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

Abstract

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.

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References

  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 1574–1578

  2. 2.

    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Google 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):205–219

    MathSciNet  MATH  Google Scholar 

  4. 4.

    Chong C-W, Raveendran P, Mukundan R (2003) Translation invariants of Zernike moments. Pattern Recognit 36(8):1765–1773

    MATH  Google 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 561–566

  6. 6.

    Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley, New York

    Google 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 1:5324

    Google Scholar 

  8. 8.

    Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187

    MATH  Google 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):1

    Google Scholar 

  11. 11.

    Kan C, Srinath MD (2002) Invariant character recognition with zernike and orthogonal Fourier-Mellin moments. Pattern Recognit 35(1):143–154

    MATH  Google Scholar 

  12. 12.

    Kaur L, Laxmi V (2016) Detection of unhealthy region of plant leaves using neural network. Dis Manag 1(05):34–42

    Google Scholar 

  13. 13.

    Kharde PK, Kulkarni HH (2016) An unique technique for grape leaf disease detection. IJSRSET 2:343–348

    Google Scholar 

  14. 14.

    Khotanzad A, Yaw HH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Google Scholar 

  15. 15.

    Kim W-Y, Kim Y-S (2000) A region-based shape descriptor using Zernike moments. Signal Process Image Commun 16(1):95–102

    Google 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 377–384

    Google 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):227–237

    Google 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):1894–1908

    MathSciNet  MATH  Google 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):1

    Google 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):33

    Google Scholar 

  21. 21.

    Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans image Process 10(9):1357–1364

    MathSciNet  MATH  Google 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 1:3794

    Google 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 7–11

  24. 24.

    Padol PB, Yadav AA (2016) SVM classifier based grape leaf disease detection. In: Conference on advances in signal processing (CASP), IEEE, pp 175–179

  25. 25.

    Pang Y-H (2005) Enhanced pseudo Zernike moments in face recognition. IEICE Electron Express 2(3):70–75

    Google 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 125:48–55

    Google 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):627–636

    Google 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 1–5

  29. 29.

    Sharma Ashok S, Patel Mitul M, Chaudhari Jitendra P (2013) Palm print identification using Zernike moments. Int J Eng Innov Technol 4:11

    Google Scholar 

  30. 30.

    Shen J, Shen W, Shen D (2000) On geometric and orthogonal moments. Int J Pattern Recognit Artif Intell 14(07):875–894

    MATH  Google Scholar 

  31. 31.

    Sheng Y, Shen L (1994) Orthogonal Fourier-Mellin moments for invariant pattern recognition. JOSA A 11(6):1748–1757

    Google 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):70–74

    Google Scholar 

  33. 33.

    Shu H, Luo L, Coatrieux JL (2014) Derivation of moments invariants. Moments and moments invariants. Science Gate Publishing, Xanthi

    Google Scholar 

  34. 34.

    Singh C, Aggarwal A (2014) A noise resistant image matching method using angular radial transform. Digit Signal Process 33:116–124

    Google 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):1384–1396

    Google 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):663–676

    Google Scholar 

  37. 37.

    Singh C, Walia E, Upneja R (2013) Accurate calculation of Zernike moments. Inf Sci 233:255–275

    MathSciNet  MATH  Google 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 2016:6

    Google Scholar 

  39. 39.

    Teague MR (1980) Image analysis via the general theory of moments\(\ast\). J Opt Soc Am 70(8):920–930

    MathSciNet  Google 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):496–513

    MATH  Google Scholar 

  41. 41.

    Tigadi B, Sharma B (2016) Banana plant disease detection and grading using image processing. Int J Eng Sci 7:6512

    Google 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):887–903

    Google Scholar 

  43. 43.

    Weston J, Watkins C (1998) Multi-class support vector machines. Technical report, Citeseer

  44. 44.

    Wu Y, Shen J (2005) Properties of orthogonal Gaussian–Hermite moments and their applications. EURASIP J Adv Signal Process 2005(4):439420

    MathSciNet  Google Scholar 

  45. 45.

    Xiao B, Linping Li Y, Li WL, Wang G (2017) Image analysis by fractional-order orthogonal moments. Inf Sci 382:135–149

    Google Scholar 

  46. 46.

    Yaakob SN, Saad P, Jamlos MF (2006) On analysis of invariant characteristic for moment invariant techniques. J Eng Res Educ 3:29–42

    Google Scholar 

  47. 47.

    Yap P-T, Paramesran R, Ong S-H (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377

    MathSciNet  Google Scholar 

  48. 48.

    Zhang D, Guojun L (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–19

    Google Scholar 

  49. 49.

    Zhang S, Wang Z (2016) Cucumber disease recognition based on global–local singular value decomposition. Neurocomputing 205:341–348

    Google 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 134:135–141

    Google 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):687–708

    MATH  Google 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):1688–1704

    Google Scholar 

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Correspondence to Husanbir Singh Pannu.

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Kaur, P., Pannu, H.S. & Malhi, A.K. Plant disease recognition using fractional-order Zernike moments and SVM classifier. Neural Comput & Applic 31, 8749–8768 (2019). https://doi.org/10.1007/s00521-018-3939-6

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Keywords

  • Orthogonal moments
  • Fractional-order Zernike moments
  • Image representation
  • SVM