Automatic identification of the endangered hawksbill sea turtle behavior using deep learning and cross-species transfer learning.

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

SUBMITTER: Jeantet L 

PROVIDER: S-EPMC11698059 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature