Segmentation of Cotton Bolls by Efficient Feature Selection Using Conventional Fuzzy C-Means Algorithm with Perception of Color

  • Sandeep Kumar
  • Manish Kashyap
  • Akashdeep Saluja
  • Mahua Bhattacharya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


Ad hoc method for segmentation of mature or nearly mature cotton bolls is proposed based on proper feature vector selection and efficient application of Fuzzy c-means (FCM) on images. Perception of color is used as fundamental criteria for segmentation. The results obtained are compared with conventional FCM and supremacy of the proposed work is presented. Since the technique is ad hoc, it will work only for the said purpose in the natural setting of cotton fields. Any improper acquisition of images of cotton bolls, like intense illumination or deep shadows (which is of course absent in natural settings) will produce improper results.


Fuzzy c-means Image segmentation Cotton boll segmentation Perception of color 



Authors like to acknowledge the support given by Department of Science & Technology, Ministry of Science & Technology, Government of India, Central Institute for Cotton Research, Nagpur under Indian Council of Agricultural Research (ICAR) and Central Mechanical Engineering Research Institute, Ludhiana under the Council of Scientific and Industrial Research (CSIR). This work is under the collaborative networking project Center for Precision and Conservation Farming Machinery (CPCFM) entitled: Vision-Based Expert System for Picking of Cotton.


  1. 1.
    Wang, M., Xu, K., Wei, J., Yuan, J.: A research for intelligent cotton picking robot based on machine vision. In: International Conference on Information and Automation, 20–23 June 2008Google Scholar
  2. 2.
    Wang, W., Qu, D., Ma, B.,Wang, Y.: Cotton top feature identification based on machine vision and image processing. In: Computer Science and Automation Engineering (CSAE) (2011)Google Scholar
  3. 3.
    Yong, W., Chang-ying, Ming-xia, S.J.: Model and analysis of color for different parts of ripe cotton in picking period. In: Transactions of the CSAE, vol. 23, pp. 183–185 (2007)Google Scholar
  4. 4.
    Yong, W., Chang-ying, J.: Study on discrimination of mature cotton in early scenes. Acta Agricul. Jiangxi 18(6), 141–143 (2007)Google Scholar
  5. 5.
    Yong, W., Chang-ying, J., Ming-xia, S.: Study on the recognition of mature cotton based on the chromatic aberration in natural outdoor scenes. Acta Agricul. Zhejiang 19, 385–388 (2007)Google Scholar
  6. 6.
    Basu, M.: Gaussian-based edge-detection methods–a survey. IEEE Trans. Syst., Man, Cybern. C 32, 252–260 (2002)Google Scholar
  7. 7.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. A-Math. Phys. Sci. B 207, 187–217 (1980)Google Scholar
  8. 8.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. 1986(8), 679–698 (1986)CrossRefGoogle Scholar
  9. 9.
    Witkin, A.P.: Scale space filtering. In: Proceeding 8th International Joint Conference Artifical Intelligence, pp. 1019–1022. Karlsruhe, Germany (1983)Google Scholar
  10. 10.
    Nicole R.: Study on the matlab segmentation image segmentation. In: J. Name Stand. AbbrevGoogle Scholar
  11. 11.
    Kwon, D. et al.: A image segmentation method based on improved watershed algorithm and region merging. IEEE Trans. Circuits Syst. Video Technol. 17, 517–529 (2007)Google Scholar
  12. 12.
    Ross, J.: Fuzzy Logic with Engineering Applications, 3rd ednGoogle Scholar
  13. 13.
    Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Brandt, M.E. : An error convergence simulation study of hard vs. fuzzy c-means clustering. In: IEEE World Congress on Computational Intelligence, vol. 3, pp. 1835–1839 (1994)Google Scholar
  15. 15.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab (2008)Google Scholar
  16. 16.
    Bezdek, J.: Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum, New York (1981)Google Scholar
  17. 17.
    Paudel, D.R., Eric, F., Abidi, H.N.: Evaluation of cotton fiber maturity measurements. Ind. Crops Prod. 45, 435–441 (2013)CrossRefGoogle Scholar
  18. 18.
    Balafar, M.A.: Fuzzy C-mean based brain MRI segmentation algorithms. Artif. Intell. Rev. 41(3), 441–449 (2014)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Sandeep Kumar
    • 1
  • Manish Kashyap
    • 1
  • Akashdeep Saluja
    • 1
  • Mahua Bhattacharya
    • 1
  1. 1.ABV Indian Institute of Information Technology and ManagementGwaliorIndia

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