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Neural Computing and Applications

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01-01-2019 | Original Article

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

Authors: Parminder Kaur, Husanbir Singh Pannu, Avleen Kaur Malhi

Published in: Neural Computing and Applications | Issue 12/2019

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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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Title: Plant disease recognition using fractional-order Zernike moments and SVM classifier
Authors: Parminder Kaur
Husanbir Singh Pannu
Avleen Kaur Malhi
Publication date: 01-01-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3939-6

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