Machine learning (ML) and Earth observation (EO) are complementary technologies. While EO helps us understand natural and anthropogenic changes on the Earth, ML empowers us to analyze vast amounts of imagery and build new models for EO data that would have been very difficult if not impossible using traditional physical models a few short years ago.
The promise ML and EO hold for agriculture are immense. EO satellites capture data at a global scale, and ML techniques can use these data to map croplands at local, regional, and continental levels, which provide input for farmers and policymakers alike. In particular, the ability to estimate crop yield or detect pest/disease damage during the growing season will be game-changing in addressing food insecurity problems.