Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Xiaodong, Wang, Pei, Wang, Yafei, Hu, Lian, Luo, Xiwen, Mao, Hanping, Shen, Baoguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784807354196295680
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
work_keys_str_mv AT zhangxiaodong cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT wangpei cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT wangyafei cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT hulian cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT luoxiwen cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT maohanping cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz
AT shenbaoguo cucumberpowderymildewdetectionmethodbasedonhyperspectraterahertz