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

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

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.

Keywords

Orthogonal moments Fractional-order Zernike moments Image representation SVM 

Notes

Compliance with ethical standards

Conflict of interest

There is no conflict of interest involved in this research.

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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

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