Quality estimation of nuts using deep learning classification of hyperspectral imagery

Abstract

Rapid quality assessment of nuts is important to increase the shelf life and minimise the nut loss due to rancidity. Existing methods for nut quality estimation are usually slow and destructive. In this study, a quick and non-destructive method using hyperspectral imaging (HSI) coupled with deep learning classification was applied for the quality estimation of unblanched kernels in Canarium indicum categorized by peroxide values (PV). A set of 2300 sub-images of 289 C. indicum samples were used to train a convolutional neural network (CNN) to estimate quality levels. Series of ablation experiments showed that the highest overall accuracy of PV estimation on the test set reached 93.48%, with 95.59%, 90.00%, and 95.83% for good, medium, and poor quality nuts, respectively. The results indicate that deep learning classification of hyperspectral imagery offers a great potential for accurate, real-time, and non-destructive quality estimation of nuts in practical applications.