Integrated framework for multipurpose UAV Path Planning in hedgerow systems considering the biophysical environment

This study presents a new path-planning framework for precision agriculture, designed for hedgerow systems, which combines cutting-edge technology and data analysis to enhance crop management in light of climate change challenges. The framework creates detailed digital field models by employing Unmanned Aerial Vehicles (UAVs), or drones, either with high-precision LiDAR or Structure-from-Motion (SfM) data. Then, these models are inputs for the path planning algorithm, crucial for directing drones on the most efficient paths for surveys or spraying. The key feature is its ability to adjust to the specific conditions of agricultural fields, considering the current biophysical environment, ensuring paths are closely aligned with crop rows and adapting to vegetation changes. This leads to significant efficiency improvements, especially in cases of irregular row spacing or heterogeneous vegetation, achieving paths up to 40% shorter than traditional geometry-based methods. The effectiveness of the algorithm relies on the quality of input data, with LiDAR being recommended due to its higher accuracy despite its longer processing time. Field tests were conducted in a vineyard in Spain to validate the effectiveness of the framework. Integrating drone technology with precise routing and high-quality data, the proposed framework can potentially enhance the sustainable and efficient management of woody crops. • Incorporating biophysical environment information is vital for precision agriculture, precise disease and pest management and accurate path planning. • The workflow enables efficient UAV aerial inspections and precise spraying in hedgerow vineyards using biophysical data. • The algorithm creates paths up to 40% shorter, improving efficiency through vegetation density consideration. • Comparing SfM and LiDAR inputs, LiDAR has slightly longer paths due to its higher accuracy in detecting vegetation.