Precision agriculture in mountainous regions faces challenges due to complex topography and limited accessibility, making traditional crop monitoring and fertilization inefficient. This study proposes and evaluates a drone-based precision system to optimize adaptive fertilization and crop health monitoring in such environments. A quadcopter drone equipped with RGB and multispectral sensors captured high-resolution aerial imagery, which was processed into Digital Surface Models (DSM) and NDVI maps for accurate land mapping and plant health assessment. Spatial NDVI data enabled targeted liquid fertilizer application, improving precision and reducing waste. Results showed that NDVI-based monitoring allowed early detection of plant stress, and drone-generated maps exhibited high accuracy with minimal deviation from manual observations. Compared to traditional methods, drone-assisted adaptive fertilization improved time and labor efficiency, and reduced fertilizer usage by up to 25% without compromising yields. Statistical analysis confirmed the system's effectiveness in optimizing inputs and enhancing operational efficiency. Despite the advantages, adoption remains limited among smallholder farmers due to cost and accessibility concerns. This research contributes to sustainable agriculture by demonstrating how precision drones can enhance resource management and productivity in hilly terrains, supporting the broader adoption of digital agricultural practices.
Siska Almaniar (Tue,) studied this question.