An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning

Agriculture is a vital sector in developing nations such as India, and the use of autonomous vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming practices. Unmanned Aerial Vehicles (UAVs) are becoming increasingly important in agriculture, as they can provide valuable data for crop monitoring and pest control. In this study, we investigate the reliability of a Multi-Agent Reinforcement Learning (MARL) method for UAV field coverage. The algorithm enables a group of UAVs equipped with ground-facing cameras to learn how to provide complete coverage of an unknown Field of Interest (FoI) while minimizing camera view overlap. We test the algorithm in scenarios where the FoI and camera Field of View (FoV) are dynamically updated in the environment, to evaluate its performance under more dynamic conditions. Our results demonstrate the effectiveness and resilience of the proposed method in varying environmental conditions, highlighting its potential for Precision Agriculture (PA) applications.