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Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features

by Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 11
Year of Publication: 2014
Authors: Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi
10.5120/16259-5910

Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi . Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features. International Journal of Computer Applications. 93, 11 ( May 2014), 17-24. DOI=10.5120/16259-5910

@article{ 10.5120/16259-5910,
author = { Jagadeesh D. Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi },
title = { Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 11 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number11/16259-5910/ },
doi = { 10.5120/16259-5910 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:32.147426+05:30
%A Jagadeesh D. Pujari
%A Rajesh Yakkundimath
%A Abdulmunaf S. Byadgi
%T Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 11
%P 17-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we present a reduced feature set based approach for recognition and classification of normal and affected agriculture produce types. Color and texture features are extracted from normal and affected image samples of agriculture produce. The color features are reduced from eighteen to eight and texture features are reduced from thirty to five. A classifier based on Back Propagation Neural Network (BPNN) is developed which uses reduced color and texture features to recognize and classify the different normal and affected agricultural produce. A feedback from classifier performance is used in reducing the features. The average classification accuracies using reduced color features are 78. 08% and 75. 17% for normal and affected agriculture produce type respectively. The average classification accuracies using reduced texture features are 85. 53% and 77. 43% for normal and affected agriculture produce type respectively. The average classification accuracies have increased to 88. 28% and 83. 80% for normal and affected agriculture produce type respectively, when the reduced color and texture features are combined. The work finds application in developing a machine vision system in agriculture fields in the area of recognition and classification of agriculture produce.

References
  1. Abdul Malik Khan. and Andrew P. Papli?ski. 2008. Blemish detection in citrus fruits, Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India, Vol. 1, Pages: 203-211.
  2. Ahmada, U. , Naoshi, K. , Mitsuji, M. , and Haruhiko. 2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks, Computers and Electronics in Agriculture, Vol. 29, Issue. 1-2, Pages: 135 - 147.
  3. Biswas, H. and Hossain F. 2013. Automatic Vegetable Recognition System, International Journal of Engineering Science Invention, Vol. 2, Issue 4, Pages: 37-41.
  4. Blasco, J. , Aleixos, N. , Molto, E. 2003. Machine Vision System for Automatic Quality Grading of Fruit, Biosystems Engineering,Vol. 85, Issue 4, Pages:415–423.
  5. Brosnan, T. and Da-Wen Sun. 2004. Improving quality inspection of food products by Computer vision-a review, Journal of Food Engineering, Vol. 61, Pages: 3-16.
  6. Dubey, S. , Dixit, P. , Nishant Singh. , and Gupta, J. 2013. Infected Fruit part Detection using K-means Clustering Segmentation technique, International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Issue. 2.
  7. Dubey, S. and Anand Singh, J. 2013. Species and variety detection of fruits and vegetables from images, International Journal of Applied Pattern Recognition, Vol. 1, Issue. 1.
  8. Kataoka, T. , Hiroshi, O. , and Shun-ichi H. 2001. Automatic detecting system of apple harvest season for robotic apple harvesting. Presented at the 2001 ASAE Annual international meeting, Sacramento, California. Paper No. 01-3132.
  9. Kim, M. S. , Chen, Y. R. , and Mehl, P. M. 2001. Hyperspectral reflectance and fluorescence imaging system for food quality and safety, Transactions of ASAE, Vol. 44, Issue. 3, Pages: 721:729.
  10. Londe, D. , Nalawade, S. , Pawar, G. , Atkari, V. , and Wandkar, S. 2013. Grader: A review of different methods of grading for fruits and vegetables, Agriculture Engineering International: CIGR Journal, Vol. 15, Issue. 3, Pages: 217-230.
  11. Luo, X. , Jayas, D. S. , and Symons, S. J. 1999. Identification of Damaged Kernels in Wheat using a Color Machine Vision System, Journal of Cereal Science, Volume 30, Issue 1, Pages: 49-59.
  12. Majumdar S and Jayas D S, (2000a), Classification of Cereal Grains using Machine Vision. II. Color Models, Transactions of American Society of Agriculture Engineers, Volume 43(6), Pages: 1677-1680.
  13. Majumdar S and Jayas D S, (2000b), Classification of Cereal Grains using Machine Vision. II. Texture Models, Transactions of American Society of Agriculture Engineers, Volume 43(6), Pages: 1681-1687.
  14. Maliappis, M. T. , Ferentinos. K. P. , Passam, H. C. , and Sideridis, A. B. 2008. Gims: A Web-based Greenhouse Intelligent Management System, World Journal of Agricultural Sciences, Vol. 4, Issue. 5, Pages: 640-647.
  15. May, Z. , Amaran, M. H. 2011. Automated Oil Palm Fruit Grading System using Artificial Intelligence, International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol: 11 No: 03.
  16. Narendra, V. G. and Hareesh, K. S. 2010. Prospects of Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation, International Journal of Computer Applications, Vol. 11, No. 4.
  17. Neuman, M. , Sapirstein, H. D. , Shwedyk, E. , and Bushuk W. 1989. Wheat Grain Color Analysis by Digital Image Processing, Journal of Cereal Science, Vol. 10, pages: 175-182.
  18. Ning, K. , Zhang, M. , Ruan, R. , and Chen, P. L 2001. Computer vision for objective inspection of beans quality, ASAE Annual international meeting, Sacramento, California, Paper No. 01-3059.
  19. Parmar, R. R. , Jain, K. R. , and Modi, C. K. 2011. Unified Approach in Food Quality Evaluation Using Machine Vision Advances in Computing and Communications Communications in Computer and Information Science, Vol. 192, Pages 239-248.
  20. Paliwal, J. , Jayas, D. S. , and Borhan, M, S. 2004, Classification of Cereal Grains using a Flatbed Scanner, Canadian Bio-systems Engineering, Vol. 46, Pages:3. 1-3. 5.
  21. Raji, A. O. and Alamutu, A. O. 2005. Prospects of Computer Vision Automated Sorting Systems in Agricultural Process Operations in Nigeria, Agricultural Engineering International: the CIGR Journal of Scientific Research and Development, Vol. 7.
  22. Razmjooya. , N. , Mousavib, B. S. and Soleymani, F. 2012. A real-time mathematical computer method for potato inspection using machine vision, Computers and Mathematics with Applications, Vol. 63, Pages: 268–279.
  23. Riquelme, M. T. , Barreiro, P. , Ruiz-Altisen, M. and Valero, C. 2008. Olive Classification according to external damage using image analysis, Journal of Food Engineering, Vol. 87, Isuue. 3, Pages. 371-379.
  24. Xiu, Liming. and Zhao, Yanchao, Zhao. 2010. Automated strawberry grading system based on image processing, Computers and Electronics in Agriculture, Vol. 71, Pages: 32-39.
  25. Yud-Ren, Chen. , Kuanglin, Chao. and Kim M S. 2002. Machine vision technology for agricultural Applications, Computers and Electronics in Agriculture, Vol. 36, Pages: 173-191.
Index Terms

Computer Science
Information Sciences

Keywords

agriculture produce color features texture features bulk normal produce bulk affected produce artificial neural network

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