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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10010490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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