Skip to main content
SpringerLink
Log in

Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper introduces a new segmentation technique to segment incomplete nutrient-deficient crop images by imputing missing pixels. Usually, each image contains pixels holding information about intensity, but sometimes image can miss some pixels (that is, the pixel without an appropriate intensity value). An image with missing pixels is called an incomplete image. Intuitionistic fuzzy clustering algorithm is a useful tool for clustering images, but it is not directly applicable for incomplete images. For example, segmentation of nutrient deficiency portions in the presence of missing pixels leads to error in segmentation. Crop images with nutrient deficiency might have missing pixels due to inherent defects in imaging equipment or due to environmental conditions. In this paper, nutrient deficiency in crop images is segmented after imputation of missing pixels based on intuitionistic fuzzy C-means color clustering algorithm. Experiments are performed on various incomplete crop images. Through the derived results and evaluated comparisons with other methods, namely K-means, fuzzy K-means, principal component analysis, regularized expectation maximization and fuzzy C-means algorithms, it has been proven that the proposed method performs well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Du, J.X., Huang, D.S., Wang, X.F., Gu, X.: Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Trans. Inst. Meas. Control 28, 275–284 (2006)

    Article  Google Scholar 

  2. Camargo, A., Smith, J.S.: An image processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102, 9–21 (2009)

    Article  Google Scholar 

  3. Cunha, J.B.: Applications of image processing techniques in the characterization of plant leafs. In: Proceedings of IEEE International Symposium on Industrial Electronics, pp. 612–616 (2003)

  4. Runtz, K.J.: Electronic recognition of plant species for machine vision sprayer control system. In: Proceedings of IEEE Western Canada Conference on Computer, Power and Communication Rural Environment (WESCANEX’ 91), pp. 84–88 (1991)

  5. Dave, S., Runtz, K.: Image processing methods for identifying species of the plants. In: Proceedings of IEEE Western Canada Conference on Computer, Power and Communication Rural Environment (WESCANEX’ 95), pp. 403–408 (1995)

  6. Banish, D.A., Braik, M., Ahamad, S.B.: A frame work for detecting and classification of plant leaf and stem disease. In: International Conference on Signal and Image Processing pp. 113–118 (2010)

  7. 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 

  8. 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)

    Article  Google Scholar 

  9. Dixon, J.K.: Pattern recognition with partly missing data. IEEE Trans. Syst. Man Cybern. 9, 617–621 (1979)

    Article  Google Scholar 

  10. Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data, 2nd edn. Wiley-Interscience, New York (1981)

    MATH  Google Scholar 

  11. Hathway, R.J., Bezdek, J.C.: Fuzzy C-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. 31, 735–744 (2001)

    Article  Google Scholar 

  12. Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)

    Article  Google Scholar 

  13. Chaira, T., Anand, S.: A novel intuitionistic fuzzy approach for tumor/hemorrhage detection in medical images. J. Sci. Ind. Res. 70, 427–434 (2011)

    Google Scholar 

  14. Shyu, K.K., Pham, V.T., Tran, T.T., Lee, P.L.: Global and local fuzzy energy-based active contours for image segmentation. Nonlinear Dyn. 67, 1559–1578 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Shyu, K.K., Tran, T.T., Pham, V.T., Lee, P.L., Shang, L.J.: Fuzzy distribution fitting energy-based active contours for image segmentation. Nonlinear Dyn. 69, 295–312 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. Jameel, R., Manza, R.: Color image segmentation using wavelet. Int. J. Appl. Inf. Syst. 1, 1–4 (2012)

    Google Scholar 

  17. Juang, L.H., Nu, M.N.: MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement 43, 941–949 (2010)

    Article  Google Scholar 

  18. Lim, Y.W., Lee, S.U.: On the color image segmentation algorithm based on the thresholding and fuzzy C-means technique. Pattern Recognit. 23, 935–952 (1990)

    Article  Google Scholar 

  19. Chaira, T.: A novel intuitionistic fuzzy C-means clustering algorithm and its application to medical images. Appl. Soft Comput. 11, 1711–1717 (2011)

    Article  Google Scholar 

  20. Wan, M., Li, L., Xiao, J., Yang, Y., Wang, C., Guo, X.: CAS based clustering algorithm for Web users. Nonlinear Dyn. 61, 347–361 (2010)

    Article  MATH  Google Scholar 

  21. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  22. Balasubramaniam, P., Ananthi, V.P.: Image fusion using intuitionistic fuzzy sets. Inf. Fusion 20, 21–30 (2014)

    Article  Google Scholar 

  23. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  24. Khatibi, V., Montazer, G.A.: Intuitionistic fuzzy sets vs fuzzy set application to medical pattern recognition. Artif. Intell. Med. 47, 43–52 (2009)

    Article  Google Scholar 

  25. Zhang, Z.S., Chen, Q.: On clustering approach to intuitionistic fuzzy sets. Control Decis. 22, 882–888 (2007)

    MathSciNet  MATH  Google Scholar 

  26. Perc, M.: The Matthew effect in empirical data. J. R. Soc. Interface 11, 1–15 (2014)

    Article  Google Scholar 

  27. Zhang, J., Zhang, C., Chu, T., Perc, M.: Resolution of the stochastic strategy spatial prisoners dilemma by means of particle swarm optimization. PLoS One 6, e21787 (2011)

    Article  Google Scholar 

  28. Atanassov, K.T., Stoeva, S.: Intuitionistic fuzzy set. In: Proceedings of Polish Symposium on Interval Fuzzy Mathematics, pp. 23–26. Poznan

  29. Sugeno, M.: Fuzzy measure and fuzzy intergrals. In: Gupta, M., Sardis, G.N., Gains, B.R. (eds.) Fuzzy Automata and Decision Process, pp. 82–102. North Holland, Amesterdam (1977)

    Google Scholar 

  30. Kim, D.W., Lee, K.W., Lee, D.: A novel initialization scheme for the fuzzy C-means algorithm for colour clustering. Pattern Recognit. Lett. 25, 227–237 (2004)

    Article  Google Scholar 

  31. Chein, C.L., Tseng, D.C.: Colour image enhancement with exact HSI color model. Int. J. Innov. Comput. Inf. Control 7, 6691–6710 (2011)

    Google Scholar 

  32. Pelekis, N., Iakovidis, D.K., Kotsifakos, E.E., Kopanakis, I.: Fuzzy clustering of intuitionistic fuzzy data. Int. J. Busin. Intell. Data Min. 3, 45–65 (2008)

    Article  Google Scholar 

  33. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45, 427–437 (2009)

    Article  Google Scholar 

  34. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  35. Nelson, R.R.: Climate effects on the incidence of plant diseases: the epidemiology of southern corn leaf blight. In: Proceedings of the Symposium on Climate and Rice, pp. 393–415. Philippines (1976)

  36. Mohamed, M.H., Hashem, A.A., Abdelsamea, M.M.: Scalable algorithms for missing value imputation. Int. J. Comput Appl. 87, 35–42 (2014)

  37. Li, D., Deogun, J., Spaulding, W., Shuart, B.: Towards missing data imputation: a study of fuzzy k-means clustering method. In: Tsumoto, S., Slowinski, R., Komorowski, J., Grzymala-Busse, J.W. (eds.) Rough Sets and Current Trends in Computing, pp. 573–579. Springer, Berlin (2004)

  38. Ilin, A., Raiko, T.: Practical approaches to principal component analysis in the presence of missing values. J. Mach. Learn. Res. 11, 1957–2000 (2010)

    MathSciNet  MATH  Google Scholar 

  39. Schneider, T.: Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Climate 14, 853–871 (2001)

    Article  Google Scholar 

  40. Huang, Z.K., Liu, D.H.: Segmentation of color image using EM algorithm in HSV color space. In: Proceedings of International Conference on Information Acquisition, pp. 9–11. Korea (2007)

Download references

Acknowledgments

This work is supported by the University Grants Commission-Basic Science Research (UGC-BSR)—Research fellowship in Mathematical Sciences-2013–2014, Govt. of India, New Delhi. This work is also supported by the Engineering Faculty of the University of Malaya under Grant No. UM.C/HIR/MOHE/ENG/42. The authors wish to thank all the reviewers and editors for their fruitful comments and suggestions for significant improvement of the manuscript.

Author information

Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural Institute-Deemed University, Gandhigram, Dindigul, Tamil Nadu, India

    P. Balasubramaniam & V. P. Ananthi

Authors
  1. P. Balasubramaniam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. P. Ananthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Balasubramaniam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balasubramaniam, P., Ananthi, V.P. Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm. Nonlinear Dyn 83, 849–866 (2016). https://doi.org/10.1007/s11071-015-2372-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-015-2372-y

Keywords

Search

Navigation