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Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model
Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-idea...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412535/ https://www.ncbi.nlm.nih.gov/pubmed/32708130 http://dx.doi.org/10.3390/s20144045 |
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author | Li, Yuhua Luo, Zhihui Wang, Fengjie Wang, Yingxu |
author_facet | Li, Yuhua Luo, Zhihui Wang, Fengjie Wang, Yingxu |
author_sort | Li, Yuhua |
collection | PubMed |
description | Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases. |
format | Online Article Text |
id | pubmed-7412535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74125352020-08-26 Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model Li, Yuhua Luo, Zhihui Wang, Fengjie Wang, Yingxu Sensors (Basel) Article Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases. MDPI 2020-07-21 /pmc/articles/PMC7412535/ /pubmed/32708130 http://dx.doi.org/10.3390/s20144045 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Yuhua Luo, Zhihui Wang, Fengjie Wang, Yingxu Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title | Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title_full | Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title_fullStr | Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title_full_unstemmed | Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title_short | Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model |
title_sort | hyperspectral leaf image-based cucumber disease recognition using the extended collaborative representation model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412535/ https://www.ncbi.nlm.nih.gov/pubmed/32708130 http://dx.doi.org/10.3390/s20144045 |
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