LAESI: Leaf Area Estimation with Synthetic Imagery

Jacek Kałużny, Yannik Schreckenberg, Karol Cyganik, Peter Annighöfer, Soren Pirk, Dominik Michels, Mikolaj Cieslak, Farhah Assaad, Bedrich Benes, Wojtek Palubicki

Keywords: Visual Computing, Computer Vision, Procedural Modeling, Computer Graphics, 3D, Generative AI, Plant Biology

TL;DR: Leaf Area Estimation with Synthetic Imagery

Abstract: We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resource for leaf morphology analysis primarily aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area prediction 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 provides an efficient framework based on 3D procedural models and generative AI for the large-scale, controllable generation 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 which allow training the highest performing vision models.

Submission Number: 49