Machine learning models outperform simple mechanistic models in predicting pollinator visitation rates. We use deep learning to infer rules from land cover maps to estimate pollination services globally. Results suggest deep learning can improve predictions by identifying complex patterns in landscape composition, especially in data-rich but knowledge-poor areas. The challenge is to make deep learning algorithms more interpretable so that experts can validate prediction rules for pollination.