inis 18: e1

Research Article

Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties

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  • @ARTICLE{10.4108/eai.7-3-2022.173605,
        author={Tran Thi Kim Nga and Tuan Pham-Viet and Dang Minh Nhat and Dang Minh Tam and Insoo Koo and Vladimir Y. Mariano and Tuan Do-Hong},
        title={Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={INIS},
        year={2022},
        month={3},
        keywords={rice varieties, local ternary pattern, improved local ternary pattern, texture feature, support vector machine},
        doi={10.4108/eai.7-3-2022.173605}
    }
    
  • Tran Thi Kim Nga
    Tuan Pham-Viet
    Dang Minh Nhat
    Dang Minh Tam
    Insoo Koo
    Vladimir Y. Mariano
    Tuan Do-Hong
    Year: 2022
    Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties
    INIS
    EAI
    DOI: 10.4108/eai.7-3-2022.173605
Tran Thi Kim Nga1,2,3,*, Tuan Pham-Viet4, Dang Minh Nhat1,2, Dang Minh Tam5, Insoo Koo6, Vladimir Y. Mariano7, Tuan Do-Hong1,2
  • 1: Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam
  • 2: Vietnam National University, Ho Chi Minh City, Vietnam
  • 3: NongLam University, Ho Chi Minh City, Vietnam
  • 4: University of Education, Hue University, Hue City, Vietnam
  • 5: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam
  • 6: School of Electrical Engineering, University of Ulsan, Ulsan, South Korea
  • 7: College of Computing and Information Technologies, National University, Manila, Philippines
*Contact email: ttknga.sdh16@hcmut.edu.vn

Abstract

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.