Michigan State Researchers Use AI to Detect Calf Pain

New machine learning technology analyzes animal behavior to identify pain during standard farm procedures.

Updated on Oct. 21, 2026 in Veterinary Medicine

Michigan State Researchers Use AI to Detect Calf Pain

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Michigan State University researchers are developing an artificial intelligence application designed to detect and measure pain in dairy calves. The system uses video analytics and behavioral data to automate pain assessment during routine procedures.

Why it matters

This technology aims to help dairy producers better understand animal welfare, allowing for more individualized care. By identifying pain levels more accurately, researchers expect to see improvements in medication use, disease risk, and labor management.

Researchers are utilizing video analytics and behavioral data collected during hot-iron disbudding, a procedure performed to prevent horn growth in cattle. This data serves as the foundation for machine learning models to assess pain.

The players

Michigan State University

This is a public research university in East Lansing, Michigan, that frequently leads agricultural and veterinary studies.

The details

The project uses machine learning to create automated systems that provide direct measurements of physical distress in calves. Researchers are also investigating how these monitoring applications could eventually be adapted for use with beef cattle, pigs, and horses.

October 21, 2026: Researchers shared updates on the AI pain detection project.

Culture Shift

This development represents a broader trend toward digitizing animal welfare monitoring to meet modern ethical standards in agriculture. It marks a transition from subjective visual observation toward data-driven care in livestock management.

This technology could eventually lead to more efficient herd management for Michigan dairy farmers by allowing for targeted animal care. Owners may see reduced veterinary costs and lower disease rates as the system helps in the early identification of physical distress.

The takeaway

Advancements in machine learning are making it possible to quantify animal welfare in ways that were previously dependent on human intuition. Farmers can look toward integrating these digital monitoring tools to streamline their care routines and improve animal health outcomes.