A Review on Snake type detection and venomous detection Using Machine Learning Algorithms

A Review on Snake type detection and venomous detection Using Machine Learning Algorithms

Borale sandeep Arjun 1, Dr. G. J Sahani2

1CSMSS, Chatrpati Shahu College of Engineering, Kanchanwadi Chh. Sambhajinagar

2CSMSS, Chatrpati Shahu College of Engineering, Kanchanwadi Chh. Sambhajinagar

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Abstract -Developing reliable techniques for identifying snake species and determining their venomous nature is critical given the tremendous health danger presented by snakebite venomation in many parts of the world. This study utilizes deep learning to identify snake kinds and categories them as poisonous based on photographs. We offer a multi-task model that employs CNNs to detect and classify snake species as venomous or non-venomous using input photos. The algorithm was trained and tested using snake pictures, yielding good accuracy in species classification and poison prediction. This approach can improve snakebite management by instantly detecting the type of snake and its potential hazard, resulting in faster reaction times during crises. This technique demonstrates the effectiveness of deep learning in identifying animals and improving human safety, with potential for future applications in ecological monitoring and public health.

Key Words: Snake detection, Venom Detection, Farmer deaths, Machine Learning, Feature Extraction.