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

Yuheng Cao, Ning Wang, Bin Wu, Xin Zhang, Yaxiong Wang, Shuting Xu, Man Zhang, Yanlong Miao () and Feng Kang
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Yuheng Cao: School of Technology, Beijing Forestry University, Beijing 100083, China
Ning Wang: School of Technology, Beijing Forestry University, Beijing 100083, China
Bin Wu: Beijing Shoufa Highway Maintenance Engineering Co., Ltd., Beijing 102600, China
Xin Zhang: School of Technology, Beijing Forestry University, Beijing 100083, China
Yaxiong Wang: School of Technology, Beijing Forestry University, Beijing 100083, China
Shuting Xu: School of Technology, Beijing Forestry University, Beijing 100083, China
Man Zhang: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, China
Yanlong Miao: School of Technology, Beijing Forestry University, Beijing 100083, China
Feng Kang: School of Technology, Beijing Forestry University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 14, 1-25

Abstract: 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.

Keywords: point cloud segmentation; regional growth; trunk–branch segmentation; adaptive cuboid; terrestrial laser scanning (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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