Abstract: Venomous snakebites are a significant public health concern in India, where numerous venomous snake species coexist with humans. Prompt and accurate identification of venomous snakes is crucial for effective medical treatment and wildlife management. In this study, we propose a Convolutional Neural Network (CNN)-based approach for venomous snake detection, specifically focusing on the classification of Indian snake species. Our methodology involves assembling a diverse dataset comprising images of various venomous and non-venomous snake species found in India. Through rigorous preprocessing and augmentation techniques, we train a CNN model capable of accurately distinguishing between venomous and non-venomous snakes. Leveraging the deep learning capabilities of CNNs, our model automatically extracts intricate features from snake images and learns discriminative patterns for classification. Evaluation of the model's performance using standard metrics demonstrates its effectiveness in venomous snake detection. These findings underscore the potential of CNN-based approaches in aiding venomous snakebite mitigation efforts and contributing to wildlife conservation endeavours in India and similar biodiversity-rich regions.
Keywords: Venomous snakes, Deep Learning, Classification, Indian snake species.
Cite:
K Ankith, Manoj, Mohammed Nihal, Mohinuddin Razi, Ms. Shwetha CH, "Venomous Snake Detection: A CNN-Based Classification of Indian Snake Species", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13362.
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DOI:
10.17148/IJARCCE.2024.13362
