AI-Driven Snake Species Identification: Using Deep Learning and Geographic Filtering

DOI

10.2991/978-94-6463-831-8_37How to use a DOI?

Keywords

Snake species identification; MobileNetV2; Geographic Filtering

Abstract

In this research, it proposes a snake species identification AI model based on MobileNetV2 to aid in the prompt medical treatment of snake bites. The dataset consists of 1014 reptile images comprising images from 10 snake species. The performance per-class was measured using precision, recall and F1-score. The highest validation accuracy reached at the 25 epochs training is 87% with a macro average precision of 0.88, recall of 0.912, and F1-score of 0.90. The weighted averages were 0.874 for precision, 0.863 for recall, and 0.886 for F1-score. This technique shows great promise for instantaneous snake identification and venom exposure risk reduction.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Zhang Enhong
AU  - Wanas Srimaharaj
PY  - 2025
DA  - 2025/08/31
TI  - AI-Driven Snake Species Identification: Using Deep Learning and Geographic Filtering
BT  - Proceeding of the 1st International Conference on Lifespan Innovation (ICLI 2025)
PB  - Atlantis Press
SP  - 306
EP  - 313
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-831-8_37
DO  - 10.2991/978-94-6463-831-8_37
ID  - Enhong2025
ER  -