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Multiclass Twin Support Vector Machine for plant species identification

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Abstract

Automatic plant species identification is one of the recent and fascinating research area as plants are crucial element of ecosystem. Several plant species exist with significant importance but most of us are unaware of the diversity of plant species available on earth. Their utility to humans starts as oxygen provider, food source, and medicinal compounds essential for medicines that are difficult to develop in right proportions. Being the first living habitants of earth, they have roots far deeper in the ecosystem than any living being. Hence, it is utmost important to develop automatic plant species identification system in which the digital image of the plant is given as input and the label of the plant is determined by the system. In this paper, we have focused on three different aspects (i) Significance of threshold (ii) Feature descriptor that can best describe the leaf images and (iii) Proposed a novel classification method called Multi class Twin Support Vector Machine which in an extension of widely used Twin Support Vector Machine classifier. The performance of the proposed method is compared with SVM, Multi Birth SVM and Probabilistic Neural Network. It is observed that the proposed classifier outperforms all the aforementioned classifiers on publicly available datasets.

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Acknowledgements

We acknowledge University Grant Commission, India for supporting this research by providing fellowship to one of the author, Ms. Neha Goyal. We are also thankful to the reviewers for their valuable and constructive comments and suggestions for the paper. Their inputs have helped us in strengthening the overall quality of the paper.

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Authors and Affiliations

  1. NIT, Kurukshetra, India

    Neha Goyal & Kapil Gupta

  2. NIT, Uttarakhand, India

    Nitin Kumar

Authors
  1. Neha Goyal
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  2. Kapil Gupta
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  3. Nitin Kumar
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Correspondence to Neha Goyal.

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Goyal, N., Gupta, K. & Kumar, N. Multiclass Twin Support Vector Machine for plant species identification. Multimed Tools Appl 78, 27785–27808 (2019). https://doi.org/10.1007/s11042-019-7588-2

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  • DOI: https://doi.org/10.1007/s11042-019-7588-2

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