An Integrated Framework for Branch Detection and Depth Estimation in UAV Stereo Vision for Forestry Pruning
Sofia Keller,
Wageningen University and Research, Wageningen, the Netherlands
Sofia Novak
Wageningen University and Research, Wageningen, the Netherlands
Theodore Abernathy
Wageningen University and Research, Wageningen, the Netherlands
Keywords: Unmanned Aerial Vehicles,, Stereo Vision, Branch Detection, Forestry Automation,, Semantic Segmentation
Abstract
The automation of forestry management practices, particularly selective branch pruning, represents a significant challenge in modern silviculture. Manual pruning is labor-intensive, time-consuming, and presents considerable safety risks to human operators. While Unmanned Aerial Vehicles have been extensively deployed for passive remote sensing and canopy analysis, their application in active physical interaction tasks such as pruning remains limited by the complexities of aerial manipulation in unstructured environments. A critical prerequisite for autonomous aerial pruning is the precise visual identification and spatial localization of target branches. This paper proposes a comprehensive and integrated framework that seamlessly combines deep learning based semantic segmentation for robust branch detection with binocular stereo vision for high accuracy depth estimation. The proposed system is designed to operate onboard a resource constrained Unmanned Aerial Vehicle, processing complex canopy imagery to output isolated three-dimensional branch coordinates suitable for guiding a robotic pruning effector. By integrating a lightweight convolutional neural network with a highly optimized semi-global stereo matching algorithm, the framework addresses the inherent challenges of dynamic lighting, heavy visual occlusion, and background clutter characteristic of forest environments. Extensive field experiments and mock-up trials demonstrate the efficacy of the proposed pipeline. The semantic segmentation module achieves high pixel wise accuracy in isolating branch structures from surrounding foliage, while the stereo vision component provides reliable depth maps with a minimal margin of error. The synthesized spatial data allows for the accurate extraction of branch cutting points. This research contributes a crucial foundational technology toward the realization of fully autonomous aerial forestry tools, bridging the gap between passive observation and active robotic intervention in complex natural landscapes.
References
Lin, Y., Xue, B., Zhang, M., Schofield, S., & Green, R. (2025, November). YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications. In 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
Lin, Y., Xue, B., Zhang, M., Schofield, S., & Green, R. (2024, December). Deep Learning-Based Depth Map Generation and YOLO-Integrated Distance Estimation for Radiata Pine Branch Detection Using Drone Stereo Vision. In 2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Tang, Y., Zhang, G., Liu, J. K., & Qin, R. (2025). Weakly supervised land-cover classification of high-resolution images with low-resolution labels through optimized label refinement. International Journal of Remote Sensing, 46(5), 1913-1937.
Wan, Y., Zhang, K., Xia, R., Li, Z., Zhang, Y., & Genovese, P. V. (2026). Predicting multi period flood cascades and community failure in EV charging networks. npj Natural Hazards, 3(1), 2.
Zhang, C., & Zhao, Y. (2017). High precision deep sea geomagnetic data sampling and recovery with three-dimensional compressive sensing. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, 100(9), 1760-1762.
HAN, Z., ZHANG, L., ZHANG, B., ZOU, F., & SHANG, N. (2024). Progress on research and application of new non-destructive testing techniques in tomato quality inspection. Food Science, 45(1), 289-300.
Lijun, X., Yehui, Z., Yue, S., Fanglei, Z., Honghua, J., & Guangming, W. (2022). Research on the current situation of continuously variable transmission and electric drive technology. Journal of Chinese Agricultural Mechanization, 43(7), 81.
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., et al. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2), RG2004.
Zou, F., Hao, H., Yang, M., Tan, C., Chen, L., Wu, J., & Wang, H. (2025). Enhancing cyclodextrin glycosyltransferase-mediated potato starch modification via plasma-activated water and twin-screw extrusion treatment. Food Chemistry, 491, 145247.
Lin, Y., Xue, B., Zhang, M., Schofield, S., & Green, R. (2025, November). Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems. In 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
Song, S., Tang, Y., & Qin, R. (2025). Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection. IEEE Transactions on Geoscience and Remote Sensing.
Ahmad, N. R. (2025). Exploring the impact of inflation on Pakistani society: Challenges, causes, and long-term consequences for economic stability and social well-being. https://doi.org/10.63075/7vtnh777
Ahmad, N. R. (2025). Business ethics in the age of automation: How companies can balance profitability with responsibility. Punjab Model Bazaars Management Company.
