Precision technology, machine learning lead to early diagnosis of calf pneumonia
Wearable sensors, automatic feeders yield clues about onset of bovine respiratory disease

Date:
July 14, 2023
Source:
Penn State
Summary:
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, according to a new
study. The novel approach -- a result of crosscutting -- will
offer dairy producers an opportunity to improve the economies of
their farms, according to researchers.


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FULL STORY ========================================================================== 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, according to a new study. The novel approach -- a
result of crosscutting collaboration by a team of researchers from Penn
State, University of Kentucky and University of Vermont -- 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
in Penn State's College of Agricultural Sciences. Cantor noted 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 closely watch and analyze the condition of calves.

Such IoT devices generate a huge amount of 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% 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 largest
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." In the study, data was collected 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 data-collection results
and manual data-collection results and compared the two.

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 is able to identify calves that developed bovine respiratory
disease sooner. Numerically, the system achieved an accuracy of 88%
for labeling sick and healthy calves.

Seventy percent of sick calves were predicted four days prior to
diagnosis, and 80% 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." Contributing to the research were: Enrico Casella, Department of Animal and Dairy Science, University of Wisconsin-Madison; Melissa Cantor, Department of Animal Science, Penn State University;
Megan Woodrum Setser, Department of Animal and Food Sciences, University
of Kentucky; Simone Silvestri, Department of Computer Science, University
of Kentucky; and Joao Costa, Department of Animal and Veterinary Sciences, University of Vermont.

This work was supported by the U.S. Department of Agriculture and the
National Science Foundation.

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Story Source: Materials provided by Penn_State. Original written by Jeff Mulhollem. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Enrico Casella, Melissa C. Cantor, Megan M Woodrum Setser, Simone
Silvestri, Joao H.C. Costa. A Machine Learning and Optimization
Framework for the Early Diagnosis of Bovine Respiratory
Disease. IEEE Access, 2023; 1 DOI: 10.1109/ACCESS.2023.3291348 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/07/230714131136.htm

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