SELF-SUSTAINING IOT NETWORKS AND AI-DRIVEN ENERGY HARVESTING FOR SMART AGRICULTURE

Abstract

The growing need of sustainable farming methods has prompted the use of Internet of Things (IoT) technology in precision farming. IoT devices to traditional sources of power however is a key obstacle to scalability and long term deployment particularly in remote or resource constrained farming regions. This paper discusses the integration of self-sustaining IoT networks to optimize energy harvesting opportunities and artificial intelligence (AI)-driven energy optimization strategies to attain agricultural efficiency and resiliency. The qualitative research design, which was used to collect the data, was semi-structured interviews and focus group discussions with farmers, technology specialists, policy makers and agribusiness representatives. Thematic analysis revealed that self-sustainable IoT systems where the stakeholders perceive the systems to be highly useful in the improvement of crop monitoring, resource management and decision making. Being the possible solution to the constraint of the infrastructures, participants focused to the renewable energy harvesting and listed the cost, technical literacy, and trust in AI-based systems as the impediments. The findings show that although AI-based optimization has enhanced the autonomy and reliability of the IoT networks, the implementation of AI will only be successful with the assistance of positive policies, financial incentives, and capacity-building initiatives. The study is drawing a conclusion of the fact that self-sufficient IoT networks can be used to revolutionize smart agriculture to a new stage and instill low-maintenance, energy-saving and data-driven smart farming. It is recommended on the further development of large-scale adoption on the basis of collaborative relationships, demonstration initiatives, and a particular policy framework.