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Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits...
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/PMC7706343/ https://www.ncbi.nlm.nih.gov/pubmed/33313550 http://dx.doi.org/10.34133/2020/3414926 |
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author | Hu, Weijuan Zhang, Can Jiang, Yuqiang Huang, Chenglong Liu, Qian Xiong, Lizhong Yang, Wanneng Chen, Fan |
author_facet | Hu, Weijuan Zhang, Can Jiang, Yuqiang Huang, Chenglong Liu, Qian Xiong, Lizhong Yang, Wanneng Chen, Fan |
author_sort | Hu, Weijuan |
collection | PubMed |
description | The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R(2) values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding. |
format | Online Article Text |
id | pubmed-7706343 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-77063432020-12-10 Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography Hu, Weijuan Zhang, Can Jiang, Yuqiang Huang, Chenglong Liu, Qian Xiong, Lizhong Yang, Wanneng Chen, Fan Plant Phenomics Research Article The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R(2) values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding. AAAS 2020-05-02 /pmc/articles/PMC7706343/ /pubmed/33313550 http://dx.doi.org/10.34133/2020/3414926 Text en Copyright © 2020 Weijuan Hu 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 Hu, Weijuan Zhang, Can Jiang, Yuqiang Huang, Chenglong Liu, Qian Xiong, Lizhong Yang, Wanneng Chen, Fan Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title_full | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title_fullStr | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title_full_unstemmed | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title_short | Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography |
title_sort | nondestructive 3d image analysis pipeline to extract rice grain traits using x-ray computed tomography |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706343/ https://www.ncbi.nlm.nih.gov/pubmed/33313550 http://dx.doi.org/10.34133/2020/3414926 |
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