Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation
The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the...
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MDPI AG
2023-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/7/1321 |
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| author | Yongzhe Sun Zhixin Zhang Kai Sun Shuai Li Jianglin Yu Linxiao Miao Zhanguo Zhang Yang Li Hongjie Zhao Zhenbang Hu Dawei Xin Qingshan Chen Rongsheng Zhu |
| author_facet | Yongzhe Sun Zhixin Zhang Kai Sun Shuai Li Jianglin Yu Linxiao Miao Zhanguo Zhang Yang Li Hongjie Zhao Zhenbang Hu Dawei Xin Qingshan Chen Rongsheng Zhu |
| author_sort | Yongzhe Sun |
| collection | DOAJ |
| description | The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans. |
| first_indexed | 2024-03-11T01:24:07Z |
| format | Article |
| id | doaj.art-012736ec1c674177ae84ce178495e872 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T01:24:07Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-012736ec1c674177ae84ce178495e8722023-11-18T17:52:08ZengMDPI AGAgriculture2077-04722023-06-01137132110.3390/agriculture13071321Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ SegmentationYongzhe Sun0Zhixin Zhang1Kai Sun2Shuai Li3Jianglin Yu4Linxiao Miao5Zhanguo Zhang6Yang Li7Hongjie Zhao8Zhenbang Hu9Dawei Xin10Qingshan Chen11Rongsheng Zhu12College of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin 150030, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin 150030, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin 150030, ChinaCollege of Agriculture, Northeast Agricultural University, Harbin 150030, ChinaCollege of Agriculture, Northeast Agricultural University, Harbin 150030, ChinaCollege of Agriculture, Northeast Agricultural University, Harbin 150030, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin 150030, ChinaThe study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.https://www.mdpi.com/2077-0472/13/7/13213D reconstructionthe whole growth periodsoybeanpoint cloud segmentationdataset |
| spellingShingle | Yongzhe Sun Zhixin Zhang Kai Sun Shuai Li Jianglin Yu Linxiao Miao Zhanguo Zhang Yang Li Hongjie Zhao Zhenbang Hu Dawei Xin Qingshan Chen Rongsheng Zhu Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation Agriculture 3D reconstruction the whole growth period soybean point cloud segmentation dataset |
| title | Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation |
| title_full | Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation |
| title_fullStr | Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation |
| title_full_unstemmed | Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation |
| title_short | Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation |
| title_sort | soybean mvs annotated three dimensional model dataset of whole growth period soybeans for 3d plant organ segmentation |
| topic | 3D reconstruction the whole growth period soybean point cloud segmentation dataset |
| url | https://www.mdpi.com/2077-0472/13/7/1321 |
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