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Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality...

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Autores principales: Zou, Zhiyong, Chen, Jie, Wu, Weijia, Luo, Jinghao, Long, Tao, Wu, Qingsong, Wang, Qianlong, Zhen, Jiangbo, Zhao, Yongpeng, Wang, Yuchao, Chen, Yongming, Zhou, Man, Xu, Lijia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010490/
https://www.ncbi.nlm.nih.gov/pubmed/36923124
http://dx.doi.org/10.3389/fpls.2023.1127108
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author Zou, Zhiyong
Chen, Jie
Wu, Weijia
Luo, Jinghao
Long, Tao
Wu, Qingsong
Wang, Qianlong
Zhen, Jiangbo
Zhao, Yongpeng
Wang, Yuchao
Chen, Yongming
Zhou, Man
Xu, Lijia
author_facet Zou, Zhiyong
Chen, Jie
Wu, Weijia
Luo, Jinghao
Long, Tao
Wu, Qingsong
Wang, Qianlong
Zhen, Jiangbo
Zhao, Yongpeng
Wang, Yuchao
Chen, Yongming
Zhou, Man
Xu, Lijia
author_sort Zou, Zhiyong
collection PubMed
description Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R(2) was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
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spelling pubmed-100104902023-03-14 Detection of peanut seed vigor based on hyperspectral imaging and chemometrics Zou, Zhiyong Chen, Jie Wu, Weijia Luo, Jinghao Long, Tao Wu, Qingsong Wang, Qianlong Zhen, Jiangbo Zhao, Yongpeng Wang, Yuchao Chen, Yongming Zhou, Man Xu, Lijia Front Plant Sci Plant Science Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R(2) was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor. Frontiers Media S.A. 2023-02-27 /pmc/articles/PMC10010490/ /pubmed/36923124 http://dx.doi.org/10.3389/fpls.2023.1127108 Text en Copyright © 2023 Zou, Chen, Wu, Luo, Long, Wu, Wang, Zhen, Zhao, Wang, Chen, Zhou and Xu 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
Zou, Zhiyong
Chen, Jie
Wu, Weijia
Luo, Jinghao
Long, Tao
Wu, Qingsong
Wang, Qianlong
Zhen, Jiangbo
Zhao, Yongpeng
Wang, Yuchao
Chen, Yongming
Zhou, Man
Xu, Lijia
Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title_full Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title_fullStr Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title_full_unstemmed Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title_short Detection of peanut seed vigor based on hyperspectral imaging and chemometrics
title_sort detection of peanut seed vigor based on hyperspectral imaging and chemometrics
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010490/
https://www.ncbi.nlm.nih.gov/pubmed/36923124
http://dx.doi.org/10.3389/fpls.2023.1127108
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