Real-Time Birds Breed Classification and Health Monitoring Using YOLOv8

The advancement of computer vision technologies has revolutionized pet health management by enabling real-time detection systems that can classify pet breeds and identify disease symptoms with high accuracy. A real-time detection system using the YOLOv8 algorithm offers significant potential for automated breed classification of pet birds and the detection of disease symptoms; aiding veterinarians in health monitoring and helping pet owners better manage their pets' well-being. By recognizing the general characteristics and features from images or live video feeds from multiple sources, this system can accurately identify the breed of a pet and detect visible signs of common illnesses such as avian influenza. This capability enables early intervention, allowing for a more proactive approach to pet health management. The integration of computer vision techniques overcomes challenges associated with manual assessments, enhancing the accuracy and consistency of health monitoring. Additionally, the system's design incorporates adjustable parameters to accommodate a wide variety of pet breeds and health conditions, ensuring adaptability and comprehensive coverage for effective pet health management.