A Deep Convolutional Neural Network Architecture for Boosting Image Discrimination Accuracy of Rice Species

In this paper, we proposed a machine vision system based on deep convolutional neural network (DCNN) architecture for improving the accuracy of classifying three distinct groups of rice kernel images compared with the traditional approaches. The main advantage of the presented method was able to avoid many heuristics as well as manual labor to tune complex parameters according to the domain to reach a modest level of accuracy in the classical feature extraction algorithms. We trained our models using stochastic gradient descent with momentum of 0.9 and weight decay of 0.0005 to optimize the network parameters and minimize the back-propagation error on the training dataset. We used a batch size between 15 and 150 and epochs time configured between 10 and 25. The experiment results showed that the highest accuracy of 99.4% obtained in the training process with batch size of 15 and epoch time of 20. We also compared the DCNN method with the traditional hand-engineered approaches of PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM for rice kernel classification. The results showed that DCNN routinely outperforms other methods in similar machine vision tasks. The prediction accuracy results for test datasets by PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM models were 89.1, 76.9, 90.6, and 92.1%, respectively. The highest prediction accuracy of DCNN is 95.5%, which showed the effectiveness of our proposed method for rice kernel classification. The aim of this study is to set up an automatic and accurate intelligent detection system and offering much value to current rice processing industry. With the comparably high classification accuracy, developed neural network could be used as a tool to achieve better and more objective rice quality evaluation at trading points within the rice marketing system.