ArticleThe Journal of experimental biology2024
Automatic identification of the endangered hawksbill sea turtle behavior using deep learning and cross-species transfer learning.
Article in The Journal of experimental biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife.iScience · 2026Article
- Classifying behaviors from animal-borne cameras using machine learning: automated identification of breathing events in sea turtles.The Journal of experimental biology · 2026Article
- Review
- Accelerometry is a valid method to distinguish between healthy and 6-OHDA-lesioned parkinsonian rats.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The accelerometer, an onboard sensor, enables remote monitoring of animal posture and movement, allowing researchers to deduce behaviors. Despite the automated analysis capabilities provided by deep learning, data scarcity remains a challenge in ecology. We explored transfer learning to classify behaviors from acceleration data of critically endangered hawksbill sea turtles (Eretmochelys imbricata). Transfer learning reuses a model trained on one task from a large dataset to solve a related task. We applied this method using a model trained on green turtles (Chelonia mydas) and adapted it to identify hawksbill behaviors such as swimming, resting and feeding. We also compared this with a model trained on human activity data. The results showed an 8% and 4% F1-score improvement with transfer learning from green turtle and human datasets, respectively. Transfer learning allows researchers to adapt existing models to their study species, leveraging deep learning and expanding the use of accelerometers for wildlife monitoring.
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Registered trials
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