Plant disease recognition using fractional-order Zernike moments and SVM classifier

  • Parminder Kaur
  • Husanbir Singh PannuEmail author
  • Avleen Kaur Malhi
Original Article


Orthogonal moments are the projections of image functions onto particular kernel functions. They play vital role in digital image feature extraction being rotation, scaling, translation invariant, robust to image noise and contain minimal information redundancy. These moments are derived from statistically independent orthogonal polynomials which can be continuous or discrete. Most of the modern researches have explored integer-order orthogonal moments, but fractional-order moments are in fact superclass of integer order and more efficient but underrated. This paper proposes fractional-order Zernike moments (FZM) along with SVM to recognize grape leaf diseases. Comparative analysis with integer-order Zernike moments along with other feature selection methods has been explored. FZM–SVM-based technique outperforms other state-of-art techniques yielding \(97.34\%\) at order 30.


Orthogonal moments Fractional-order Zernike moments Image representation SVM 


Compliance with ethical standards

Conflict of interest

There is no conflict of interest involved in this research.


  1. 1.
    Arvacheh EM, Tizhoosh HR (2005) Pattern analysis using Zernike moments. In: Proceedings of the IEEE instrumentation and measurement technology conference, IEEE. IMTC 2005, vol 2, pp 1574–1578Google Scholar
  2. 2.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  3. 3.
    Chen Z, Sun S-K (2010) A zernike moment phase-based descriptor for local image representation and matching. IEEE Trans Image Process 19(1):205–219MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chong C-W, Raveendran P, Mukundan R (2003) Translation invariants of Zernike moments. Pattern Recognit 36(8):1765–1773CrossRefGoogle Scholar
  5. 5.
    Es-saady Y, El Massi I, El Yassa M, Mammass D, Benazoun A (2016) Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. In: 2016 International conference on electrical and information technologies (ICEIT), IEEE, pp 561–566Google Scholar
  6. 6.
    Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley, New YorkCrossRefGoogle Scholar
  7. 7.
    Hanson J, Anandhakrishnan MG, Annette J, Jerin F (2017) Plant leaf disease detection using deep learning and convolutional neural network. Int J Eng Sci 1:5324Google Scholar
  8. 8.
    Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187CrossRefGoogle Scholar
  9. 9.
    Hughes D, Salathé M, et al (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060
  10. 10.
    Kakade NR, Ahire DD (2015) Real time grape leaf disease detection. Int J Adv Res Innov Ideas Educ (IJARIIE) 1(04):1Google Scholar
  11. 11.
    Kan C, Srinath MD (2002) Invariant character recognition with zernike and orthogonal Fourier-Mellin moments. Pattern Recognit 35(1):143–154CrossRefGoogle Scholar
  12. 12.
    Kaur L, Laxmi V (2016) Detection of unhealthy region of plant leaves using neural network. Dis Manag 1(05):34–42Google Scholar
  13. 13.
    Kharde PK, Kulkarni HH (2016) An unique technique for grape leaf disease detection. IJSRSET 2:343–348Google Scholar
  14. 14.
    Khotanzad A, Yaw HH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497CrossRefGoogle Scholar
  15. 15.
    Kim W-Y, Kim Y-S (2000) A region-based shape descriptor using Zernike moments. Signal Process Image Commun 16(1):95–102CrossRefGoogle Scholar
  16. 16.
    Kole DK, Ghosh A, Mitra S (2015) Detection of downy mildew disease present in the grape leaves based on fuzzy set theory. In: Kumar Kundu M, Mohapatra DP, Konar A, Chakraborty A (eds) Advanced computing, networking and informatics, vol 1. Springer, Berlin, pp 377–384Google Scholar
  17. 17.
    Li S, Lee M-C, Pun C-M (2009) Complex Zernike moments features for shape-based image retrieval. IEEE Trans Syst Man Cybern A Syst Hum 39(1):227–237CrossRefGoogle Scholar
  18. 18.
    Liu X, Han G, Jiasong W, Shao Z, Coatrieux G, Shu H (2017) Fractional Krawtchouk transform with an application to image watermarking. IEEE Trans Signal Process 65(7):1894–1908MathSciNetCrossRefGoogle Scholar
  19. 19.
    Mengistu AD, Mengistu SG, Alemayehu DM (2016) Image analysis for Ethiopian coffee plant diseases identification. Int J Biom Bioinf (IJBB) 10(1):1Google Scholar
  20. 20.
    Mohan KJ, Balasubramanian M, Palanivel S (2016) Detection and recognition of diseases from paddy plant leaf images. Int J Comput Appl 144(12):33Google Scholar
  21. 21.
    Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans image Process 10(9):1357–1364MathSciNetCrossRefGoogle Scholar
  22. 22.
    Naik MR, Reddy S, Chandra M (2016) Plant leaf and disease detection by using HSV features and SVM classifier. Int J Eng Sci 1:3794Google Scholar
  23. 23.
    MW Nasrudin, Yaakob SN, Othman RR, Ismail I, Jais MI, Nasir ASA (2014) Analysis of geometric, Zernike and united moment invariants techniques based on intra-class evaluation. In: 2014 5th international conference on intelligent systems, modelling and simulation (ISMS), IEEE, pp 7–11Google Scholar
  24. 24.
    Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: Conference on advances in signal processing (CASP), IEEE, pp 175–179Google Scholar
  25. 25.
    Pang Y-H (2005) Enhanced pseudo Zernike moments in face recognition. IEICE Electron Express 2(3):70–75CrossRefGoogle Scholar
  26. 26.
    Pires RDL, Gonçalves DN, Oruê JPM, Kanashiro WES, Rodrigues JF, Machado BB, Gonçalves WN (2016) Local descriptors for soybean disease recognition. Comput Electron Agric 125:48–55CrossRefGoogle Scholar
  27. 27.
    Revaud J, Lavoué G, Baskurt A (2009) Improving Zernike moments comparison for optimal similarity and rotation angle retrieval. IEEE Trans Pattern Anal Mach Intell 31(4):627–636CrossRefGoogle Scholar
  28. 28.
    Sannakki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural networks. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), IEEE, pp 1–5Google Scholar
  29. 29.
    Sharma Ashok S, Patel Mitul M, Chaudhari Jitendra P (2013) Palm print identification using Zernike moments. Int J Eng Innov Technol 4:11Google Scholar
  30. 30.
    Shen J, Shen W, Shen D (2000) On geometric and orthogonal moments. Int J Pattern Recognit Artif Intell 14(07):875–894CrossRefGoogle Scholar
  31. 31.
    Sheng Y, Shen L (1994) Orthogonal Fourier-Mellin moments for invariant pattern recognition. JOSA A 11(6):1748–1757CrossRefGoogle Scholar
  32. 32.
    Shu H, Luo L, Caatrieux J (2007) Moment-based approaches in imaging 1 basic features [a look at...]. IEEE Eng Med Biol Mag 26(5):70–74CrossRefGoogle Scholar
  33. 33.
    Shu H, Luo L, Coatrieux JL (2014) Derivation of moments invariants. Moments and moments invariants. Science Gate Publishing, XanthizbMATHGoogle Scholar
  34. 34.
    Singh C, Aggarwal A (2014) A noise resistant image matching method using angular radial transform. Digit Signal Process 33:116–124CrossRefGoogle Scholar
  35. 35.
    Singh C et al (2011) Improving image retrieval using combined features of Hough transform and Zernike moments. Opt Lasers Eng 49(12):1384–1396CrossRefGoogle Scholar
  36. 36.
    Singh C, Pooja S, Upneja R (2011) On image reconstruction, numerical stability, and invariance of orthogonal radial moments and radial harmonic transforms. Pattern Recognit Image Anal 21(4):663–676CrossRefGoogle Scholar
  37. 37.
    Singh C, Walia E, Upneja R (2013) Accurate calculation of Zernike moments. Inf Sci 233:255–275MathSciNetCrossRefGoogle Scholar
  38. 38.
    Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016:6CrossRefGoogle Scholar
  39. 39.
    Teague MR (1980) Image analysis via the general theory of moments\(\ast\). J Opt Soc Am 70(8):920–930MathSciNetCrossRefGoogle Scholar
  40. 40.
    Teh C-H, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10(4):496–513CrossRefGoogle Scholar
  41. 41.
    Tigadi B, Sharma B (2016) Banana plant disease detection and grading using image processing. Int J Eng Sci 7:6512Google Scholar
  42. 42.
    Wang X-Y, Miao E-N, Yang H-Y (2012) A new SVM-based image watermarking using Gaussian–Hermite moments. Appl Soft Comput 12(2):887–903CrossRefGoogle Scholar
  43. 43.
    Weston J, Watkins C (1998) Multi-class support vector machines. Technical report, CiteseerGoogle Scholar
  44. 44.
    Wu Y, Shen J (2005) Properties of orthogonal Gaussian–Hermite moments and their applications. EURASIP J Adv Signal Process 2005(4):439420MathSciNetCrossRefGoogle Scholar
  45. 45.
    Xiao B, Linping Li Y, Li WL, Wang G (2017) Image analysis by fractional-order orthogonal moments. Inf Sci 382:135–149CrossRefGoogle Scholar
  46. 46.
    Yaakob SN, Saad P, Jamlos MF (2006) On analysis of invariant characteristic for moment invariant techniques. J Eng Res Educ 3:29–42Google Scholar
  47. 47.
    Yap P-T, Paramesran R, Ong S-H (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377MathSciNetCrossRefGoogle Scholar
  48. 48.
    Zhang D, Guojun L (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–19CrossRefGoogle Scholar
  49. 49.
    Zhang S, Wang Z (2016) Cucumber disease recognition based on global–local singular value decomposition. Neurocomputing 205:341–348CrossRefGoogle Scholar
  50. 50.
    Zhang S, Xiaowei W, You Z, Zhang L (2017) Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135–141CrossRefGoogle Scholar
  51. 51.
    Zhu H, Shu H, Liang J, Luo L, Coatrieux J-L (2007) Image analysis by discrete orthogonal racah moments. Signal Process 87(4):687–708CrossRefGoogle Scholar
  52. 52.
    Zhu H, Shu H, Zhou J, Luo L, Coatrieux J-L (2007) Image analysis by discrete orthogonal dual Hahn moments. Pattern Recognit Lett 28(13):1688–1704CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Parminder Kaur
    • 1
  • Husanbir Singh Pannu
    • 1
    Email author
  • Avleen Kaur Malhi
    • 1
  1. 1.Computer Science and Engineering DepartmentThapar Institute of Engineering and TechnologyPatialaIndia

Personalised recommendations