Animal Detection and Classification to Prevent Human-Animal Conflict Using YOLOv8

In areas where wildlife and the human population exist, human-animal conflict is a rising difficulty. It can result in an economic downturn, injuries, and the loss of services for both parties. With a cutting-edge machine learning-based system that can acknowledge and classify animals in real time and send timely alerts to ensure harmonic, this research seeks to ease and reduce hostilities. With the use of the leading edge YOLOV8 model, which is well-known for its rapidity and authenticity in object identification tasks, the system is made to diagnose a wide range of animal species reliably. To ensure stability, the model is trained on several datasets that span different surroundings and animal behaviors. The system launches an alert mechanism by detecting animals and fostering timely human action to prevent conflicts. This System's interaction with the current surveillance framework can greatly lower the chance of interactions between people and animals, preserving both human safety and nature. This method can be crucial in places where conflicts often occur between humans and wildlife as it is flexible and adaptable to different geographic locations.