Precision leads to early diagnosis
Automatic feeders record calf-feeding behaviors such as the number of visits and liters of consumed milk.
'Internet of things' devices such as automatic feeders can help detect behavioral changes before outward clinical signs of disease appear.
Monitoring dairy calves with precision technologies based on "internet of things" leads to earlier diagnosis of bovine respiratory disease, according to results from a new study. The new technologies are becoming increasingly affordable. That offers farmers opportunities to detect animal-health problems soon enough to intervene, saving calves and the investment they represent, said Melissa Cantor, an assistant professor of precision dairy science at Pennsylvania State University.
Internet of things refers to embedded devices featuring sensors, processing and communication abilities, software and other technologies to connect and exchange data with other devices over the internet. In the Pennsylvania State University study, internet-of-things technologies such as wearable sensors and automatic feeders were used to watch and analyze the condition of calves.
Such devices generate a huge amount of data by monitoring animal behavior. To make the data easier to interpret and provide clues to calf-health problems, the researchers adopted machine learning. That’s a branch of artificial intelligence that learns hidden patterns in data to discriminate between sick and healthy calves, given input from internet of things devices.
“We put leg bands on the calves, which record activity-behavior data such as the number of steps and lying time,” Cantor said. “And we used automatic feeders, which dispense milk and grain, and record feeding behaviors, such as the number of visits and liters of consumed milk. Information from those sources signaled when a calf’s condition was on the verge of deteriorating.”
Diagnosing bovine respiratory disease requires intensive and specialized labor that’s difficult to find, she said.
“So precision technologies based on internet-of-things devices such as automatic feeders, scales and accelerometers can help detect behavioral changes before outward clinical signs of the disease are manifested,” she said.
She and her colleagues from Pennsylvania State University collected data from 159 dairy calves using precision livestock technologies. University of Kentucky researchers performed daily physical health exams on the calves. Researchers recorded both automatic-data-collection results and manual-data-collection results, and compared the two.
The researchers reported that the approach is able to identify more quickly calves that developed bovine respiratory disease. Numerically the system achieved an accuracy of 88 percent for labeling sick and healthy calves. Seventy percent of sick calves were predicted four days prior to diagnosis. Eighty percent of calves that developed a chronic case of the disease were detected within the first five days of sickness.
“We were really surprised to find out that the relationship with the behavioral changes in those animals was very different than animals that got better with one treatment,” she said. “We came up with the concept that if these animals actually behave differently, then there's probably a chance that internet-of-things technologies empowered with machine-learning-inference techniques could actually identify them sooner, before anybody can with the naked eye. That offers producers options.”
Enrico Casella of the University of Wisconsin-Department of Animal and Dairy Science was one of the researchers contributing to the study. Visit animalscience.psu.edu – search “Melissa Cantor” – for more information.
Jeff Mulhollem is a science writer for the Pennsylvania State University.
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