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Artificial intelligence is already changing how people work, communicate online, create art and manage businesses. Now the technology is being used in every aspect of our food systems.

AI holds the promise of making agriculture more efficient and sustainable, yielding healthier food with less impact on the planet, according to Ilias Tagkopoulos, director of the , or AIFS, at UC ����ԭ��.

Take the humble tomato, for example. In California, where some were harvested last year, researchers from UC ����ԭ�� and other institutions are using AI to reduce loss of tomatoes as they are trucked from field to cannery. AI can help us develop new tomato varieties adaptable to a changing climate and screen fruit for quality in the processing plant.

AI algorithms can make predictions and recommendations based on very large amounts of data. in Woodland, California, uses data every step of the way including sorting the fruit — and AI can lead to even more efficiency, said Dan Vincent, who retired last year after 19 years as cannery CEO.

“We’re already using ‘I,’ in AI. The ‘I’ are people, and they do a very good job, but they’re overwhelmed with data. So can AI help those people do their jobs better?” Vincent said.

Decisions from big data

AI is essentially a set of tools that creates decisions from data, said Tagkopoulos, who is also a professor in the UC ����ԭ�� Department of Computer Science and Genome Center. Those tools can support human decision-making as our systems become more complex.

“The more complex the system becomes, the more the analysis of data and finding the best point of operation becomes difficult. And the food system is really, really complex,” he said.

The seed for the AIFS began with a White House initiative on artificial intelligence in 2019. In 2020 the National Science Foundation and partner agencies issued a call for proposals for institutes working on AI in various fields, including food systems. The AIFS, led by UC ����ԭ�� and including UC Berkeley, the University of Illinois Urbana-Champaign, Cornell University and the University of California’s Division of Agriculture and Natural Resources, was established that year with a grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture.

In its first three years, the institute has funded research projects on AI technology across the food system, from cheaper sensors for agricultural production to digital simulators to manage indoor farming and technology to predict the nutritional content of food. 

As large-scale data collection and AI become routine at different steps in the food supply chain, those steps can themselves be integrated by AI.

“Where the opportunity lies is in integration — connecting the dots throughout the supply chain,” Tagkopoulos said. In 2015, Tagkopoulos founded , an AI company to help ingredient and consumer packaged goods companies come up with better products from farm to fork.

For example, information gathered by AI about crops growing in a field can be integrated with the AI for plant breeding. That integration up and down the supply chain is a key goal for the AIFS as it enters its fourth year, Tagkopoulos said.

Making the seeds: AI in plant breeding

One of the original innovations in agriculture was to select and breed plants that make good crops. Today, plant breeders and geneticists continue to push for plants that are tastier, hardier, and more drought or pest resistant. With the rise of modern genetics, breeders can use genetic information to select plants to cross with each other and create new varieties.

“The genetics helps inform the breeding,” said Christine Diepenbrock, assistant professor of plant sciences at UC ����ԭ��. “We can find different regions of the genome that are favorable for traits of interest, then cross the best lines together and make sure we get all those different favorable regions.”

The more information breeders have about the plants, the more quickly they can create the best crosses. Artificial intelligence can support those decisions, Diepenbrock said.

“So we can get gains more quickly if we can accurately predict how the next generation is going to perform, or how a given crop variety might perform in a new environment where we haven’t tested it before,” she said.

Artificial intelligence is particularly useful for finding complex, nonlinear relationships, Diepenbrock said. For example, recent work by one of Diepenbrock’s students studied ways to use DNA sequences to