Rice Classification with Fractal-based Features based on Sparse Structured Principal Component Analysis and Gaussian Mixture Model

Mavaddati, S.; Mavaddati, S.

doi:10.22044/jadm.2021.9583.2090

Document Type : Original/Review Paper

Authors

S. Mavaddati ¹
S. Mavaddati ²

¹ Department of Engineering and Technology, University of Mazandaran, Babolsar, Iran.

² Sari Agricultural Sciences and Natural Resources University, Iran.

https://doi.org/10.22044/jadm.2021.9583.2090

Abstract

Development of an automatic system to classify the type of rice grains is an interesting research area in the scientific fields associated with modern agriculture. In recent years, different techniques are employed to identify the types of various agricultural products. Also, different color-based and texture-based features are used to yield the desired results in the classification procedure. This paper proposes a classification algorithm to detect different rice types by extracting features from the bulk samples. The feature space in this algorithm includes the fractal-based features of the extracted coefficients from the wavelet packet transform analysis. This feature vector is combined with other texture-based features and used to learn a model related to each rice type using the Gaussian mixture model classifier. Also, a sparse structured principal component analysis algorithm is applied to reduce the dimension of the feature vector and lead to the precise classification rate with less computational time. The results of the proposed classifier are compared with the results obtained from the other presented classification procedures in this context. The simulation results, along with a meaningful statistical test, show that the proposed algorithm based on the combinational features is able to detect precisely the type of rice grains with more than 99% accuracy. Also, the proposed algorithm can detect the rice quality for different percentages of combination with other rice grains with 99.75% average accuracy.

Keywords

20.1001.1.23225211.2021.9.2.10.3

References

[1] Food and agriculture organization of the United nations (FAO), “Rice Market Monitor,” vol. 18, pp. 1-18, 2015.

[2] K. P. Neelamma, S. M. Virendra, and M. Y. Ravi, "Color and texture based identification and classification of food grains using different color models and haralick features," International Journal on Computer Science and Engineering (IJCSE), vol. 3, pp. 3669-3680, 2011.

[3] S. Majumdar, and D. S. Jayas, "Classification of cereal grains using machine vision: I. Morphology models," Transactions of the ASAE, vol. 43, no. 6, pp. 1669-1675, 2000.

[4] N. S. Visen, D. S. Jayas, J. Paliwal, and N. D. G. White, "Comparison of two neural network architectures for classification of singulated cereal grains," Journal of Canadian Biosystem Engineering, vol. 46, pp. 7-14, 2004.

[5] J. Paliwal, M. S. Borhan, and D. S. Jayas, "Classification of cereal grains using a flatbed scanner," Canadian Biosystems Engineering, vol. 46, no.3, pp. 1-35, 2004.

[6] Z. Y. Liu, F. Cheng, Y. B. Ying, and X. Q. Rao, "Identification of rice seed varieties using neural network," Journal of Zhejiang University. Science.B, vol. 6, no. 11, 1095-1100, 2005.

[7] B. Verma, "Image processing techniques for grading & classification of rice," In International conferences on computer and communication technology, India, pp. 220-223, 2010.

[8] M. Jinorose, S. Prachayawarakorn, and S. Soponronnarit, "Development of a computer vision system and novel evaluation criteria to characterize color and appearance of rice," Drying Technology, vol. 28, pp. 1118-1124, 2010.

[9] G. V. Dalen, "Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis," Food research international, vol. 37, pp.51-58, 2004.

[10] L. Pabamalie, and H. Premaratne, "An intelligent rice quality classifier," International Journal of Internet Technology and Secured Transactions, vol. 3, pp. 386-406, 2011.

[11] M. Hatami, A. Rahmanididar, J. Khazaee, "Identification of common Iranian rice varieties using machine vision techniques," Sixth National Congress of Agricultural Machinery Engineering and Mechanization, pp. 1-9, 2010.

[12] S. Faiazi, M. H. Abaspourfard, S. A. Monajemi, H. Sadrnia, and A. Rouhani, "Identification and separation of three Iranian rice products in mixed samples using texture based feature and LVQ," Journal of Agricultural Mechanization, vol. 1, pp. 35-44, 2013.

[13] F. A. Mousavirad, and K. Mollazade, "Real time identification of rice varieties using texture features based on classifier fusion method," In the 16th CSI International Symposium on Artificial Intelligence and Signal Processing, Shiraz University, Iran, 2012.

[14] F. A. Mousavirad, and K. Mollazade, "Identification of Rice Variaties Using Cooccurence Matrix Features of Bulk Samples and Support Vector Machine," Presented at the 20^th Iranian Conference on Electrical Engineering, University of Tehran, Teharn, Iran, 1833-1837, 2012 .

[15] I. Golpour, J. Amiripanah, R. Amirichaichan, and J. Khazaee, "Detection of rice varieties, brown rice and white rice based on image and artificial neural network," Journal of Agricultural Machinery, vol. 1, no. 5, pp. 73-81, 2015.

[16] S. Mavaddati, "Rice classification and quality detection based on sparse coding technique," IJE TRANSACTIONS B: Applications, vol. 31, no. 11, pp. 1910-1917, 2018.

[17] S. J. Mousavirad, and F. Akhlaghiantab, "Design of an expert system to recognize the rice quality by a combination of texture properties of bulk rice samples," Journal of machine vision and image processing, vol. 1, no. 1, 2013.

[18] S. Mavaddati, "Sparse structured principal component analysis and model learning for classification and quality detection of rice grains," Journal of AI and Data Mining, vol. 8, no. 2, pp. 161-175, 2020.

[19] A. F. Costa, G. E. Humpire-Mamani, and A. J. M. Traina, "An efficient algorithm for fractal analysis of textures," In SIBGRAPI 2012 (XXV Conference on Graphics, Patterns and Images), pp. 39-46, 2012.

[20] T. Ojala, and M. Pietikäinen, "Unsupervised texture segmentation using feature distributions," Pattern recognition, vol. 32, no. 3, pp. 477-486, 1999.

[21] S. Majumdar, D. S. Jayas, "Classification of cereal grains using machine vision: I. Morphology models," Transactions of the ASAE, vol. 43, no. 6, pp. 1669-1675, 2000.

[22] M. Nixon, A. Aguado, Feature extraction & image processing, 2nd Edition, Academic Press, Cambridge, 2008.

[23] J. Flusser, T. Suk, and B. Zitová, Moments and moment invariants in pattern recognition, Hichester, West Sussex, U.K: J. Wiley, 2009.

[24] I.T. Jolliffe, Principal Component Analysis, Springer, 2nd edition, 2002.

[25] H. Zou, T. Hastie, R. Tibshirani, "Sparse principal component analysis," Journal of Computational and Graphical Statistics, vol. 15, pp. 265-286, 2006.

[26] R. Jenatton, G. Obozinski, and F. Bach, "Structured Sparse Principal Component Analysis," In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 2010.

[27] R. Jenatton, J.Y. Audibert, and F. Bach, "Structured variable selection with sparsity-inducing norms," Technical report, arXiv:0904.3523, 2009.

[28] D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 4nd ed. Boca Raton, FL: Chapman & Hall/CRC, 2000.

[29] https://www.danielsoper.com/statcalc/calculator.aspx?id=4.

[30]http://onlinestatbook.com/2/calculators/normal_dist.html.

Rice Classification with Fractal-based Features based on Sparse Structured Principal Component Analysis and Gaussian Mixture Model

References

References

Volume 9, Issue 2April 2021Pages 235-244

Volume 9, Issue 2
April 2021
Pages 235-244