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...
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doaj-a50693733a274caab3c72c47e8f1f2ed2020-11-25T03:00:33ZengAmerican Association for the Advancement of SciencePlant Phenomics2643-65152020-01-01202010.34133/2020/3414926Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed TomographyWeijuan Hu0Weijuan Hu1Can Zhang2Can Zhang3Yuqiang Jiang4Yuqiang Jiang5Chenglong Huang6Chenglong Huang7Qian Liu8Qian Liu9Lizhong Xiong10Lizhong Xiong11Wanneng Yang12Wanneng Yang13Fan Chen14Fan Chen15Crop Phenomics Joint Research Center,Wuhan 430070,ChinaInstitute of Genetics and Developmental Biology Chinese Academy of Sciences,Beijing 100101,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaBritton Chance Center for Biomedical Photonics,Wuhan National Laboratory for Optoelectronics,and Key Laboratory of Ministry of Education for Biomedical Photonics,Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaInstitute of Genetics and Developmental Biology Chinese Academy of Sciences,Beijing 100101,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaNational Key Laboratory of Crop Genetic Improvement,National Center of Plant Gene Research,Agricultural Bioinformatics Key Laboratory of Hubei Province,and College of Engineering, Huazhong Agricultural University, Wuhan 430070,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaBritton Chance Center for Biomedical Photonics,Wuhan National Laboratory for Optoelectronics,and Key Laboratory of Ministry of Education for Biomedical Photonics,Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaNational Key Laboratory of Crop Genetic Improvement,National Center of Plant Gene Research,Agricultural Bioinformatics Key Laboratory of Hubei Province,and College of Engineering, Huazhong Agricultural University, Wuhan 430070,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaNational Key Laboratory of Crop Genetic Improvement,National Center of Plant Gene Research,Agricultural Bioinformatics Key Laboratory of Hubei Province,and College of Engineering, Huazhong Agricultural University, Wuhan 430070,ChinaCrop Phenomics Joint Research Center,Wuhan 430070,ChinaInstitute of Genetics and Developmental Biology Chinese Academy of Sciences,Beijing 100101,ChinaThe 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 R2 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.http://dx.doi.org/10.34133/2020/3414926 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weijuan Hu Weijuan Hu Can Zhang Can Zhang Yuqiang Jiang Yuqiang Jiang Chenglong Huang Chenglong Huang Qian Liu Qian Liu Lizhong Xiong Lizhong Xiong Wanneng Yang Wanneng Yang Fan Chen Fan Chen |
spellingShingle |
Weijuan Hu Weijuan Hu Can Zhang Can Zhang Yuqiang Jiang Yuqiang Jiang Chenglong Huang Chenglong Huang Qian Liu Qian Liu Lizhong Xiong Lizhong Xiong Wanneng Yang Wanneng Yang Fan Chen Fan Chen Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography Plant Phenomics |
author_facet |
Weijuan Hu Weijuan Hu Can Zhang Can Zhang Yuqiang Jiang Yuqiang Jiang Chenglong Huang Chenglong Huang Qian Liu Qian Liu Lizhong Xiong Lizhong Xiong Wanneng Yang Wanneng Yang Fan Chen Fan Chen |
author_sort |
Weijuan Hu |
title |
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_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_sort |
nondestructive 3d image analysis pipeline to extract rice grain traits using x-ray computed tomography |
publisher |
American Association for the Advancement of Science |
series |
Plant Phenomics |
issn |
2643-6515 |
publishDate |
2020-01-01 |
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 R2 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. |
url |
http://dx.doi.org/10.34133/2020/3414926 |
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