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Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, an...

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Autores principales: Xiao, Qinlin, Wu, Na, Tang, Wentan, Zhang, Chu, Feng, Lei, Zhou, Lei, Shen, Jianxun, Zhang, Ze, Gao, Pan, He, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834998/
https://www.ncbi.nlm.nih.gov/pubmed/36643292
http://dx.doi.org/10.3389/fpls.2022.1080745
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author Xiao, Qinlin
Wu, Na
Tang, Wentan
Zhang, Chu
Feng, Lei
Zhou, Lei
Shen, Jianxun
Zhang, Ze
Gao, Pan
He, Yong
author_facet Xiao, Qinlin
Wu, Na
Tang, Wentan
Zhang, Chu
Feng, Lei
Zhou, Lei
Shen, Jianxun
Zhang, Ze
Gao, Pan
He, Yong
author_sort Xiao, Qinlin
collection PubMed
description Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application.
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spelling pubmed-98349982023-01-13 Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves Xiao, Qinlin Wu, Na Tang, Wentan Zhang, Chu Feng, Lei Zhou, Lei Shen, Jianxun Zhang, Ze Gao, Pan He, Yong Front Plant Sci Plant Science Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application. Frontiers Media S.A. 2022-12-20 /pmc/articles/PMC9834998/ /pubmed/36643292 http://dx.doi.org/10.3389/fpls.2022.1080745 Text en Copyright © 2022 Xiao, Wu, Tang, Zhang, Feng, Zhou, Shen, Zhang, Gao and He 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
Xiao, Qinlin
Wu, Na
Tang, Wentan
Zhang, Chu
Feng, Lei
Zhou, Lei
Shen, Jianxun
Zhang, Ze
Gao, Pan
He, Yong
Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title_full Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title_fullStr Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title_full_unstemmed Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title_short Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
title_sort visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9834998/
https://www.ncbi.nlm.nih.gov/pubmed/36643292
http://dx.doi.org/10.3389/fpls.2022.1080745
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