Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches
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 ecosystem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1372 |
| _version_ | 1797263898354647040 |
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| author | Ruymán Hernández-López Carlos M. Travieso-González |
| author_facet | Ruymán Hernández-López Carlos M. Travieso-González |
| author_sort | Ruymán Hernández-López |
| collection | DOAJ |
| description | 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 <i>deep learning (DL)</i> 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 <i>EfficientNetV2B3</i> base model, which has a mean <i>Accuracy</i> of 98.75%. |
| first_indexed | 2024-04-25T00:20:19Z |
| format | Article |
| id | doaj.art-a047e69f8c5541a2a71c92319fed5cfc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:20:19Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a047e69f8c5541a2a71c92319fed5cfc2024-03-12T16:54:33ZengMDPI AGSensors1424-82202024-02-01245137210.3390/s24051372Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning ApproachesRuymán Hernández-López0Carlos M. Travieso-González1Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainSignals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, SpainThe 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 <i>deep learning (DL)</i> 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 <i>EfficientNetV2B3</i> base model, which has a mean <i>Accuracy</i> of 98.75%.https://www.mdpi.com/1424-8220/24/5/1372transfer learningdeep learningwildlife recognitionanimal identificationCanarian endemic speciesinvasive alien species |
| spellingShingle | Ruymán Hernández-López Carlos M. Travieso-González Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches Sensors transfer learning deep learning wildlife recognition animal identification Canarian endemic species invasive alien species |
| title | Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches |
| title_full | Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches |
| title_fullStr | Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches |
| title_full_unstemmed | Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches |
| title_short | Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches |
| title_sort | reptile identification for endemic and invasive alien species using transfer learning approaches |
| topic | transfer learning deep learning wildlife recognition animal identification Canarian endemic species invasive alien species |
| url | https://www.mdpi.com/1424-8220/24/5/1372 |
| work_keys_str_mv | AT ruymanhernandezlopez reptileidentificationforendemicandinvasivealienspeciesusingtransferlearningapproaches AT carlosmtraviesogonzalez reptileidentificationforendemicandinvasivealienspeciesusingtransferlearningapproaches |