Positioning radiata pine branches requiring pruning by drone stereo vision

arXiv cs.CV / 4/21/2026

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Key Points

The paper introduces a drone-mounted stereo-vision pipeline to detect and localize radiata pine branches to enable autonomous pruning.
It evaluates branch segmentation models (YOLOv8/Yolo v9 and Mask R-CNN) using a custom dataset of 71 stereo image pairs captured with a ZED Mini camera.
For depth estimation, it compares classical stereo (SGBM with WLS filtering) against multiple deep-learning stereo approaches (e.g., PSMNet, ACVNet, GWCNet, MobileStereoNet, RAFT-Stereo, NeRF-supervised deep stereo).
The authors propose centroid-based triangulation with MAD outlier rejection to compute branch distance from segmentation masks and disparity maps.
Qualitative tests at 1–2 meters show deep-learning disparity estimates yield more coherent depth than SGBM, supporting the feasibility of low-cost stereo vision for automated forestry operations.

Abstract

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.