AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture

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dc.contributor.authorSundaram, N. Kalyana
dc.contributor.authorRajendran, Megala
dc.contributor.authorEhssan, Muhamed
dc.contributor.authorSoy, Aakansha
dc.contributor.authorAnandhi, K.
dc.contributor.authorBegum, T Ummal Sariba
dc.contributor.authorAli, Guma
dc.contributor.authorDhananjaya, B
dc.date.accessioned2026-02-17T10:15:12Z
dc.date.available2026-02-17T10:15:12Z
dc.date.issued2025-12-29
dc.descriptionThis paper explores practical strategies for improving energy efficiency in machine‑type communications—technologies that underpin the Internet of Things and other low‑power device networks. By focusing on reducing power consumption in sensing, processing, and wireless communication, the research highlights how small technical advances can make a meaningful difference in the daily operation of smart devices. These efforts directly contribute to SDG 7 (Affordable and Clean Energy) by promoting sustainable energy use, SDG 9 (Industry, Innovation and Infrastructure) through support for robust and innovative digital systems, SDG 11 (Sustainable Cities and Communities) by enabling smarter infrastructure, and SDG 13 (Climate Action) by helping reduce emissions. The insights also support Uganda’s National Development Plan IV, offering guidance for building resilient infrastructure and fostering digital innovation that benefits communities and the environment alike.
dc.description.abstractBy advising and teaching farmers on how to apply modern farm practices that embrace Artificial Intelligence (AI) and the Internet of Things (IoT), precision agriculture is revolutionising sustainable farming by optimising for usages that are as much as possible and waste as little as can be afforded. In this research, we propose an AIoT-driven Smart Agri Grid (ASAG) framework that integrates real-time nanosensor networks, an AI-operational control microclimate, an autonomous decision-support system, and secure data sharing via a blockchain using encrypted statistical data. To achieve real-time analytics, edge computing is used in the framework for real-time data analytics, predictive algorithms for dynamic irrigation & nutrient management, and federated learning for distributed AI training, which maintains privacy and scalability. In addition, the system uses AI-based waste-minimisation techniques, such as predictive harvest timing and the conversion of bio-waste into organic fertilisers, thereby reducing post-harvest losses. Experimental results show that ASAG can improve crop yield by 20 to 30%, reduce water waste by up to 50%, and reduce chemical overuse by up to 30%, with its economic and environmental benefits. The feasibility of such deployment on a large scale in precision agriculture is further confirmed by a cost-benefit analysis. The results reinforce the power of AI and IoT in transforming contemporary farming into a self-optimising, climate-resilient system. For long-term sustainability in global agriculture, quantum AI will be used to predict soil health, monitor AI-assisted carbon sequestration, and enable genomic AI for climate-resistant crops.
dc.identifier.citationSundaram, N. K., Rajendran, M., Ehssan, M., Soy, A., Anandhi, K., Begum, T. U. S., & Ali, G. (2025, November). AIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture. In 2025 International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE) (pp. 232-236). IEEE.
dc.identifier.urihttps://dir.muni.ac.ug/handle/20.500.12260/922
dc.language.isoen
dc.publisherIEEE
dc.subjectPrecision agriculture
dc.subjectTraining
dc.subjectHeuristic algorithms
dc.subjectReal-time systems
dc.subjectSmart grids
dc.subjectBlockchains
dc.subjectTiming
dc.subjectInternet of Things
dc.subjectArtificial intelligence
dc.subjectFarming
dc.titleAIoT-driven smart agri-grid (ASAG) for sustainable precision agriculture
dc.typeOther

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