PyTeak

In large-scale poultry farms, disease outbreaks among chickens can lead to massive losses due to rapid contagion. Early detection of sick chickens is critical to prevent the spread of infections, minimize economic damage, and ensure food safety. However, manual monitoring is labor-intensive, inefficient, and prone to human error. A scalable and automated solution is needed to detect and isolate sick chickens before a full-blown outbreak occurs.

Our solution uses computer vision and AI-powered image processing to monitor chicken behavior and physical health indicators continuously. Cameras installed in the farm capture live footage, which is analyzed using deep learning models trained to detect signs of illness such as lethargy, abnormal movement, or changes in physical appearance. Upon detecting a sick chicken, the system sends instant alerts to farm operators, allowing for quick isolation and treatment, thus preventing the spread of disease and improving overall farm productivity.

To leverage advanced computer vision technology to enable real-time, accurate, and automated detection of sick chickens in poultry farms, ensuring early intervention, reducing losses, and promoting healthier farming practices.