Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud
Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/4/893 |
| _version_ | 1827622102389227520 |
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| author | Yunling Liu Guoli Zhang Ke Shao Shunfu Xiao Qing Wang Jinyu Zhu Ruili Wang Lei Meng Yuntao Ma |
| author_facet | Yunling Liu Guoli Zhang Ke Shao Shunfu Xiao Qing Wang Jinyu Zhu Ruili Wang Lei Meng Yuntao Ma |
| author_sort | Yunling Liu |
| collection | DOAJ |
| description | Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R<sup>2</sup> (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants. |
| first_indexed | 2024-03-09T11:16:30Z |
| format | Article |
| id | doaj.art-021190c5124a4056acb38254ab537614 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T11:16:30Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-021190c5124a4056acb38254ab5376142023-12-01T00:27:48ZengMDPI AGAgronomy2073-43952022-04-0112489310.3390/agronomy12040893Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point CloudYunling Liu0Guoli Zhang1Ke Shao2Shunfu Xiao3Qing Wang4Jinyu Zhu5Ruili Wang6Lei Meng7Yuntao Ma8College of Information and Electrical Engineering, China Agricultural University, Beijing 100081, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100081, ChinaInner Mongolia Autonomous Region Biotechnology Research Institute, Huhehaote 010010, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaInner Mongolia Autonomous Region Biotechnology Research Institute, Huhehaote 010010, ChinaDepartment of Geography, Environment, and Tourism, Western Michigan University, Kalamazoo, MI 49008, USACollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaAccurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R<sup>2</sup> (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.https://www.mdpi.com/2073-4395/12/4/8933D point cloudregion-growing algorithmmultiscale tensor voting method (MSTVM)phenotyping |
| spellingShingle | Yunling Liu Guoli Zhang Ke Shao Shunfu Xiao Qing Wang Jinyu Zhu Ruili Wang Lei Meng Yuntao Ma Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud Agronomy 3D point cloud region-growing algorithm multiscale tensor voting method (MSTVM) phenotyping |
| title | Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud |
| title_full | Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud |
| title_fullStr | Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud |
| title_full_unstemmed | Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud |
| title_short | Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud |
| title_sort | segmentation of individual leaves of field grown sugar beet plant based on 3d point cloud |
| topic | 3D point cloud region-growing algorithm multiscale tensor voting method (MSTVM) phenotyping |
| url | https://www.mdpi.com/2073-4395/12/4/893 |
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