AIoT-Driven Smart Agri-Grid (ASAG) for Sustainable Precision Agriculture

By 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.

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