EDGE INTELLIGENCE FOR SMART AGRICULTURE: AN ARTIFICIAL INTELLIGENCE FRAMEWORK FOR PRECISION FARMING AND SUSTAINABLE CROP MANAGEMENT

The rapid growth of the global population has intensified the demand for sustainable agricultural practices capable of increasing crop productivity while minimizing environmental impact. Conventional farming techniques often rely on manual observation and generalized resource allocation, leading to inefficient utilization of water, fertilizers, pesticides, and energy. Recent advances in Artificial Intelligence (AI), Edge Computing, and the Internet of Things (IoT) have enabled intelligent precision agriculture systems that provide real-time monitoring and autonomous decision-making. Edge Intelligence, which combines AI with distributed edge devices, processes agricultural data closer to its source, reducing communication delays and dependence on cloud infrastructure. This paper presents a comprehensive review of Edge Intelligence applications in smart agriculture and proposes an AI-enabled precision farming framework integrating IoT sensors, unmanned aerial vehicles (UAVs), machine learning, computer vision, and edge computing. The framework aims to improve crop health monitoring, irrigation management, pest detection, soil analysis, and yield prediction while reducing operational costs and environmental impact. The paper also discusses current challenges, security considerations, and future research directions. The findings indicate that Edge Intelligence has significant potential to transform modern agriculture by enabling efficient, scalable, and sustainable farming practices.