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Cost Effective Grading Process for Grape Raisins based on HSI and Fuzzy Logic Algorithms

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 67 - Number 22
Year of Publication: 2013
Authors:
Swati P. Pawar
Amit Sarkar
10.5120/11527-7335

Swati P Pawar and Amit Sarkar. Article: Cost Effective Grading Process for Grape Raisins based on HSI and Fuzzy Logic Algorithms. International Journal of Computer Applications 67(22):18-22, April 2013. Full text available. BibTeX

@article{key:article,
	author = {Swati P. Pawar and Amit Sarkar},
	title = {Article: Cost Effective Grading Process for Grape Raisins based on HSI and Fuzzy Logic Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {67},
	number = {22},
	pages = {18-22},
	month = {April},
	note = {Full text available}
}

Abstract

Farmers of several progressive countries like India are producing the grape raisins. However, the existing grading systems in these countries are human expert based and judgmental. The image based sorting and grading systems developed in the advanced countries are costly and are sometimes slow as they do analysis of individual raisin. This work proposes to develop a cost effective grading process for grape raisins which will give judgment about grading of bulk of raisins sorted manually or mechanically. In this study, the database is developed using the images taken by simple webcam from the local raisin market. Based on the opinion of the raisin experts, these images are grouped into four classes. The Hue, Saturation and Intensity (HSI) color features of these images are obtained to develop the fuzzy logic system for the classification of the images of different grades. The Gaussian membership function is used for developing the four rules for four grades. The performance of the fuzzy classification system is measured in the form of Success Rate. The results show that for more than four features, the raisin grading can be classified with 100 % success rates.

References

  • K. Mollazade , M. Omid , A. Arefi "Comparing data mining classifiers for grading raisins based on visual features", Journal of Computers and Electronics in Agriculture, vol. 84,pp. 124-131,2012.
  • X. Yu, K. Liu, D. Wu and Yong He "Raisin Quality Classification Using Least Squares Support Vector Machine (LSSVM) Based on combined Color and Texture Features" Journal of Food And Bioprocess Technology,vol. 5, pp. 1152-1563, 2012.
  • L. Xiaoling "Detection Level of Raisins Based on Neural Network and Digital Image" Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference pp. 1-3,july 2011.
  • M. Abbasgholipour , M. Omid, A. Keyhani, S. S. Mohtasebi"Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions" journal of Expert Systems with Applications,vol. 38,pp. 3671-3678, 2011.
  • M. Omid, M. Sharouzi and A. R. Keyhani "Development of an Automated Machine for Grading Raisins based on Color and Size" Journal of Modeling and Simulation of Systems, Vol. 1,pp. 157-162,2010
  • A. Raji and A. Alamutu. "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. VII. Invited Overview. February 2005.
  • Lee, W. S. , Slaughter, D. C. and Giles, D. K. . Robotic weed control system for tomatoes. Precision Agriculture, vol. 1, pp 95–113, 1999.
  • Majumdar, S. & Jayas, D. S. (2000). Classification of cereal grains using machine vision II. Color models, Transactions of the ASAE, vol. 43(6), pp. 1677-1680
  • Shahin, M. A. and Symons, S. J. , "A machine vision system for grading lentils". Canadian Biosystems Engineering, Vol. 7,pp. 7-14, 2001.
  • Paliwal, J. , Borhan, M. S. & Jayas, D. S. Classification of cereal grains using a flatbed scanner. Transactions of the ASAE, pp. 036103, 2003
  • Shigeta, K. , Motonaga, Y. , Kida, T. and Matsuo, M. "Distinguishing damaged and undamaged chaff in rice whole crop silage by image processing", Transactions of the ASAE Annual Meeting, pp. 043125, 2004
  • Omid, M. , Mahmoudi, A. and Omid, M. H. " An intelligent system for sorting pistachio nut varieties", Expert Systems with Applications, vol. 36(9), pp. 11528–11535,2009.
  • Khojastehnazhand, M. , Omid M. and Tabatabaeefar, A. "Determination of orange volume and surface area using image processing technique". International Agrophysics, vol. 23, pp. 237-242, 2009.