Skip to main navigation menu Skip to main content Skip to site footer
Academia Open

Food Science

Vol 9 No 2 (2024): December

Classification of Rice Grains by Image Processing
Klasifikasi Butir Beras dengan Pemrosesan Citra



(*) Corresponding Author
DOI
https://doi.org/10.21070/acopen.9.2024.10303
Published
October 15, 2024

Abstract

General Background: The assessment of the surface quality of pre-treated rice grains is crucial for determining their market acceptability, storage stability, processing quality, and overall consumer satisfaction. Traditional evaluation methods are often time-consuming and yield subjective classifications. Specific Background: The lack of a comprehensive tagged image dataset hinders the application of advanced convolutional neural networks (CNN) for detailed damage classification of healthy rice grains. Knowledge Gap: Existing datasets and methods limit the effective exploration of sophisticated CNN models for categorizing rice types, particularly in identifying subtle damage characteristics. Aims: The study aims to create a robust rice grain classification system using image processing techniques, primarily deep learning algorithms, to improve the classification of rice varieties. Results: Utilizing a dataset of 75,000 images across five widely cultivated rice varieties in Turkey, we achieved classification accuracies of 100% for CNN, 99.95% for deep neural networks (DNN), and 99.87% for artificial neural networks (ANN). Novelty: The proposed approach represents a significant advancement in rice classification technology, employing a combination of image acquisition, feature extraction, and machine learning to streamline the process, effectively addressing the challenges faced in traditional methods. Implications: The findings underscore the potential for improved sorting and grading efficiency in the rice industry, facilitating better market outcomes and consumer satisfaction through enhanced quality control

Highlights:

 

  1. Innovative: Uses CNN for accurate rice variety classification.
  2. Data-Driven: Analyzes 75,000 images for enhanced quality evaluation.
  3. Impactful: Increases efficiency in sorting and grading processes.

 

Keywords: Rice Classification, Image Processing, CNN, Machine Learning, Agricultural Technology

References

  1. . A. Pasoriya, S. Chavhan, S. Gotarkar, and R. C. Dharmik, "Rice Quality Analysis Using Image," in Rice Quality Analysis Using Image, R. C. Dharmik, S. Chavhan, S. Gotarkar, and A. Pasoriya, Eds. New Delhi, India: National Institute of Technology, 2022, pp. 158-164.
  2. . C. C. Hortineal, J. R. Balbin, J. C. Fausto, A. D. Catli, K. J. R. Cui, J. A. F. Tan, and E. O. S. Zunega, "Milled Rice Grain Grading Using Raspberry Pi With Image Processing and Support Vector Machines With Adaptive Boosting," in Proc. IEEE, 2020.
  3. . H. O. Velesaca, P. L. Suárez, R. Mira, and A. D. Sappa, "Computers and Electronics in Agriculture," Computers and Electronics in Agriculture, vol. 177, 2021.
  4. . I. Chatnuntawech, K. Tantisantisom, P. Khanchaitit, T. Boonkoom, B. Bilgic, and E. Chuangsuwanich, "Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network," IJERT, vol. 9, no. 4, 2020.
  5. . J. S. Aulakh and Dr. V. K. Banga, "Quality Assessment of Rice," Indian Journal of Agricultural Research, vol. 47, no. 2, pp. 116-120, 2012.
  6. . P. Patil, "Reliable Quality Analysis of Indian Basmati," IJERT, vol. 3, no. 6, 2014.
  7. . S. K. Dhyawa, A. Srivastava, S. Pal, and P. L. Bhutia, "Identification and Classification of Rice Varieties Using Image Processing," International Journal of Advanced Research in Computer Engineering & Technology, vol. 2, no. 5, 2014.
  8. . W. P. N. W. M. Tahir, N. Hussin, Z. Z. Htike, and W. Y. N. Naing, "Rice Grading Using Image Processing," ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 20, pp. 9634-9638, 2015.
  9. . Z. Z. Htike and W. Y. N. Naing, W. P. N. W. M. Tahir, and N. Hussin, "Rice Grading Using Image Processing," ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 20, pp. 9639-9643, 2015.
  10. . C. V. Maheshwari, K. R. Jain, and C. K. Modi, "Non-Destructive Quality Analysis of Indian Gujarat-17 Oryza Sativa SSP Indica (Rice) Using Image Processing," International Journal of Computational Engineering Science, vol. 2, pp. 48-54, 2012.

Downloads

Download data is not yet available.