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Ant genera identification using an ensemble of convolutional neural networks

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  • Alan Caio R Marques
  • Marcos M. Raimundo
  • Ellen Marianne B. Cavalheiro
  • Luis F. P. Salles
  • Christiano Lyra
  • Fernando J. Von Zuben

Abstract

Works requiring taxonomic knowledge face several challenges, such as arduous identification of many taxa and an insufficient number of taxonomists to identify a great deal of collected organisms. Machine learning tools, particularly convolutional neural networks (CNNs), are then welcome to automatically generate high-performance classifiers from available data. Supported by the image datasets available at the largest online database on ant biology, the AntWeb (www.antweb.org), we propose here an ensemble of CNNs to identify ant genera directly from the head, profile and dorsal perspectives of ant images. Transfer learning is also considered to improve the individual performance of the CNN classifiers. The performance achieved by the classifiers is diverse enough to promote a reduction in the overall classification error when they are combined in an ensemble, achieving an accuracy rate of over 80% on top-1 classification and an accuracy of over 90% on top-3 classification.

Suggested Citation

  • Alan Caio R Marques & Marcos M. Raimundo & Ellen Marianne B. Cavalheiro & Luis F. P. Salles & Christiano Lyra & Fernando J. Von Zuben, 2018. "Ant genera identification using an ensemble of convolutional neural networks," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0192011
    DOI: 10.1371/journal.pone.0192011
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    References listed on IDEAS

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    1. Norman MacLeod & Mark Benfield & Phil Culverhouse, 2010. "Time to automate identification," Nature, Nature, vol. 467(7312), pages 154-155, September.
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    Cited by:

    1. Marco Seeland & Patrick Mäder, 2021. "Multi-view classification with convolutional neural networks," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.

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