Positioning radiata pine branches requiring pruning by drone stereo vision
This work addresses the need for automated branch detection in forestry, enabling precision pruning with drones, though the approach is incremental and tested on a small custom dataset.
The paper develops a drone-mounted stereo vision system to detect and localize radiata pine branches for autonomous pruning, comparing multiple segmentation and depth estimation methods. Results show deep learning-based disparity maps outperform traditional SGBM at 1-2 m distances, demonstrating feasibility of low-cost stereo vision for branch positioning.
This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning. The proposed pipeline comprises two stages: branch segmentation and depth estimation. For segmentation, YOLOv8, YOLOv9, and Mask R-CNN variants are compared on a custom dataset of 71 stereo image pairs captured with a ZED Mini camera. For depth estimation, both a traditional method (SGBM with WLS filtering) and deep-learning-based methods (PSMNet, ACVNet, GWCNet, MobileStereoNet, RAFT-Stereo, and NeRF-Supervised Deep Stereo) are evaluated. A centroid-based triangulation algorithm with MAD outlier rejection is proposed to compute branch distance from the segmentation mask and disparity map. Qualitative evaluation at distances of 1-2 m indicates that the deep learning-based disparity maps produce more coherent depth estimates than SGBM, demonstrating the feasibility of low-cost stereo vision for automated branch positioning in forestry.