In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.
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.
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