A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species

Accurate acquisition of the phenotypic information of trunk-shaped fruit trees plays a crucial role in intelligent orchard management, pruning during dormancy, and improving fruit yield and quality. However, the precise segmentation of trunks and branches remains a significant challenge, limiting the accurate measurement of phenotypic parameters and high-precision pruning of branches. To address this issue, a novel adaptive cuboid regional growth segmentation algorithm is proposed in this study. This method integrates a growth vector that is adaptively adjusted based on the growth trend of branches and a growth cuboid that is dynamically regulated according to branch diameters. Additionally, an innovative reverse growth strategy is introduced to enhance the efficiency of the growth process. Furthermore, the algorithm can automatically and effectively identify the starting and ending points of growth based on the structural characteristics of fruit tree branches, solving the problem of where to start and when to stop. Compared with PointNet++, PointNeXt, and Point Transformer, ACRGS achieved superior performance, with F 1 -scores of 95.75% and 96.21% and mIoU values of 0.927 and 0.933 for apple and cherry trees. The results show that the method enables high-precision and efficiency trunk–branch segmentation, providing data support for fruit tree phenotypic parameter extraction and pruning.