Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.

Autor: Lonati M; College of Science and Engineering, James Cook University, Douglas, Australia.; Marine Data Technology Hub, James Cook University, Townsville, Australia.; AIMS@JCU, Townsville, Australia., Jahanbakht M; Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University, Douglas, Australia., Atkins D; College of Science and Engineering, James Cook University, Douglas, Australia., Bierwagen SL; AIMS@JCU, Townsville, Australia.; AIMS@JCU, Division of Research and Innovation, James Cook University, Townsville, Australia.; Australian Institute of Marine Science, Cape Cleveland, Australia., Chin A; College of Science and Engineering, James Cook University, Douglas, Australia., Barnett A; College of Science and Engineering, James Cook University, Douglas, Australia.; Marine Data Technology Hub, James Cook University, Townsville, Australia.; Biopixel Oceans Foundation, Cairns, Australia., Rummer JL; College of Science and Engineering, James Cook University, Douglas, Australia.
Jazyk: angličtina
Zdroj: Journal of fish biology [J Fish Biol] 2024 Dec; Vol. 105 (6), pp. 1572-1587. Date of Electronic Publication: 2024 Aug 10.
DOI: 10.1111/jfb.15887
Abstrakt: Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs well with some species of elasmobranchs (i.e., sharks, skates, and rays) where individuals have distinctive skin patterns. However, the unique skin patterns used for ID must be stable through time to allow re-identification of individuals in future sampling events. More recently, artificial intelligence (AI) models have substantially decreased the labor-intensive process of matching photos in extensive photo ID libraries and increased the reliability of photo ID. Here, photo ID and AI are used for the first time to identify epaulette sharks (Hemiscyllium ocellatum) at different life stages for approximately 2 years. An AI model was developed to assess and compare the reliability of human-classified ID patterns in juvenile and neonate sharks. The model also tested the persistence of unique patterns in adult sharks. Results indicate that immature life stages are unreliable for pattern identification, using both human and AI approaches, due to the plasticity of these subadult growth forms. Mature sharks maintain their patterns through time and can be identified by AI models with approximately 86% accuracy. The approach outlined in this study has the potential of validating the stability of ID patterns through time; however, testing on wild populations and long-term datasets is needed. This study's novel deep neural network development strategy offers a streamlined and accessible framework for generating a reliable model from a small data set, without requiring high-performance computing. Since many photo ID studies commence with limited datasets and resources, this AI model presents practical solutions to such constraints. Overall, this approach has the potential to address challenges associated with long-term photo ID data sets and the application of AI for shark identification.
(© 2024 The Author(s). Journal of Fish Biology published by John Wiley & Sons Ltd on behalf of Fisheries Society of the British Isles.)
Databáze: MEDLINE
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