Integrating Satellite and UAV Data through Artificial Intelligence for Real Time Crop Health Surveillance

Chayanika Nath, Sarmistha Borgohain

Abstract:

Timely and accurate monitoring of crop health is essential for achieving sustainable and efficient agricultural production. The fusion of satellite remote sensing and unmanned aerial vehicle (UAV) data provides a robust approach for generating high spatial and temporal resolution information on vegetation status. Artificial Intelligence (AI) plays a crucial role in analyzing and integrating these large datasets to enable real time crop health surveillance. The combination of satellite and UAV data supported by AI algorithms enhances the precision of vegetation stress detection and yield estimation. This paper elaborates on the principles of remote sensing, methodologies of data fusion, AI based analytical frameworks, and the applications of this integrated system in crop stress diagnosis, disease detection, and precision resource management. The limitations and future directions for practical implementation are also discussed. The convergence of these technologies offers new possibilities for sustainable, data driven agricultural management that ensures food security and environmental resilience.