Monitoring croplands at scale with machine learning

Faculty members David Mulla (Department of Soil, Water and Climate) and Philip Pardey (Department of Applied Economics), along with colleagues in the Department of Computer Science, are exploring how machine learning, paired with satellite imagery, can transform how croplands are monitored across large regions and long time periods.

Their research shows how automated, AI-driven approaches can map where crops are grown more efficiently and consistently than traditional methods. These advances could improve decision-making related to food security, land use and environmental sustainability, while also identifying technical hurdles that must be addressed before these tools can reach their full potential.