Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

Golpour, I.; Amiri Parian, J.; Amiri Chayjan, R.; Khazaei, J.

doi:10.22067/jam.v5i1.21550

Document Type : Research Article

Authors

¹ Bu-Ali Sina University

² University of Tehran

https://doi.org/10.22067/jam.v5i1.21550

Abstract

Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.

Keywords

20.1001.1.22286829.1394.5.1.8.9

References

1. Anami, B. S., J. D. Pujari, and R. Yakkundimath. 2011. Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction. International Journal of Computer Applications in Engineering Sciences 1: 356-360.

2. Arefi, A., A. Modarres Motlagh, and R. Farrokhi Teimourlou. 2011. Wheat class identification using computer vision system and artificial neural networks. International Agrophysics 25: 319-323.

3. ElMasry, G. N., A. Wang, A. ElSayed, and M. Ngadia. 2006. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering 81 (1): 98-107.

4. Haykin, S. 1999. Neural networks: A comprehensive foundation. 2nd ed. Prentice Hall. New York.

5. Haralick, R. M., K. Shanmugam, and I. H. Dinstein. 1973. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3: 610-621.

6. Heinzow, T., and R. S. J. Tol. 2003. Prediction of crop yields across four climate zones in Germany: An artificial neural network approach. FNU-34. Centre for Marine and Climate Research, Hamburg University, Hamburg, Germany.

7. Kannur, A., A., Kannur, V. S. Rajpurohit. 2011. Classification and grading of bulk seeds using artificial neural network. International Journal of Machine Intelligence 3: 62-73.

8. Khazaei, J., M. R. Naghavi, M. R. Jahansouz, and G. Salimi-Khorshidi 2008. Yield estimation and clustering of chickpea genotypes using soft computing techniques. Agronomy Journal 100: 1077-1087.

9. Majumdar, S., and D.S. Jayas. 1999. Classification of bulk samples of cereal grains using machine vision system. Journal of Agricultural Engineering Research 73 (1): 35-47.

10. Majumdar, S., and D.S. Jayas. 2000. Classification of cereal grains using machine vision. I. Morphology models. Transactions of the ASAE, 43 (6): 1669-1675.

11. Majumdar, S. and D.S. Jayas 2000c. Classification of cereal grains using machine vision. III. Texture models. Transactions of the ASAE, 43 (6): 1681-1687.

12. Narendra, V. G., and K. S. Hareesh. 2011. Cashew kernels classification using color features. International Journal of Machine Intelligence 3 (2): 52-57.

13. Neelamma, K. P., S. M. Virendra, and M. Y. Ravi. 2011. 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) 3: 3669-3680.

14. Paliwal, J., M. S. Borhan, and D. S. Jayas. 2004. Classification of cereal grains using a flatbed scanner. Canadian Biosystems Engineering 46: 3.1-3.5.

15. Petersen, P. H. 1992. Weed seed identification by shape and texture analysis of microscope images. Unpublished Ph.D. Dissertation. The Danish Institute of Plant and Soi1 Science. Copenhagen, Denmark.

16. Shahin, M. A., and S. J. Symons. 2001. A machine vision system for grading lentils. Canadian Biosystems Engineering 43: 7-14.

17. Shantaiya, S., and U. Ansari. 2010. Identification of food grain and its quality using pattern classification. International Conference [ICCT-2010], December 3–5. Special Issue of IJCCT, 2, 3, 4: 70–74.

18. Tsheko, R. 2002. Discrimination of plant species using co-occurrence matrix of leaves. Agricultural Engineering International. The CIGR Journal of Scientific Research and Development, IV (May), Manuscript IT 01 004.

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

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Journal of Agricultural Machinery

Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

References

References

Send comment about this article

Volume 5, Issue 1 - Serial Number 9
Spring and Summer
2015
Pages 73-81

Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

References

References

Send comment about this article

Volume 5, Issue 1 - Serial Number 9Spring and Summer 2015Pages 73-81

Volume 5, Issue 1 - Serial Number 9
Spring and Summer
2015
Pages 73-81