Improving Up-Close Remote Sensing of Occluded Areas in Vineyards through Customized Multiple-Unmanned-Aerial-Vehicle Path Planning

This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The proposed method implements the Ant Colony Optimization (ACO) algorithm and enables single and multiple UAVs to fly synchronously while ensuring a safe distance between platforms. The results highlight that ACO is able to generate optimal and safe routes, considering the vegetation and covering the whole agricultural area. Moreover, it shows potential to solve partial leaf occlusion for fruit identification.