Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level

It is difficult to differentiate small, but harmful, shell fragments of Chinese hickory nuts from their kernels since they are very similar in color. Including shell fragments of Chinese hickory nuts by mistake may create safety hazards for consumers. Therefore, there is a need to develop an effective method to differentiate the shells from the kernels of Chinese hickory nuts. In this study, a deep learning approach based on a two-dimensional convolutional neural network (2D CNN) and long short-term memory (LSTM) integrated with hyperspectral imaging for distinguishing the shells and kernels of Chinese hickory nuts at the pixel level was proposed. Two classical classification methods, principal component analysis-K-nearest neighbors (PCA-KNN) and the support vector machine (SVM), were employed to establish identification models for comparison. The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%. Moreover, the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies.