Generating Diverse Agricultural Data for Vision-Based Farming Applications

arXiv:2403.18351v1 Announce Type: cross
Abstract: We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive …

abstract applications arxiv crops cs.ai cs.cv cs.gr cs.lg data diverse farming growth integration lighting plants synthetic type vision weeds world