - 639 DO - 10.1016/B978-0-323-89778-5.00035-0 UR - https://api.elsevier.com/content/article/eid/3-s2.0-B9780323897785000350 UR - https://api.elsevier.com/content/abstract/scopus_id/85142630071 N1 - Cited By (since 2022): 1 N2 - Modern farming has progressed by espousing technological developments, for instance, machines intended for tillage and harvesting, controlled irrigation, fertilizers, pesticides, crop breeding, genetics research, and biotechnological tools for trait enhancement. These innovations facilitated farmers to yield a large quantity of superior crops yield. Conversely, to triumph the unsurpassed possible yield from various types of soil is still in progress, and there are chief losses related to food wastage—especially during and postharvest—where the production is not scrutinized and touched well. The industry prerequisites a shrewd and precise solution that is possible through new technologies. Smart farming targets custom modern technological gears to rally crop yield and product quality, for case in point, accurate agricultural information, a site-specific crop management concept that monitors, measures, and measures the variability in crops and field variability. The kin use of a decision support system [artificial intelligence (AI)] countenances farmers to use fertilizer pesticides given to crops as per the requisite. Thus such monitoring could be accomplished by integrating employing suitable electronic sensing devices that record data in soil, environment, or crops. The data can run useful information related to what the crop required. To sort the best possible use of soil in a particular area, control crop care and yield postharvest. Informed decisions have to be made approximately allocating with has been intricated in the development and use of sensors to help establish the quality of a wide range of horticultural products, including fruits. Therefore AI is indicated as a data-driven elucidation with many recompenses. The technique could help to diminish the loss of fruits and vegetables laterally with the supply chain from the farm. ER - TY - Conference Paper T1 - SAF: A Peer to Peer IoT LoRa System for Smart Supply Chain in Agriculture A1 - Karras, A Y1 - 2022/// KW - Internet of Things KW - Smart agriculture KW - Supply chain management KW - Event detection KW - Trajectory modeling KW - LPWAN KW - LoRa JF - IFIP Advances in Information and Communication Technology VL - 647 SP - 41 EP - 50 SN - 1868-4238 DO - 10.1007/978-3-031-08337-2_4 UR - https://api.elsevier.com/content/abstract/scopus_id/85133220672 N1 - Cited By (since 2022): 3 N2 - In the dairy industry farming as well as transportation conditions are paramount to product quality and to the overall supply chain resiliency. However, modern farms are complex installations with a broad spectrum of factors such as atmospheric conditions, including rain and humidity, ground composition, and highly irregular animal motion making difficult the deployment of digital telemetry systems. These conditions in turn translate to technical requirements including easy maintenance, scalability, wide coverage, low power consumption, strong signal resiliency, and high spatial resolution. Perhaps the best way to meet them is an LPWAN based IoT deployment. Along this line of reasoning, here is presented the architecture of SAF, an integrated IoT system built on LoRa technology for monitoring the supply chain of a dairy farm ensuring livestock and food safety with emphasis placed on monitoring the states of sheep, milk refrigerator, and milk trucks. LoRa was selected after an extensive comparison between the major latest generation LPWAN protocols. SAF is slated to be implemented in a local cooperative to monitor the production of protected designation of origin products ER - TY - Article T1 - SMART SYSTEM ARCHITECTURE DESIGN IN THE FIELD OF PRECISION AGRICULTURE BASED ON IOT IN BANGLADESH A1 - Sourav, A I Y1 - 2022/// KW - Internet of Things KW - Precision agriculture KW - Bangladesh KW - Irrigation KW - Simula- tion JF - ICIC Express Letters VL - 16 IS - 10 SP - 1111 EP - 1118 DO - 10.24507/icicel.16.10.1111 UR - https://api.elsevier.com/content/abstract/scopus_id/85138756673 L1 - file:///C:/Users/sonsu/Downloads/SmartSystemArchitectureDesignintheFieldofPrecisionAgricultureBasedonIoTinBangladesh.pdf N1 - Cited By (since 2022): 1 N2 - Despite being the largest production sector, agriculture in Bangladesh is still backdated. Applying a technology-based smart system architecture in precision agriculture can better smoothen Bangladesh’s traditional farming activities. This study aims to in- troduce a smart system architecture in precision agriculture based on IoT in Bangladesh. The study combines several sensors compatible with the microcontroller unit, IoT cloud platform, and mobile application. The system is simulated in Cisco Packet Trace (Ver- sion 7.3.1) software. The sensors collect environmental data from the fields and process them in a cloud platform to produce information. Based on the information, farmers can access the information through a mobile application and make smart decisions to improve crop production. The system supports remote monitoring and control. The simulated re- sult showed that the system could maintain a suitable environment for the crops. ER - TY - Conference Paper T1 - Security Challenges and Solutions for Internet of Things based Smart Agriculture: A Review A1 - Basharat, A Y1 - 2022/// KW - Internet of Things KW - smart agriculture KW - security challenges KW - blockchains KW - Software Defined Network JF - 4th International Conference on Smart Sensors and Application: Digitalization for Societal Well-Being, ICSSA 2022 SP - 102 EP - 107 DO - 10.1109/ICSSA54161.2022.9870979 UR - https://api.elsevier.com/content/abstract/scopus_id/85138697953 N1 - Cited By (since 2022): 2 N2 - The Internet of Things (IoT) is a massive and evolving technology that has the potential to revolutionize our world. It enables devices to connect to the internet via sensors, drones, satellites, and so on. Agriculture is critical to the economy of any country. With the world’s population growing, traditional agricultural methods are finding it hard to fulfill the world’s food needs. It is the need of the hour to incorporate IoT in agriculture to digitalize the farming methods to increase the production level. IoT-based modernization in agriculture helps the farmers to check soil moisture, humidity, water level, climate conditions, crop production, etc. IoT applications also promise to maximize the comfort level and efficiency and offer automation to the farmers. With rising cyber-attacks on IoT, smart devices need better security, confidentiality, integrity, and recovery from attacks. In this review, our main focus is on the security challenges faced by resource constraints, low storage, and low power devices. The basic security requirements for IoT devices in smart agriculture are briefly reviewed. Some state-of-the-art security technologies are explored, including machine learning, fog computing, edge computing, SDN, and Blockchains. This analysis of security concerns and security measures will give future scholars a research direction. ER - TY - Article T1 - Security in IoT-enabled smart agriculture: architecture, security solutions and challenges A1 - Vangala, A Y1 - 2023/// KW - Smart Agriculture KW - Internet of Things KW - Authentication KW - Access Control KW - Security KW - Testbeds JF - Cluster Computing VL - 26 IS - 2 SP - 879 EP - 902 DO - 10.1007/s10586-022-03566-7 UR - https://api.elsevier.com/content/abstract/scopus_id/85128293836 N1 - Cited By (since 2023): 9 N2 - Agricultural industry is one of the most vital industries that has a major contribution to the economy due to its share in the Gross Domestic Product (GDP) and as a source of employment. The past few decades have seen immense change in the operation of agricultural sector with the introduction of precision farming in conjunction with Internet of Things (IoT). The application of such advancements is highly based on exchange of messages between various devices in the farming. This paper aims to study the security scenarios applicable in husbandry through the analysis of possible attacks and threats. The testbeds available for agriculture based on IoT have been studied. An architecture for smart farming is proposed which is independent of the underlying technologies that may be used and the requirements of security have been laid out based on the proposed architecture. A literature survey of security protocols for various subsectors of security in smart agriculture along with authentication protocols in smart applications provides a detailed direction of the progress in each of farming security sub-areas and identifies the dearth of existing protocols. The current progress in development of IoT-based tools and systems from industry has also been studied. ER - TY - Conference Paper T1 - Sensor Based Smart Agriculture with IoT Technologies: A Review A1 - Pyingkodi, M Y1 - 2022/// KW - IoT Sensors KW - Smart farming KW - Agriculture Sensor KW - Smart Agriculture KW - Precision Agriculture JF - 2022 International Conference on Computer Communication and Informatics, ICCCI 2022 DO - 10.1109/ICCCI54379.2022.9741001 UR - https://api.elsevier.com/content/abstract/scopus_id/85128715591 L1 - file:///C:/Users/sonsu/Downloads/Sensor_Based_Smart_Agriculture_with_IoT_Technologies_A_Review.pdf N1 - Cited By (since 2022): 10 N2 - The IoT is a new technology trend used in almost every area thing, when connected to the internet and to each other, when you connect to the internet or interconnect, your entire system will be smarter. We have used IoT in all areas of our lives, including smart cities, smart homes, and smart retail. Much more. From 9.6 billion by 2050, agriculture needs to deliver even faster to meet this type of demand. This is possible with the latest technology, especially the IoT. The IoT enables labour free farms. Not only can it be used for large-scale agriculture, but it can also be used for livestock, greenhouse management, and agricultural land management. The most significant tool for the IoT is the sensor. A sensor is a device that collects important data that is interpreted to obtain the required analysis. The important objective of sensors are used to determine the soil's physical qualities and the environment. The main applications of sensors are control and supervise, safety, alarm, diagnostics, and analytics. Sensors make innovative agriculture more effective and trouble-free. In agriculture, the sensor is mainly used for measuring, measuring NPK (Nitrogen, Phosphorus, Potassium) levels, and detecting disease and soil moisture content. The main solution to this problem is smart farming, which modernizes traditional farming practices. This paper narrates the role of IoT application in smart agriculture. Smart farming is also known as precision farming hence it uses accurate information to draw outcomes. It demonstrates the different sensors, applications, challenges, strengths and weaknesses that support the IoT and agriculture. ER - TY - Article T1 - Sensor-Based Technologies in Sugarcane Agriculture A1 - Garcia, A P Y1 - 2022/// KW - Sugarcane harvester KW - Sugarcane planter KW - Sprayer KW - Subsoiler KW - Automation KW - Control JF - Sugar Tech VL - 24 IS - 3 SP - 679 EP - 698 DO - 10.1007/s12355-022-01115-5 UR - https://api.elsevier.com/content/abstract/scopus_id/85129134691 N1 - Cited By (since 2022): 2 N2 - The sensors allow the delivery of real-time information on a range of production systems, making each day relevant to the development of autonomous agricultural machinery. The sensors implementation in sugarcane operations is no exception. The objective of this work was to present the technological evolution and the state of the art of the sensors used in the monitoring, decision-making and management process for sugarcane cultivation. This review addresses the sensors involved in the agricultural operations of sugarcane production. For soil preparation and properties, the sensors involved in the diagnosis of compacted areas, depth of the soil preparation tool and monitoring the traction force. In planting, the monitored variable is the sugarcane stalks mass flow poured into the planting furrows. Optical remote sensing, vegetative index, ultrasonic, photoelectric and cameras are tools that help in the task of monitoring sugarcane cultivation. One of the significant challenges for fertilization is to quantify the soil need for nutrient replacement. In spraying, the implementation of sensors to detect pests or weeds. At harvest, the sensors used real-time yield estimation and the sensors for base cut height controlling. Finally, it was found that the integration of sensors of different physical principles to measure crop and soil attributes. Machine operational variables are a trend together with the use of intelligent control systems. This trend generates an increase in the volume of data on the crop, improving the management of the fleet and inputs,and demanding a robust data network in the field. ER - TY - Article T1 - Sensors for UAVs dedicated to agriculture: current scenarios and challenges A1 - Szczepanski, C J Y1 - 2022/// KW - agricultural onboard sensors KW - agriculural uav KW - uav sensors JF - Aircraft Engineering and Aerospace Technology VL - 94 IS - 1 SP - 31 EP - 44 DO - 10.1108/AEAT-11-2020-0257 UR - https://api.elsevier.com/content/abstract/scopus_id/85108194619 N1 - Cited By (since 2022): 3 N2 - Purpose The unmanned aerial vehicles (UAVs) entered into their development stage when different applications became real. One of those application areas is agriculture. Agriculture and transport currently follow infrastructure as the top industries in the world UAV market. The agricultural UAV can be acquired as a ready-made, built by its future user or UAV-as-a-service (UaaS) way. This paper aims to help the UAVs’ users to choose the right sensors for agricultural purposes. For that sake, the overview of the types and application areas of onboard sensors is presented and discussed. Some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring UAVs for their purposes related to the agricultural area. Design/methodology/approach The agricultural UAVs’ onboard specialised sensors have been analysed, described and evaluated from the farmer’s operational point of view. That analysis took into consideration the agricultural UAVs’ types of missions, sensor characteristics, basics of the data processing software and the whole set of UAV-sensor-software operational features. As the conclusions, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented. Findings Services performed by the UAVs for the agriculture businesses are the second in the UAV services world market, and their growth potential is around 17% compound annual growth rate in the next years. As one of the quickest developing businesses, it will attract substantial investments in all related areas. They will be done in the research, development and market deployment stages of that technology development. The authors can expect the new business models of the equipment manufacturers, service providers and sellers of the equipment, consumables and materials. The world agricultural UAVs’ services market will be divided between the following two main streams: the UAVs’ solutions dedicated to the individual farmers, systems devoted to the companies giving the specialised services to individual farmers, in the form of UaaS. It will be followed by the two directions of the agriculture UAV set optimisation, according to each of the above streams’ specific requirements and expectations. Solutions for the individual users will be more straightforward, universal and more comfortable to operate but less effective and less accurate than systems dedicated to the agricultural service provider. UAVs are becoming important universal machines in the agriculture business. They are the newcomers in that business but can change the processes performed traditionally. Such an example is spraying the crops. UAVs spray the rice fields in Japan on at least half of them every year. The other is defoliating the cotton leaves, which only in one China province takes place on a few million hectares every year (Kurkute et al., 2018). That trend will extend the range of applications of UAVs. The agricultural UAV will take over process after process from the traditional machines. The types and number of missions and activities performed by agricultural UAVs are growing. They are strictly connected with the development of hardware and software responsible for those missions’ performance. New onboard sensors are more reliable, have better parameters and their prices are reasonable. Onboard computers and data processing and transmitting methods allow for effective solutions of automatisation and autonomy of the agricultural UAVs’ operation. Automatisation and autonomous performance of the UAVs’ agricultural missions are the main directions of the future development of that technology. Changing the UAV payload allows for its application to a different mission. Changing the payload, like effectors, is quite simple and does not require any special training or tooling. It can be done in the field during the regular operation of the agricultural UAV. Changing the sensor set can be more complicated, because of the eventually required calibrating of those sensors. The same set of sensors gives a possibility to perform a relatively broad range of missions and tasks. The universal setup consists of the multispectral and RGB camera. The agricultural UAV equipped with such a set of sensors can effectively perform most of the crop monitoring missions. The agriculture business will accept the optimised sensor-computer-software UAV payload set, where its exploitation cost and operational simplicity are the critical optimisation factors. Simplicity, reliability and effectiveness of the everyday operation are the vital factors of accepting the agricultural UAV technology as a widespread working horse. Research limitations/implications Performed research studies have been done taking into consideration the factors influencing the real operational decisions made by the farmers or companies offering UAV services to them. In that case, e.g. the economical factors have been considered, which could prevail the technical complexity or measuring accuracy of the sensors. Then, drawn conclusions can be not accurate from the scientific research studies point of view, where the financing limits are not so strict. Practical implications The main goal of the paper is to present the reasons and factors influencing the “optimised” solution of the configuration of agricultural UAV onboard sensors set. It was done at the level useful for the readers understanding the end-users expectations and having a basic understanding of the sensors-related technologies. The paper should help them to configure an acceptable agricultural UAV for the specific missions or their servicing business. Social implications Understanding the technology implications related to the applying of agricultural UAVs into everyday service is one of the main limits of that technology market deployment. The conclusions should allow for avoiding the misunderstanding of the agricultural UAVs’ capabilities and then increasing their social acceptance. That acceptance by the farmers is the key factor for the effective introduction of that technology into the operation. Originality/value Presented conclusions have been drawn on the base of the extensive research of the existing literature and web pages, and also on the own experience in forestry and agriculture and other technical applications of the onboard sensors. The experience in practical aspects of the sensors choosing and application into several areas have been also used, e.g. manned and unmanned aeroplanes and helicopters applied in similar and other types of missions. ER - TY - Conference Paper T1 - Smart Agriculture Framework Implemented Using the Internet of Things and Deep Learning A1 - Aishwarya, R Y1 - 2022/// KW - Smart agriculture KW - Machine learning KW - Internet of things KW - Sensors KW - Artificial neural network JF - Smart Innovation, Systems and Technologies VL - 271 SP - 639 EP - 648 SN - 2190-3018 DO - 10.1007/978-981-16-8739-6_56 UR - https://api.elsevier.com/content/abstract/scopus_id/85132718844 N1 - Cited By (since 2022): 1 N2 - In the recent world, the Internet of things (IoT) is a rising trend among a variety of real-world applications which tends to collect real-time data from a variety of sensors which are connected together with an Internet of things (IoT) chip. Our proposed model aims for the implementation of a smart agriculture framework which is implemented using an Internet of things architecture built on a system that comprises of various sensors and layers which can collect data based on various parameters. Our proposed system is expected to revolutionize the agriculture heralding in the era of smart agriculture where the farmers can rely on smart sensors and intelligent systems to perform accurate predictions based on the data collected. Smart agriculture is efficient on resource usage, scalability, and flexibility it offers along with the automation that it offers by implementing various layers in the architecture. ER - TY - Conference Paper T1 - Smart Agriculture Implementation—Blockchain IoT-Based Approach A1 - Bhattacharya, S Y1 - 2022/// KW - Smart agriculture KW - Internet of Things KW - Blockchain KW - Ethereum KW - Smart contracts JF - Lecture Notes in Electrical Engineering VL - 815 SP - 87 EP - 97 SN - 1876-1100 DO - 10.1007/978-981-16-7011-4_9 UR - https://api.elsevier.com/content/abstract/scopus_id/85125234117 N1 - Cited By (since 2022): 2 N2 - Over the years it has really been challenging working with Internet of Things (IoT) devices in terms of security as every IoT device offers a potential entry point. Blockchain is an emerging technology which is capable of minimizing the inherent risk of security and privacy issues of IoT. In this paper, blockchain IoT (BIoT)-based approach has been proposed to develop a smart agricultural system. It uses a distributed ledger which automatically stores the sensor data across different locations in a decentralized manner. Proposed method is beneficial compared to conventional IoT-based system as BIoT offers public access to the distributed ledger and time stamping of the sensor data. For the smart agriculture system, temperature and humidity of environment, moisture content and dielectric soil moisture content which are the fundamental parameters are measured here with three different sensors. NodeMCU has been used as IoT device. An Ethereum blockchain created via Ganache. Latency rate for the same is calculated. ER - TY - Conference Paper T1 - Smart Agriculture Monitoring System Using Internet of Things (IoT) A1 - Manglani, T Y1 - 2022/// KW - IoT KW - Smart Device KW - Smart Agriculture KW - Sensor KW - Automation KW - Crop Monitoring KW - Plant JF - Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022 SP - 501 EP - 505 DO - 10.1109/ICEARS53579.2022.9752446 UR - https://api.elsevier.com/content/abstract/scopus_id/85128946203 N1 - Cited By (since 2022): 1 N2 - The New beginning of computing technology is arriving such as the Internet of Things (IoT). It is a kind of Global Neural Network the cloud that interfaces various gadgets. Human life and the way work have been revolutionized by the Internet in the past decade. Currently, IoT is changing the trends of human life as the use of emerging technologies which consist of heterogeneous communication devices is increasing. IoT are relevant in different strategies of agriculture. India has agriculture as its essential occupation. As per IBEF (India Brand Equity Foundation), 58% individuals living in rural areas in India are reliant upon agriculture. The agricultural advancement is sped up with the increment in the profitability and up gradation of the plantation frameworks. The IoT has the capacity to change the world. In any case, the use of innovation like IoT in agriculture could have the best effect. Smart Agriculture is an idea wherein data and correspondence innovation is carried out to deal with every one of the exercises and cycles identified with the agriculture space. With the quick improvement of the world population, huge space of land is used to foster lodging and the capacity of creating food is decreased. Farming has gotten essential in present pattern and keeps food on the tables. Farming with IoT helps in moderating the lack of food by requesting the current land for more grounded usage at least expense. Smart agriculture is an idea that rapidly snaps on the agricultural field. In this paper present a novel design that are developing an automated system which is able to control the crop monitoring of the agriculture lands, which is quite difficult for human beings. ER - TY - Conference Paper T1 - Smart Agriculture Monitoring and Management System using IoT-enabled Devices based on LoRaWAN A1 - Supanirattisai, P Y1 - 2022/// KW - Smart farming KW - Precision agriculture KW - Internet of Things KW - IoTs KW - LoRa KW - LoRaWAN KW - Node-RED JF - ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications SP - 679 EP - 682 DO - 10.1109/ITC-CSCC55581.2022.9894956 UR - https://api.elsevier.com/content/abstract/scopus_id/85140602137 N1 - Cited By (since 2022): 2 N2 - A fully automated, smart agriculture system using IoT-enabled devices connected to LoRaWAN network (Long Range Wide Area Network) is proposed in this study. The system is capable of measuring crop growing parameters using low-power and low-cost sensor devices. Environmental conditions are automatically regulated through actuator end devices that receive activation command from a network server, allowing precise control of the water and mist pumps. Real-time data and system status are sent to the cloud and can be accessed via customizable dashboard. The transmission range between LoRa end devices and gateway is found to vary from 300 m to 1700 m, depending on the quality of the LoRa antenna. Compared to a Wi-Fi implemented system, LoRa provides for a longer range of communication and 2.4 times the power reduction when operating in Working state. In an Idle state, the end device conserves power by entering a deep-sleep mode which offers up to 86% reduction in power when compared to an active mode. ER - TY - Conference Paper T1 - Smart Agriculture Using Internet of Things: An Empirical Study A1 - Saini, M K Y1 - 2022/// KW - Smart farming KW - IOT KW - Sensors KW - Wi-Fi KW - Agriculture JF - Lecture Notes in Electrical Engineering VL - 855 SP - 163 EP - 175 SN - 1876-1100 DO - 10.1007/978-981-16-8892-8_13 UR - https://api.elsevier.com/content/abstract/scopus_id/85128959044 N1 - Cited By (since 2022): 1 N2 - IOT is the life-changing technology nowadays. That represents the attribute of computing & communication. Nowadays, IOT is using everywhere like home automation, smart health, smart cities, air smog monitoring, water distribution system, etc. The part of IOT is very huge and can be implemented everywhere. IOT also play a very important role in the area of agriculture. In the traditional agriculture system, farmers are using the oldest system of the farming. There is no technique of soil monitoring, rain water monitoring system etc. In this paper, I surveyed typical agriculture methods, which is used by the farmers in and what are the problems farmers are facing? I personally meet the farmer and visited their fields for collecting more information about the new technology which can be implementing in field nowadays. The goal of this study is to show how to use the Internet of Things to monitor humidity, soil condition, temperature, and give water to the field, as well as climate conditions. This report's IoT-based Smart Farming System is integrated with several sensors and a Wi-Fi module, resulting in a live data feed that can be accessed online. ER - TY - Review T1 - Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture A1 - Dhanaraju, M Y1 - 2022/// KW - crop management KW - sustainable agriculture KW - smart farming KW - internet of things KW - advanced agriculture practices KW - issues and problems JF - Agriculture (Switzerland) VL - 12 IS - 10 SN - 2077-0472 DO - 10.3390/agriculture12101745 UR - https://api.elsevier.com/content/abstract/scopus_id/85141822628 L1 - file:///C:/Users/sonsu/Downloads/agriculture-12-01745-v2.pdf N1 - Cited By (since 2022): 11 N2 - Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated. ER - TY - Book Chapter T1 - Smart Technologies in Agriculture as the Basis of Its Innovative Development: AI, Ubiquitous Computing, IoT, Robotization, and Blockchain A1 - Savelyeva, N K Y1 - 2022/// KW - Smart technology KW - Agriculture KW - Innovative development KW - AI KW - Ubiquitous computing KW - internet of things KW - Robots KW - Blockchain JF - Smart Innovation, Systems and Technologies VL - 264 SP - 29 EP - 35 SN - 2190-3018 DO - 10.1007/978-981-16-7633-8_4 UR - https://api.elsevier.com/content/abstract/scopus_id/85126220231 N1 - Cited By (since 2022): 3 N2 - The paper aims to determine the contribution of various smart technologies (artificial intelligence [AI], ubiquitous computing, the Internet of Things [IoT], robotization, and blockchain) to food security. This study also seeks to develop recommendations for improving the innovative development of agriculture for ensuring food security and determining the limits of the implementation of the “Zero hunger” sustainable development goal based on smart technology. The authors apply the method of regression and correlation analysis. The paper substantiates that different smart technology contributes to food security in different ways. The most contribution is registered on the part of the blockchain (− 1.54 points). The contribution of AI, ubiquitous computing, and IoT is also quite significant (− 0.46 points). The contribution of robotization is much less pronounced, especially in countries dependent on food imports. The authors developed recommendations to improve the innovative development of agriculture for food security based on blockchain, AI, ubiquitous computing, and the IoT. The authors revealed the limits of implementation of the second SDG based on smart technology and quantitative availability (non-deficiency) of food. It is shown that the implementation of the given recommendations increases the affordability of food to the maximum. ER - TY - Review T1 - Smart microalgae farming with internet-of-things for sustainable agriculture A1 - Lim, H R Y1 - 2022/// KW - Artificial intelligence KW - Internet of things KW - Machine learning KW - Microalgae KW - Smart farming. JF - Biotechnology Advances VL - 57 SN - 0734-9750 DO - 10.1016/j.biotechadv.2022.107931 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0734975022000271 UR - https://api.elsevier.com/content/abstract/scopus_id/85126293178 N1 - Cited By (since 2022): 25 N2 - Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era. ER - TY - Article T1 - Solar PV fed brushless drive with optical encoder for agriculture applications using IoT and FPGA A1 - Alqahtani, A S Y1 - 2022/// KW - Optical encoder KW - Perovskite based solar cell KW - Brushless DC motor drive KW - Hydroponics JF - Optical and Quantum Electronics VL - 54 IS - 11 DO - 10.1007/s11082-022-04065-0 UR - https://api.elsevier.com/content/abstract/scopus_id/85138158470 N1 - Cited By (since 2022): 7 N2 - In modern agriculture, IoT is playing a vital part in monitoring and controlling the parameters that has been influenced the growth of the plants. The proposed Hydroponics is sourced by the Solar PV System. It is coupled with the Brushless DC Motor Drive with optical encoder to fulfill the requirement of water in Hydroponics through DC to DC Converter and Inverter that drives water pump. An analytical tool which is executed to visualize and investigate the live field data through IoT platform has been implemented in this paper. Raspberry Pi controller can act as an interface as well as a bi-directional communication between FPGA and cloud to monitor and control process. To validate the proposed system, an experimental setup is implemented. Perovskite Based Solar PV array of 1.5 KW supplies to a Hydroponics network for growing greens plants through deep water culture method. For the effective growth of plant, distinct parameters such as humidity, water level, pH level, temperature and nutrition amount of the hydroponics field are monitored through IoT. Raspberry Pi 3 B+ controller sends the data to the web server which can be adopted for data visualization. Experimental results are proved the effectiveness of monitoring and controlling system. ER - TY - Article T1 - Special report: The Internet of Things for Precision Agriculture (IoT4Ag) A1 - Kagan, C R Y1 - 2022/// KW - Applied computing KW - Social and professional topics JF - Computers and Electronics in Agriculture VL - 196 DO - 10.1016/j.compag.2022.106742 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S016816992200059X UR - https://api.elsevier.com/content/abstract/scopus_id/85124178615 N1 - Cited By (since 2022): 7 N2 - The National Science Foundation (NSF) Engineering Research Center (ERC) for the Internet of Things for Precision Agriculture (IoT4Ag) was established on September 1; 2020 and launched its collaborative programs across the four NSF ERC pillars of convergent research; engineering workforce development; diversity and culture of inclusion; and innovation ecosystem. IoT4Ag unites an interdisciplinary cadre of faculty and students from the University of Pennsylvania; Purdue University; the University of California-Merced; and the University of Florida; with partners in education; government; industry; and the end-user farming community. The IoT4Ag mission is to create and translate to practice Internet of Things (IoT) technologies for precision agriculture and to train an educated and diverse workforce that will address the societal grand challenge of food; energy; and water security for decades to come. ER - TY - Book Chapter T1 - Sustainable Smart Farming for Masses Using Modern Ways of Internet of Things (IoT) Into Agriculture A1 - Chowhan, R S Y1 - 2022/// JF - Research Anthology on Strategies for Achieving Agricultural Sustainability SP - 531 EP - 556 DO - 10.4018/978-1-6684-5352-0.ch028 UR - https://api.elsevier.com/content/abstract/scopus_id/85138988175 N1 - Cited By (since 2022): 1 N2 - Modern technologies are revolutionizing the way humans have lived. The world's population is expected to reach 9.6 billion by year 2050 and to serve this much population, the agricultural industries and layman farmers need to embrace IoT and e-agriculture or ICT in agriculture. Feeding the global population is the biggest problem of the world. The terminology has advanced from IIoT (Industrial Internet of Things), IoFT (Internet of Farm Things), IoSFT (Internet of Smart Farming Things), etc. The agriculture industries are open for ideas, advances, and technically trained workforce to help sustain ever increasing needs of food and allocate better choices of resources. Smart farming is less labor intensive and more capital intensive. Smart farming is furthering the Third Green Revolution around the globe by using various ICT technologies in agriculture. ER - TY - Article T1 - Sustainable agriculture by the Internet of Things – A practitioner's approach to monitor sustainability progress A1 - Wolfert, S Y1 - 2022/// KW - Applied computing JF - Computers and Electronics in Agriculture VL - 200 DO - 10.1016/j.compag.2022.107226 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169922005403 UR - https://api.elsevier.com/content/abstract/scopus_id/85135511410 N1 - Cited By (since 2022): 4 N2 - Sustainability is a major challenge in agri-food systems. Digital technologies, such as Internet of Things (IoT) hold substantial promises for attaining the sustainability goals of the economy, environment and society at large. However, in practice it is difficult to evaluate to which extent these technologies contribute to sustainable development raising doubts about their impact. This paper demonstrates a stepwise approach that allows for measuring and monitoring IoT contribution to sustainability in a real-life context. The UN sustainable development goals (SDGs) underpin the principles of the approach by a typology and by framing the sustainability impact in terms of business opportunities. The approach has been developed and evaluated by 33 use cases in the EU-funded IoF2020 project. The research illustrates how the measurement and monitoring tool is applied in 5 of these use cases from different agricultural subsectors showing how the approach is applied and validated. The results indicate an overall positive impact of IoT on improving sustainability, although these results are also partly determined by other influential external factors that cannot be easily discerned in a practical situation. The main contribution of this approach is the set of instruments for practitioners to measure and monitor the impact of fast-changing technologies such as IoT to sustainability in a real-life context. This set of instruments can also be used by other stakeholders in large IoT projects where strategic sustainability objectives should be supported by IoT solutions. The stepwise approach is easy to communicate and supports stakeholders such as farmers in decision-making, but also policy makers and investors in funding projects. ER - TY - Article T1 - Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture A1 - Lambertini, A Y1 - 2022/// KW - UAV KW - UAS KW - precision agriculture KW - mission planning KW - checklist KW - thermal camera KW - SfM JF - Remote Sensing VL - 14 IS - 19 DO - 10.3390/rs14194954 UR - https://api.elsevier.com/content/abstract/scopus_id/85140270359 L1 - file:///C:/Users/sonsu/Downloads/remotesensing-14-04954-v2.pdf N1 - Cited By (since 2022): 1 N2 - Precision Agriculture (PA) is an approach to maximizing crop productivity in a sustainable manner. PA requires up-to-date, accurate and georeferenced information on crops, which can be collected from different sensors from ground, aerial or satellite platforms. The use of optical and thermal sensors from Unmanned Aerial Vehicle (UAV) platform is an emerging solution for mapping and monitoring in PA, yet many technological challenges are still open. This technical note discusses the choice of UAV type and its scientific payload for surveying a sample area of 5 hectares, as well as the procedures for replicating the study on a larger scale. This case study is an ideal opportunity to test the best practices to combine the requirements of PA surveys with the limitations imposed by local UAV regulations. In the field area, to follow crop development at various stages, nine flights over a period of four months were planned and executed. The usage of ground control points for optimal georeferencing and accurate alignment of maps created by multi-temporal processing is analyzed. Output maps are produced in both visible and thermal bands, after appropriate strip alignment, mosaicking, sensor calibration, and processing with Structure from Motion techniques. The discussion of strategies, checklists, workflow, and processing is backed by data from more than 5000 optical and radiometric thermal images taken during five hours of flight time in nine flights throughout the crop season. The geomatics challenges of a georeferenced survey for PA using UAVs are the key focus of this technical note. Accurate maps derived from these multi-temporal and multi-sensor surveys feed Geographic Information Systems (GIS) and Decision Support Systems (DSS) to benefit PA in a multidisciplinary approach. ER - TY - Conference Paper T1 - The Application of Artificial Intelligence (AI) and Internet of Things (IoT) in Agriculture: A Systematic Literature Review A1 - Abreu, C L de Y1 - 2022/// KW - Artificial Intelligence KW - Internet of Things KW - Agriculture 4.0 KW - Smart sensors KW - Precision farming KW - Systematic literature review JF - Communications in Computer and Information Science VL - 1551 SP - 32 EP - 46 SN - 1865-0929 DO - 10.1007/978-3-030-95070-5_3 UR - https://api.elsevier.com/content/abstract/scopus_id/85125258698 N1 - Cited By (since 2022): 3 N2 - The World Resource Institute estimates that by 2050 there will be a shortfall between food being produced and the amount needed to feed an estimated 10 billion people. With the quantity of available arable land on the decline, the scarcity of water and limiting factors and growing challenges such as soil quality, pest and weed infestations, it is increasingly important that innovative approaches to food production are implemented to optimise agricultural practices. This paper presents a systematic literature review aimed at exploring the use of Artificial Intelligence (AI) and the Internet of Things (IoT) in agriculture. A total of 50 articles were identified and analysed according to the PRISMA approach to understanding the current applications, challenges, and future benefits of AI and IoT in agriculture and how it has the potential to reduce resource wastage and assist in feeding the world’s growing population. Based on the data, it is expected that this review will serve as a reference to supplement the reader’s knowledge of AI and IoT in the agricultural industry. ER - TY - Book Chapter T1 - The integration of blockchain and IoT edge devices for smart agriculture: Challenges and use cases A1 - Nigam, S Y1 - 2022/// KW - Blockchain KW - internet of things KW - B-IoTAgriculture JF - Advances in Computers VL - 127 SP - 507 EP - 537 SN - 0065-2458 DO - 10.1016/bs.adcom.2022.02.015 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0065245822000444 UR - https://api.elsevier.com/content/abstract/scopus_id/85127317923 N1 - Cited By (since 2022): 3 N2 - Growth of IoT (Internet of Things) has proved its importance in the various sector. But due to some limitations of security, privacy, etc. it is not possible to use IoT devices in the field of agriculture at its fullest. To overcome these limitations Blockchain is used as it provides security, privacy and it helps in monitoring, examining, to authenticate the agriculture data. With the help of Blockchain, traditional methods of collection, rearranging and distribution of Agri-products can be replaced by more trust-worthy, decentralized, vitreous, and immutable style. In agriculture sector, Blockchain and Internet of things can be amalgamated to have better results which leads us to one level up in the field of agriculture and may control or improve the supply chain in proper manner. The consequences of using blockchain and IoT in combination will result in better understanding to supervise and managing the agriculture effectively. This chapter will illustrate the importance of using blockchain and IoT collectively to develop smart agriculture from traditional agriculture. A model is also proposed to overcome the challenges encountered in agriculture sector, based on IoT applications with the help of blockchain. Also, a review is mentioned about the main characteristics and functions of blockchain used in agriculture sector such as livestock grazing, crops and food supply chain. Finally, some of the open issues Blockchain and security challenges are elaborate. ER - TY - Conference Paper T1 - The security risks from the application of 5G and GPS in agriculture A1 - Wu, Y Y1 - 2022/// KW - 5G KW - GPS KW - agriculture KW - precision agriculture KW - security KW - risks JF - INES 2022 - 26th IEEE International Conference on Intelligent Engineering Systems 2022, Proceedings SP - 115 EP - 119 DO - 10.1109/INES56734.2022.9922616 UR - https://api.elsevier.com/content/abstract/scopus_id/85141856768 L1 - file:///C:/Users/sonsu/Downloads/publication-WuYue-20_ines2022.pdf N1 - Cited By (since 2022): 1 N2 - As the big data era comes, the rapid development of ICT and IoT promise more and more efficient technologies in all industries. The use of 5G and GPS is also becoming more and more popular in agriculture around the world. 5G and GPS are providing optimal ways to realize more resilient agriculture and create more agricultural yields. However, each new technology has risks in its application. The improper use of 5G and GPS may result in further catastrophic loss and potential risks. The first step to better leverage the benefits of 5G and GPS in agriculture is to point out the security risks. This review research used content analysis to clarify the security risks of 5G and GPS in agriculture, such as the passive and active attacks, 5G architecture core network risks, network access risks, hardware risks etc. from 5G technology, and the disruption of position and timing systems, confidential data loss etc. from GPS technology in agriculture. Besides, both 5G and GPS have the technology immaturity and high-cost constraints to be adopted in agriculture. And we also provide suggestions for further research about 5G and GPS adoption in agriculture. ER - TY - Article T1 - Throughput optimization in backscatter-assisted wireless-powered underground sensor networks for smart agriculture A1 - Lin, K Y1 - 2022/// JF - Internet of Things (Netherlands) VL - 20 DO - 10.1016/j.iot.2022.100637 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S2542660522001184 UR - https://api.elsevier.com/content/abstract/scopus_id/85142326535 N1 - Cited By (since 2022): 2 N2 - Wireless underground sensor networks (WUSNs) using wirelessly-connected buried sensors enable smart agriculture through real-time soil sensing, timely decision-making, and precise remote operation. Energy harvesting technology is adopted in WUSNs, implying wireless-powered underground sensor networks (WPUSNs), to prolong the network lifetime. In addition, the backscatter communication (BSC) technology seems promising for improving the utilization of resources and network throughput according to preliminary studies in terrestrial wireless-powered communication networks. However, this technique has not yet been investigated in WPUSNs, where channel impairments are incredibly severe. In this work, we aim to assess BSC’s performance in WPUSNs and evaluate its feasibility for sustainable smart agriculture. For this, we first conceptualize a multi-user backscatter-assisted WPUSN (BS-WPUSN), where a set of energy-constrained underground sensors (USs) backscatter and/or harvest the radio frequency energy emitted by an above-ground power source before the sensed data are transmitted to a nearby above-ground access point. Then, we formulate the optimal time allocation to maximize the network throughput while assuring real-world users’ quality of service (QoS). Our analysis considers the non-linearities of practical energy harvesting circuits and severe signal attenuation in underground channels. By simulating a realistic farming scenario, we show that our proposed solution outperforms two baseline schemes, i.e., underground harvest-then-transmit and underground BSC, by an average of 12% and 358% increase in network throughput (when USs are buried at 0.35 m), respectively. Additionally, several trade-offs between the network throughput, time allocation, network configurations, and underground parameters are identified to facilitate the practical implementation of BS-WPUSNs. ER - TY - Conference Paper T1 - TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform A1 - Nicolas, C Y1 - 2022/// KW - Agriculture KW - Artificial Intelligence KW - Internet of Things KW - LoRaWAN KW - Smart Farming KW - TinyML JF - International Conference on Ubiquitous and Future Networks, ICUFN VL - 2022 SP - 256 EP - 259 SN - 2165-8528 DO - 10.1109/ICUFN55119.2022.9829675 UR - https://api.elsevier.com/content/abstract/scopus_id/85135207391 N1 - Cited By (since 2022): 2 N2 - Smart agriculture researchers bring numerous tools and prospects to the farm ecosystem to improve its productivity and, mainly, its sustainability. Artificial Intelligence (AI) is widely used in precision agriculture as Internet of Things (IoT) technologies have brought a huge volume of data to exploit to provide useful insights for farmers such as weather prediction, pest development detection, or harvest time estimation. AI algorithms are mostly executed in the cloud due to their inherent computing constraints, thus requiring the different sensors to offload their data to the appropriate server. Depending on the amount and volume of data exchanged, the need for computer offloading may induce privacy, security, and latency issues in addition to weighting on the sensor’s battery consumption as wireless transmission methods have a high-energy demand. To overcome this difficulty, recent research has tried to bring AI computation closer to the end device with edge or fog computing and more recently with the Tiny Machine Learning (TinyML) paradigm that aims to embed the AI algorithm directly into the sensor’s microcontroller. In that context, this paper proposes a prototype of smart sensor capable of detecting fruits presence with TinyML. We then study the energy consumption of our system in different IoT scenarios. ER - TY - Article T1 - Towards Optimized Security Attributes for IoT Devices in Smart Agriculture Based on the IEC 62443 Security Standard A1 - Shaaban, A M Y1 - 2022/// KW - security measures KW - potential threats KW - attack propagation KW - internet of things KW - cybersecurity KW - security standard JF - Applied Sciences (Switzerland) VL - 12 IS - 11 DO - 10.3390/app12115653 UR - https://api.elsevier.com/content/abstract/scopus_id/85131801274 L1 - file:///C:/Users/sonsu/Downloads/applsci-12-05653.pdf N1 - Cited By (since 2022): 1 N2 - Implementing applicable security measures into system engineering applications is still one of the most challenging processes in building secure infrastructure. This process needs to consider a variety of security attributes to support securing system components against numerous cyberattacks that could exploit vulnerable points in the system. The redundancy in these attributes is also another challenge that could degrade system functionality and impact the availability of the system’s services. Therefore, it is crucial to choose appropriate security properties by considering their ability to address cyber threats with minimal negative impacts on the system’s functionality. This process is still subjected to inconsistencies due to ad- oc determinations by a specialist. In this work, we propose a novel algorithm for optimizing the implementation of security mechanisms in IoT applications for the agricultural domain to ensure the effectiveness of the applied mechanisms against the propagation of potential threats. We demonstrate our proposed algorithm on an IoT application in the farming domain to see how the algorithm helps with optimizing the applied security mechanisms. In addition, we used THREATGET to analyze cyber risks and validate the optimized security attributes against the propagation of cyber threats. ER - TY - Article T1 - Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture A1 - Thilakarathne, N N Y1 - 2023/// JF - Frontiers in Plant Science VL - 13 DO - 10.3389/fpls.2022.1030168 UR - https://api.elsevier.com/content/abstract/scopus_id/85146311440 L1 - file:///C:/Users/sonsu/Downloads/fpls-13-1030168.pdf N1 - Cited By (since 2023): 1 N2 - Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment. ER - TY - Article T1 - UAV-Based Wireless Data Collection from Underground Sensor Nodes for Precision Agriculture A1 - Holtorf, L Y1 - 2023/// KW - precision agriculture KW - unmanned aerial vehicle KW - drone KW - LoRa KW - Internet of Things KW - Internet of Underground Things JF - AgriEngineering VL - 5 IS - 1 SP - 338 EP - 354 DO - 10.3390/agriengineering5010022 UR - https://api.elsevier.com/content/abstract/scopus_id/85150950412 L1 - file:///C:/Users/sonsu/Downloads/agriengineering-05-00022-v3.pdf N1 - Cited By (since 2023): 1 N2 - In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture. ER - TY - Review T1 - UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review A1 - Awais, M Y1 - 2023/// KW - Unmanned aerial vehicle KW - Crop water stress index KW - Precision agriculture KW - Vegetation index KW - Image processing KW - Intelligent irrigation JF - International Journal of Environmental Science and Technology VL - 20 IS - 1 SP - 1135 EP - 1152 SN - 1735-1472 DO - 10.1007/s13762-021-03801-5 UR - https://api.elsevier.com/content/abstract/scopus_id/85122316399 N1 - Cited By (since 2023): 11 N2 - Water management is becoming a critical issue for sustainable agriculture, especially in the semi-arid region, where problems with water scarcity are rising. More accurate water status recovery in crops is required for precise irrigation through remote sensing technologies. These technologies have a lot of potential in intelligent irrigation because they allow for real-time environmental data collection. Nowadays, digital practices have been used, such as unmanned aerial vehicle (UAV), which plays an essential role in various applications related to crop management. Drones offer an exciting opportunity to track crop fields with high spatial and temporal resolution remote sensing to enhance water stress management in irrigation. Farmers have historically depended on soil moisture measurements and weather conditions to detect crop water status for irrigation scheduling. This review paper summarizes the use of UAV remote sensing data in crops for estimating the water status and gives a detailed summary of the potential capacity of UAV remote sensing for water stress application. The remote sensing techniques help modify agricultural practices to meet this significant challenge by providing repeated information on crop status at different scales and various performances during the season. UAVs successful implementation in water stress estimations depends on UAV features, such as flexibility of use in flight planning, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. UAV with a thermal sensor is considered the most effective technique for detecting water stress using specific indices. Thermal imaging can identify water status variations and crop water stress index (CWSI). This CWSI acquired through UAV thermal sensors imagery can be acceptable for managing real-time irrigation to achieve optimum crop water efficiency. ER - TY - Conference Paper T1 - Usage of Internet of Things Based Devices in Smart Agriculture for Monitoring the field and Pest Control A1 - Singh, G Y1 - 2022/// KW - smart agriculture KW - internet of things KW - IoT-based devices KW - unmanned aerial vehicles KW - sensors JF - 2022 IEEE Delhi Section Conference, DELCON 2022 DO - 10.1109/DELCON54057.2022.9753021 UR - https://api.elsevier.com/content/abstract/scopus_id/85129404480 N1 - Cited By (since 2022): 1 N2 - Today's agriculture is now become smarter, precise, and data-centered than ever. The continuous developments on the Internet of Things-based technologies have mostly changed agriculture to smart-agriculture. This revolution has changed the existing agriculture method and created new scope with many of challenges. This article brings focus to the potential based on Internet of Things based (IoT) devices in smart agriculture, and the challenges expected when integrating these devices in traditional farming. Different Internet of Things-based devices and other sensors are also available for specified agricultural applications like crop status, preparation of soil, pest and insect control, irrigation are listed. Also, the usage of unmanned aerial vehicles (UAV Drones) in agriculture field surveillance is considered in this article. Various latest review papers have been taken into consideration for the Internet of Things-based Smart Farming. A critical review has been done on the use of Smart devices in agriculture. Lastly based on rigorous reviews we can find the current and upcoming trends of Internet of Things-based devices in agriculture. This research focuses on how to agriculture production can be enhanced by using Internet of Things (IoT) based devices. This article helps engineers and researchers for implement the Internet of Things (IoT) based devices and technology to obtain the required smart agriculture using modern Internet of Things (IoT) based devices. At last, an automated sprayer done is proposed for spraying pesticides in the agricultural field. ER - TY - Book Chapter T1 - Use of Machine Learning and IoT in Agriculture A1 - Mehla, A Y1 - 2023/// KW - Internet of Things KW - Machine learning KW - Smart agriculture KW - Precision agriculture KW - IoT sensors KW - Crop yield KW - Crop diseases KW - Farming JF - EAI/Springer Innovations in Communication and Computing SP - 277 EP - 293 SN - 2522-8595 DO - 10.1007/978-3-031-04524-0_16 UR - https://api.elsevier.com/content/abstract/scopus_id/85139459246 N1 - Cited By (since 2023): 1 N2 - In recent years, the agricultural sector has come under tremendous pressure due to the very high growth of the population. The need for food increased at a quadratic rate, and even the Food and Agriculture Organization (FAO) of the United Nations estimated that food production would have to grow by 70% worldwide to meet the global food demand. Due to limited arable lands and countful availability of renewable resources, there is a need to increase the agricultural yield even more seriously. Agriculture is affected significantly by recent technological advancements in the domains of Internet of Things (IoT), Machine Learning (ML), and deep learning (DL). Researchers are helping farmers by applying these technologies to precisely automate crop cultivation methods, management, and production. This chapter provides the latest insights into the latest research initiatives that significantly impact smart agriculture and farming. It provides a detailed impact of IoT, machine learning, and data analytics that can be used for disease control; monitoring the climate; measuring soil temperature, nutrient value, and moisture levels; controlling and analyzing water consumption; and much more. These shall help follow scientific procedures for plant growth and increase crop yield. It refers to the latest work of researchers to provide solutions to various agricultural challenges, using several ways to automate and maximize agricultural produce. ER - TY - Review T1 - Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture A1 - Bayih, A Z Y1 - 2022/// KW - Internet of Things KW - wireless sensor network KW - affordable digital data infrastructure KW - technology assist in smallholder data acquisition KW - smart agriculture JF - Sensors VL - 22 IS - 9 SN - 1424-8220 DO - 10.3390/s22093273 UR - https://api.elsevier.com/content/abstract/scopus_id/85128770051 L1 - file:///C:/Users/sonsu/Downloads/sensors-22-03273.pdf N1 - Cited By (since 2022): 2 N2 - Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science. ER - TY - Book Chapter T1 - Vertical Farming Trends and Challenges: A New Age of Agriculture Using IoT and Machine Learning A1 - Swain, M Y1 - 2022/// JF - Internet of Things for Agriculture 4.0: Impact and Challenges SP - 1 EP - 16 UR - https://api.elsevier.com/content/abstract/scopus_id/85131975744 N1 - Cited By (since 2022): 2 N2 - Vertical farming (VF) is a new age of agriculture technique. It has the potential to fulfill the food requirement in the future. Looking toward the current trend of agriculture, it seems to be that VF could enable all the vertices of farming in various dimensions. It is an unconventional way of agriculture to meet the food requirement as the farming lands are getting shirked day by day. Implementing cutting-edge technologies like IoT (Internet of things), AI (artificial intelligence), and machine learning, the productivity and quality factors would enhance VF. This chapter illustrates how advanced technology is integrated into the farming for overall growth of farmer and economy of a country. By deploying sensor nodes, farming monitoring is reliable and effective way to handle day-to-day activities in farming land. ER - TY - Article T1 - Viable smart sensors and their application in data driven agriculture A1 - Paul, K Y1 - 2022/// KW - Applied computing KW - Computer systems organization KW - Embedded and cyber-physical systems JF - Computers and Electronics in Agriculture VL - 198 DO - 10.1016/j.compag.2022.107096 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S0168169922004136 UR - https://api.elsevier.com/content/abstract/scopus_id/85131221996 N1 - Cited By (since 2022): 14 N2 - Smart sensors are useful in professional farming approach by which one can use the digital technology to monitor, visualize, generate digital data, to control the application of resources, to improve quality and productivity of agriculture produce. Novel sensors add value in soil-less farming through automation and IoT (Internet of Things) based operation management digital tools. Data-driven technologies by using smart sensors can find a solution to many glitches in agriculture practices and it could improve new efficiencies. The principles of smart sensors as well as the most viable sensors that are used for monitoring soil and plant physicochemical parameters in field cultivation processes, greenhouse and indoor hydroponics are being discussed. Digital technologies in precision farming, automation in agro machinery, Precision Livestock Farming (PLF), TV White Spaces (TVWS) remote connectivity, Unmanned Aerial Vehicles (UAVs) based imagery, application of IoTs can help farming communities to use resources accurately based on real-time farm data acquired and improve crop yield without any wastage. Smart sensors helps the entire food value chain, the precision to productivity quest of growers and could enable new business models. This article provides a wide understanding of novel smart sensors, wireless sensor network architectures, and applications of these sensors to inculcate sustainable farming practices, value chain traceability and create secured income. ER - TY - Article T1 - Wearable sensors made with solution-blow spinning poly(lactic acid) for non-enzymatic pesticide detection in agriculture and food safety A1 - Paschoalin, R T Y1 - 2022/// KW - Agriculture and food safety KW - Carbendazim KW - Diquat KW - Electrochemical detection KW - Wearable device KW - poly(lactic acid) fibers JF - Biosensors and Bioelectronics VL - 199 DO - 10.1016/j.bios.2021.113875 UR - https://api.elsevier.com/content/article/eid/1-s2.0-S095656632100912X UR - https://api.elsevier.com/content/abstract/scopus_id/85121730406 N1 - Cited By (since 2022): 23 N2 - On-site monitoring the presence of pesticides on crops and food samples is essential for precision and post-harvest agriculture, which demands nondestructive analytical methods for rapid, low-cost detection that is not achievable with gold standard methods. The synergy between eco-friendly substrates and printed devices may lead to wearable sensors for decentralized analysis of pesticides in precision agriculture. In this paper we report on a wearable non-enzymatic electrochemical sensor capable of detecting carbamate and bipyridinium pesticides on the surface of agricultural and food samples. The low-cost devices (