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

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.

Code and data availability

The paper describes a walnut tree point cloud dataset (100 trees), annotations, and the Walnut-PointNet model, but the Data Availability Statement only offers contact with the corresponding author; no public dataset, code, or model repository is provided.

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