According to Penn State, a new study monitoring dairy calves with precision technologies based on the “Internet of Things,” or IoT, leads to the earlier diagnosis of calf-killing bovine respiratory disease. The novel approach will offer dairy producers an opportunity to improve the economies of their farms, according to researchers.
This is not your grandfather’s dairy farming strategy, notes lead researcher Melissa Cantor, assistant professor of precision dairy science at Penn State’s College of Agricultural Sciences. The work results from crosscutting collaboration by a team of researchers from Penn State, the University of Kentucky, and the University of Vermont.
Cantor notes that new technology is becoming increasingly affordable, offering farmers opportunities to detect animal health problems soon enough to intervene, saving the calves and the investment they represent.
IoT refers to embedded devices equipped with sensors, processing and communication abilities, software, and other technologies to connect and exchange data with other devices over the Internet. In this study, Cantor explained, IoT technologies such as wearable sensors and automatic feeders were used to watch and analyze the condition of calves closely.
Such IoT devices generate massive data by closely monitoring the cows’ behavior. To make such data easier to interpret and provide clues to calf health problems, the researchers adopted machine learning — a branch of artificial intelligence that learns the hidden patterns in the data to discriminate between sick and healthy calves, given the input from the IoT devices.
“We put leg bands on the calves, which record activity behavior data in dairy cattle, 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.”
Bovine respiratory disease is an infection of the respiratory tract that is the leading reason for antimicrobial use in dairy calves and represents 22 percent of calf mortalities. The costs and effects of the ailment can severely damage a farm’s economy since raising dairy calves is one of the most significant economic investments.
“Diagnosing bovine respiratory disease requires intensive and specialized labor that is hard to find,” Cantor said. “So, precision technologies based on IoT devices such as automatic feeders, scales, and accelerometers can help detect behavioral changes before outward clinical signs of the disease are manifested.”
The study collected data from 159 dairy calves using precision livestock technologies and by researchers who performed daily physical health exams on the calves at the University of Kentucky. Researchers recorded both automatic and manual data-collection results and compared the results.
In findings recently published in IEEE Access, a peer-reviewed open-access scientific journal published by the Institute of Electrical and Electronics Engineers, the researchers reported that the proposed approach can identify calves that develop bovine respiratory disease sooner. Numerically, the system achieved an accuracy of 88 percent for labeling sick and healthy calves. Seventy percent of sick calves were predicted four days before diagnosis, and 80 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. “And nobody had ever looked at that before. We came up with the concept that if these animals actually behave differently, then there’s probably a chance that IoT technologies empowered with machine learning inference techniques could actually identify them sooner before anybody can with the naked eye. That offers producers options.”
