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Non-Destructive Quality Analysis of JIRASAR Oryza Sativa SSP Indica (Indian Rice) using Feed Forward Neural Network

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Niky K. Jain, Samrat O. Khanna, Chetna K. Shah

Niky K Jain, Samrat O Khanna and Chetna K Shah. Non-Destructive Quality Analysis of JIRASAR Oryza Sativa SSP Indica (Indian Rice) using Feed Forward Neural Network. International Journal of Computer Applications 172(7):35-39, August 2017. BibTeX

	author = {Niky K. Jain and Samrat O. Khanna and Chetna K. Shah},
	title = {Non-Destructive Quality Analysis of JIRASAR Oryza Sativa SSP Indica (Indian Rice) using Feed Forward Neural Network},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {7},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {35-39},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017915186},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Carrying out compelling and reasonable agriculture product has turned into an important issue in recent years. Agricultural production needs to stay aware with an ever-increasing population. A key to this is the utilization of present day strategies (for precision agriculture) to exploit the quality in the market. Classification of rice seeds from the exposed human hands is neither savvy nor prescribed. The automatic grading for examination of quality has turned into the need of great importance. This paper prescribes an extra way to deal with quality specialists for the quality investigation of INDIAN JIRASAR Rice using computer vision and soft computing techniques. Computer Vision gives a grading methodology, non-destructive technique, along with multi-layer feed forward neural networking which achieves high degree of quality than human vision inspection.


  1. Abdullah MZ, Fathinul-Syahir AS, Mohd-Azemi BMN, “Automated inspection system for color and shape grading of star fruit (Averrhoa carambola L.) using Computer vision sensor,” Transactions of the Institute of Measurement and Control, 27 (2), 65-87, 2005.
  2. Abutaleb AS, “Automatic thresholding of grey-level pictures using two-dimensional entropies.” Pattern Recognition, 47(1), 22-32, 1989.
  3. Ballard, D. A., & Brown, C. M. “ Computer vision”, Englewood Cliffs, NJ, USA: Prentice-Hall ,1982.
  4. Blasco J, Aleixos N, Molt E, “Computer vision system for automatic quality grading of fruit”, Biosystems Engineering, 415-423, 2003.
  5. Chetna Maheshwari, Kavindra Jain, Chintan Modi, “Novel approach for Oryza sativa L.(Rice) based on Computer vision technology,” PEPCCI, National Conference ,ISBN No.-978-93-81286-06-7,2012.
  6. Du CJ, Sun D-W, “Recent development in the applications of image processing techniques for food quality evaluation.” Trends in Food Science and Technology, 15,230-249, 2004.
  7. Du C-J, Sun D-W, “Learning techniques used in computer vision for food quality evaluation: a review”, Journal of Food Engineering, 72(1), 39-55, 2006.
  8. Gunasekaran Sundaram, Kexiang Ding, “Computer vision technology for food quality assurance,”. Trends in Food Science and Technology, 7, 245-256, 1996.
  9. Jain AK, ”Fundamentals of Digital Image Processing,” Englewood Cliffs: Prentice-Hall 1989.
  10. Kavindra Jain, Chintan K. Modi, Kunal Pithadiya, “Non Destructive quality evaluation in spice industry with specific reference to Cuminum Cyminum L (Cumin) seeds,” International Conference on Innovations & Industrial Applications, Malaysia, (IEEE) 2009.
  11. M. Kurita and N. Kondo, “Agricultural product grading method by image processing (part 1) - effectiveness of direct lighting method”, J.SHITA 18(1): 9-17,2006.
  12. Shen Castan, Sian Zhao,”A Comparitive study of Performance of Noisy roof edge detection”, 5th International conference on Computer analysis of Images and Patterns, volu.179, pp 170-174
  13. Tadhg Brosnan, Da-Wen Sun, “Improving quality inspection of food products by computer vision-a review”, Journal of Food Engineering 61, pp. 3–16, 2004.
  14. Xiaopei Hu, ParmeshwaraK.M, DavidV. “Development of Non Destructive Methods To Evaluate Oyster Quality By Electronic Nose Technology”, Springer Science Business Media, LLC, 2008.
  15. Niky K. Jain,Samrat O. Khanna,Chetna Maheshwari,,”Feed Forward Neural Network Classification for INDIAN Krishna Kamod Rice”International Journal of Computer Applications(0975-8887),Volume 134 – No. 14,January 2016


Computer Vision, Soft Computing technique, digital image processing, Indian Jirasar rice seeds, non-destructive.