Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud
To address the inefficiency and significant errors in the manual measurement of phenotypic parameters of <i>Phoebe zhennan</i> seedlings, a non-destructive automated method based on a 3D point cloud was proposed for extracting phenotypic parameters of stem and leaves following stem and l...
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MDPI AG
2025-04-01
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| Series: | Agriculture |
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| author | Yang Zhou Yikai Qi Longbin Xiang |
| author_facet | Yang Zhou Yikai Qi Longbin Xiang |
| author_sort | Yang Zhou |
| collection | DOAJ |
| description | To address the inefficiency and significant errors in the manual measurement of phenotypic parameters of <i>Phoebe zhennan</i> seedlings, a non-destructive automated method based on a 3D point cloud was proposed for extracting phenotypic parameters of stem and leaves following stem and leaf segmentation. First, the processed point cloud image was aligned using the Sample Consensus Initial Aligment (SAC-IA) and Iterative Closest Point (ICP) algorithms to generate a three-dimensional model of the seedlings. The stem point cloud was extracted from the model using the median normalized growth vector-based search (MNVG) method, with the current growth vector refined based on previous growth points and vectors. These corrective processes enhanced the accuracy of stem extraction. The leaves were separated from the stem through streamlined projection, after which the remaining leaf point cloud was individually extracted using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The extracted stem data were used to measure stem length and stem diameter, and for each extracted leaf, leaf length, width, and area were measured, yielding accuracies of 97.7%, 93.2%, 96.4%, 88.02%, and 85.84%, respectively. The results of this study provide a valuable reference for forest breeding and the cultivation of high-quality tree seedlings. |
| format | Article |
| id | doaj-art-ead191b414a54b458a42b4fb2e137ab3 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-ead191b414a54b458a42b4fb2e137ab32025-08-20T02:24:41ZengMDPI AGAgriculture2077-04722025-04-0115883410.3390/agriculture15080834Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point CloudYang Zhou0Yikai Qi1Longbin Xiang2School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaTo address the inefficiency and significant errors in the manual measurement of phenotypic parameters of <i>Phoebe zhennan</i> seedlings, a non-destructive automated method based on a 3D point cloud was proposed for extracting phenotypic parameters of stem and leaves following stem and leaf segmentation. First, the processed point cloud image was aligned using the Sample Consensus Initial Aligment (SAC-IA) and Iterative Closest Point (ICP) algorithms to generate a three-dimensional model of the seedlings. The stem point cloud was extracted from the model using the median normalized growth vector-based search (MNVG) method, with the current growth vector refined based on previous growth points and vectors. These corrective processes enhanced the accuracy of stem extraction. The leaves were separated from the stem through streamlined projection, after which the remaining leaf point cloud was individually extracted using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The extracted stem data were used to measure stem length and stem diameter, and for each extracted leaf, leaf length, width, and area were measured, yielding accuracies of 97.7%, 93.2%, 96.4%, 88.02%, and 85.84%, respectively. The results of this study provide a valuable reference for forest breeding and the cultivation of high-quality tree seedlings.https://www.mdpi.com/2077-0472/15/8/8343D point cloudpoint cloud alignmentmedian normalized growthstreamline projectionhole repairphenotypic extraction |
| spellingShingle | Yang Zhou Yikai Qi Longbin Xiang Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud Agriculture 3D point cloud point cloud alignment median normalized growth streamline projection hole repair phenotypic extraction |
| title | Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud |
| title_full | Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud |
| title_fullStr | Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud |
| title_full_unstemmed | Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud |
| title_short | Automatic Extraction Method of Phenotypic Parameters for <i>Phoebe zhennan</i> Seedlings Based on 3D Point Cloud |
| title_sort | automatic extraction method of phenotypic parameters for i phoebe zhennan i seedlings based on 3d point cloud |
| topic | 3D point cloud point cloud alignment median normalized growth streamline projection hole repair phenotypic extraction |
| url | https://www.mdpi.com/2077-0472/15/8/834 |
| work_keys_str_mv | AT yangzhou automaticextractionmethodofphenotypicparametersforiphoebezhennaniseedlingsbasedon3dpointcloud AT yikaiqi automaticextractionmethodofphenotypicparametersforiphoebezhennaniseedlingsbasedon3dpointcloud AT longbinxiang automaticextractionmethodofphenotypicparametersforiphoebezhennaniseedlingsbasedon3dpointcloud |