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Hernández-López, Ruymán and Travieso-González, Carlos M. (2024) Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches. Sensors, 24 (5). p. 1372. ISSN 1424-8220

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Abstract

The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.

Item Type: Article
Subjects: GO for ARCHIVE > Multidisciplinary
Depositing User: Unnamed user with email support@goforarchive.com
Date Deposited: 21 Feb 2024 05:41
Last Modified: 21 Feb 2024 05:41
URI: http://eprints.go4mailburst.com/id/eprint/2151

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