Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments
As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy s...
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MDPI AG
2025-07-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/13/2080 |
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| author | Wenqin Wang Chengda Lin Haiyu Shui Ke Zhang Ruifang Zhai |
| author_facet | Wenqin Wang Chengda Lin Haiyu Shui Ke Zhang Ruifang Zhai |
| author_sort | Wenqin Wang |
| collection | DOAJ |
| description | As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R<sup>2</sup>) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. |
| format | Article |
| id | doaj-art-b3f1efa297884a9e88f9ad7c9fae8e7c |
| institution | Matheson Library |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-b3f1efa297884a9e88f9ad7c9fae8e7c2025-07-11T14:42:09ZengMDPI AGPlants2223-77472025-07-011413208010.3390/plants14132080Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy EnvironmentsWenqin Wang0Chengda Lin1Haiyu Shui2Ke Zhang3Ruifang Zhai4College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaAs a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R<sup>2</sup>) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems.https://www.mdpi.com/2223-7747/14/13/2080adaptive symmetry self-matchingpoint cloud completionsymmetry analysistomato phenotypingsmart agriculture |
| spellingShingle | Wenqin Wang Chengda Lin Haiyu Shui Ke Zhang Ruifang Zhai Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments Plants adaptive symmetry self-matching point cloud completion symmetry analysis tomato phenotyping smart agriculture |
| title | Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments |
| title_full | Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments |
| title_fullStr | Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments |
| title_full_unstemmed | Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments |
| title_short | Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments |
| title_sort | adaptive symmetry self matching for 3d point cloud completion of occluded tomato fruits in complex canopy environments |
| topic | adaptive symmetry self-matching point cloud completion symmetry analysis tomato phenotyping smart agriculture |
| url | https://www.mdpi.com/2223-7747/14/13/2080 |
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