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Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging
This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400–1000 nm were collected...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729219/ https://www.ncbi.nlm.nih.gov/pubmed/36477460 http://dx.doi.org/10.1038/s41598-022-23326-2 |
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author | Zhou, Yongxin Chen, Jiaze Ma, Jinfang Han, Xueqin Chen, Bijuan Li, Guilian Xiong, Zheng Huang, Furong |
author_facet | Zhou, Yongxin Chen, Jiaze Ma, Jinfang Han, Xueqin Chen, Bijuan Li, Guilian Xiong, Zheng Huang, Furong |
author_sort | Zhou, Yongxin |
collection | PubMed |
description | This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400–1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model’s prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato. |
format | Online Article Text |
id | pubmed-9729219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97292192022-12-09 Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging Zhou, Yongxin Chen, Jiaze Ma, Jinfang Han, Xueqin Chen, Bijuan Li, Guilian Xiong, Zheng Huang, Furong Sci Rep Article This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400–1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model’s prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato. Nature Publishing Group UK 2022-12-07 /pmc/articles/PMC9729219/ /pubmed/36477460 http://dx.doi.org/10.1038/s41598-022-23326-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhou, Yongxin Chen, Jiaze Ma, Jinfang Han, Xueqin Chen, Bijuan Li, Guilian Xiong, Zheng Huang, Furong Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title | Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title_full | Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title_fullStr | Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title_full_unstemmed | Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title_short | Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging |
title_sort | early warning and diagnostic visualization of sclerotinia infected tomato based on hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729219/ https://www.ncbi.nlm.nih.gov/pubmed/36477460 http://dx.doi.org/10.1038/s41598-022-23326-2 |
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