By Jeff Mulhollem
Bovine respiratory disease (BRD) a type of pneumonia is the leading cause of death for dairy calves after they become accustomed to food other than their mothers’ milk, resulting in economic losses at over $1 billion annually for the U.S. cattle industry. To detect BRD in dairy calves before they show obvious symptoms and reduce those costly losses, a team of researchers at Penn State, the University of Kentucky and the University of Delaware, funded by a new three-year, $1 million grant from the U.S. National Science Foundation, intend to create a system that uses modern sensing technologies and advanced artificial intelligence (AI).
“We know that early detection can save the lives of calves, reduce antibiotic use and improve farmers’ profitability,” said Melissa Cantor, assistant professor of precision dairy science and lead collaborator at Penn State, in the College of Agricultural Sciences. “So, we will build an explainable, affordable and widely applicable AI system called CalfHealth to detect calf pneumonia early using wearable sensors and robotic smart feeders, among other tools. We’ll also develop an understanding of how to get farmers to adopt and trust such a system that combines computer science, animal science, economics and behavioral science.”
The other co-principal investigators on the project are Simone Silvestri, professor in the Department of Computer Science at the University of Kentucky, and Michelle Segovia, associate professor of food and agribusiness marketing at the University of Delaware. The work is a continuation of research Cantor and Silvestri published two years ago.
The CalfHealth system will incorporate several innovations, according to Cantor. It will feature multimodal detection, meaning it will monitor several data types, combining behavior data from low-cost sensors calves will wear, such as accelerometers that track the young animals’ steps and lying down resting times. The plan is that the system will observe feeding behavior from precision robotic feeders, and it will detect breathing patterns using a non-invasive and inexpensive Wi-Fi-based sensing system.
Source : psu.edu