Cargando…
Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods
The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9127793/ https://www.ncbi.nlm.nih.gov/pubmed/35620676 http://dx.doi.org/10.3389/fpls.2022.849495 |
_version_ | 1784712430380646400 |
---|---|
author | Wang, Zheli Huang, Wenqian Tian, Xi Long, Yuan Li, Lianjie Fan, Shuxiang |
author_facet | Wang, Zheli Huang, Wenqian Tian, Xi Long, Yuan Li, Lianjie Fan, Shuxiang |
author_sort | Wang, Zheli |
collection | PubMed |
description | The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment. |
format | Online Article Text |
id | pubmed-9127793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91277932022-05-25 Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods Wang, Zheli Huang, Wenqian Tian, Xi Long, Yuan Li, Lianjie Fan, Shuxiang Front Plant Sci Plant Science The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment. Frontiers Media S.A. 2022-05-10 /pmc/articles/PMC9127793/ /pubmed/35620676 http://dx.doi.org/10.3389/fpls.2022.849495 Text en Copyright © 2022 Wang, Huang, Tian, Long, Li and Fan. 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 Wang, Zheli Huang, Wenqian Tian, Xi Long, Yuan Li, Lianjie Fan, Shuxiang Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title | Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title_full | Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title_fullStr | Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title_full_unstemmed | Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title_short | Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods |
title_sort | rapid and non-destructive classification of new and aged maize seeds using hyperspectral image and chemometric methods |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9127793/ https://www.ncbi.nlm.nih.gov/pubmed/35620676 http://dx.doi.org/10.3389/fpls.2022.849495 |
work_keys_str_mv | AT wangzheli rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods AT huangwenqian rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods AT tianxi rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods AT longyuan rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods AT lilianjie rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods AT fanshuxiang rapidandnondestructiveclassificationofnewandagedmaizeseedsusinghyperspectralimageandchemometricmethods |