Ash dieback disease poses a severe threat to European ash trees, necessitating improved monitoring and management. However, datasets for training computer vision models for automated ash diabeck disease detection remain limited. To address this, our study investigates a practical computer vision approach to ash dieback detection, using limited real leaflet data augmented by a conditional generative adversarial network (cGAN). A two-phase cGAN training strategy enabled the production of synthetic leaflet images that capture ash-specific features. We test our synthetic data generation on a range of tasks, including classification with models like ResNet and ResNeXt, as well as object detection using YOLO. Results show our synthetic augmentation improves model performance across all tasks. We propose two distinct frameworks to support surveys through semantic segmentation and enable automated data collection for further research. Overall, our approach considers cGANs to enrich limited domain-specific datasets and improve model accuracy across diverse vision tasks, and offers headway in applying learning frameworks to enhance biodiversity conservation over current methods.