We introduce LAESI, a Synthetic Leaf Dataset of 100K synthetic leaf images on millimeter paper, each with seman- tic masks and surface area labels. This dataset provides a resource for leaf morphology analysis aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area pre- diction and semantic segmentation, using real images for validation. Our validation shows that these models can be trained to predict leaf surface area with a relative error not greater than an average human annotator. LAESI also pro- vides an efficient framework based on 3D procedural models and generative AI for the large-scale, controllable genera- tion of data with potential further applications in agriculture and biology. We evaluate the inclusion of generative AI in our procedural data generation pipeline and show how data filtering based on annotation consistency results in datasets allows training the highest performing vision models.