Pruning Branch Recognition and Pruning Point Localization for Walnut ( Juglans regia L.) Trees Based on Point Cloud Semantic Segmentation

Wei Zhu, Xiaopeng Bai, Daochun Xu and Wenbin Li ()
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Wei Zhu: School of Technology, Beijing Forestry University, Beijing 100083, China
Xiaopeng Bai: School of Technology, Beijing Forestry University, Beijing 100083, China
Daochun Xu: School of Technology, Beijing Forestry University, Beijing 100083, China
Wenbin Li: School of Technology, Beijing Forestry University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 8, 1-22

Abstract: Intelligent pruning technology is significant in reducing management costs and improving operational efficiency. In this study, a branch recognition and pruning point localization method was proposed for dormant walnut ( Juglans regia L.) trees. First, 3D point clouds of walnut trees were reconstructed from multi-view images using Neural Radiance Fields (NeRFs). Second, Walnut-PointNet was improved to segment the walnut tree into Trunk, Branch, and Calibration categories. Next, individual pruning branches were extracted by cluster analysis and pruning rules were adjusted by classifying branches based on length. Finally, Principal Component Analysis (PCA) was used for length extraction, and pruning points were determined based on pruning rules. Walnut-PointNet achieved an OA of 93.39%, an ACC of 95.29%, and an mIoU of 0.912 on the walnut tree dataset. The mean absolute errors in length extraction for the short-growing branch group and the water sprout were 28.04 mm and 50.11 mm, respectively. The average success rate of pruning point recognition reached 89.33%, and the total time for pruning branch recognition and pruning point localization for the entire tree was approximately 16 s. This study provides support for the development of intelligent pruning for walnut trees.

Keywords: walnut tree; intelligent pruning; 3D point cloud; semantic segmentation; PointNet++; branch recognition; pruning point localization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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