Generating Diverse Agricultural Data for Vision-Based Farming
  Applications
Mikolaj Cieslak Umabharathi Govindarajan Alejandro Garcia Anuradha Chandrashekar Torsten Hädrich Aleksander Mendoza-Drosik Dominik L. Michels Sören Pirk Chia-Chun Fu Wojciech Pałubicki
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 resource for computer vision tasks in
precision agriculture, such as semantic segmentation for autonomous weed
control. We validate our model's effectiveness by comparing the synthetic data
against real agricultural images, demonstrating its potential to significantly
augment training data for machine learning models in agriculture. This approach
not only provides a cost-effective solution for generating high-quality,
diverse data but also addresses specific needs in agricultural vision tasks
that are not fully covered by general-purpose models.