Pine wilt disease (PWD) is a global devastating threat to forest ecosystems. Therefore, a feasible and effective approach to precisely monitor PWD infection is indispensable, especially at the early stages. However, a precise definition of “early stage” and a rapid and high-efficiency method to detect PWD infection have not been well established. In this study, we systematically divided the PWD infection into green, early, middle, and late stages based on the needle color, the resin secretion, and whether the pine wood nematode (PWN) was carried. Simultaneously, an unmanned aerial vehicle (UAV) equipped with multispectral cameras was used to obtain images. Two target detection algorithms (Faster R-CNN and YOLOv4) and two traditional machine learning algorithms based on feature extraction (random forest and support vector machine) were employed to realize the recognition of infected pine trees. Moreover, we took into consideration of the influence of green broad-leaved trees on the identification of pine trees at the early stage of PWD infection. We obtained the following results: (1) the accuracy of Faster R-CNN (60.98–66.7%) was higher than that of YOLOv4 (57.07–63.55%), but YOLOv4 outperformed in terms of model size, processing speed, training time, and testing time; (2) although the traditional machine learning models had higher accuracy (73.28–79.64%), they were not able to directly identify the object from the images; (3) the accuracy of early detection of PWD infection showed an increase of 3.72–4.29%, from 42.36–44.59% to 46.08–48.88%, when broad-leaved trees were considered. In this study, the combination of UAV-based multispectral images and target detection algorithms allowed us to monitor the occurrence of PWD and obtain the distribution of infected trees at an early stage, which can provide technical support for the prevention and control of PWD.

松枯病 (PWD) 是对森林生态系统的全球毁灭​​性威胁。因此，一种可行且有效的方法来精确监测 PWD 感染是必不可少的，尤其是在早期阶段。然而，“早期”的准确定义以及检测 PWD 感染的快速高效方法尚未得到很好的确立。在本研究中，我们根据针叶颜色、树脂分泌情况以及是否携带松材线虫（PWN），系统地将 PWD 感染分为绿色、早期、中期和晚期。同时，使用配备多光谱相机的无人机 (UAV) 获取图像。采用两种目标检测算法（Faster R-CNN 和 YOLOv4）和两种基于特征提取的传统机器学习算法（随机森林和支持向量机）来实现对受感染松树的识别。此外，我们还考虑了绿色阔叶树对 PWD 感染早期松树识别的影响。我们得到了以下结果：（1）Faster R-CNN（60.98-66.7%）的准确率高于YOLOv4（57.07-63.55%），但YOLOv4在模型大小、处理速度、训练时间等方面表现优于YOLOv4，和测试时间；(2) 传统机器学习模型虽然有较高的准确率（73.28-79.64%），但无法直接从图像中识别出物体；(3)PWD感染早期检测准确率提高了3。当考虑阔叶树时，72-4.29%，从 42.36-44.59% 到 46.08-48.88%。本研究将基于无人机的多光谱图像与目标检测算法相结合，能够对PWD的发生进行监测，及早获取受感染树木的分布情况，为PWD的防控提供技术支持。