Digital Twin-Driven Teat Localization and Shape Identification for Dairy Cow (Student Abstract)

Dairy owners invest heavily to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce costs, yet obstacles arise when adapting these advanced tools to farming environments. In this work, we applied AI tools to dairy cow teat localization and teat shape classification, obtaining a model that achieves a mean average precision of 0.783. This digital twin-driven approach is intended as a first step towards automating and accelerating the detection and treatment of hyperkeratosis, mastitis, and other medical conditions that significantly burden the dairy industry.

Authors

• Aarushi Gupta Cornell University
• Yuexing Hao Cornell University
• Yuting Yang Cornell University
• Tiancheng Yuan Cornell University
• Matthias Wieland Cornell University
• Parminder S. Basran Cornell University
• Ken Birman Cornell University

Keywords

• Applications Of AI
• Computer Vision
• Dairy Health & Management
• Machine Learning
• Teat Localization
• Teat Shape Identification