Generating Photorealistic Synthetic Data for Mushroom Segmentation with AI

This research explores a novel method for generating training data, which could significantly improve the performance of computer vision models in agricultural applications. The combination of procedural 3D graphics and diffusion models represents a promising approach to creating realistic synthetic images.

• •The study leverages procedural 3D graphics and guided diffusion models.
• •The goal is to generate photorealistic synthetic training data.
• •Application is within the domain of white button mushroom segmentation.