Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization
Abstract Due to the small and irregular shapes of vegetable seeds, modeling them is challenging, and the imprecision of physical parameters hinders the performance of vegetable seeders, impeding simulation development. In this study, seeds of cucumber, pepper, and tomato were seen as examples. A 3D...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84375-3 |
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| _version_ | 1841559716994482176 |
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| author | Yuling Shao Qing Wang Hao Sun Xinting Ding |
| author_facet | Yuling Shao Qing Wang Hao Sun Xinting Ding |
| author_sort | Yuling Shao |
| collection | DOAJ |
| description | Abstract Due to the small and irregular shapes of vegetable seeds, modeling them is challenging, and the imprecision of physical parameters hinders the performance of vegetable seeders, impeding simulation development. In this study, seeds of cucumber, pepper, and tomato were seen as examples. A 3D point cloud reconstruction method based on Structure-from-Motion Multi-View Stereo (SfM-MVS) was employed to accurately extract 3D models of small and irregularly shaped seeds. Corresponding discrete element models were established. Combining physical and simulation experiments on seed angle of repose(AOR), significant parameters influencing seed AOR and their ranges were identified through Plackett–Burman Design (PBD) and steepest ascent test. Within this range, the GA-BP-GA algorithm was used to accurately inverse the optimal parameter combination. The results indicate that the SfM-MVS 3D point cloud reconstruction method can extract more detailed shape information of small and irregularly shaped seeds. The GA-BP-GA algorithm achieved an inversion of physical parameters with the smallest relative error of cucumber, pepper, and tomato seeds being 0.26%, 0.98%, and 0.51%, respectively. Through experimental comparative analysis, the feasibility and accuracy of this method in calibrating discrete element parameters for small and irregularly shaped seeds were validated. The established seed models and calibrated parameters in this study can be implemented to the simulation optimization design of vegetable seeders, enhancing development efficiency and operational performance. |
| format | Article |
| id | doaj-art-1fd82eab4e3648b59428fdb98d79d1e6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1fd82eab4e3648b59428fdb98d79d1e62025-01-05T12:14:01ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-84375-3Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimizationYuling Shao0Qing Wang1Hao Sun2Xinting Ding3College of Engineering, Shandong Yingcai UniversityCollege of Engineering, Shandong Yingcai UniversityWeihai Mangosteen Model Co., Ltd.College of Mechanical and Electronic Engineering, Northwest A&F UniversityAbstract Due to the small and irregular shapes of vegetable seeds, modeling them is challenging, and the imprecision of physical parameters hinders the performance of vegetable seeders, impeding simulation development. In this study, seeds of cucumber, pepper, and tomato were seen as examples. A 3D point cloud reconstruction method based on Structure-from-Motion Multi-View Stereo (SfM-MVS) was employed to accurately extract 3D models of small and irregularly shaped seeds. Corresponding discrete element models were established. Combining physical and simulation experiments on seed angle of repose(AOR), significant parameters influencing seed AOR and their ranges were identified through Plackett–Burman Design (PBD) and steepest ascent test. Within this range, the GA-BP-GA algorithm was used to accurately inverse the optimal parameter combination. The results indicate that the SfM-MVS 3D point cloud reconstruction method can extract more detailed shape information of small and irregularly shaped seeds. The GA-BP-GA algorithm achieved an inversion of physical parameters with the smallest relative error of cucumber, pepper, and tomato seeds being 0.26%, 0.98%, and 0.51%, respectively. Through experimental comparative analysis, the feasibility and accuracy of this method in calibrating discrete element parameters for small and irregularly shaped seeds were validated. The established seed models and calibrated parameters in this study can be implemented to the simulation optimization design of vegetable seeders, enhancing development efficiency and operational performance.https://doi.org/10.1038/s41598-024-84375-3Seeds with irregular shapeDiscrete element method3D point cloud reconstructionSfM-MVSGA-BP neural network |
| spellingShingle | Yuling Shao Qing Wang Hao Sun Xinting Ding Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization Scientific Reports Seeds with irregular shape Discrete element method 3D point cloud reconstruction SfM-MVS GA-BP neural network |
| title | Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization |
| title_full | Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization |
| title_fullStr | Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization |
| title_full_unstemmed | Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization |
| title_short | Irregular seeds DEM parameters prediction based on 3D point cloud and GA-BP-GA optimization |
| title_sort | irregular seeds dem parameters prediction based on 3d point cloud and ga bp ga optimization |
| topic | Seeds with irregular shape Discrete element method 3D point cloud reconstruction SfM-MVS GA-BP neural network |
| url | https://doi.org/10.1038/s41598-024-84375-3 |
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