Integration of Edge AI and IoT Connected UAV Platforms for Smart Agricultural Systems

Abstract

Precision agriculture increasingly depends on intelligent, autonomous technologies to improve crop productivity and optimize resource usage. This study proposes an integrated architecture that combines Edge Artificial Intelligence (Edge AI) with Internet of Things (IoT) connected Unmanned Aerial Vehicles (UAVs) to enable real time monitoring and decision making in smart agricultural systems. In the proposed framework, UAVs equipped with multispectral and RGB imaging sensors collect high resolution field data, while embedded edge AI modules perform onboard processing for early detection of crop diseases, nutrient deficiencies, and environmental stress indicators. A distributed IoT communication layer links UAVs with ground-based sensors and cloud platforms, enabling continuous data synchronization, environmental analysis, and predictive modeling. By shifting computation from the cloud to the edge, the system significantly reduces latency, enhances operational autonomy, and remains functional in remote farming regions with limited connectivity. Experimental results demonstrate notable improvements in detection accuracy, processing efficiency, and communication reliability compared to traditional centralized UAV based agricultural systems. The findings confirm that integrating Edge AI with IoT enabled UAV platforms provides a scalable, energy efficient, and robust technological solution for next generation smart agriculture.