CNN-Driven Terrain Analysis and Infrared Soil Hydration Control in Autonomous UAV-Based Urban Reforestation

This paper presents an innovative AI-driven plantation drone designed to automate the process of land selection, irrigation, and seed planting. The system utilizes a web camera for real-time image processing to identify empty and fertile land for planting. An infrared humidity sensor monitors soil moisture, and the drone sprays water in areas with insufficient humidity. Once the ideal moisture level is reached, a seed ball, containing both seeds and fertilizer, is shot into the soil using a built-in dispenser. This fully autonomous process aims to reduce human intervention in plantation activities while optimizing land usage and improving crop success rates. The AI algorithms ensure precise detection and planting, while the drone's navigation system enhances efficiency. Preliminary testing demonstrates significant improvements in water conservation, planting accuracy, and time efficiency compared to manual methods, showcasing the system's potential for sustainable agricultural practices.