Advertisement

Computer Vision Based Classification of Indian Gujarat-17 Rice Using Geometrical Features and Cart

  • Chetna V. Maheshwari
  • Niky K. Jain
  • Samrat Khanna
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Agricultural production has to be kept up with an ever-increasing population demand which is an important issue in recent years. The paper presents a solution for quality evaluation, grading and classification of INDIAN Gujarat-17 Rice using Computer Vision and image processing. In this paper basic problem of rice industry for quality assessment is defined and Computer Vision provides one alternative for an automated, non-destructive and cost-effective technique. In this paper we quantify the quality of ORYZA SATIVA SSP Indica (Gujarat-17) based on features which affect the quality of the rice. Based on these features CART (Classification and Regression Tree Analysis) is being proposed to evaluate the rice quality.

Keywords

Machine vision Combined parameters Oryza sativa SSP indica Mining techniques, CART 

Notes

Acknowledgments

The author expresses their gratitude to Mr. H.N. Shah the owner of Shri Krishna Rice and Pulse Mill, Borsad for the system model as per the requirements desired by the image processing group.

References

  1. 1.
    Abdullah, M.Z., Fathinul-Syahir, A.S., Mohd-Azemi, B.M.N.: Automated inspection system for color and shape grading of star fruit (Averrhoa carambola L.) using Computer Vision sensor. Trans. Inst. Meas. Control. 27(2), 65–87 (2005)Google Scholar
  2. 2.
    Shantaiya, S., Ansari, U.: Identification of food grains and its quality using pattern classification. In: International Conference [ICCT], 3rd–5th December. IJCCT, vol. 2 issue 2, 3, 4, pp. 70–74 (2010)Google Scholar
  3. 3.
    Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)CrossRefGoogle Scholar
  4. 4.
    Sundaram, G., Ding, K.X.: Computer vision technology for food quality assurance. Trends Food Sci. Technol. 7, 245–256 (1996)CrossRefGoogle Scholar
  5. 5.
    Guzman, J.D., Peralta, E.K.: Classification of Philippine rice grains using computer vision and artificial neural networks. In: Iaald Afita WCCA World conference on Agricultural information and IT (2008)Google Scholar
  6. 6.
    Jain, K., Modi, Pithadiya, K.: Non Destructive quality evaluation in spice industry with specific reference to Cuminum Cyminum L (Cumin) seeds. In: International Conference on Innovations and Industrial Applications Malaysia, IEEE (2009)Google Scholar
  7. 7.
    Maheswari, C.V., Jain, K., Chintan, Modi, K.: Non destructive quality evaluation of Indian basmati Oryza sativa SSP Indica rice using image processing. In: CSNT-Rajkot, IEEE, pp. 189–193 (2012)Google Scholar
  8. 8.
    Salem, M., Alfatni, M., Rashid, A., Shariff, M., Osama, M., Saaed, B.: The design and process of the external inspection system in agricultural engineering. Map Asia and ISG (2010)Google Scholar
  9. 9.
    Rani, N.S., Pandey, M.K., Prasad, G.S.V., Sudharshan, I.: Historical significance, grain quality features and precision breeding for improvement of export quality Gujarat-17 varieties in India. Indian J. Crop Sci. 1(1–2), 29–41 (2006)Google Scholar
  10. 10.
    Abdullah, M.Z., Aziz, A.S., Dos-Mohamed, A.M.: Quality inspection of bakery products using color-based computer vision system. J. Food Qual. 23, 39–50 (2000)CrossRefGoogle Scholar
  11. 11.
    Verma, B.: Image processing techniques for grading and classification of rice. In: International Conference on Computer and Communication Technology [ICCCT] (2010)Google Scholar
  12. 12.
    Ballard, D.A., Brown, C.M.: Computer Vision. Prentice-Hall, Englewood Cliffs (1982)Google Scholar
  13. 13.
    Blasco, J., Aleixos N., Molt, E: Computer vision system for automatic quality grading of fruit. Biosyst. Eng. 85(4) (2003)Google Scholar
  14. 14.
    Guo, A., Yang, O., Gao, R.J., Liu, Yan de, Sun, X.D., Pan, Y., Dong, X.L.: An automatic method for identifying different variety of rice seeds using computer vision technology. In: Sixth International Conference on Natural Computation ICNC (2010)Google Scholar
  15. 15.
    Gumuş, B., Balaban, M.O., Unlusayın, M.: Computer vision applications to aquatic foods: a review. Turk. J. Fish. Aquat. Sci. 11, 171–181 (2011)Google Scholar
  16. 16.
    Batchelor, B.G.: Lighting and viewing techniques In: Bachelor, B.G., Hill, D.A., Hodgson, D.C. (eds.) Automated Visual Inspection, pp. 103–179. IFS Publication Ltd, Bedford (1985)Google Scholar
  17. 17.
    Birewar, B.R., Kanjilal, S.C.: Hand operated batch type grain cleaner. J. Agric. Eng. Today 6(6), 3–5 (1982)Google Scholar
  18. 18.
    Yadav, B.K., Jindal, V.K.: Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis. Food Engineering and Bioprocess Technology. Asian Institute of TechnologyGoogle Scholar
  19. 19.
    Liu, C.C., Shaw, J.T., Poong, K. Y., Hong, M.C., Shen, M.L.: Classifying paddy rice by morphological and color features using computer vision. In: AACC International /CC-82-0649 (2005)Google Scholar
  20. 20.
    Du, C.J., Sun, W.: Learning techniques used in computer vision for food quality evaluation: a review. J. Food Eng. 72(1), 39–55 (2006)CrossRefGoogle Scholar
  21. 21.
    Openg, D.X., Yong, L.: Research on the rice chalkiness measurement based on the image processing technique. In: IEEE 978-1-61284-840-2/11/$26.00 (2011)Google Scholar
  22. 22.
    Parmar, R.R., Jain, K.R., Modi, K.: Unified approach in food quality evaluation using Computer Vision. In: ACC 2011, Part III, CCIS, vol. 192, pp. 239–248 (2011)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Chetna V. Maheshwari
    • 1
  • Niky K. Jain
    • 2
  • Samrat Khanna
    • 2
  1. 1.G.H. Patel College of Engineering and TechnologyV.V. NagarIndia
  2. 2.ISTARV.V. NagarIndia

Personalised recommendations