The issue for agriculture is using the massive amounts of data that has been collected to determine the best path forward in developing high-performing plants and animals.

Once the data was collected the challenge became how to best use it.

Professor Lee Hickey, team leader of predictive agriculture at the Queensland Alliance for Agriculture and Food Innovation said the university was integrating AI with what it terms “speed breeding” of plants to rapidly stack multiple disease resistance in wheat and barley.

“It’s all about bringing together traits as quickly as possible,” he said.

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“For the last 10 years, I have invested a lot of time to develop protocols for speed breeding and communicating the benefits of speed breeding and working with programs around the world to develop the technology.

“Integrating speed breeding with genomics and particular AI where we tap into big data sets is the new frontier for plant and animal breeding.

“This will be a game changer, bringing desirable traits together faster than ever before, particularly when it comes to multiple traits governed by multiple genes.”

Professor Ben Hayes, a co-inventor of genomic prediction, said even deciding what to breed had become a complex question and consumer acceptance was a big part of that.

He said AI was a good way of pulling together the preferences of millions of people as well as analysing images taken on a large scale to capture genetic variation between related lines of plants and certain traits in animals that were wanted.

AI could also be used to take genetic markers to predict how good a variety was going to be for breeding.

He said AI could also be used to shift the way researchers approached breeding from looking at individual genetic lines to thinking about a breeding population as a collection of chunks of DNA that were good for a trait.