Cattle are a major source of animal protein and income for hundreds of thousands of people, with beef and dairy exports exceeding $18 billion in the United States1. Calf raising is costly, mainly due to feed costs and labor. Calves are also at greater risk of mortality and diseases that can affect their long-term health and productivity. The beef herd is at historic lows, and dairy heifer replacement costs have nearly doubled from $2,200 in 2020 to over $4,000 in 2026, emphasizing the importance of calf raising for both sectors. Calf ranches are viewed as a way to improve efficiency and reduce costs, raising about 10 million dairy and beef calves annually2. However, these large populations create logistical challenges, such as health monitoring, which are exacerbated by labor shortages in agriculture. These challenges are not unique to large operations; labor shortages and calf raising costs are major bottlenecks for small and medium cattle enterprises as well. Like many modern technologies, activity-monitoring and deep learning-based image analysis can transform cattle production. However, the wide variety of facilities and terrain has posed a major challenge for developing and adopting such technologies across large, medium, and small calf-raising operations.

This work will benefit U.S. dairy and beef producers raising pre-weaned calves, whose rearing costs have risen while labor shortages limit health monitoring and animal welfare. We propose studying three robotic systems for automated calf health surveillance and disease detection, which could reduce labor, boost calf survival and welfare, lower treatment costs, and increase productivity. Designed for diverse environments, the systems can assist calf ranches, dairies, beef farms, and calf-growing enterprises nationwide.