Home Identification and Classification of Three Iranian Rice Varieties in Mixed Bulks Using Image Processing and MLP Neural Network
Article
Licensed
Unlicensed Requires Authentication

Identification and Classification of Three Iranian Rice Varieties in Mixed Bulks Using Image Processing and MLP Neural Network

  • Saeideh Fayyazi EMAIL logo , Mohammad Hossein Abbaspour-Fard , Abbas Rohani , S. Amirhassan Monadjemi and Hassan Sadrnia
Published/Copyright: April 7, 2017

Abstract

Due to variation in economic value of different varieties of rice, reports indicating the possibility of mixing different varieties on the market. Applying machine vision techniques to classify rice varieties is a method which can increase the accuracy of classification process in real applications. In this study, several morphological and textural features of rice seeds’ images were examined to evaluate their efficacy in identification of three Iranian rice varieties (Tarom, Fajr, Shiroodi) in their mixed samples. On the whole, 666 images of rice seeds (222 images of each variety) were acquired at a stable illumination condition and totally, 17 morphological and 41 textural features were extracted from seeds images. Principal component analysis (PCA) method was employed to select and rank the most significant features for the classification. Subsequently, the MLP neural network classifier was employed for classification of rice varieties in the mixed bulks of three and two varieties, using top selected features. The network was three-layered feed forward type and trained using two training algorithms (BB and BDLRF). The classification accuracy of 55.93, 84.62 and 82.86 % for Fajr, Tarom and Shiroodi, 86.96 and 93.02 % for Fajr and Shiroodi, 86.84 and 96.08 % for Tarom and Shiroodi and 91.49 and 95.24 % for Fajr and Tarom were obtained in test phase, respectively.

References

1. Rohani A, Abbaspour-Fard MH, Abdolahpour S. Prediction of tractor repair and maintenance costs using artificial neural network. Expert Syst Appl. 2011;38(7):8999–9007.10.1016/j.eswa.2011.01.118Search in Google Scholar

2. Liu Y, Ouyang A, Wu J, Ying Y. Paper presented at the Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality. An automatic method for identifying different variety of rice seeds using machine vision technology. 2005 08November.10.1117/12.631004Search in Google Scholar

3. Verma B. Paper presented at the International Conference on Computer and Communication Technology. Image processing techniques for grading & classification of rice. Dept. of ECE, Lovely Prof. University of Phagwara, 2010 17–19SeptemberPhagwara, India.10.1109/ICCCT.2010.5640428Search in Google Scholar

4. Wu JH, Liu YD, Ouyang AG. Research on real time identification of seed variety by machine vision technology. Chin J Sensors Actuators. 2005;18(4):742–744.Search in Google Scholar

5. Jana A, Bandyopadhyay R, Tudu B, Roy JK, Bhattacharyya N, Adhikari B. Classification of aromatic and non-aromatic rice using electronic nose and artificial neural network. Paper presented at the Recent Advances in Intelligent Computational Systems (RAICS). 22–24 Sep,Trivandrum, India. 2011.10.1109/RAICS.2011.6069320Search in Google Scholar

6. Marini F, Bucci R, Magri AL, Magri AD, Acquistucci R, Francisci R. Classification of 6 durum wheat cultivars from Sicily (Italy) using artificial neural networks. Chemometrics Intell Lab Syst. 2008;90(1):1–7.10.1016/j.chemolab.2007.06.009Search in Google Scholar

7. Pazoki A, Pazoki Z. Classification system for rain fed wheat grain cultivars using artificial neural network. Afr J Biotechnol. 2011;10(41):8031–8038.10.5897/AJB11.488Search in Google Scholar

8. OuYang AG, Gao RJ, Sun XD, Pan YY, Dong XL. An automatic method for identifying different variety of rice seeds using machine vision technology. IEEE, 2010 Sixth International Conference on Natural Computation (1 August), 2010:84–88.10.1109/ICNC.2010.5583370Search in Google Scholar

9. MousaviRad SJ, Akhlaghian Tab F, Mollazade K. Classification of rice varieties using optimal color and texture features and BP neural networks. Nov 2011 7th Iranian Conference on Machine Vision and Image Processing. IEEE, 2011:1–5.10.1109/IranianMVIP.2011.6121583Search in Google Scholar

10. MousaviRad SJ, Rezaee K, Nasri K. A new method for identification of Iranian rice kernel varieties using optimal morphological features and an ensemble classifier by image processing. Majlesi J Multimedia Process. 2012;1(3):1–7Search in Google Scholar

11. Liu ZY, Cheng F, Ying YB, Rao XQ. Identification of rice seed varieties using neural network. J Zhejiang Univ Sci B. 2005;6(11):1095.10.1631/jzus.2005.B1095Search in Google Scholar PubMed PubMed Central

12. Silva CS, Sonnadara U. Paper presented at the Proceedings of Technical Sessions. Classification of rice grains using neural networks. 2013.Search in Google Scholar

13. Guzman JD, Peralta EK. Classification of Philippine rice grains using machine vision and artificial neural networks. World conference on agricultural information and IT, August 2008, Tokyo, Japan, 2008:24–27.Search in Google Scholar

14. Gonzalez RC, Woods RE. Digital image processing. New Jersey: Pearson, 2008Search in Google Scholar

15. Majumdar S, Jayas DS. Classification of bulk samples of cereal grains using machine vision. J Agric Eng Res. 1999;73(1):35–47.10.1006/jaer.1998.0388Search in Google Scholar

16. Manickavasagan A, Sathya G, Jayas DS, White ND. Wheat class identification using monochrome images. J Cereal Sci. 2008;47(3):518–527.10.1016/j.jcs.2007.06.008Search in Google Scholar

17. Vakil-Baghmisheh MT. Farsi character recognition using artificial neural networks (Ph.D Thesis). Ljubljana: University of Ljubljana, , 2002.Search in Google Scholar

18. NeuroDimensions Inc. NeuroSolutions tool for excel 2002.Search in Google Scholar

19. Zhang YR, Fuh JY. A neural network approach for early cost estimation of packaging products. Comput Ind Eng. 1998;34(2):433–450.10.1016/S0360-8352(97)00141-1Search in Google Scholar

20. Bowers W, Hunt DR. Application of mathematical formula to repair cost data. Trans ASAE. 1970;13:806–809.10.13031/2013.38725Search in Google Scholar

21. Vakil-Baghmisheh MT, Pavešic N. A fast simplified fuzzy ARTMAP network. Neural Process Lett. 2003;17(3):273–301.10.1023/A:1026004816362Search in Google Scholar

22. Vakil-Baghmisheh MT, Pavešic N. Back-propagation with declining learning rate. Paper presented at the 10th Electrotechnical and Computer Science Conference, Slovenia, Portorozˇ, 2001.Search in Google Scholar

23. Haykin S. Neural networks: a comprehensive foundation. New York: McMillan College Publishing Company, 1994.Search in Google Scholar

24. Gupta MM, Jin J, Homma N. Static and dynamic neural networks: from fundamentals to advanced theory. Hoboken, NJ: John Wiley & Sons, Inc, 2003.10.1002/0471427950Search in Google Scholar

Published Online: 2017-4-7

© 2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 1.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijfe-2016-0121/pdf
Scroll to top button