Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds
Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cu...
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2025-01-01
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| Series: | Agronomy |
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| author | Lili Zhang Shuangyue Shi Muhammad Zain Binqian Sun Dongwei Han Chengming Sun |
| author_facet | Lili Zhang Shuangyue Shi Muhammad Zain Binqian Sun Dongwei Han Chengming Sun |
| author_sort | Lili Zhang |
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| description | Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-9c97ea8456e44dc5a14769c0af189ea52025-01-24T13:17:19ZengMDPI AGAgronomy2073-43952025-01-0115124510.3390/agronomy15010245Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point CloudsLili Zhang0Shuangyue Shi1Muhammad Zain2Binqian Sun3Dongwei Han4Chengming Sun5Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaPoint cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed.https://www.mdpi.com/2073-4395/15/1/245rapeseedpoint cloud denoisingpoint cloud segmentationbinocular stereo vision |
| spellingShingle | Lili Zhang Shuangyue Shi Muhammad Zain Binqian Sun Dongwei Han Chengming Sun Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds Agronomy rapeseed point cloud denoising point cloud segmentation binocular stereo vision |
| title | Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds |
| title_full | Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds |
| title_fullStr | Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds |
| title_full_unstemmed | Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds |
| title_short | Evaluation of Rapeseed Leave Segmentation Accuracy Using Binocular Stereo Vision 3D Point Clouds |
| title_sort | evaluation of rapeseed leave segmentation accuracy using binocular stereo vision 3d point clouds |
| topic | rapeseed point cloud denoising point cloud segmentation binocular stereo vision |
| url | https://www.mdpi.com/2073-4395/15/1/245 |
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