Artificial intelligence could hasten detection of gait problems in horses

The technology will use artificial intelligence to analyze high-definition video of horses as they move.

Artificial intelligence will be used in research involving horses in a bid to identify mobility issues in livestock faster and more accurately.

Scientists with the University of Florida will use the technology to analyze high-definition video of horses as they move. They hope the findings will ultimately help a range of livestock, improving both animal health and production.

Geneticist Samantha Brooks and her colleagues at the University of Florida researchers have received a $US49,713 grant from the Agricultural Genome to Phenome Initiative for their research, which will combine machine learning with gait analyzes to speed assessments of livestock mobility.

Brooks, an associate professor of equine physiology, provides an example of how this technology can help. In horses, a veterinarian can do a basic lameness exam in about 15 minutes.

“Our long-term goal is to build an automated pipeline that could produce results nearly in real-time, just seconds after the animal passes by the camera,” she says. “This pilot project is a first step toward that goal.”

Brooks and her colleagues work primarily with horses because they are an excellent model for locomotion and because scientists can gather a lot of data quickly.

They are already working with about 2000 video clips of moving horses. Brooks credits the hard work of graduate student Madelyn Smythe and the generosity of hundreds of central Florida horse owners for the footage.

Digital labels applied to this video of a horse in motion using new Artificial Intelligence programs will soon help UF/IFAS researchers detect limb problems in horses and other livestock. Source: University of Florida/YouTube

“The large library of video will enable construction of accurate models to track the animals’ movement in the video frame,” Brooks said. “Although we’ve started with the horse, what we learn here will translate to similar models for other four-legged farm animals.”

For their project, they will also build artificial intelligence models to analyze videos of cattle, swine and small ruminants.

As they review the data, researchers will look at such horse traits as stance time, stride length and limb extension. In cattle and swine, scientists are more interested in asymmetry and postures that indicate pain for abnormal function in one or more limbs.

Brooks said she wants to help other scientists and owners of farm animals because artificial intelligence, while helpful, is not always intuitive.

“Artificial intelligence approaches can accelerate our ability to measure complex locomotor traits in livestock, with better accuracy than the human eye,” she says. “Yet, AI tools often are not biologist-friendly, nor are they ready for challenging, on-farm applications.

“To cope with these issues, we hope to adapt and assemble existing AI methodologies into an analysis package accessible to scientists of diverse backgrounds and deployable in a variety of livestock management settings.”

For instance, the technology could detect lameness in livestock as they pass a camera each day. Envision, for example, dairy cattle coming in for milking, alerting the farmer to potentially serious health issues early on, and with less effort from farm staff.

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