Morphological Estimation of Primary Branch Inclination Angles in Jujube Trees Based on Improved PointNet++

The segmentation of jujube tree branches and the estimation of primary branch inclination angles (IAs) are crucial for achieving intelligent pruning. This study presents a primary branch IA estimation algorithm for jujube trees based on an improved PointNet++ network. Firstly, terrestrial laser scanners (TLSs) are used to acquire jujube tree point clouds, followed by preprocessing to construct a point cloud dataset containing open center shape (OCS) and main trunk shape (MTS) jujube trees. Subsequently, the Chebyshev graph convolution module (CGCM) is integrated into PointNet++ to enhance its feature extraction capability, and the DBSCAN algorithm is optimized to perform instance segmentation of primary branch point clouds. Finally, the generalized rotational symmetry axis (ROSA) algorithm is used to extract the primary branch skeleton, from which the IAs are estimated using weighted principal component analysis (PCA) with dynamic window adjustment. The experimental results show that compared to PointNet++, the improved network achieved increases of 1.3, 1.47, and 3.33% in accuracy (Acc), class average accuracy (CAA), and mean intersection over union (mIoU), respectively. The correlation coefficients between the primary branch IAs and their estimated values for OCS and MTS jujube trees were 0.958 and 0.935, with root mean square errors of 2.38° and 4.94°, respectively. In summary, the proposed method achieves accurate jujube tree primary branch segmentation and IA measurement, providing a foundation for intelligent pruning.