Generating Diverse Agricultural Data for Vision-Based Farming Applications

TL;DR

This paper introduces a procedural method for creating highly realistic synthetic agricultural images of soybean fields, aiming to improve computer vision applications in farming through diverse, annotated datasets.

Contribution

A novel procedural model that generates diverse, photorealistic agricultural scenes with detailed annotations, tailored for agricultural vision tasks.

Findings

Synthetic data improves model training effectiveness

Model-generated images closely resemble real agricultural scenes

Dataset supports semantic segmentation for weed control

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 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.