Agriculture is ready for AI, but its data isn’t

Artificial intelligence offers significant potential in agriculture, including improving crop yield by 26%, reducing water use by 41%, and cutting chemical usage by 33%. However, effective AI implementation requires clean, accurate data, which is often lacking due to fragmented sensor data, inconsistent historical records, and complex farm-specific variables like soil variation. Without a solid data foundation, AI risks generating misleading outputs that could harm productivity and resource management.