Disease is the largest factor negatively affecting beef production and its related profitability. Acute or chronic illness reduces weight gain, feed conversion rates and overall performance. Efficiency losses grow and mortalities increase when disease isn’t handled effectively in a timely manner.

Derksen bruce
Freelance Writer
Bruce Derksen is a freelance writer based in Lacombe, Alberta.

The industry is constantly searching for better ways to detect, identify and treat disease. Technology is helping.

Luis Burciaga-Robles, director of Latin America services and business development at Feedlot Health Management Services, believes the first question to ask not only in the U.S. but on a global scale, relates to the level of the technology’s value proposition. It must deliver a return on investment for it to be implemented.

“There’s a broad definition of what technology is and what the cost of implementation would be,” Burciaga-Robles says. “It could be something as simple as automatic data modeling or as complex as a camera used for facial recognition and sickness detection.”

He says that while producers receive social pressure for their license to operate, to say animal health-based technologies will benefit the animal could imply we’re not doing a good enough job now.


“I think the industry worldwide is doing an outstanding job of being stewards, so I’m not sure saying a technology is better for animals is right,” he says. “The industry is very conscious of this and has spent a lot of money and resources making sure all their practices are animal welfare- and well-being-approved. It’s important to understand technology implementation facilitates and improves labor and efficiency, not necessarily to be beneficial for the cattle.”

A few health and management companies are focusing on early disease detection with the goal of reducing antibiotic use.


HerdWhistle, a Calgary, Alberta-based company, is building a tool for beef producers and veterinarians alike.

“If we catch an at-risk animal in an initial stage of illness, assess them and use our health management software to make a decision on treatment, we’re ahead of the game,” says William Torres, HerdWhistle’s director of cattle operations. “Because of this early identification, we can make better decisions on what drug to choose, if any. Rather than using our most potent and expensive drug, we might be able to use our lower-end antibiotic to achieve the same result due to more optimal timing.”

Earlier this year, HerdWhistle acquired the licensing for internet protocol (IP) technology dubbed BigEye. It incorporates their current use of ultra-high-frequency (UHF) eartags with an all-in-one UHF tag reader and multispectral camera. The technology uses infrared thermography and a high-resolution 3D lens to create a fourth dimension with an algorithm calculating weights and detecting at-risk cattle up to six days prior to noticeable illness.

“Even the most experienced pen riders, cowboys, veterinarians and Ph.D.s can only confirm sickness when the clinical signs are already there,” Torres says. “Our technology makes an early prediction in a mere three-and-a-half seconds. It’s a game-changer.”

The BigEye camera is mounted at water sources and along feedbunks, capturing side profiles of the cheek and eye. An infrared thermography image enables the reading of energy being released into the atmosphere, which is translated into temperature. HerdWhistle’s portable handheld scanner can be used to locate the sick animal.

“The AI [artificial intelligence] part of our technology measures this energy and compares it to the individual’s baseline and others nearby,” Torres says. “The AI sends an alert of an at-risk animal, and while it may not show an immediate increase in rectal temperature, as the days pass, the illness will be confirmed.”

Within seconds, an accurate decision could be made to use either a $3 or a $30 antimicrobial.

“It’s empowering producers and feeders to make real-time decisions, putting lost margins back in their pocket,” he says. “Plus, it gives our cattle a much better chance of recovery.”


MyAniML, an animal health technology company based in Kansas City, is also focusing on early disease detection.

Their system uses motion sensor cameras on bunks or trucks to photograph faces for analysis. Acting in combination with eartag sensors, AI breaks down the data and sends automatic notifications of health records derived from the video and pictures.

“Our technology focuses on muzzles,” says Shekhar Gupta, MyAniML’s founder and CEO. “Each muzzle is unique, like a fingerprint. By studying the minute changes in a muzzle’s ridges, we can predict health events two to three days before any noticeable symptoms occur.”

Gupta says early detection delivers the opportunity to isolate sick animals, saving money on treatment costs and reducing the risk of transmission. As individuals live close to one another in a feedlot, transmission is always a concern. If sold, they move from one location to another; when sickness accompanies them, early identification reduces the risk of an outbreak, lowers re-treatment rates and targets the use of antibiotics.

He explains that facial recognition is contributing to the individual identification aspect of the supply chain and becoming a larger aspect of overall biosecurity in agriculture.

MyAniML’s technology currently provides sick or healthy indications, but its database is rapidly expanding. Gupta hopes machine learning will help transition from these basic determinations to specific disease recognition, such as bovine respiratory disease (BRD) and bovine viral diarrhea (BVD).

“We want to go as far as possible with animal health in the future,” he says. “Our goal is to bring large numbers of diseases to our dataset and eventually move into other livestock species. We foresee this as an excellent human health feature as well, as we reduce the use of antibiotics, ensuring they don’t find their way into the food chain.”

Burciaga-Robles believes early disease identification health technologies are changing management systems and practices, but he also warns that work needs to be accomplished studying how much this ability changes survivability outcomes.

“Overall, I think we’ll see a slow adoption of technology, since it normally requires an overhead cost to implement,” he says. “With the tight margins in the cattle industry, producers aren’t eager to increase these costs or expand their technology scale if they’re unsure of a return on investment. It’s still a big part of the bottom line and the decisions affecting it.”