Chicken Behavior Analysis

I successfully completed a project focused on monitoring chicken behavior through the application of cutting-edge computer vision techniques. Leveraging the power of deep convolutional neural networks, I implemented an approach for chicken detection, to accurately identify and track individual chickens across consecutive video frames. This innovative framework enabled a comprehensive analysis of chicken behavior, providing valuable insights into their movements and interactions within the poultry. I won a prestigious prize for the development and application of this novel technology in the realm of poultry behavior monitoring. This accomplishment not only highlights the effectiveness of my work but also underscores the significance of utilizing AI technologies to enhance our understanding of animal behavior and welfare.

The application of artificial intelligence (AI) in the context of animal welfare has the potential to revolutionize the way we understand, monitor, and enhance the well-being of animals across various environments. One of the primary benefits lies in the ability of AI to provide continuous and real-time monitoring of animal behavior, health, and overall conditions. For instance, AI-powered systems can analyze data from sensors and cameras to detect signs of distress, illness, or abnormal behavior in livestock, enabling prompt intervention and veterinary care. Additionally, AI facilitates the development of predictive models that can anticipate potential issues or health concerns before they escalate. This proactive approach allows for preventive measures to be implemented, ensuring a higher standard of care for animals. The integration of machine learning algorithms can also aid in identifying patterns related to optimal living conditions, feeding schedules, and environmental preferences, contributing to the creation of more tailored and animal-centric practices.

Project GitHub page: https://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis