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.

利用最新发布的 Ground-Truth 数据集，本论文提供了关于肯尼亚西部地区新数据集的背景信息并使用快速、可解释和可扩展的 k 最近邻算法，对在非典型 2019 年种植季节中收集的数据集进行分类，结果显示玉米和木薯的分类准确率分别为 64% 和 70%，这为未来的工作提供了基线。