YOLOv8-Based Real-Time System for Breed and Disease Detection in Birds to Enhance Health Monitoring

As the popularity of pet birds continues to grow, there is an increasing demand for advanced health monitoring systems that can provide timely and accurate care. Conventional methods of bird health monitoring rely heavily on manual observation, which can be subjective, error-prone, and reactive rather than proactive. This project proposes an automated system utilizing the YOLOv8 deep learning model to detect bird breeds and identify symptoms of diseases in real-time. By leveraging a large and diverse dataset, this system uses computer vision to continuously monitor birds, enabling early disease detection through high-speed video processing. The system is designed to send alerts to pet owners and veterinarians at the first signs of health issues, facilitating timely interventions and improving overall health outcomes. The inclusion of a feedback loop allows the model to continuously improve its accuracy through user input and retraining with new data. This methodology provides a scalable and adaptable solution for pet bird care, reducing dependence on physical examinations and providing comprehensive monitoring 24 hours a day. The project highlights substantive advances in real-time animal care technology with potential applications across a wide bird species and environmental spectrum.