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Complex-Canopy Pear Branch Parameter Estimation via Three-Stage Point Cloud Segmentation and Label-Constrained Skeletonization
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DOI：10.3390/agriculture16131479.png)
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Abstract
En 中文
Fruit tree pruning improves orchard ventilation and light interception, thereby enhancing fruit quality and yield. Accurate measurement of pear tree branch parameters, such as branch length, growth angle, and spacing between growth points, is essential for intelligent pruning. However, pear tree canopies often exhibit severe branch crossing and adhesion, ambiguous branch-type boundaries, and difficulties in identifying target branches. To address these challenges, this study proposes a three-stage branch point cloud segmentation framework and a skeleton-based structural parameter measurement method. First, Point Transformer V3 classifies the whole-tree point cloud into annual shoots, primary branches, and the trunk. Second, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) coarsely divides the annual shoots into local adhesive clusters. Third, PlantNet performs fine instance segmentation of individual annual shoots, after which the local labels are remapped to the whole-tree coordinate space through inverse normalisation. Semantic and instance labels are then embedded into Laplacian contraction to construct the Semantic and Instance Label-Constrained Laplacian Skeleton Extraction (SILC-LSE) method. The proposed framework achieved mPrec, mRec, mF1, and mIoU values of 90.31%, 89.82%, 90.07%, and 83.76%, respectively. The mean absolute errors (MAEs) for the length estimation of annual shoots and primary branches were 0.13 m and 0.12 m, respectively. The MAE for spacing between growth points was 0.07 m, while the MAEs for growth angle estimation between different branch types were 6.70° and 7.27°, respectively.
Keywords:
intelligent pruning
pear tree
point cloud segmentation
skeleton extraction
structural parameter measurement
deep learning
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