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Cucumber powdery mildew detection method based on hyperspectra-terahertz
To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558005/ https://www.ncbi.nlm.nih.gov/pubmed/36247642 http://dx.doi.org/10.3389/fpls.2022.1035731 |
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author | Zhang, Xiaodong Wang, Pei Wang, Yafei Hu, Lian Luo, Xiwen Mao, Hanping Shen, Baoguo |
author_facet | Zhang, Xiaodong Wang, Pei Wang, Yafei Hu, Lian Luo, Xiwen Mao, Hanping Shen, Baoguo |
author_sort | Zhang, Xiaodong |
collection | PubMed |
description | To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases. |
format | Online Article Text |
id | pubmed-9558005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95580052022-10-14 Cucumber powdery mildew detection method based on hyperspectra-terahertz Zhang, Xiaodong Wang, Pei Wang, Yafei Hu, Lian Luo, Xiwen Mao, Hanping Shen, Baoguo Front Plant Sci Plant Science To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9558005/ /pubmed/36247642 http://dx.doi.org/10.3389/fpls.2022.1035731 Text en Copyright © 2022 Zhang, Wang, Wang, Hu, Luo, Mao and Shen 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 Zhang, Xiaodong Wang, Pei Wang, Yafei Hu, Lian Luo, Xiwen Mao, Hanping Shen, Baoguo Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title | Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title_full | Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title_fullStr | Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title_full_unstemmed | Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title_short | Cucumber powdery mildew detection method based on hyperspectra-terahertz |
title_sort | cucumber powdery mildew detection method based on hyperspectra-terahertz |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558005/ https://www.ncbi.nlm.nih.gov/pubmed/36247642 http://dx.doi.org/10.3389/fpls.2022.1035731 |
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