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 creat...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.展开更多
基金The authors gratefully acknowledge the financial support of the National Key Research and Development Program of China(Grant No.2017YFC1600805)the help of Jie Yang in studying convolution neural networks.Trade and manufacturer names are necessary to report factually on the available data。
文摘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.
