Automated marine turtle photograph identification using artificial neural networks, with application to green turtles

Steven Carter, Ian Bell, Jessica Miller, Peter Gash

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    Marine turtle population studies to date have relied on flipper tags or other physical markers to identify individuals previously caught and released. This approach is not entirely successful, motivating us to develop a method for producing an automated turtle photograph identification (photo ID) system. This advancement uses artificial neural networks to compare a digital photo of an individual turtle with a database of turtle photos. Unlike many animals, marine turtles have distinctive facial characteristics, making them ideal candidates for automated photo ID systems. It is easy to gather the large number of good photos of tagged turtles needed to train and test the system; the pattern of interest can be distinguished in a relatively small number of pixels; and it is possible to take suitable photos of both nesting and free swimming turtles. We have used this method to develop a photo ID system, MYDAS, for green turtles (Chelonia mydas), with individual animals identified by their distinctive post-ocular scute patterns. MYDAS has a success rate better than 95% in correctly determining whether a new photo matches a photo in a database, and is now being applied to the green turtle population of Lady Elliot Island in the southern Great Barrier Reef.

    Original languageEnglish
    Pages (from-to)105-110
    Number of pages6
    JournalJournal of Experimental Marine Biology and Ecology
    Volume452
    DOIs
    Publication statusPublished - Mar 2014

    Keywords

    • Artificial neural networks
    • Automated photo identification
    • Bio-monitoring
    • Marine turtle populations

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