Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.

In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hy...

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Autores principales:	Shuang Liu, Haiye Yu, Yuanyuan Sui, Haigen Zhou, Junhe Zhang, Lijuan Kong, Jingmin Dang, Lei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b6e2d1cf8a34a96885c1b0ab47b2219
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spelling	oai:doaj.org-article:7b6e2d1cf8a34a96885c1b0ab47b22192021-12-02T20:14:49ZClassification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.1932-620310.1371/journal.pone.0257008https://doaj.org/article/7b6e2d1cf8a34a96885c1b0ab47b22192021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257008https://doaj.org/toc/1932-6203In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).Shuang LiuHaiye YuYuanyuan SuiHaigen ZhouJunhe ZhangLijuan KongJingmin DangLei ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257008 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuang Liu Haiye Yu Yuanyuan Sui Haigen Zhou Junhe Zhang Lijuan Kong Jingmin Dang Lei Zhang Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
description	In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
format	article
author	Shuang Liu Haiye Yu Yuanyuan Sui Haigen Zhou Junhe Zhang Lijuan Kong Jingmin Dang Lei Zhang
author_facet	Shuang Liu Haiye Yu Yuanyuan Sui Haigen Zhou Junhe Zhang Lijuan Kong Jingmin Dang Lei Zhang
author_sort	Shuang Liu
title	Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
title_short	Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
title_full	Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
title_fullStr	Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
title_full_unstemmed	Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
title_sort	classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7b6e2d1cf8a34a96885c1b0ab47b2219
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Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance.

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