INVESTIGATING THE ROLE OF AUTONOMOUS DRONES IN PRECISION AGRICULTURE FOR REAL-TIME CROP MONITORING AND YIELD PREDICTION

Abstract

 The rapid advancement of precision agriculture has highlighted the need for efficient, accurate, and real-time crop monitoring systems to enhance productivity and sustainability. This study investigates the role of autonomous drones equipped with multispectral and thermal sensors for real-time crop monitoring and yield prediction. Drone-acquired data were used to extract key agronomic indicators, including Normalized Difference Vegetation Index (NDVI), soil moisture, and canopy temperature, which were further analyzed using data-driven yield prediction models. The results demonstrate that autonomous drones enable high-resolution spatial and temporal monitoring of crop health, effectively capturing field variability and stress conditions. The integration of multi-temporal drone observations significantly improved yield prediction accuracy and robustness across different field plots. The findings confirm that drone-based monitoring provides timely insights for precision farming decisions, supporting optimized resource management and improved crop productivity. Overall, the study establishes autonomous drones as a reliable and scalable solution for real-time crop assessment and yield forecasting in modern agriculture.