Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone species threatens biodiversity and may lead to significant habitat degradation. Since no cure exists, early detection and removal of infected trees are critical to slowing the spread of the disease. Traditional identification methods rely on visual assessments of canopy loss, which are inefficient and impractical for large-scale monitoring. Leveraging advancements in computer vision and deep learning, our key objective is to develop a tool to detect ash dieback symptoms at the leaf level, classifying leaves into three categories: healthy, early-stage infection, and mid-stage infection. Since there is no known available dataset for ash dieback at the leaf level, we generated a new synthetic dataset and trained a YOLOv5 single-stage object detection model. The final model achieves mean Average Precision (mAP) scores of above 90% for each category. Evaluations on real ash tree leaf footage captured using uncrewed aerial vehicles (UAVs) show strong alignment between the model’s detections and expert annotations. Our tool demonstrates the potential of integrating advanced computer vision techniques into tree health monitoring platforms. In the near future, this can provide conservationists and researchers with a novel, efficient means of early disease identification.