Comparative Analysis of Unmanned Aerial Vehicle and Ground-Level Data Using Computer Vision in Agriculture

Machine learning and computer vision advancements are transforming precision agriculture by enhancing crop monitoring, disease detection, and yield forecasting. This study compares aerial imagery from unmanned aerial vehicles (UAVs) with ground-level photos for mapping key agricultural tasks, evaluating both using a unified deep learning framework on classification accuracy, inference speed, and feature extraction. UAV images achieved 92.3% accuracy - a 28% reduction in processing time versus ground-level data - while providing superior vegetation index estimation and broader spatial coverage. For specialized applications like weed and disease detection, UAV models matched ground-based methods in accuracy and F1 scores. Supporting literature further underscores UAV efficacy in canopy analysis, biomass estimation, and overall crop monitoring. These findings position UAV-derived imagery as a scalable, reliable data source for precision agriculture, offering actionable insights for future research and implementation.