14 Nebraska Cattleman October 2023 a change in behavior, such as a cow not coming to feed or leaving water for an extended period, it flags this as a potential problem and notifies management. The key is first to collect quality data and develop clever algorithms; when trained properly, an algorithm can detect patterns, which may be invisible to the human eye. CONTINUED ON PAGE 16 AI IN CATTLE MANAGEMENT • CONTINUED FROM PAGE 12 For example, one avenue of collecting livestock behavior data is through continuous recording. Using this type of dataset, the algorithm can be trained to recognize certain alarming patterns and flag such behavior next time it “sees” that pattern. “If a cow comes in with a limp or is favoring a leg, that's fairly easy to identify, using some sort of AI algorithm with either a camera or continuous recording,” Xiong says. “By labeling that behavior, the AI can associate that limping behavior to a sort of lameness problem, and then the AI can learn from that. So, then the next time the camera sees that a cow is limping, it will notify the producer that the animal is having trouble and should be checked on.” This eliminates the need to have eyes on the cattle at all times. By implementing AI technology into cattle management, producers could potentially see a decrease in labor costs, or given labor-constraints, even an improvement in production performance. Xiong believes AI can play a big role in cattle management. “For example, traditionally, this type of management is done by riding horseback to check the animals,” she says. “If you have either a huge feedlot or an extensive rangeland situation, you're going to take a whole day just to do this. AI can potentially help make tasks like this timelier or more efficient, noticing illness or injury before it's too late.” The biggest and most promising AI application would be to identify or predict cattle health issues. One of the foremost health threats to cattle in both feedlot and rangeland scenarios is bovine respiratory disease (BRD). Xiong is hopeful that through extensive data collection where behavioral and biomarker indicators can be associated to BRD, AI can be trained to identify early BRD indicators. “I am fairly optimistic that soon we can use such AI technology to, at the very least, identify and hopefully predict the onset of BRD,” she says. “Another A computer screen shows virtual fence management in real-time. Photo courtesy of Chandra Spangler. A cow equipped with a virtual fence collar grazes in the Nebraska Sandhills. Photo courtesy of Yijie Xiong.
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