Design and Field Validation of a Modular Vision-Guided UAV System for Real-Time Adaptive Vegetative Restoration

Vegetative restoration in degraded landscapes requires scalable deployment strategies capable of adapting to heterogeneous terrain conditions. Conventional aerial seeding methods typically operate in open-loop mode, distributing seeds uniformly without considering terrain suitability. This study presents a modular, vision-guided unmanned aerial vehicle (UAV) system for real-time adaptive seed deployment based on the closed-loop integration of onboard perception and actuation under embedded computational constraints. The proposed system combines RGB-based terrain classification, embedded processing, and altitude-adaptive seed dispensing within a unified perception–decision–actuation framework, enabling selective and context-aware seed deployment during flight. Terrain suitability is evaluated onboard using three convolutional neural network (CNN) models and a color-based baseline to distinguish sowable and non-sowable areas. A confidence-based decision strategy with temporal filtering improves reliability, while an altitude-adaptive control mechanism regulates seed distribution across varying flight heights. Field experiments conducted in semi-arid environments demonstrate classification accuracy above 85% with inference latency below 100 ms on a Jetson Nano platform. Additional offline evaluation under varying altitude, speed, illumination, and terrain conditions confirms the robustness of the perception module. The results demonstrate the feasibility of integrating real-time perception with adaptive actuation, enabling UAVs to transition from passive sensing platforms to active agents for environmental intervention. The proposed system provides a practical and scalable approach for precision vegetative restoration in heterogeneous environments.