Invasive plant species are detrimental to the ecology of both agricultural
and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that
has spread through much of North America from Eastern Europe. When paired with
contemporary computer vision systems, unmanned aerial vehicles, or drones,
offer the means to track expansion of problem plants, such as leafy spurge, and
improve chances of controlling these weeds. We gathered a dataset of leafy
spurge presence and absence in grasslands of western Montana, USA, then
surveyed these areas with a commercial drone. We trained image classifiers on
these data, and our best performing model, a pre-trained DINOv2 vision
transformer, identified leafy spurge with 0.84 accuracy (test set). This result
indicates that classification of leafy spurge is tractable, but not solved. We
release this unique dataset of labelled and unlabelled, aerial drone imagery
for the machine learning community to explore. Improving classification
performance of leafy spurge would benefit the fields of ecology, conservation,
and remote sensing alike. Code and data are available at our website:
leafy-spurge-dataset.github.io.

研究通过使用商业无人机和计算机视觉系统，对西蒙塔纳州的草地进行航拍，训练了图像分类器以识别叶苣草的存在，并获得了 0.84 的准确率，为生态学，保护和遥感领域提供了改进叶苣草分类性能的机会。