Artificial intelligence improves the identification of fur seals recorded at Southern Brazilian coast

Authors

  • Carlos Oliveira Universidade do Vale do Rio dos Sinos
  • Camila Spaniol Klagenberg Laboratório de Ecologia de Mamíferos, Universidade do Vale do Rio dos Sinos, Av. Unisinos, 950, São Leopoldo – RS
  • Maurício Roberto Veronez Advanced Visualization & Geoinformatics Laboratory (VizLab), Universidade do Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo – RS https://orcid.org/0000-0002-5914-3546
  • Larissa Rosa Oliveira Laboratório de Ecologia de Mamíferos, Universidade do Vale do Rio dos Sinos, Av. Unisinos, 950, São Leopoldo – RS https://orcid.org/0000-0002-5735-3697

DOI:

https://doi.org/10.5597/lajam00334

Abstract

The genus Arctocephalus represents the group of fur seals that mainly inhabit the Southern Hemisphere. In general, Arctocephalus species are extremely similar in appearance, often making it very difficult to impossible to distinguish them only by characteristics of their external morphology. In this context, it is important to find new tools to differentiate them, especially in locations outside of their traditional distribution area, such as Brazilian waters, in order to take appropriate actions for their management. This study proposes the use of an artificial intelligence method, based on machine learning and convolutional neural networks, to classify and identify three species of southern fur seals by analysing 121 facial images from living specimens of Arctocephalus australis, A. gazella, and A. tropicalis found on the Brazilian coast. The image database

Keywords:

citizen science convolutional neural networks, machine learning, pinnipeds.

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Published

2024-11-27

How to Cite

Oliveira, C., Klagenberg, C. S., Veronez, M. R., & Oliveira, L. R. (2024). Artificial intelligence improves the identification of fur seals recorded at Southern Brazilian coast. Latin American Journal of Aquatic Mammals, 19(2), 182-189. https://doi.org/10.5597/lajam00334

Issue

Vol. 19 No. 2 (2024)

Section

Articles

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