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Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology
Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few pare...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554440/ https://www.ncbi.nlm.nih.gov/pubmed/36247557 http://dx.doi.org/10.3389/fpls.2022.1015891 |
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author | Zhang, Han Hou, Qiling Luo, Bin Tu, Keling Zhao, Changping Sun, Qun |
author_facet | Zhang, Han Hou, Qiling Luo, Bin Tu, Keling Zhao, Changping Sun, Qun |
author_sort | Zhang, Han |
collection | PubMed |
description | Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future. |
format | Online Article Text |
id | pubmed-9554440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95544402022-10-13 Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology Zhang, Han Hou, Qiling Luo, Bin Tu, Keling Zhao, Changping Sun, Qun Front Plant Sci Plant Science Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9554440/ /pubmed/36247557 http://dx.doi.org/10.3389/fpls.2022.1015891 Text en Copyright © 2022 Zhang, Hou, Luo, Tu, Zhao and Sun https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Zhang, Han Hou, Qiling Luo, Bin Tu, Keling Zhao, Changping Sun, Qun Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title | Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title_full | Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title_fullStr | Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title_full_unstemmed | Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title_short | Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
title_sort | detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554440/ https://www.ncbi.nlm.nih.gov/pubmed/36247557 http://dx.doi.org/10.3389/fpls.2022.1015891 |
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