Developing a Data-Driven IoT-Based Smart Agriculture Model for Sustainable Food Security in Indonesia

Solly Aryza : Universitas Pembangunan Panca Budi Medan , M. Syahputra Novelan : Universitas Pembangunan Panca Budi Medan , Pasya Fahira Hutasuhut : Universitas Pembangunan Panca Budi Medan
Submitted: 01-10-2026 || Accepted: 01-10-2026 || Published: 30-09-2026

Abstract


The increasing demand for food, climate variability, resource constraints, and fragmented agricultural information systems have created substantial challenges for achieving sustainable food security in Indonesia. Although Internet of Things (IoT) technologies have been increasingly adopted in smart agriculture, many existing approaches remain focused on real-time monitoring and lack an integrated data-driven framework that translates heterogeneous agricultural data into actionable decisions for improving production efficiency and food-system resilience. This study aims to develop a data-driven IoT-based smart agriculture model that integrates real-time environmental and agricultural data, data analytics, predictive modeling, and decision support to strengthen sustainable food security in Indonesia. The proposed model combines IoT sensors for collecting key parameters, including soil moisture, temperature, humidity, light intensity, and water-related variables, with data processing and analytical methods to identify agricultural conditions and support predictive decision-making. The model is evaluated through system performance, prediction accuracy, resource-use efficiency, and selected indicators of agricultural productivity and food-security resilience. The research adopts a design and development approach followed by empirical validation using field-based agricultural data. The expected contribution is an integrated conceptual and technological framework that connects data acquisition, intelligent analytics, and agricultural decision-making rather than treating IoT merely as a monitoring technology. The study is expected to provide practical support for precision agriculture and evidence-based resource management while contributing to the development of scalable smart agriculture strategies for sustainable food security in Indonesia.

Keywords


Smart Agriculture; Internet Of Things (Iot); Data-Driven Agriculture; Precision Agriculture; Predictive Analytics; Sustainable Food Security; Decision Support System; Indonesia.

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References


Camel, A, et al (2025). Servitizing for sustainability: Leveraging technology-enabled platforms and service innovation for carbon reduction in Africa’s agri-food sector: A dynamic capabilities perspective. Journal of Business Research, 189, 115166. https://doi.org/10.1016/j.jbusres.2024.115166

Dhal, S., et al (2023). Internet of Things (IoT) in digital agriculture: An overview. Agronomy Journal, 116(3), 1144–1163. https://doi.org/10.1002/agj2.21385

Duguma, A. L., & Bai, X. (2025). How the Internet of Things technology improves agricultural efficiency. Artificial Intelligence Review, 58, 63. https://doi.org/10.1007/s10462-024-11046-0

Elavarasan, R. et al. (2024). Applications of Internet of Things (IoT) and sensors technology to increase food security and agricultural sustainability: Benefits and challenges. Ain Shams Engineering Journal, 15(3), 102509. https://doi.org/10.1016/j.asej.2023.102509

Finger, R. etal (2022). Precision agriculture for sustainable intensification. Springer.

Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, UNICEF, World Food Programme, & World Health Organization. (2024). The state of food security and nutrition in the world 2024: Financing to end hunger, food insecurity and malnutrition in all its forms. FAO.

Getahun, S., et al (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024, 2126734. https://doi.org/10.1155/2024/2126734

Hair, J. F., Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Intergovernmental Panel on Climate Change. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press. https://doi.org/10.1017/9781009325844

Kasera, R. et al (2024). A comprehensive survey on IoT and AI based applications in different pre-harvest, during-harvest and post-harvest activities of smart agriculture. Computers and Electronics in Agriculture, 216, 108522. https://doi.org/10.1016/j.compag.2023.108522

Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24, 100347. https://doi.org/10.1016/j.gfs.2019.100347

Mhlanga, D., & Ndhlovu, E. (2023). Digital transformation and the future of agriculture: Opportunities and challenges for sustainable food systems. Sustainability, 15, 1–20.

Naseer, M. A., et al. (2024). Adoption of Internet of Things technologies in agriculture: Determinants, opportunities, and challenges. Agriculture, 14, 1–20.

Ndlovu, B., & Maguraushe, K. (2026). Leveraging Internet of Things and Artificial Intelligence in smart agriculture to enhance food security and sustainable farming: A systematic review. Scientific Journal of Informatics, 13(3). https://doi.org/10.15294/sji.v13i3.49540

Rejeb, A., Rejeb, K., Abdollahi, A., & Treiblmaier, H. (2022). Digitalization of the agrifood sector: A systematic literature review. Journal of Cleaner Production, 355, 131728.

Shahab, H., et al (2024). IoT-based agriculture management techniques for sustainable farming: A comprehensive review. Computers and Electronics in Agriculture, 220, 108851. https://doi.org/10.1016/j.compag.2024.108851

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064. https://doi.org/10.1002/mar.21640

Tzachor, A., Richards, C. E., & Holt, L. (2022). Future foods for sustainable food systems: The role of digital agriculture and emerging technologies. Frontiers in Sustainable Food Systems, 6, 877434.

Thilakarathne, N. et al (2025). Internet of Things enabled smart agriculture: Current status, latest advancements, challenges and countermeasures. Heliyon, 11(3), e42136. https://doi.org/10.1016/j.heliyon.2025.e42136

Walter, A., et al (2017). Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences, 114(24), 6148–6150. https://doi.org/10.1073/pnas.1707462114




DOI: https://doi.org/10.30596/jcositte.v7i2.32345

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