A Lightweight AI System for Snake Species Identification and Emergency Medical Guidance

Zhang Enhong; Seung Hwan Kang

doi:10.2991/978-94-6239-797-2_14

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A Lightweight AI System for Snake Species Identification and Emergency Medical Guidance

Authors

Zhang Enhong¹, Seung Hwan Kang¹^{, *}

¹Payap University, Chiang Mai, 50000, Thailand

^*Corresponding author. Email: seung_h@payap.ac.th

Corresponding Author

Seung Hwan Kang

Available Online 14 October 2026.

DOI: 10.2991/978-94-6239-797-2_14 How to use a DOI?
Keywords: Snake Species Identification; MobileNetV2; Image Classification
Abstract: This project aims to establish an automated snake species classification system that can reduce the dangers posed during snake bite incidence in rural areas. Even though it is essential to determine the snake species to react accordingly in time, this is a challenging task, especially for laymen. Therefore, this paper proposes a lightweight deep learning model, implemented using the Mobilenetv2 model, which is more conducive and well-balanced between complicity and accuracy on standard computer hardware. This experiment utilized a collection of images of five different snake species obtained from the Kaggle platform, which consists of more than 17,000 images. The proposed method is equipped with rigorous preprocessing of images, involving normalization, edge detection processes using OpenCV to focus on skin patterns of the snakes, and data augmentation approaches, including rotation and zooming techniques, resulting in significantly improved accuracy of the model. Even though the accuracy level was approximately 77% during the primary training process, it significantly improved to 90% after the finetuning and optimization processes of the model.
Copyright: © 2026 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.

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Volume Title: Proceedings of the 2nd International Conference on Lifespan Innovation (ICLI 2026) national Conference on Lifespan Innovation
Series: Advances in Social Science, Education and Humanities Research
Publication Date: 14 October 2026
ISBN: 978-94-6239-797-2
ISSN: 2352-5398
DOI: 10.2991/978-94-6239-797-2_14 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Zhang Enhong
AU  - Seung Hwan Kang
PY  - 2026
DA  - 2026/10/14
TI  - A Lightweight AI System for Snake Species Identification and Emergency Medical Guidance
BT  - Proceedings of the 2nd International Conference on Lifespan Innovation (ICLI 2026) national Conference on Lifespan Innovation
PB  - Atlantis Press
SP  - 126
EP  - 134
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-94-6239-797-2_14
DO  - 10.2991/978-94-6239-797-2_14
ID  - Enhong2026
ER  -

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