  Accurate crop type maps provide critical information for ensuring food
security, yet there has been limited research on crop type classification for
smallholder agriculture, particularly in sub-Saharan Africa where risk of food
insecurity is highest. Publicly-available ground-truth data such as the
newly-released training dataset of crop types in Kenya (Radiant MLHub) are
catalyzing this research, but it is important to understand the context of
when, where, and how these datasets were obtained when evaluating
classification performance and using them as a benchmark across methods. In
this paper, we provide context for the new western Kenya dataset which was
collected during an atypical 2019 main growing season and demonstrate
classification accuracy up to 64% for maize and 70% for cassava using k Nearest
Neighbors--a fast, interpretable, and scalable method that can serve as a
baseline for future work.
