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A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
INTRODUCTION: Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and tak...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272841/ https://www.ncbi.nlm.nih.gov/pubmed/37332705 http://dx.doi.org/10.3389/fpls.2023.1180203 |
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author | Chu, Xuan Zhang, Kun Wei, Hongyu Ma, Zhiyu Fu, Han Miao, Pu Jiang, Hongzhe Liu, Hongli |
author_facet | Chu, Xuan Zhang, Kun Wei, Hongyu Ma, Zhiyu Fu, Han Miao, Pu Jiang, Hongzhe Liu, Hongli |
author_sort | Chu, Xuan |
collection | PubMed |
description | INTRODUCTION: Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. METHODS: This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. RESULTS: The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. DISCUSSION: These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day. |
format | Online Article Text |
id | pubmed-10272841 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102728412023-06-17 A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae Chu, Xuan Zhang, Kun Wei, Hongyu Ma, Zhiyu Fu, Han Miao, Pu Jiang, Hongzhe Liu, Hongli Front Plant Sci Plant Science INTRODUCTION: Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures. METHODS: This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison. RESULTS: The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. DISCUSSION: These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272841/ /pubmed/37332705 http://dx.doi.org/10.3389/fpls.2023.1180203 Text en Copyright © 2023 Chu, Zhang, Wei, Ma, Fu, Miao, Jiang and Liu 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 Chu, Xuan Zhang, Kun Wei, Hongyu Ma, Zhiyu Fu, Han Miao, Pu Jiang, Hongzhe Liu, Hongli A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae |
title | A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
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title_full | A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
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title_fullStr | A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
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title_full_unstemmed | A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
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title_short | A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae
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title_sort | vis/nir spectra-based approach for identifying bananas infected with colletotrichum musae |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272841/ https://www.ncbi.nlm.nih.gov/pubmed/37332705 http://dx.doi.org/10.3389/fpls.2023.1180203 |
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