Identification the appearance quality of rice kernels by vision technology and neural network classifier

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In this study, the appearance quality of Hashemi variety of rice grains was evaluated using image processing and artificial neural network (ANN) classifier. Non-touching kernel images of different classes in a Hashemi rice sample were acquired using a flatbed scanner. Then preprocessing, segmentation, feature extraction and effective feature selection process were done on each objects of image. To categorized grains, various structures of ANN consisting network with one and two hidden layer with different hidden nodes, different training and transfer functions were considered. Results of validation stage showed ANN with 13-18-18-5 topology and LM training and tansig transfer functions had highest mean of classification accuracy (97.33%) and the lowest value of RMSE (0.08361). It’s concluded that the suggested method uses low cost equipment to identify quality of rice with acceptable accuracy. Results of this research can be used for fast and accurate grading and developing an efficient rice sorting system.



  • Keywords

    Artificial Neural Network; Classification; Image Processing; Quality; Rice.

  • References

      [1] M.R Alizadeh, S. Minaei, T. Tavakoli, M.H. Khoshtaghaza, Effect of de-awning on physical properties of paddy, Pakistan Journal of Biological Sciences 9 (2006), 1726-1731.

      [2] D. Mohapatra, S. Bal, Effect of degree of milling on specific energy consumption, optical measurements and cooking quality of rice, Journal of Food Engineering 80 (1) (2007) 119-125.

      [3] M. Frei, P. Siddhuraju, K. Becker, Studies on the in vitro starch digestibility and glycemic index of six different indigenous rice cultivars from the Philippines, Food Chemistry 83 (3) (2003) 395-402.

      [4] M.R. Alizadeh, A. Dabbaghi, F. Rahimi Ajdadi, Effect of final paddy moisture content on breaking force and milling properties of rice varieties, Elixir Agriculture 36 (2011) 3186-3189.

      [5] H. Zareiforoush, M. Komarizadeh, M.R. Alizadeh, Effects of crop-machine variables on paddy grain damage during handling with an inclined screw auger, Biosystems Engineering 106 (3) (2010) 234–242.

      [6] T. Tashiro, I.F. Wardlaw, The effect of high temperature on kernel dimensions and the type and occurrence of kernel damage in rice, Australian Journal of Agricultural Research, 42 (1991), 485–496.

      [7] K. Bhavesh, Analysis of rice grains through digital image processing, International Journal of Scientific Research in Science and Technology 1 (1) (2015) 1-3.

      [8] C.M. Christensen, C.M. Kaufmann, Deterioration of stored grains by fungi, Annual Review of Phytopathology 3 (1965) 69-84.

      [9] N.M. Sahay, S. Gangopadhyay, Effect of wet harvesting on biodeterioration of rice, Cereal Chemistry 62 (2) (1985) 80-83.

      [10] J.S. Aulakh, V. Banga, Percentage purity of rice sample by image processing, International conference on trends in electrical, electronics and power engineering (ICTEEP), July 15–16, Singapore (2012) 15-16.

      [11] H.H. Wang, D.W. Sun, Evaluation of the functional properties of cheddar cheese using a computer vision method, Journal of Food Engineering 49 (1) (2001) 47–51.

      [12] H.H. Wang, D.W. Sun, Correlation between cheese meltability determined with a computer vision method and with Arnott and Schreiber tests, Journal of Food Science 67 (2) (2002) 745–749.

      [13] J Paliwal, N.S. Visen, D.S. Jayas, AE-Automation and emerging technologies: Evaluation of neural network architectures for cereal grain classification using morphological features, Journal of Agricultural Engineering Research 79 (4) (2011) 361–370.

      [14] N.S. Visen, J. Paliwal, D.S. Jayas, N.D.G. white, Specialist neural networks for cereal grain classification, Biosystems Engineering 82 (2002) 151–159.

      [15] I. Golpour, R. Chayjan, Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network, Czech Journal of Food Sciences 32 (3) (2014) 280-287.

      [16] B.B. Prajapati, S. Patel, Algorithmic approach to quality analysis of Indian basmati rice using digital image processing, International journal of emerging technology and advanced engineering 3 (3) (2013) 503–504.

      [17] G. Ajay, M. Suneel, K.K. Kumar, P.S. Prasad, Quality evaluation of rice grains using morphological methods, International journal of soft computing and engineering 2 (2013) 35–37.

      [18] V.G. Dalen, Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis, Food Research International 37 (1) (2004) 51–58.

      [19] H. Zareiforoush, S. Minaei, M.R. Alizadeh, A. Banakar, Qualitative classification of milled rice grains using computer vision and metaheuristic techniques, Journal of Food Science and Technology 53 (1) (2016) 118–131.

      [20] B.K. Yadav, V. Jindal, Monitoring milling quality of rice by image analysis, Computers and Electronics in Agriculture 33 (1) (2001) 19-33.

      [21] Y. Yoshioka, H. Iwata, M. Tabata, S. Ninomiya, R. Ohsawa, Chalkiness in rice: potential for evaluation with image analysis, Crop Science 47 (2007) 2113-2120.

      [22] J.D. Guzman, E.K. Peralta, Classification of Philippine rice grains using machine vision and artificial neural networks, World conference on agricultural information and IT, Tokyo, Japan, (2008) 41–48.

      [23] A. Pazoki, Z. Pazoki, Classification system for rain fed wheat grain cultivars using artificial neural network, African Journal of Biotechnology 10 (41) (2011) 8031-8038.

      [24] K. Mollazade, M. Omid, F. Akhlaghian Tab, Y. Rezaei Kalaj, S. Mohtasebi, M. Zude, Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging, Computers and Electronics in Agriculture 98 (2013) 34-45.

      [25] A. Fielding, Cluster and classification techniques for the biosciences, Cambridge University Press, UK, 2007.

      [26] K. Mollazade, M. Omid, A. Arefi, Comparing data mining classifiers for grading raisins based on visual features, Computers and Electronics in Agriculture 84 (2012) 124–131.

      [27] M. Omid, A. Mahmoudi, M.H. Omid, Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics, Expert Systems with Applications 37 (10) (2010) 7205–7212.

      [28] M. Hall, Correlation-based feature selection for machine learning, PhD Thesis, University of Waikato, Hamilton, New Zealand, 1999.

      [29] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining software: an update, SIGKDD Explorations11 (1) (2009) 10–18.

      [30] J. Rexce, K. Usha Kingsly Devi, Classification of milled rice using image processing, International Journal of Scientific and Engineering Research 8 (2) (2017) 10-15.

      [31] N. Teimouri, M. Omid, K. Mollazade, A. Rajabipour, A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow, Computers and Electronics in Agriculture 105 (2014) 34–43.

      [32] A. Khoshroo, A. Arefi, A. Masoumiasl, GH.H. Jowkar, Classification of wheat cultivars using image processing and artificial neural networks, Agricultural Communications 2 (1) (2014) 17-22.

      [33] Z. Liu, F. Cheng, Y. Ying, X. Rao, Identification of rice seed varieties using neural network, Journal of Zhejiang University Science B. 6, (11) (2005) 1095-1100.

      [34] C.S. Silva, U. Sonnadara, Classification of rice grains using neural networks, Proceedings of Technical Sessions 29 (2013) 9-14.

      [35] S. Shantaiya, U. Ansari, Identification of food grains and its quality using pattern classification, 12th IEEE International Conference on Communication Technology (ICCT), Nanjing, China, 11–14 November 2010.




Article ID: 29739
DOI: 10.14419/ijet.v9i1.29739

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.