Site-specific weed management (on the scale of a few meters or less) has the potential to greatly reduce pesticide use and its associated environmental and economic costs. A prerequisite for site-specific weed management is the availability of accurate maps of the weed population that can be generated quickly and cheaply. Improvements and cost reductions in unmanned aerial vehicles (UAVs) and camera technology mean these tools are now readily available for agricultural use. We used UAVs to collect aerial images captured in both RGB and multispectral formats of 12 cereal fields (wheat [Triticum aestivum L.] and barley [Hordeum vulgare L.]) across eastern England. These data were used to train machine learning models to generate prediction maps of locations of black-grass (Alopecurus myosuroides Huds.), a prolific weed in UK cereal fields. We tested machine learning and data set resampling methods to obtain the most accurate system for predicting the presence and absence of weeds in new out-of-sample fields. The accuracy of the system in predicting the absence of A. myosuroides is 69% and its presence above 5 g in weight with 77% accuracy in new out-of-sample fields. This system generates prediction maps that can be used by either agricultural machinery or autonomous robotic platforms for precision weed management. Improvements to the accuracy can be made by increasing the number of fields and samples in the data set and the length of time over which data are collected to gather data across the entire growing season.

特定地点的杂草管理（几米或更小的规模）有可能大大减少农药的使用及其相关的环境和经济成本。特定地点杂草管理的先决条件是能够快速且廉价地生成准确的杂草种群地图。无人机 (UAV) 和摄像技术的改进和成本降低意味着这些工具现在可以随时用于农业。我们使用无人机收集了英格兰东部12 个谷物田（小麦 [ Triticum aestivum L.] 和大麦 [ Hordeum vulgare L.]）以 RGB 和多光谱格式拍摄的航空图像。这些数据用于训练机器学习模型，以生成黑草（Alopecurus myosuroides Huds.）位置的预测地图，黑草是英国谷物田中的一种多产杂草。我们测试了机器学习和数据集重采样方法，以获得最准确的系统来预测新的样本外田地中是否存在杂草。在新的样本外区域中，系统预测A. myosuroides不存在的准确度为 69%，预测其重量超过 5 g 的准确度为 77%。该系统生成的预测图可供农业机械或自主机器人平台使用，以进行精确的杂草管理。可以通过增加数据集中的田地和样本数量以及收集数据的时间长度来提高准确性，以收集整个生长季节的数据。