Smart Farming Application Training for Agricultural Communities Using IoT-Based Monitoring Tools
Downloads
Background. The advancement of agricultural technology in the digital era has opened up new opportunities to improve productivity, sustainability, and efficiency in farming practices. However, many smallholder farmers in rural communities remain unfamiliar with smart farming tools, particularly those involving Internet of Things (IoT) technologies. Limited access to training and digital infrastructure further hinders their ability to adapt to modern agricultural systems.
Purpose. This study aims to implement a community-based training program that focuses on the use of IoT-based monitoring applications for agriculture.
Method. The primary objective of this research is to empower local farming communities by enhancing their technical competencies in operating and interpreting data from smart farming systems. A participatory action research (PAR) design was employed, involving 20 smallholder farmers from a rural agricultural village in Central Java, Indonesia. The training included device installation (temperature, soil moisture, and humidity sensors), mobile application usage, and basic data analysis for crop management decision-making.
Results. The results indicate that participants demonstrated improved understanding and practical skills in using IoT tools to monitor crop conditions. Farmers reported increased awareness of real-time data utilization, enabling more informed decisions regarding irrigation, fertilization, and harvesting schedules. Engagement levels were high, with 85% of participants able to operate the system independently after the training.
Conclusion. This study concludes that integrating IoT training into community-based agricultural empowerment programs significantly boosts farmer readiness for smart farming adoption. The findings support broader implementation of accessible, localized, and low-cost digital training models for sustainable agriculture.
Abdullahi, H. O. (2023). A Bibliometric Analysis of the Evolution of IoT Applications in Smart Agriculture. Ingenierie des Systemes d’Information, 28(6), 1495–1504. https://doi.org/10.18280/isi.280606
Adam, A. H. (2019). Low-Cost Green Power Predictive Farming Using IOT and Cloud Computing. Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019, Query date: 2025-05-17 13:25:30. https://doi.org/10.1109/ViTECoN.2019.8899500
Ahmad, U. (2022). Solar Fertigation: A Sustainable and Smart IoT-Based Irrigation and Fertilization System for Efficient Water and Nutrient Management. Agronomy, 12(5). https://doi.org/10.3390/agronomy12051012
Ali, A. (2023). Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production. Agriculture (Switzerland), 13(2). https://doi.org/10.3390/agriculture13020397
Al-Shareeda, M. A. (2022). Intelligent Drone-based IoT Technology for Smart Agriculture System. 2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022, Query date: 2025-05-17 13:25:30, 41–45. https://doi.org/10.1109/ICDSIC56987.2022.10076170
Alves, R. G. (2023). Development of a Digital Twin for smart farming: Irrigation management system for water saving. Journal of Cleaner Production, 388(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.jclepro.2023.135920
Anand, A. (2022). Applications of Internet of Things(IoT) in Agriculture: The Need and Implementation. Proceedings - International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022, Query date: 2025-05-17 13:25:30. https://doi.org/10.1109/ICADEIS56544.2022.10037505
Aryal, S. K. (2021). Admission of Turkey into the European Union: “Does religion matter?” Vestnik RUDN. International Relations, 21(3), 571–582. https://doi.org/10.22363/2313-0660-2021-21-3-571-582
Badoni, P. (2023). Enhancing Water Efficiency and Crop Yield in Agriculture Sector using IoT. 2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, Query date: 2025-05-17 13:25:30, 1039–1044. https://doi.org/10.1109/ICAICCIT60255.2023.10466092
Bertoglio, R. (2021). The Digital Agricultural Revolution: A Bibliometric Analysis Literature Review. IEEE Access, 9(Query date: 2025-05-17 13:25:30), 134762–134782. https://doi.org/10.1109/ACCESS.2021.3115258
Cakir, L. V. (2023). Digital Twin Middleware for Smart Farm IoT Networks. 2023 International Balkan Conference on Communications and Networking, BalkanCom 2023, Query date: 2025-05-17 13:25:30. https://doi.org/10.1109/BalkanCom58402.2023.10167962
Chandra, R. (2021). Digital agriculture for small-scale producers. Communications of the ACM, 64(12), 75–84. https://doi.org/10.1145/3454008
Chehri, A. (2020). A framework of optimizing the deployment of IoT for precision agriculture industry. Procedia Computer Science, 176(Query date: 2025-05-17 13:25:30), 2414–2422. https://doi.org/10.1016/j.procs.2020.09.312
Dahane, A. (2020). An IoT based smart farming system using machine learning. 2020 International Symposium on Networks, Computers and Communications, ISNCC 2020, Query date: 2025-05-17 13:25:30. https://doi.org/10.1109/ISNCC49221.2020.9297341
Delgado, J. A. (2019). Big Data Analysis for Sustainable Agriculture on a Geospatial Cloud Framework. Frontiers in Sustainable Food Systems, 3(Query date: 2025-05-17 13:25:30). https://doi.org/10.3389/fsufs.2019.00054
Dey, K. (2021). Blockchain for sustainable e-agriculture: Literature review, architecture for data management, and implications. Journal of Cleaner Production, 316(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.jclepro.2021.128254
Dutta, G. (2020). Digital transformation priorities of India’s discrete manufacturing SMEs – a conceptual study in perspective of Industry 4.0. Competitiveness Review, Query date: 2025-05-17 13:25:30, 289–314. https://doi.org/10.1108/CR-03-2019-0031
Frikha, T. (2023). Integrating blockchain and deep learning for intelligent greenhouse control and traceability. Alexandria Engineering Journal, 79(Query date: 2025-05-17 13:25:30), 259–273. https://doi.org/10.1016/j.aej.2023.08.027
Fuentes-Peñailillo, F. (2024). Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management. Journal of Sensor and Actuator Networks, 13(4). https://doi.org/10.3390/jsan13040039
Goel, R. K. (2021). Smart agriculture – Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems, 30(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.suscom.2021.100512
Gómez-Chabla, R. (2019). IoT Applications in Agriculture: A Systematic Literature Review. Advances in Intelligent Systems and Computing, 901(Query date: 2025-05-17 13:25:30), 68–76. https://doi.org/10.1007/978-3-030-10728-4_8
Gupta, N. (2020). Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Applied Intelligence, 50(11), 3990–4016. https://doi.org/10.1007/s10489-020-01744-x
Gurewitz, O. (2022). Data Gathering Techniques in WSN: A Cross-Layer View. Sensors, 22(7). https://doi.org/10.3390/s22072650
Hang, L. (2020). A secure fish farm platform based on blockchain for agriculture data integrity. Computers and Electronics in Agriculture, 170(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.compag.2020.105251
Javaid, M. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3(Query date: 2025-05-17 13:25:30), 150–164. https://doi.org/10.1016/j.ijin.2022.09.004
Khaleefah, R. M. (2023). Optimizing IoT Data Transmission in Smart Agriculture: A Comparative Study of Reduction Techniques. HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, Query date: 2025-05-17 13:25:30. https://doi.org/10.1109/HORA58378.2023.10156757
Leduc, G. (2021). Innovative blockchain-based farming marketplace and smart contract performance evaluation. Journal of Cleaner Production, 306(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.jclepro.2021.127055
Lima, G. C. (2020). Agro 4.0: Enabling agriculture digital transformation through IoT. Revista Ciencia Agronomica, 51(5). https://doi.org/10.5935/1806-6690.20200100
Lugonja, D. (2022). Smart Agriculture Development and Its Contribution to the Sustainable Digital Transformation of the Agri-Food Sector. Tehnicki Glasnik, 16(2), 264–267. https://doi.org/10.31803/tg-20210914162640
Lutta, P. (2021). The complexity of internet of things forensics: A state-of-the-art review. Forensic Science International: Digital Investigation, 38(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.fsidi.2021.301210
Masuda, Y. (2021). Internet of robotic things with digital platforms: Digitization of robotics enterprise. Smart Innovation, Systems and Technologies, 189(Query date: 2025-05-17 13:25:30), 381–391. https://doi.org/10.1007/978-981-15-5784-2_31
Mentsiev, A. U. (2020). IoT and mechanization in agriculture: Problems, solutions, and prospects. IOP Conference Series: Earth and Environmental Science, 548(3). https://doi.org/10.1088/1755-1315/548/3/032035
Onwude, D. I. (2020). Recent advances in reducing food losses in the supply chain of fresh agricultural produce. Processes, 8(11), 1–31. https://doi.org/10.3390/pr8111431
Pal, D. (2023). AI, IoT and Robotics in Smart Farming: Current Applications and Future Potentials. 2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 - Proceedings, Query date: 2025-05-17 13:25:30, 1096–1101. https://doi.org/10.1109/ICSCDS56580.2023.10105101
Rani, D. (2019). Implementation of an Automated Irrigation System for Agriculture Monitoring using IoT Communication. Proceedings of IEEE International Conference on Signal Processing,Computing and Control, 2019(Query date: 2025-05-17 13:25:30), 138–143. https://doi.org/10.1109/ISPCC48220.2019.8988390
Tholhappiyan, T. (2023). Agriculture Monitoring System with Efficient Resource Management using IoT. Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, Query date: 2025-05-17 13:25:30, 1628–1633. https://doi.org/10.1109/ICAISS58487.2023.10250720
Vasileiou, M. (2024). Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning. Crop Protection, 176(Query date: 2025-05-17 13:25:30). https://doi.org/10.1016/j.cropro.2023.106522
Xu, X. (2019). Design and implementation of cloud storage system for farmland internet of things based on NoSQL database. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 35(1), 172–179. https://doi.org/10.11975/j.issn.1002-6819.2019.01.021
Zgank, A. (2020). Bee swarm activity acoustic classification for an iot-based farm service. Sensors (Switzerland), 20(1). https://doi.org/10.3390/s20010021
Zhang, C. (2020). A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166(Query date: 2024-05-25 16:57:52), 183–200. https://doi.org/10.1016/j.isprsjprs.2020.06.003
Zhou, T. (2020). Design and Implementation of Agricultural Internet of Things System Based on Aliyun IoT Platform and STM32. Journal of Physics: Conference Series, 1574(1). https://doi.org/10.1088/1742-6596/1574/1/012159
Copyright (c) 2024 Heri Setiyawan, Rohmat Sahirin, Vanna Sok
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
