Abstract: Objectives: Pine Wilt Disease (PWD) is a global disease with devastating threat to forests, causing great ecological and economic losses in China every year. It is important to monitor and map the infection status of pine trees accurately for industry sectors to control the spread of such diseases timely. Traditional PWD detection methods such as manual inspection and visual interpretation of remote sensing images are inefficient. Recently, deep learning algorithms have applied in PWD detection task, bringing significant performance gains. However, the current methods usually adopt supervised learning strategy, which requires lots of professional labeling that is time-consuming and labor-intensive. To this end, we proposed a semi-supervised PWD detection method in a teacher-student mechanism. Methods: First, we collected 16310 images in Wudu River Region in Yichang City, Hubei Province, labeled 697 images for model training, and utilized unlabeled 15613 images for testing; then, the YOLOv5 model was adopted as a student model to learn features of input images, the learnt features were updated by exponential moving average algorithm to build a teacher model for generating pseudo-labels of unlabeled test images. The test images with pseudo-labels were mixed with train dataset and fed into the student model to optimize features and generated a stronger teacher model. With the corporation of student and teacher model, the network was more capable to learn robust features for detecting PWD in UAV images. Results: Extensive experiments on the dataset demonstrated that our semi-supervised method could effectively detect the diseased wood, achieving a promising performance with precision of 85.05% and recall of 91.36% on the test dataset, the ground truth of which was provided by third-party professionals. Conclusions: This paper proposed a semi-supervised object detection method using a small portion of labeled data to robustly learn features in large scale UAV images for high-quality PWD detection. The performance on test dataset showed that our method could be well applied to the application of rapid dynamic monitoring of PWD epidemics in forestry field, which could benefit for the sustainable development of forest ecosystem.