Artificial Intelligence and Food Safety

The year 2023 has been to artificial intelligence (AI) what 1993 was to the internet: the year it became available to the masses. While the public debate on the impact of AI on society has just started, one of its most fascinating aspects is its potential to generate elaborate predictions based on an analysis of immense volumes of data.

For the past few years, researchers and regulators have been trying to apply this ability to food safety. FDA has made data analytics a part of its New Era of Smarter Food Safety Blueprint, an initiative the agency launched in 2020 that seeks to reduce the number of foodborne illnesses by leveraging technology to create a safer, more digital, and more easily traceable food system.

Data Sharing in the Field

Food safety organizations have also joined the AI movement. One online platform developed by the Western Growers Association, a trade organization comprising more than 2,200 farmers, aims to allow users to share food safety data. This network, called GreenLink, started in 2021 in partnership with Creme Global, an Ireland-based data analytics company, and six participating members and has grown tremendously, reaching 140 growers and 6 million data points. “Our goal is to capture and analyze field food safety data so that each operation can view it individually and compare it with an aggregated data of other operations,” says De Ann Davis, PhD, senior vice president of science for the Western Growers Association.

The GreenLink platform plans to use both descriptive and predictive models for analysis. “For example, if a water test comes back high in E. coli, we would like to be able to use descriptive analytics to explain what’s likely causing that, and predictive analytics to understand [whether] that value is expected to be high in that period of the year,” says Dr. Davis. The use of predictive analytics, however, hasn’t been implemented; GreenLink’s datasets are not yet consistent enough to start making predictions. “That doesn’t mean that in six months we won’t be able to do that, though,” she adds.

This insufficient level of consistency has to do with the freedom that the project leaves to participants to decide what data to share­—for example, field location, water or pathogen testing results, or bird activity. Such flexibility is meant to encourage members to share information that is normally treated as confidential.

The challenge of collecting non-public data is an aspect of AI in which the human factor is very much present. When sensitive company data is essential for developing AI tools, sharing it is not a spontaneous act done for the sake of the algorithm; rather, it’s a business decision taken to gauge risk versus reward.

Dr. Davis says this is a chicken-and-egg problem: “People want to know what you’re going to deliver before they go all the way in with the data, but you can’t deliver anything if they don’t provide data first. So, it’s also a matter of balancing the value they’re getting out with the amount of data they’re putting in.”

Why the Produce Industry Is Ripe for AI

Indeed, growers may be receptive to the idea of sharing data. Matt Stasiewicz, PhD, an associate professor of applied food safety at the University of Illinois Urbana-Champaign, says, “While the produce industry is well controlled, we’re still seeing outbreaks. Yet, no single company is going to observe enough contamination events to understand truly what’s driving that risk. People are starting to realize that sharing data across companies may be the way to find answers to those questions.”

Dr. Stasiewicz is one of his university’s site leads for the AI Institute for Food Systems (AIFS), a consortium formed by six universities and USDA. One of the group’s aims is to create an AI-powered database based on information gathered from public research projects, with a specific focus on microbiological testing data from growing fields: “Just knowing that a test was positive or negative is not really predictive,” says Dr. Stasiewicz. “It’s much more useful to find out what else about that sample could help predict the result, such as how the sample was taken, its size, the assay method, or the size of the field. That can be combined with publicly available data such as weather patterns, the presence of migratory birds, or a specific wind pattern that may be blowing dust in from somewhere else.”