Kansas State University is developing new technology to capitalize on the power of artificial intelligence to build a database of facial recognition technology for the cattle industry. Such a system could boost biosecurity efforts and work into animal disease traceability systems, the university noted.
Just like people, each bovine has a set of unique facial features that modern technology can scan and later use to track the animal throughout its life, Kansas State said.
“We’re talking about a system here that has an incremental cost that is close to zero, and nobody would be (forced) to use it,” said KC Olson, a beef cattle scientist with Kansas State Research & Extension who helped develop the idea. “There would be economic incentives provided by the beef industry to participate.”
Human facial recognition is becoming more common in secure locations, such as airports.
“The technology is based on the geometry of the human face. It uses a bunch of intricate biometric measurements to put a permanent identification on a human being so that, later on, when that person needs to get on a flight or something similar, the technology will identify who they are. For humans, that technology is capable of nearly 100% accuracy,” Olson said.
“Our thinking is, ‘Why can’t we have something like that for beef cattle, which could then be used to create a national animal disease traceability system?’ The need for such a system has never been greater” Olson added. “We need this extra layer of protection for our industry against a foreign animal disease or .... possible malfeasance by somebody who’s an enemy of this nation.”
Olson and a group of Kansas State researchers in computer engineering, veterinary medicine and animal science began discussing the idea late in 2019. While much of the world slowed down during the COVID-19 pandemic, they were busier than ever putting together the intricacies of facial recognition for cattle.
“Initially, we made short videos of 1,000 feeder cattle that were restrained in a chute, taking a panoramic view of each calf’s head,” Olson said.
From the videos, computer engineers parsed individual images of each cow’s head and uploaded it to a neural network — a self-learning form of artificial intelligence. Once the pictures are loaded, Olson said the system “teaches itself which of the biometric measurements are critical.”
Recently, the Kansas State team tested the reliability of the network, feeding it images of cattle already in the system and some that had not yet been entered. Olson said the technology was accurate 94% of the time.
“Given the fact that this was a really small data set, there are some risks,” he said. “You can actually over-train a neural network so that it gets really good with the database that was used to create it, but it’s a little helpless when you give it new material. The major limitation right now is the size of the database. The bigger it becomes — in other words, the smarter the neural network is — the higher the accuracy becomes. Achieving buy-in from the beef industry is absolutely essential to make this as robust as possible.”
Kansas State is working with Kansas City-based Black Hereford Holdings to build a smartphone app called Cattletracs, which will allow producers to submit pictures of their cattle. The app is due to be released soon, although its full capability is not likely to be in place for several months, Olson said.
“For producers who don’t want anything to do with a national disease traceability system, that’s fine. Nobody is compelled to participate,” Olson said. “An animal could be read into the database anytime, such as at the first point of sale after leaving its ranch of origin or anytime after that.
“We do know that there can be economic incentives for animals with desired traits, and this system could help with that, but we would potentially get a lot more, including that all-critical element of biosecurity for our industry. The thinking is that this will eventually be applicable to most mammalian livestock species, including hogs and dairy cattle,” Olson added.