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A Hybrid Method for the Classification of Paddy Varieties Based on Image Segmentation

P. Suganya, K.S. Tamil Selvan

Abstract


In Seed Processing Plant, different paddy varieties are identified and classified manually by visual inspection which is a tedious and less accuracy process. To overcome this, we developed an automated system for identifying and classifying the five different varieties such as ADT-38, ADT-39, ADT-43, ASD-16, and TKM-9 based on their morphological features.  In this paper, three phases are involved. In First Phase, Image of paddy seeds are acquired by a digital camera, which is collected from seed unit and the acquired image is stored in JPEG format. The stored image is given to wiener filter for Image pre-processing such as edge detection, image denoising and image enhancement. In Second Phase, Image Segmentation is performed using Dual Tree Complex Wavelet Transform (DTCWT). In Third Phase, from segmented image features are extracted and given to Neural Network to classify the paddy varieties according to the extracted color features, morphological features and shape factors of each paddy grain.

Keywords


ADT-43, ADT-39, ADT-38, ASD-16, TKM-9, DT-CWT, Morphological Features, Seed Processing Unit.

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References


D. Savakar, “Recognition And Classification Of Similar Looking Grain Images Using Artificial Neural Networks”, Journal of Applied Computer Science and Mathematics, 2012.

Harpreet Kaur, Baljit Singh, “Classification and Grading Rice Using Multi-Class SVM”, International Journal of Scientific and Research Publications, Volume 3, Issue 4, April 2013.

H.K. Mebatsion, J. Paliwal, D.S. Jayas, “Automatic classification of non-touching cereal grains in digital images using limited morphological and color features” Elsevier, Computers and Electronics in Agriculture 90,2013.

L.A.I.Pabamalie, H.L.Premaratne, “A Grain Quality Classification System”, Institute of Electrical and Electronics Engineers, 2010.

Huang, X.Y., Li, J., Jiang, S., “Study on identification of rice varieties using computer vision”, 2004.

M. Zhao, W. Wu, Y. Q. Zhang, and X. Li, “Combining genetic algorithm and SVM for corn variety identification,” in Proceedings of the International Conference on Mechatronic Science, Electric Engineering and Computer (MEC ’11), pp. 990–993, Jilin, China, August 2011.

K. Kiratiratanapruk and W. Sinthupinyo, “Color and texture for corn seed classification by machine vision,” in Proceedings of the 19th IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS ’11), Chiang Mai, Thailand, December 2011.

J. Paliwal, N. S. Visen, and D. S. Jayas, “Evaluation of neural network architectures for cereal grain classification using morphological features,” Journal of Agricultural Engineering Research, vol. 79, no. 4, pp. 361–370, 2001.

A. Douik, M. Abdellaoui, and E. N. I. de Monastir, 2010, "Cereal Grain Classification by Optimal Features and Intelligent Classifiers," International Journal of Computers Communications & Control, vol. 5, pp. 506-516.

P. M. Granitto, H. D. Navone, P. F. Verdes, and H. Ceccatto, 2002, "Weed seeds identification by machine vision," Computers and Electronics in Agriculture, vol. 33, pp. 91-103.

B. Dubey, S. Bhagwat, S. Shouche, and J. Sainis, 2006, "Potential of artificial neural networks in varietal identification using morphometry of wheat grains," Biosystems engineering, vol. 95, pp. 61-67.

I. Zayas, Y. Pomeranz, and F. Lai, 1989, "Discrimination of wheat and nonwheat components in grain samples by image analysis," Cereal Chem, vol. 66, pp. 233-237. [7] I. Y. Zayas, C. Martin, J. Steele, and A. Katsevich, 1996, "Wheat classification using image analysis and crush-force parameters," Trans. ASAE, vol. 39, pp. 2199-2204.

S. Shouche, R. Rastogi, S. Bhagwat, and J. K. Sainis, 2001, "Shape analysis of grains of Indian wheat varieties," Computers and Electronics in Agriculture, vol. 33, pp. 55-76.

R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern classification," New York: John Wiley, Section, vol. 10, p. l, 2001.

G. Van Dalen, 2004 "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.


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