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 th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/14/1463 |
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| author | Yuheng Cao Ning Wang Bin Wu Xin Zhang Yaxiong Wang Shuting Xu Man Zhang Yanlong Miao Feng Kang |
| author_facet | Yuheng Cao Ning Wang Bin Wu Xin Zhang Yaxiong Wang Shuting Xu Man Zhang Yanlong Miao Feng Kang |
| author_sort | Yuheng Cao |
| collection | DOAJ |
| description | 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<sub>1</sub>-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. |
| format | Article |
| id | doaj-art-b38e6869e357400eabe60ecc4b81c5d8 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-b38e6869e357400eabe60ecc4b81c5d82025-08-20T03:55:49ZengMDPI AGAgriculture2077-04722025-07-011514146310.3390/agriculture15141463A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree SpeciesYuheng Cao0Ning Wang1Bin Wu2Xin Zhang3Yaxiong Wang4Shuting Xu5Man Zhang6Yanlong Miao7Feng Kang8School of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaBeijing Shoufa Highway Maintenance Engineering Co., Ltd., Beijing 102600, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaKey Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, Beijing 100083, ChinaAccurate 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<sub>1</sub>-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.https://www.mdpi.com/2077-0472/15/14/1463point cloud segmentationregional growthtrunk–branch segmentationadaptive cuboidterrestrial laser scanning |
| spellingShingle | Yuheng Cao Ning Wang Bin Wu Xin Zhang Yaxiong Wang Shuting Xu Man Zhang Yanlong Miao Feng Kang A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species Agriculture point cloud segmentation regional growth trunk–branch segmentation adaptive cuboid terrestrial laser scanning |
| title | A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species |
| title_full | A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species |
| title_fullStr | A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species |
| title_full_unstemmed | A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species |
| title_short | A Novel Adaptive Cuboid Regional Growth Algorithm for Trunk–Branch Segmentation of Point Clouds from Two Fruit Tree Species |
| title_sort | novel adaptive cuboid regional growth algorithm for trunk branch segmentation of point clouds from two fruit tree species |
| topic | point cloud segmentation regional growth trunk–branch segmentation adaptive cuboid terrestrial laser scanning |
| url | https://www.mdpi.com/2077-0472/15/14/1463 |
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