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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023077678 |
| _version_ | 1797646521914621952 |
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| author | Jetsada Posom Chutatip Duangpila Khwantri Saengprachatanarug Seree Wongpichet Jiraporn Onmankhong |
| author_facet | Jetsada Posom Chutatip Duangpila Khwantri Saengprachatanarug Seree Wongpichet Jiraporn Onmankhong |
| author_sort | Jetsada Posom |
| collection | DOAJ |
| 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 (LDA), 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. |
| first_indexed | 2024-03-11T15:02:51Z |
| format | Article |
| id | doaj.art-77c0d38fd72743df8fb1ee27d4813d3c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T15:02:51Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-77c0d38fd72743df8fb1ee27d4813d3c2023-10-30T06:06:34ZengElsevierHeliyon2405-84402023-10-01910e20559Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava rootJetsada Posom0Chutatip Duangpila1Khwantri Saengprachatanarug2Seree Wongpichet3Jiraporn Onmankhong4Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen,40002, ThailandDepartment of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen,40002, ThailandDepartment of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen,40002, ThailandDepartment of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen,40002, ThailandDepartment of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand; Corresponding author.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 (LDA), 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.http://www.sciencedirect.com/science/article/pii/S2405844023077678Cassava rootThermal imagingMachine learningDeterioration levels |
| spellingShingle | Jetsada Posom Chutatip Duangpila Khwantri Saengprachatanarug Seree Wongpichet Jiraporn Onmankhong Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root Heliyon Cassava root Thermal imaging Machine learning Deterioration levels |
| 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 | Cassava root Thermal imaging Machine learning Deterioration levels |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023077678 |
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