CLASSIFICATION OF RICE USING CONVOLUTIONAL NEURAL NETWORK (CNN)

AUTHOR(S)

Pradeep M, Dr. M Siddappa

DOI: https://doi.org/10.46647/ijetms.2023.v07i05.056

ABSTRACT
This paper describes the technique for automatic recognition and classification of different rice grain samples using neural network classifier. The Red Green Blue (RGB), Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) color models of the image were considered for extracting 18 color features. The classification was carried out using color and texture features separately. The color image was converted to Gray scale image and the Gray Level Co-occurrence Matrixes (GLCM) for four different directions was calculated. A total of eight texture features were calculated from the Co-occurrence matrices. Convolutional Neural Network (CNN) is used for the classification process. The classification accuracy with color features and texture features were compared. Result shows that the classification base on texture features outperform the color feature-based classification even with lesser number of features. It is found that Convolutional Neural network was able to classify two varieties of rice with 100% accuracy using texture features and the edge detection with Sobel and Canny edge detection of the fiber features in the food grain.
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References:

Anami, B.S., Burkpalli, V., Angadi, S.A. Patil, N.M. (2003), “Neural network approach for grain classification and gradation”, Proceedings of the second national conference on document analysis and recognition, held at Mandya, India, during 11-12 July, 2003., pp. 394-405.

B. S. Anami, D. G. Savakar, Aziz Makandar, and P. H. Unki (2005). “A neural network model for classification of bulk grain samples based on color and texture”. Proceedings of International Conference on Cognition and Recognition, held at mandya, India, on 22 & 23 December 2005.pp. 359 – 368.

N.S Visen, J. Paliwal, D.S Jayas and N.D.G White.: ‘Image analysis of bulk grain samples using neural network’, Canadian Biosystem Engineering, vol 46 (2004), pp. 7.11-7.15.

B.S Anami1, D.G. Savakar2, Aziz Makandar3 and P. H. Unki4. “A Neural Network Model for Classification of Bulk Grain Samples Based on Color and Texture” Proceeding on International conference on cognition and recognition (2005).

J. Paliwal; N. S. Visen; D. S. Jayas. “Evaluation of Neural Network Architectures for Cereal Grain Classification using Morphological Features” Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Silsoe Research Institute, 2001

J. Paliwal; N.S. Visen; D.S. Jayas; N.D.G. White” Cereal Grain and Dockage Identification using Machine Vision” 1Department of Biosystems Engineering, University of Manitoba, Cereal Research Centre, Agriculture and Agri-Food Canada, Biosystems Engineering (2003) 85 (1), 51–57

B. S. Anami, D. G. Savakar, P. H. Unki and S.S. Sheelawant (2006). “An Artificial Neural Network Model for Separation, Classification and Gradation of Bulk Grain samples”. IEEE First International Conference on Signal and Image Processing held at Hubli, India during 7-9 December 2006.pp 511-520.

Cheng-Jin Du and Da-Wen Sun (2007) “multi-classification of pizza using computer vision and support vector machine”, Journal of Food Engineering, volume 86, Issue 2, pp 234- 242.

Dah-Jye Lee, Robert. Schoenberger, James. Archibald and SteveMcCollum (2007), “Development of machine vision system for automatic date grading using digital reflective near-infrared imaging”, Journal of Food Engineering, volume 86, Issue 3, pp 388-398.

Haralick, R.M., Shanmugam, K., and Dinstein, I. (1973), “Texture features for image classification”, IEEE Trans. on System., Man, and cybernetics,610-621. Volume: 3, Issue: 6.pp: 610-621.

Keefe, P.D. (1992), “A dedicated Wheat grading system”, Plant Varieties and Seeds, vol. 5, pp. 27-33.

Kıvanç Kılıç, İsmail Hakki Boyac, Hamit Köksel and İsmail Küsmenoğlu (2006) “A classification system for beans using computer vision system and artificial neural networks.”, Journal of Food Engineering, volume 78, Issue 3, pp 897-904.

B Saritha, G Gokila, A Gokul Priya, K Mahalakshmi, “Adulteration Identification of Papaya Seeds in Black Pepper Using Digital Image Processing” International Journal of Innovative Research in Computer and Communication Engineering ( IJIRCCE), Vol. 5, Issue 3, March 2017.

Harish S Gujjar, Dr M Siddappa, “A Method for identification of Basmati Rice grain of India and its Quality using Pattern Recognition” International Journal of Engineering Research and Applications(IJERA) ISSN: 2248-9622 Vol. 3, Issue 1, January- February 2013, pp.268-273.

Mayur P. Raj and Dr. Priya R. Swaminarayan “Applications of Image Processing for Grading Agriculture products” International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Vol 3 Issue 3 1194 – 1201.

How to Cite This Article:
Pradeep M, Dr. M Siddappa . ijetms;7(5):455-463. DOI: 10.46647/ijetms.2023.v07i05.056