Segmentation of Crop Nutrient Deficiency Using Intuitionistic Fuzzy C-Means Color Clustering Algorithm

  • P. Balasubramaniam
  • V. P. Ananthi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

Nowadays, crop nutrient deficiency is common in most of the agricultural fields in India due to inadequate and imbalanced fertilization. The main aim of this work is to segment and calculate the percentage of nutrient deficiency which helps to predict the rate of fertilization needed for that crop. In this paper, a new intuitionistic fuzzy c-means color clustering algorithm (IFCM) is introduced using intuitionistic fuzzy sets (IFSs) with its distance function defined from similarity measure. Initially, all the experimental images are preprocessed. Then the preprocessed images are segmented by using the proposed clustering algorithm. The experimental results obtained by IFCM algorithm are compared with fuzzy c-means algorithm (FCM) to show the effectiveness of the proposed algorithm. Comparison results reveal that the proposed segmentation method is capable of segmenting uncertain crop images with nutrient deficiency.

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References

  1. 1.
    Camargo, A., Smith, J.S.: An image processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102, 9–21 (2009)CrossRefGoogle Scholar
  2. 2.
    Mao, H.P., Zhang, Y.C., Hu, B.: Segmentation of crop disease leaf images using fuzzy c-means clustering algorithm. Trans. Chin. Soc. Agric. Eng. 24, 136–140 (2008)Google Scholar
  3. 3.
    Hu, J., Li, D., Chen, G., Duan, Q., Han, Y.: Image segmentation method for crop nutrient deficiency based on fuzzy c-means algorithm. Intell. Autom. Soft Comput. 18, 1145–1155 (2012)CrossRefGoogle Scholar
  4. 4.
    Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets and Syst. 20, 87–96 (1986)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Chaira, T.: A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl. Soft Comput. 11, 1711–1717 (2011)CrossRefGoogle Scholar
  6. 6.
    Bustince, H., Kacpryzk, J., Mohedano, Z.: Intuitionistic fuzzy generators application to intuitionistic fuzzy complementation. Fuzzy Sets and Syst. 114, 485–504 (2000)CrossRefMATHGoogle Scholar
  7. 7.
    Burillo, P., Bustince, H.: Entropy on intuitionistic fuzzy sets and on interval- valued fuzzy set. Fuzzy Sets ans Syst. 78, 305–316 (1996)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    Pelekis, N., Iakovidis, D.K., Kotsifakos, E.E., Kopanakis, I.: Fuzzy clustering of intuitionistic fuzzy data. Int. J. Bus. Intell. Data Min. 3, 45–65 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • P. Balasubramaniam
    • 1
  • V. P. Ananthi
    • 1
  1. 1.Department of MathematicsGandhigram Rural Institute - Deemed UniversityIndia

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