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.

中文翻译：

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具有 k 个最近邻的田间作物类型分类：新肯尼亚小农数据集的基线

准确的作物类型地图为确保粮食安全提供了关键信息，但对小农农业作物类型分类的研究有限，特别是在粮食不安全风险最高的撒哈拉以南非洲地区。公开可用的地面实况数据，例如肯尼亚新发布的作物类型训练数据集 (Radiant MLHub) 正在推动这项研究，但在评估时了解这些数据集是何时、何地以及如何获得的背景很重要分类性能并将它们用作跨方法的基准。在本文中，我们提供了在 2019 年非典型主要生长季节收集的新的肯尼亚西部数据集的背景，并使用 k 最近邻证明了玉米的分类准确度高达 64%，木薯的分类准确度高达 70%——这是一个快速、可解释的、