Deep Learning-Based Depth Map Generation and YOLO-Integrated Distance Estimation for Radiata Pine Branch Detection Using Drone Stereo Vision

This research focuses on the development of a deep learning based method to enable a drone equipped with a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to Semi-Global Block Matching(SGBM), deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process with deep neural networks is applied to generate the depth map that most closely approximates the ground-truth. This methodology achieves accurate branch detection and precise distance measurement, addressing key challenges in automating pruning operations. The results indicate substantial improvements in accuracy, though further optimization is required to enhance processing speed, demonstrating the potential of deep learning to advance automation in agricultural systems.