Hybrid Fuzzy–PPO Control for Precision UAV Spraying

Precision agriculture increasingly relies on autonomous UAVs for tasks, such as crop monitoring and targeted pesticide spraying. However, maintaining stable flight and precise spray delivery under varying payloads and wind disturbances remains challenging. This paper proposes a hybrid control architecture that combines interpretable Mamdani fuzzy logic controllers with a deep reinforcement learning (DRL) agent (Proximal Policy Optimization, PPO). The fuzzy controllers encode expert-crafted rules for baseline altitude and attitude stabilization, while the PPO agent adaptively adjusts setpoints to optimize spray coverage and energy efficiency. We train the agent in a realistic PyBullet simulator with dynamic payload and wind conditions. In simulated precision-spraying trials, our hybrid controller outperformed both a conventional PID-based controller and a pure PPO controller. Specifically, it achieved roughly 2–3× faster disturbance rejection, near-zero overshoot, and ~30% faster settling than the baselines, resulting in more uniform coverage and reduced pesticide use. These results demonstrate that fusing fuzzy logic with deep PPO yields a UAV spray controller that is both high-performance and robust for precision agriculture applications.