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U Kentucky drone cows.jpg Photo by: Eric Sanders.
In the basement of an engineering building on UK's campus, there's a calf replica who goes by the name of Chuck, which has been instrumental in perfecting the machine-learning and UAV-formation-control technology.

UAV research aims for remote health monitoring of cows

New system aims to identify each cow in pasture through unique characteristics and measure vital health information.

Every year, nearly 3 million cows in the U.S. die from health problems, which is costing the cattle industry more than $1 billion, according to the University of Kentucky.

Combating this economic loss starts at the producer level with improved observation of cows in the pasture, which has been shown to reduce herd loss, the university said, noting that constant monitoring of beef cattle may be problematic since they spend a significant amount of time outside.

With a $900,000 grant from the U.S. Department of Agriculture, Jesse Hoagg, the Donald & Gertrude Lester professor of mechanical engineering at the University of Kentucky, is working on a non-invasive health monitoring approach using unmanned aerial vehicles (UAVs), otherwise known as drones.

The drones would provide farmers with a way to remotely and autonomously check on the location and health of each cow, thus allowing them to address cattle health and safety issues much sooner, the university said.

“This project tackles an important problem: reducing cattle loss," Hoagg said. "The approach that we are developing is highly interdisciplinary, drawing on expertise in robotics, computer science, control systems, agricultural engineering and livestock systems."

Cattle producer Josh Jackson, an assistant extension professor in the University of Kentucky College of Agriculture, Food & Environment, said the motivation for the project came while he was trying to find his Angus herd in the dark.

“Many Kentucky cattle producers have jobs off the farm, and it gets tricky to locate cows this time of the year, when the sun sets so early,” he explained. “We want to lessen producers’ stress by helping them locate their animals quicker and help sick animals faster.”

The new system aims to identify each cow in a pasture through unique characteristics such as facial features and to measure vital health information like size and physical activity.

Hoagg and his team of professor and student researchers have been conducting experiments in an engineering building on campus with a replica calf ("Chuck") that has been instrumental in perfecting the machine learning and UAV formation control technology.

Hoagg explained, “Our indoor UAV flight facility allows us to develop and test formation control approaches in a controlled environment. This is an important first step before outdoor testing.”

In the system, an observer drone hovers 50-100 ft. above the herd. Using stereo cameras, the drone tracks motion to determine the location of the cattle, Hoagg said. Meanwhile, three worker drones use that location information to track a specific cow. The worker drones then perform the health monitoring tasks.

A software program the team has custom built tells the drones when to execute maneuvers such as maintaining formations around a cow and tracking the cow.

Zack Lippay, a doctoral student leading these test flights, has dedicated more than two years to the project. “We’re trying to prove that this method is safe," he explained. "Everything is completely autonomous, but we have a fail safe where pilots can take over if things get unstable.”

Machine learning technology plays a crucial role, especially when teaching the drones how to identify one cow from another and estimate physical characteristics. Essentially, the team is training the software to recognize each cow's face so physical measurements of an individual cow can be tracked over time, the university said. To do this, 3D models will be constructed using real images of cows.

Michael Sama, associate professor of biosystems and agricultural engineering, and Ruigang Yang, professor of computer science, are leading this component of the project. “Part of this effort is to simply collect the massive amount of imagery necessary to develop custom machine learning technology suitable for individual cow identification,” Sama explained. “We’re trying to understand the best way to acquire images remotely — how to efficiently extract information from those images that provides value to cattle producers.”

Last, the team also needs to ensure that drones hovering near the cattle won’t cause any adverse effects. Trials are already underway at the C. Oran Little Research Center in Versailles, Ky., to test how cows react to the drones.

Gabriel Abdulai, a doctoral student in biosystems and agricultural engineering, is focusing his studies on cattle's response to drones. Three days a week, the team performs test flights. So far, the heart rates remain stable among the herds circled by drones, and the cattle have shown no other signs of stress.

“By studying the physiological and behavioral response of beef cattle to drones, we want to ensure that this great technology is not a stressor," Abdulai said. "This is because stressed cattle often spend less time grazing and are difficult to handle, which can impact daily weight gain and handling operations.”

Although the project is far from being completed, early results are promising. The hope is that someday the technology being developed could be commercialized and used to improve the productivity of small-herd cattle producers.

“This project aims to make transformational progress on the use of autonomous UAVs for monitoring cattle health and, thus, improve the security of a critical food resource and improve the economic outlook for rural beef producers," Hoagg said.

The project is slated to continue through February 2021.

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