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.

为精准农业中的计算机视觉任务提供包含语义标签的 12,000 张图像数据集，通过在合适的光照条件下模拟大豆作物和杂草的成长阶段、不同的土壤条件和随机的田地布局，将真实世界纹理和环境因素融入到程序生成过程中，提高合成数据的逼真度和适用性，从而有效地增加了农业机器学习模型的训练数据。