论文标题
带有K最近的邻居的现场级农作物类型分类：新的肯尼亚小型持有人数据集的基线
Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset
论文作者
论文摘要
准确的农作物类型地图提供了确保粮食安全的关键信息，但是关于农业农业的农作物类型分类的研究有限，尤其是在撒哈拉以南非洲，那里的粮食不安全风险最高。肯尼亚（Radiant MLHUB）的新发布的作物类型培训数据集诸如公共可用的基础真实数据正在催化这项研究，但是重要的是要了解何时，何时何地，何时何地以及如何在评估分类性能以及将它们用作跨方法的基础标记时获得这些数据集的上下文。在本文中，我们提供了新的肯尼亚西部数据集的背景，该数据集是在非典型2019年主要生长季节中收集的，并使用K最近的邻居（一种快速，可解释且可扩展的方法可以用作将来工作的基线，玉米的分类准确性高达64％，木薯的分类精度为70％。
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.
