A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs
Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algor...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Plants |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2223-7747/14/11/1578 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129332077330432 |
|---|---|
| author | Pengyu Chu Bo Han Qiang Guo Yiping Wan Jingjing Zhang |
| author_facet | Pengyu Chu Bo Han Qiang Guo Yiping Wan Jingjing Zhang |
| author_sort | Pengyu Chu |
| collection | DOAJ |
| description | Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R<sup>2</sup> of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton. |
| format | Article |
| id | doaj-art-75f7e505d5c94e48bf824c8075bf340f |
| institution | OA Journals |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-75f7e505d5c94e48bf824c8075bf340f2025-08-20T02:33:02ZengMDPI AGPlants2223-77472025-05-011411157810.3390/plants14111578A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple OrgansPengyu Chu0Bo Han1Qiang Guo2Yiping Wan3Jingjing Zhang4College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaPhenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R<sup>2</sup> of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton.https://www.mdpi.com/2223-7747/14/11/1578cottonplant phenotypeartificial intelligenceresidual modulepoint cloud segmentation |
| spellingShingle | Pengyu Chu Bo Han Qiang Guo Yiping Wan Jingjing Zhang A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs Plants cotton plant phenotype artificial intelligence residual module point cloud segmentation |
| title | A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs |
| title_full | A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs |
| title_fullStr | A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs |
| title_full_unstemmed | A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs |
| title_short | A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs |
| title_sort | three dimensional phenotype extraction method based on point cloud segmentation for all period cotton multiple organs |
| topic | cotton plant phenotype artificial intelligence residual module point cloud segmentation |
| url | https://www.mdpi.com/2223-7747/14/11/1578 |
| work_keys_str_mv | AT pengyuchu athreedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT bohan athreedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT qiangguo athreedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT yipingwan athreedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT jingjingzhang athreedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT pengyuchu threedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT bohan threedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT qiangguo threedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT yipingwan threedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans AT jingjingzhang threedimensionalphenotypeextractionmethodbasedonpointcloudsegmentationforallperiodcottonmultipleorgans |