CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks

Published on October 2014 by Harish S Gujjar, M. Siddappa
journal_cover_thumbnail

International Conference on Information and Communication Technologies
Foundation of Computer Science USA
ICICT - Number 1
October 2014
Authors: Harish S Gujjar, M. Siddappa
4f46d9cd-f67a-48fa-9d5f-f051ac8becd2

Harish S Gujjar, M. Siddappa . Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks. International Conference on Information and Communication Technologies. ICICT, 1 (October 2014), 12-17.

@article{
author = { Harish S Gujjar, M. Siddappa },
title = { Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks },
journal = { International Conference on Information and Communication Technologies },
issue_date = { October 2014 },
volume = { ICICT },
number = { 1 },
month = { October },
year = { 2014 },
issn = 0975-8887,
pages = { 12-17 },
numpages = 6,
url = { /proceedings/icict/number1/17959-1403/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information and Communication Technologies
%A Harish S Gujjar
%A M. Siddappa
%T Recognition and Classification of Different Types of Food Grains and Detection of Foreign Bodies using Neural Networks
%J International Conference on Information and Communication Technologies
%@ 0975-8887
%V ICICT
%N 1
%P 12-17
%D 2014
%I International Journal of Computer Applications
Abstract

This paper deals with the classification of bulk food grain samples and detection of foreign bodies in food grains. A new method for inspecting food samples is presented, using ANN and segmentation to classify grain samples and detect foreign bodies that are not detectable using conventional methods easily. A BPNN based classifier is designed to classify the unknown grain samples. The algorithms are developed to extract color, texture and combined features are extracted from grains and after normalization presented to neural network for training purpose. The trained network is then used to identify the unknown grain type and it's quality in terms of pure/impure type. A Segmentation based detection model is developed to detect the foreign body in the impure grain samples. This model accepts an impure grain samples, pre-processes and segments the image using two different thresholds T1 and T2 to detect the foreign body in impure image. Finally the success rates are observed from both classification and foreign body detection models and are recorded.

References
  1. Harish S Gujjar, Dr. M. Siddappa, "A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification" , International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 3, Issue 1, January -February 2013, pp. 268-273.
  2. Basavaraj . S. Anami and Vishwanath. C. Burkpalli, Texture based Identification and Classification of Bulk Sugary Food Objects, ICGST-GVIP Journal, ISSN: 1687-398X, Volume 9, Issue 4, August 2009.
  3. Effect of Foreign Bodies on Recognition and Classification of Bulk Food Grains Image Samples
  4. Basavaraj S. Anami, Dayanand G. Savakar Journal of Applied Computer Science, no. 6 (3) /2009, Suceava.
  5. Mowat A. D. and Poole, P. R. Non destructive discrimination of persimmon fruit quality using visible-near-infrared reflectance spectro-photometry, Act Horticulture (ISHS), Vol. 436, pp. 159-163, 2007.
  6. 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.
  7. 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.
  8. Lee, K. -M. , Li, Q. , Daley, W (2007). "Effects of Classification Methods on Color-Based Feature Detection With Food Processing Applications", IEEE Transactions on Automation Science and Engineering, Volume 4, Issue 1. pp. 40-51.
  9. 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.
  10. K?vanç K?l?ç, ?smail Hakki Boyac, Hamit Köksel and ?smail Küsmeno?lu ( 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.
  11. Anami, B. S. , Savakar, D. g. , Makandar, A. and Unki, P. H. (2005), A Neural Network Model for Classification of Bulk Grain Samples based on Color and Texture.
  12. B. S. Anami, D. G. Savakar, Aziz Makandar, and P. H. Unki , "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.
  13. Genesu, G. , Giusto, D. D. , Marganer, V. and Menlschmidt, P. Detection of Foreign Bodies in Food by Thermal Image Processing, IEEE Transactions on Industrial Electronics, vol. 51, no. 2, April 2004.
  14. Gonzalez, R. C. Rechard, E. W. (2004), Digital Image Processing, II Indian Edition, Person Education, New Delhi, India.
  15. Visen, N. S. , Paliwal, J. , Jayas, D. S. , White, N. D. G. (2004). "Image analysis of bulk grain samples using neural networks", Canadian Biosystems Engineering, vol. 46, pp. 7. 11-7. 15.
  16. Visen, N. S. , Paliwal, J. , Jayas, D. S. , White, N. D. G. (2004). "Image analysis of bulk grain samples using neural networks", Canadian Biosystems Engineering, vol. 46, pp. 7. 11-7. 15.
  17. Anami, B. S. , Burkapalli, V. , Angadio, 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, Mandya, pp. 394-405.
  18. Anami, B. S. , Burkpalli,V. , Angadi, S. A. Patil, N. M. "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.
  19. Pearson, T. C. , Doster, M. and Michailides, T. J. , Automated detection of pistachio defects by machine vision. Appl. Eng. Agri. , vol. 17, no. 5, pp. 81-84, 2001.
  20. Anami B. S. , Angadi, S. A, Amarapur, B. and Channal, S. (2000). Invariant moment method for leaf image recognition. Proceeding of the National conference on Document Analysis and Recognition, pp. 131-139.
  21. Majumdar, S. and Jayas, D. S. (2000a). "Classification of cereal grains using machine vision. I. Morphology models", Transactions of the American Society of Agriculture Engineers, vol. 43(6), pp. 1669-1675.
  22. Majumdar, S. and Jayas, D. S. (2000b). "Classification of cereal grains using machine vision. II. Color models", Transactions of the American Society of Agriculture Engineers, vol. 43(6), pp. 1677-1680.
  23. Majumdar, S. and Jayas, D. S. (2000c). "Classification of cereal grains using machine vision. III. Texture models", Transactions of the American Society of Agriculture Engineers, vol. 43(6), pp. 1681-1687.
  24. Majumdar, S. and Jayas, D. S. (2000d). "Classification of cereal grains using machine vision. IV. Combined morphology, color, and texture models", Transactions of the American Society of Agriculture Engineers, , vol. 43(6), pp. 1689-1694.
  25. Majumdar, S. and Jayas, D. S. (1999). "Classification of bulk samples of cereal grains using machine vision", Journal of Agricultural Engineering Research, vol. 731(1), pp. 35-47.
  26. Kazuhiro, N. (1997). Application of neural networks to the color grading of apples. Computer and electronics in Agriculture, vol. 18, no. 2-3, pp. 105-116.
  27. Timmermans, A. J. M. and Hulzebosch, A. A. (1996). Computer vision system for on-line sorting of pot plants using an artificial network classifier. Computer and electronics in Agriculture, vol. 15, no. 1, pp. 41-55.
  28. Majumdar, S. , Jayas, D. S. , Symons, S. J. and Bulley, N. R. (1996). "Textural features for automated grain identification", Transactions on Computer Society on agriculture Engineering, Paper no. 96-602.
  29. Brons, A. , Rabatel, G. , Ros, F. , Sevila, F. and Touzet, C. (1993). Plant grading by Vision using neural networks and statistics. Computer and Electronics in Agriculture, vol. 9, pp. 25-39.
  30. Keefe, P. D. (1992), "A dedicated Wheat grading system", Plant Varieties and Seeds, vol. 5, pp. 27-33.
  31. Neuman, M. , Sapirstein, H. D. , Shwedyk, E. , and Bushuk, W. (1989a), "Wheat grain color analysis by digital image processing: I. Methodology", Journal of Cereal Science, vol. 10, pp. 175-182.
  32. Neuman, M. , Sapirstein, H. D. , Shwedyk, E. and Bushuk, W. (1989b). "Wheat grain color analysis by digital image processing: II. Wheat class determination", Journal of Cereal Science, vol. 10, pp. 183-188.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Classification Color Segmentation Texture Feature Extraction.