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MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706320/ https://www.ncbi.nlm.nih.gov/pubmed/33313542 http://dx.doi.org/10.34133/2020/1848437 |
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author | Wu, Sheng Wen, Weiliang Wang, Yongjian Fan, Jiangchuan Wang, Chuanyu Gou, Wenbo Guo, Xinyu |
author_facet | Wu, Sheng Wen, Weiliang Wang, Yongjian Fan, Jiangchuan Wang, Chuanyu Gou, Wenbo Guo, Xinyu |
author_sort | Wu, Sheng |
collection | PubMed |
description | Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R(2)) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions. |
format | Online Article Text |
id | pubmed-7706320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-77063202020-12-10 MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction Wu, Sheng Wen, Weiliang Wang, Yongjian Fan, Jiangchuan Wang, Chuanyu Gou, Wenbo Guo, Xinyu Plant Phenomics Research Article Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R(2)) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions. AAAS 2020-03-12 /pmc/articles/PMC7706320/ /pubmed/33313542 http://dx.doi.org/10.34133/2020/1848437 Text en Copyright © 2020 Sheng Wu et al. http://creativecommons.org/licenses/by/4.0/ Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0). |
spellingShingle | Research Article Wu, Sheng Wen, Weiliang Wang, Yongjian Fan, Jiangchuan Wang, Chuanyu Gou, Wenbo Guo, Xinyu MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title | MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title_full | MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title_fullStr | MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title_full_unstemmed | MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title_short | MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction |
title_sort | mvs-pheno: a portable and low-cost phenotyping platform for maize shoots using multiview stereo 3d reconstruction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706320/ https://www.ncbi.nlm.nih.gov/pubmed/33313542 http://dx.doi.org/10.34133/2020/1848437 |
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