Could artificial intelligence (AI) help dairy cattle farmers spot sickness in calves before it spreads and becomes deadly?
Simone Silvestri, Ph.D., professor in the Department of Computer Science in the Stanley and Karen Pigman College of Engineering, is part of a research team that been awarded a grant from the U.S. National Science Foundation (NSF) to answer that very question.
The multi-institutional team will use the $1.1 million in funding to advance AI and precision agriculture technologies with the goal of improving the health and welfare of dairy calves.
"It's an honor to receive this NSF award," Silvestri said. "This project highlights how computer science can serve as a bridge across disciplines - combining AI, agriculture and animal science to create practical solutions that improve lives and make a real-world impact."
The project, titled "CalfHealth: An Explainable AI Framework for the Early Detection of Pneumonia in Dairy Calves through Precision Technologies," is funded through NSF's Cyber-Physical Systems program.
The research will be conducted in partnership with Muhammad Abu Baker Siddique, Ph.D., assistant professor in the UK Department of Computer Science, Melissa Cantor, Ph.D., from Penn State University, and Michelle Segovia, Ph.D., from the University of Delaware.
A multi-million dollar challenge
Bovine Respiratory Disease (BRD), an infection of the respiratory tract in cattle, is one of the most significant health and economic challenges facing the dairy industry.
BRD weakens young animals, stunts growth and increases the risk of death - accounting for up to 75% of all calf illnesses and costing the U.S. dairy industry nearly $900 million every year.
Farmers also face the added burden of increased veterinary care and reliance on antibiotics, which are often administered only after visible symptoms appear.
"Bovine Respiratory Disease severely impacts both animal welfare and farm profitability," Silvestri explained. "Our goal is to use low-cost, explainable AI and Internet of Things technologies to help farmers detect illness early and intervene before symptoms become severe."
The solution powered by AI
Through CalfHealth, the team will develop a comprehensive AI-powered system that combines data from wearable sensors, robotic feeders and Wi-Fi-based breathing monitors to detect early signs of illness, such as BRD.
More specifically, the system uses advanced AI to study calves' movement, feeding and breathing patterns - giving farmers early warnings of sickness and clear explanations for each prediction.
The research also explores how farmers decide to use and trust new precision technologies. The results will help develop better ways to encourage farmers in different settings to adopt these tools.
"What makes this project stand out is our interactive chatbot - a tool that helps farmers easily understand what the AI is seeing and test different 'what-if' scenarios to make better, faster decisions for their animals," Silvestri said. "It bridges the gap between technology and the farm, which allows farmers to build trust in the technology guiding their decisions."
Additionally, the project could help curb antibiotic use by enabling earlier, more precise detection and treatment of disease. Researchers say the approach could one day be applied to other livestock health challenges - even emerging outbreaks like avian influenza.
"This research represents the convergence of computer science, agricultural economics and animal science - with the potential for tremendous real-world impact," Silvestri said. "By combining expertise across these disciplines, we're not only advancing technology, but also creating practical solutions that can help farmers make more informed decisions, improve animal welfare and strengthen the overall sustainability of the dairy industry."
The project will also provide hands-on research experiences for undergraduate and graduate students and engage with farmers, veterinarians and industry partners through field demonstrations and participatory workshops.
"These partnerships are essential to ensuring our research translates beyond the lab," Silvestri said. "By working directly with producers and students, we're training the next generation of agricultural innovators, while developing tools that meet the real-world needs of Kentucky's dairy industry."
Research reported in this publication was supported by the U.S. National Science Foundation under Award No. 2529093. The opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.
