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

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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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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Parminder Kaur, Husanbir Singh Pannu & Avleen Kaur Malhi

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  1. Parminder Kaur
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  3. Avleen Kaur Malhi
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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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