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Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data

Author

Listed:
  • Li Xu

    (Harvard University)

  • Yili Hong

    (Virginia Tech)

  • Eric P. Smith

    (Virginia Tech)

  • David S. McLeod

    (Mary Baldwin University
    Smithsonian Institution)

  • Xinwei Deng

    (Virginia Tech)

  • Laura J. Freeman

    (Virginia Tech)

Abstract

As is true of many complex tasks, the work of discovering, describing, and understanding the diversity of life on Earth (viz., biological systematics and taxonomy) requires many tools. Some of this work can be accomplished as it has been done in the past, but some aspects present us with challenges which traditional knowledge and tools cannot adequately resolve. One such challenge is presented by species complexes in which the morphological similarities among the group members make it difficult to reliably identify known species and detect new ones. We address this challenge by developing new tools using the principles of machine learning to resolve two specific questions related to species complexes. The first question is formulated as a classification problem in statistics and machine learning and the second question is an out-of-distribution (OOD) detection problem. We apply these tools to a species complex comprising Southeast Asian stream frogs (Limnonectes kuhlii complex) and employ a morphological character (hind limb skin texture) traditionally treated qualitatively in a quantitative and objective manner. We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained. We further demonstrate that the algorithm can successfully classify an image into a new class if the image does not belong to the existing classes. Additionally, we use the larger MNIST dataset to test the performance of our OOD detection algorithm. We finish our paper with some concluding remarks regarding the application of these methods to species complexes and our efforts to document true biodiversity.

Suggested Citation

  • Li Xu & Yili Hong & Eric P. Smith & David S. McLeod & Xinwei Deng & Laura J. Freeman, 2024. "Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 874-894, December.
  • Handle: RePEc:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-023-00592-9
    DOI: 10.1007/s13253-023-00592-9
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    References listed on IDEAS

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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Adam Martin-Schwarze & Jarad Niemi & Philip Dixon, 2017. "Assessing the Impacts of Time-to-Detection Distribution Assumptions on Detection Probability Estimation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 465-480, December.
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