Fused Segmentation Algorithm for the Detection of Nutrient Deficiency in Crops Using SAR Images

  • V. P. Ananthi
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 24)


The main aim of this chapter is to segment nutrient deficiency in crop images using fuzzy sets (FSs) theory. Fuzziness exists in images as the quantized level of brightness in each pixels. Processing of such uncertain images can be efficiently handled by using fuzzy sets, particularly IFSs. Before initiation of segmentation, crop images taken by satellite are fused to reduce uncertainty in the captured images. Finally, the fused image is processed for segmentation of deficiency in crop images using clustering method based on interval valued intuitionistic fuzzy sets (IVIFSs) with new distance function. Quantitatively, the segmented images are evaluated using precision-recall, ROC curves, and measure for structural similarity index, and their results are compared with results of existing methods. Performance measures reveal that the proposed method seems to segment deficiency better than other comparable methods. Segmentation of nutrient deficiency using the proposed method helps the agriculturist in differentiating various types of disease thereby estimating the rate of fertilization for the crop concerned, which improves economy.


Hesitation degree Segmentation Image fusion Intuitionistic fuzzy set Nutrient deficiency 


  1. 1.
    Kurosu T, Fujita M, Chiba K (1995) Monitoring of rice crop growth from space using the ERS-1 C-band SAR. IEEE Trans Geosci Remote Sens 33(4):1092–1096CrossRefGoogle Scholar
  2. 2.
    Tan CP, Ewe HT, Chuah HT (2011) Agricultural crop-type classification of multi-polarization sar images using a hybrid entropy decomposition and support vector machine technique. Int J Remote Sens 32(22):7057–7071CrossRefGoogle Scholar
  3. 3.
    Teimouri M, Mokhtarzade M, Valadan Zoej MJ (2016) Optimal fusion of optical and sar high-resolution images for semiautomatic building detection. GIsci Remote Sens 53(1):45–62CrossRefGoogle Scholar
  4. 4.
    Massonnet D, Souyris J-C (2008) Imaging with synthetic aperture radar. EPFL, LausanneCrossRefGoogle Scholar
  5. 5.
    Zhou T, Pan J, Zhang P, Wei S, Han T (2017) Mapping winter wheat with multi-temporal sar and optical images in an urban agricultural region. Sensors 17(6):1210CrossRefGoogle Scholar
  6. 6.
    Dong Z, Wang Z, Liu D, Zhang B, Zhao P, Tang X, Jia M (2013) Spot5 multi-spectral (ms) and panchromatic (pan) image fusion using an improved wavelet method based on local algorithm. Comput Geosci 60:134–141CrossRefGoogle Scholar
  7. 7.
    Ananthi VP (2017) Studies on processing of images with Uncertainty using intuitionistic fuzzy sets. Unpublished doctoral dissertation, Gandhigram Rural Institute-Deemed University, IndiaGoogle Scholar
  8. 8.
    Zadeh LA (1965) Information and control. Fuzzy sets 8(3):338–353Google Scholar
  9. 9.
    Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96CrossRefGoogle Scholar
  10. 10.
    Chaira T (2012) A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set. Appl Soft Comput 12(4):1259–1266CrossRefGoogle Scholar
  11. 11.
    Hwang C, Rhee FC-H (2007) Uncertain fuzzy clustering: interval type-2 fuzzy approach to c-means. IEEE Trans Fuzzy Syst 15(1):107–120CrossRefGoogle Scholar
  12. 12.
    Atanassov K, Gargov G (1989) Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst 31(3):343–349MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gonzalez RC, Woods RE (2002) Digital image processing. Prentice Hall of India, New DelhiGoogle Scholar
  14. 14.
    Gonzalez RC, Woods RE (2002) Thresholding. In: Digital image processing, pp. 595–611Google Scholar
  15. 15.
    Bustince H, Barrenechea E, Pagola M, Fernández J (2009) Interval-valued fuzzy sets constructed from matrices: application to edge detection. Fuzzy Sets Syst 160(13):1819–1840MathSciNetCrossRefGoogle Scholar
  16. 16.
    Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98–110CrossRefGoogle Scholar
  17. 17.
    Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer, New YorkzbMATHGoogle Scholar
  18. 18.
    Xu Z, Wu J (2010) Intuitionistic fuzzy c-means clustering algorithms. J Syst Eng Electron 21(4):580–590CrossRefGoogle Scholar
  19. 19.
    Bustince H, Burillo P (1995) A theorem for constructing interval valued intuitionistic fuzzy sets from intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets 1(1):5–16MathSciNetzbMATHGoogle Scholar
  20. 20.
    Pal SK, King R (1981) Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst Man Cybern 11(7):494–500CrossRefGoogle Scholar
  21. 21.
    Chaira T, Ray AK (2009) Fuzzy image processing and applications with MATLAB. CRC Press, Boca RatonzbMATHGoogle Scholar
  22. 22.
    Raol JR (2015) Data fusion mathematics: theory and practice. CRC, New YorkCrossRefGoogle Scholar
  23. 23.
    Ananthi VP, Balasubramaniam P (2015) Image fusion using interval-valued intuitionistic fuzzy sets. Int J Image Data Fusion 6(3):249–269CrossRefGoogle Scholar
  24. 24.
    Meera Gandhi G, Parthiban S, Thummalu N, Christy A (2015) Ndvi: vegetation change detection using remote sensing and gis–a case study of vellore district. Proc Comput Sci 57:1199–1210CrossRefGoogle Scholar
  25. 25.
    Ortiz B, Shaw J, Fulton J (2011) Basics of crop sensing. Alabama cooperative extension system, 1–3Google Scholar
  26. 26.
    McNairn H, Wiseman G, Powers J, Merzouki A, Shang J (2014) Assessment of disease risk in canola using multi-frequency sar: preliminary results. In: EUSAR 2014; Proceedings of 10th European conference on synthetic aperture radar. VDE, Berlin, pp 1–4Google Scholar
  27. 27.
    Canisius F, Shang J, Liu J, Huang X, Ma B, Jiao X, Geng X, Kovacs JM, Walters D (2018) Tracking crop phenological development using multi-temporal polarimetric radarsat-2 data. Remote Sens Environ 210:508–518CrossRefGoogle Scholar
  28. 28.
    Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437CrossRefGoogle Scholar
  29. 29.
    Balasubramaniam P, Ananthi VP (2016) Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy c-means clustering algorithm. Nonlinear Dyn 83(1–2): 849–866CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • V. P. Ananthi
    • 1
  1. 1.Department of MathematicsGobi Arts and Science CollegeErodeIndia

Personalised recommendations