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

MIT Technology Review AI · 2 weeks ago

AI systems can improve crop yield by 26%, reduce water use by 41%, and cut chemical usage by 33%, but agricultural operations lack the clean data foundations needed to make these systems reliable. Agriculture's data challenge is uniquely complex: modern farms use disparate IoT devices, autonomous machinery, and external feeds from weather and government sources, while requiring AI systems to understand specific geographic details like GPS coordinates and field-level soil variation. Organizations must first build unified data models, governance frameworks, and security controls before deploying AI, or risk generating misleading recommendations that waste resources or cause operational damage.