Enhancing the Classification Accuracy of Rice Varieties by Using Convolutional Neural Networks

Abstract

The aim of this study is to enhance the classification accuracy of rice varieties that are quite similar in external observation. In this study, 17 rice grain varieties popularly planted in Vietnam are classified with an Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models. The two CNN models (modified VGG16 and modified ResNet50) are based on pre-trained VGG16 and Resnet50 models. Two datasets are used in the experiments: a feature dataset extracted using an extended improved local ternary pattern (extended ILTP) method, and an image dataset generated with a data augmentation technique. The feature dataset was fed into the ANN, while the image dataset was fed into the CNN models. The highest classification accuracies of ANN, modified VGG16, and modified ResNet50 models are 92.82%, 96.41%, and 97.88%, respectively. The results show that the modified VGG16 and ResNet50 models significantly improved classification accuracy of the 17 varieties of rice. In addition, the experiments show that the dimensions of the image dataset can affect the performance of the CNN models. This research can be developed for applications of rice varieties classification and identification.