How AI could bring satellite crop monitoring to the world's most vulnerable farms

New research from the Department of Computer Science and Technology shows that Tessera, an AI tool developed in the Department, could guide food security decisions.

Small farms grow much of the world's food, but from space they are nearly invisible. Their fields are tiny and ill-defined, and the satellite tools built to track crops were designed for the large uniform fields of industrial agriculture, not the small sub-hectare plots that feed many of the world's poorest people. Those figures matter because the agencies that plan for food security, among them the UN Food and Agriculture Organization, the World Bank and individual governments, rely on satellite crop maps in their decision making.

Tessera is a foundation model that has been trained on years of satellite imagery so it can be adapted to many different tasks. When it was tested on small fields in Austria, Tessera identified most crop types more accurately than methods currently in use. At the same time, it was using just 8% of the computing power, and none of the hand-tuning, that those methods require.

At that scale a small gain in accuracy can decide whether a country imports enough grain to avoid a shortfall, said lead author Madeline Lisaius, who completed the study as a PhD researcher here in this Department.

"When the decisions are being made at country and continent scale, [that] makes a really big difference in terms of food security and planning," Lisaius said. "Do we go buy 100 tonnes, or 10,000 tonnes of rice from Thailand now, because we're going to underproduce and people are going to starve in seven months?"