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


Carter, SJB and Bell, IP and Miller, JJ and Gash, PP, Automated marine turtle photograph identification using artificial neural networks, with application to green turtles, Journal of Experimental Marine Biology and Ecology, 452 pp. 105-110. ISSN 0022-0981 (2014) [Refereed Article]

DOI: doi:10.1016/j.jembe.2013.12.010


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 nowbeing applied to the green turtle population of Lady Elliot Island in the southern Great Barrier Reef.

Item Details

Item Type:Refereed Article
Keywords:artificial neural networks, automated photo identification, bio-monitoring, marine turtle populations
Research Division:Environmental Sciences
Research Group:Climate change impacts and adaptation
Research Field:Climate change impacts and adaptation not elsewhere classified
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Carter, SJB (Dr Steven Carter)
ID Code:88361
Year Published:2014
Web of Science® Times Cited:15
Deposited By:Mathematics and Physics
Deposited On:2014-01-30
Last Modified:2017-10-31

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