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

Abstract

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.

Plant phenotyping relevance

熱画像と機械学習により、キャッサバ塊根の劣化状態を非破壊推定する手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。

Code and data availability

The article describes thermal imaging of cassava tubers with MATLAB machine learning classification, but contains no data availability statement, no public repository deposit, no author code/image URL, and no supplement (pmc-prop-has-supplement no). The only URLs are the license and a cited Statista reference, neithera

No evidence-backed public reproduction asset is currently recorded.