In Oklahoma, the beef industry represents a significant agricultural commodity. However, mortality from stress in feedlot cattle threatens the sustainability of the industry and is intensified by extreme weather conditions and trends to select animals for high growth rates. No resources are currently available for producers to identify animals prone to stress and related mortality. Researchers in CAS and the Department of Animal and Food Science were awarded a United States Department of Agriculture (USDA) Agriculture and Food Research Initiative (AFRI) grant program of the National Institute of Food and Agriculture: Inter-Disciplinary Engagement in Animal Systems (IDEAS) to develop tools that help identify these animals. “The purpose of this research is to develop precision livestock management tools that use artificial intelligence and machine learning to identify cattle that are susceptible to stress,” said Dr. Ashley Railey, sociology Dr. Ashley Railey visits with a child while performing survey data collection for a previous livestock-related research project in Tanzania. assistant professor and project co-principal investigator. “Part of this research, and that which is augmented by sociology, is understanding how to disseminate the research to producers and how the tools can best be implemented amongst producers.” Each co-investigator brings preliminary data to begin the project, with Railey’s initial research suggesting producers value early detection when avoiding major economic impacts, such as mortality of animals. By working associated with the new technologies can be reduced by allowing producers to evaluate whether the technology is appropriate for their feedlots. “Artificial intelligence is used to sort through the large amounts of data from tracking animal behavior over time on the farm feedlot,” Railey said. “We will receive data from sensors on the animals and of the climate, video and audio of the farm or feedlot.” Led by Dr. NingWang, biosystems and agricultural engineering professor, and Dr. Sathyanarayanan Aakur, computer science assistant professor, the AI framework will continuously monitor data and derive insights for assessing stress levels by tracking animal behavior over time. “In the AI field, limited studies have utilized vision,” Railey said. “In this research, Dr. Aakur’s expertise in vision AI — using images to predict an outcome — is very unique.” MACHINE LEARNING IN ACADEMIA To discover more about what CAS faculty are doing in the world of AI and machine learning research, listen to a recent episode of the College of Arts and Sciences’ podcast wherein Center for the Humanities’ members Dr. Rosemary Avance, Dr. Heather Stewart and Richard and other machine learning bots on academia: okla.st/cas-podcast-ai 38 CONNECT 2023
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