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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)

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

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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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