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Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root

Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging wi...

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Detalles Bibliográficos
Autores principales: Posom, Jetsada, Duangpila, Chutatip, Saengprachatanarug, Khwantri, Wongpichet, Seree, Onmankhong, Jiraporn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570577/
https://www.ncbi.nlm.nih.gov/pubmed/37842593
http://dx.doi.org/10.1016/j.heliyon.2023.e20559
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author Posom, Jetsada
Duangpila, Chutatip
Saengprachatanarug, Khwantri
Wongpichet, Seree
Onmankhong, Jiraporn
author_facet Posom, Jetsada
Duangpila, Chutatip
Saengprachatanarug, Khwantri
Wongpichet, Seree
Onmankhong, Jiraporn
author_sort Posom, Jetsada
collection PubMed
description Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (10.13039/100003090LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels.
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spelling pubmed-105705772023-10-14 Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root Posom, Jetsada Duangpila, Chutatip Saengprachatanarug, Khwantri Wongpichet, Seree Onmankhong, Jiraporn Heliyon Research Article Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (10.13039/100003090LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels. Elsevier 2023-09-29 /pmc/articles/PMC10570577/ /pubmed/37842593 http://dx.doi.org/10.1016/j.heliyon.2023.e20559 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Posom, Jetsada
Duangpila, Chutatip
Saengprachatanarug, Khwantri
Wongpichet, Seree
Onmankhong, Jiraporn
Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title_full Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title_fullStr Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title_full_unstemmed Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title_short Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
title_sort application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570577/
https://www.ncbi.nlm.nih.gov/pubmed/37842593
http://dx.doi.org/10.1016/j.heliyon.2023.e20559
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