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Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, w...

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Autores principales: Wang, Xuemeng, Zhang, Han, Song, Rui, He, Xin, Mao, Peisheng, Jia, Shangang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434479/
https://www.ncbi.nlm.nih.gov/pubmed/34502695
http://dx.doi.org/10.3390/s21175804
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author Wang, Xuemeng
Zhang, Han
Song, Rui
He, Xin
Mao, Peisheng
Jia, Shangang
author_facet Wang, Xuemeng
Zhang, Han
Song, Rui
He, Xin
Mao, Peisheng
Jia, Shangang
author_sort Wang, Xuemeng
collection PubMed
description Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.
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spelling pubmed-84344792021-09-12 Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis Wang, Xuemeng Zhang, Han Song, Rui He, Xin Mao, Peisheng Jia, Shangang Sensors (Basel) Article Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa. MDPI 2021-08-28 /pmc/articles/PMC8434479/ /pubmed/34502695 http://dx.doi.org/10.3390/s21175804 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Xuemeng
Zhang, Han
Song, Rui
He, Xin
Mao, Peisheng
Jia, Shangang
Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title_full Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title_fullStr Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title_full_unstemmed Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title_short Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
title_sort non-destructive identification of naturally aged alfalfa seeds via multispectral imaging analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434479/
https://www.ncbi.nlm.nih.gov/pubmed/34502695
http://dx.doi.org/10.3390/s21175804
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