The project advocates for a transformative method in livestock monitoring by marrying computer vision with intelligent wearable devices. It aims to circumvent the constraints posed by traditional sensor-reliant systems and the prohibitive cost of hardware, suggesting a method for precise behavior tracking and categorization in cows, such as grazing and ruminating, via a computer vision model. This methodology is designed not just to advance behavior monitoring technologies but to also refine predictions of feed intake, conversion efficiency, and methane emissions, thereby offering an economical and scalable alternative to enhance livestock management and promote ecological sustainability.
Undergraduate
Research Goals:
- Data Collection: Create an extensive video dataset documenting diverse cow behaviors as a basis for the computer vision model's development and validation.
- Skeletal Landmark Research: Research prevalent techniques to pinpoint skeletal landmarks in animal videos, a critical step for accurate movement tracking and behavior analysis of cows.
- Model Development: Craft and train a machine learning model employing the identified skeletal landmarks, aiming to predict cow behaviors and improve monitoring and farm management strategies.
None
Robotics (405.652, Lab)