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IJEETC 2023 Vol.12(2): 150-160
doi: 10.18178/ijeetc.12.2.150-160

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

Nga Tran-Thi-Kim 1,2,3*, Tuan Pham-Viet 4, Insoo Koo 5, Vladimir Mariano 6, and Tuan Do-Hong 1
1. Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam
3. NongLam University, Ho Chi Minh City, Vietnam
4. University of Education, Hue University, Vietnam
5. University of Ulsan, Korea
6. YSEALI Academy at Fulbright University Vietnam, Ho Chi Minh City, Vietnam

Manuscript received November 18, 2022; revised January 17, 2023; accepted February 1, 2023.

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.
Index Terms—artificial neural network, convolutional neural network, local binary pattern, improved local ternary pattern, rice varieties

Cite: Nga Tran-Thi-Kim, Tuan Pham-Viet, Insoo Koo, Vladimir Mariano, and Tuan Do-Hong, "Enhancing the Classification Accuracy of Rice Varieties by Using Convolutional Neural Networks," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 2, pp. 150-160, March 2023. Doi: 10.18178/ijeetc.12.2.150-160

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.