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Artificial Intelligence Advances Food Safety

Landing AI is helping food producers overcome not only the limitations of their human workforce but of traditional machine vision as well, using machine learning to better evaluate issues around food safety.

Getty Images 476586896 Food Inspection

Machine vision has long found a place in food safety, working 24/7 without fatigue. But as data access increases and processing power improves, machine vision is finding even more opportunities through the added capabilities of artificial intelligence (AI).

To take one example, traditional machine vision tends to struggle to inspect for contamination in sun-dried tomatoes. But it’s an application that’s well suited to AI. “Similar to a human, AI is very good at dealing with a lot of variations in whatever’s being looked at,” says Quinn Killough, senior business development manager for Landing AI, a company that provides end-to-end AI platforms for manufacturing. “That type of application, because there’s so much variability in what a tomato could look like or what kind of contamination could be on it, it was a pretty tough machine vision problem in general. A human can do it easily. And it turns out AI can do it fairly easily as well. Being able to deal with all that variation in what you’re looking at, it makes it very well suited for AI.”

As a general rule of thumb, if a human could come up with a decision in their head in less than a second, it’s something that AI could handle. But then why not stick with humans for the task?


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“If you have a human out there inspecting products, they are subject to change in a lot of it. What they think might be a defective piece of food one day they might say is an OK piece of food the next day. They are impacted by a variety of things—they could be in a bad mood that day, or they could’ve forgotten their glasses that day, something like that,” Killough explains. “Machine learning will be able to perform at the best human’s ability consistently, 24/7. There’s no variation in the accuracy with which the machine learning model will look at something and provide a prediction.”

As another example where AI fits well, Killough describes a leak inspection method in which a packaged product is submerged in water with pressure added to the tank to try to pull air out of it. “The human will sit and look for bubbles coming out of this package,” he explains. “With cognitive fatigue, they would eventually miss something, or something might get in the way of their vision and bad products would get through the production line. What we did was create an algorithm that can look at these packages and detect these bubbles consistently and work 24/7.”

Another inspection problem that traditional machine vision has struggled to deal with are skewed date codes on the bottoms of soup cans. “You need to make sure that you have the right date code on that soup tin, but because there’s ridges on the bottom, it gets printed and gets all skewed,” Killough describes. “A traditional system won’t be able to read that, and AI actually does it pretty easily.”

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