Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point
High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/120 |
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| author | Xuemei Liang Wenbo Yu Li Qin Jianfeng Wang Peng Jia Qi Liu Xiaoyu Lei Minglai Yang |
| author_facet | Xuemei Liang Wenbo Yu Li Qin Jianfeng Wang Peng Jia Qi Liu Xiaoyu Lei Minglai Yang |
| author_sort | Xuemei Liang |
| collection | DOAJ |
| description | High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings. |
| format | Article |
| id | doaj-art-1408ea72c05a42b78b9d46f801780a34 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-1408ea72c05a42b78b9d46f801780a342025-01-24T13:16:48ZengMDPI AGAgronomy2073-43952025-01-0115112010.3390/agronomy15010120Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D PointXuemei Liang0Wenbo Yu1Li Qin2Jianfeng Wang3Peng Jia4Qi Liu5Xiaoyu Lei6Minglai Yang7College of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaState Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaKey Laboratory of Facility Vegetable of Jilin Province, Jilin Academy of Vegetable and Flower Sciences, Changchun 130119, ChinaState Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaHigh-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings.https://www.mdpi.com/2073-4395/15/1/120tomato seedlingphenotypic parameter extraction3D point cloudstem and leaf segmentation |
| spellingShingle | Xuemei Liang Wenbo Yu Li Qin Jianfeng Wang Peng Jia Qi Liu Xiaoyu Lei Minglai Yang Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point Agronomy tomato seedling phenotypic parameter extraction 3D point cloud stem and leaf segmentation |
| title | Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point |
| title_full | Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point |
| title_fullStr | Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point |
| title_full_unstemmed | Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point |
| title_short | Stem and Leaf Segmentation and Phenotypic Parameter Extraction of Tomato Seedlings Based on 3D Point |
| title_sort | stem and leaf segmentation and phenotypic parameter extraction of tomato seedlings based on 3d point |
| topic | tomato seedling phenotypic parameter extraction 3D point cloud stem and leaf segmentation |
| url | https://www.mdpi.com/2073-4395/15/1/120 |
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