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Advances in Nature and Biologically Inspired Computing pp 141–150Cite as

Sinuosity Coefficients for Leaf Shape Characterisation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

The design of an efficient and automated model for plant recognition and classification will give the possibility to people with little or no botanical knowledge to conduct field work. In this paper a feature for leaf shape analysis is presented: the Sinuosity coefficients. This new feature is based on the sinuosity measures, which is a value expressing the degree of meandering of a curve. The sinuosity coefficients is a vector of sinuosity measures characterising a given shape. The proposed shape feature is translation and scale invariant. This feature achieved a classification rate of 92 % with the Multilayer Perceptron (MLP) classifier, a rate of 91 % with the K-Nearest Neighbour (KNN) and a rate of 94 % with the Naive Bayes classifier, on a set of leaf images from the FLAVIA dataset.

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Authors and Affiliations

  1. University of Kwazulu-Natal, Durban, South Africa

    Jules R. Kala, Serestina Viriri & Deshendran Moodley

Authors
  1. Jules R. Kala
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  2. Serestina Viriri
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  3. Deshendran Moodley
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Corresponding author

Correspondence to Jules R. Kala .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Kala, J.R., Viriri, S., Moodley, D. (2016). Sinuosity Coefficients for Leaf Shape Characterisation. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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