IoT leads to early diagnosis of calf-killing pneumonia

The development offers dairy producers an opportunity to improve the economies of their farms.
Loukia Papadopoulos
This Holstein calf was included in the study. .jpg
This Holstein calf was included in the study.

Mellisa Cantor 

A team of researchers from Penn State, University of Kentucky and University of Vermont have conceived of an internet of things (IoT) method to monitor dairy calves leading to the earlier diagnosis of calf-killing bovine respiratory disease.

This is according to a press release by the institutions published on Friday.

The research saw data collected from 159 dairy calves at the University of Kentucky using precision livestock technologies. In addition, daily physical health exams were performed by the researchers on the calves. 

Lead researcher Melissa Cantor, assistant professor of precision dairy science in Penn State's College of Agricultural Sciences, said in the statement that the “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.”

She further explained that in the current research “IoT technologies such as wearable sensors and automatic feeders were used to closely watch and analyze the condition of calves.”

The IoT devices were able to extrapolate a significant amount of data by closely monitoring the cows' behavior. Machine learning based on the input from the IoT devices was then adopted by the researchers to make the data easier to interpret.

"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 represents 22 percent of calf mortalities and can severely damage a farm's economy, since the activity of raising dairy calves requires a significant economic investment.

"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 newly-developed system achieved an accuracy of 88 percent for labeling sick and healthy calves and 70 percent of sick calves were predicted four days prior to diagnosis.

"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," Cantor said in the statement.

"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."

The study was published in IEEE Xplore.

Study abstract:

Bovine Respiratory Disease (BRD) is an infection of the respiratory tract that is the leading reason for antimicrobial use in dairy calves and represents 22% of calf mortalities. The costs and effects of BRD can severely damage a farm’s economy, since raising dairy calves is one of the largest economic investments, and diagnosing BRD requires intensive and specialized labor that is hard to find. Precision technologies based on the Internet of Things (IoT), such as automatic feeders, scales, and accelerometers, can help detect behavioral changes before outward clinical signs of BRD. Such early detection enables early treatment, and thus faster recovery, with less long term effects. In this paper, we propose a framework for BRD diagnosis, its early detection, and identification of BRD persistency status using precision IoT technologies. We adopt a machine learning model paired with a cost-sensitive feature selection problem called Cost Optimization Worth (COW). COW maximizes prediction accuracy given a budget constraint. We show that COWis NP-Hard, and propose an efficient heuristic with polynomial complexity called Cost-Aware Learning Feature (CALF). We validate our methodology on a real dataset collected from 159 calves during the preweaning period. Results show that our approach outperforms a recent state-of-the-art solution. Numerically, we achieve an accuracy of 88% for labeling sick and healthy calves, 70% of sick calves are predicted 4 days prior to diagnosis, and 80% of persistency status calves are detected within the first five days of sickness.

Add Interesting Engineering to your Google News feed.
Add Interesting Engineering to your Google News feed.
message circleSHOW COMMENT (1)chevron
Job Board