Real-Time Snake Prediction and Detection System Using the YOLOv5

Snakebite envenomation is one of the major challenges in health, primarily viewed in snake-populous areas. The actual identification of the species of snake is integral to getting timely and effective treatment. This study uses the real-time snake prediction and detection system-based object detection algorithm using YOLOv5, especially for the detection and classification of species at runtime. It is based on a dataset of many species of snakes trained to differentiate between poisonous and harmless snakes. It provides actionable insights with the integration of a notification mechanism: sending alerts through WhatsApp, thereby allowing effective response in emergencies. It would be useful in public safety, ecological monitoring, and wildlife conservation as well. In breaking up dataset preprocessing, model architecture, training methodology, and the deployment framework, vividly illustrate the functionality of the system in saving lives and advancing automated wildlife identification systems.