CropCraft：用于农业任务机器人仿真的程序化世界生成器
CropCraft: A Procedural World Generator for Robotic Simulation of Agricultural Tasks
AI总结：
提出基于Blender和Python的开源程序化世界生成器CropCraft，通过YAML配置生成多样化农田场景，支持间作、葡萄园和杂草田，并生成带标注的3D仿真环境，用于农业机器人感知和导航算法开发。
摘要：
现代农业中 agroecological 实践的采用要求机器人系统能够在高度多样化和复杂的田间环境中运行。开发和评估此类系统严重依赖仿真，但生成代表 agroecological 多样性的逼真且可配置的3D环境仍然是一个主要挑战。本文提出了 CropCraft，一个基于 Blender 和 Python 构建的开源程序化世界生成器，旨在生成适用于农业机器人的3D仿真环境。CropCraft 通过简单的 YAML 配置文件生成作物田，支持多种场景，包括间作、葡萄园和杂草丛生的田地。该工具包含一个多生长阶段的3D植物模型库（作物、草和杂草），并使用随机放置算法真实地再现实际田地中观察到的空间变异性。生成的场景可直接导入 Gazebo 仿真器，并包含所有放置元素的地面真值标注，支持感知和导航算法的开发。为了展示 CropCraft 的实际用途，我们将其应用于使用深度学习的作物-杂草语义分割任务。生成了包含10,000张玉米田合成图像的数据集，这些图像具有不同的杂草密度、生长阶段和光照条件，并用于训练多个分割架构。仅使用合成数据训练的模型在真实田间图像上实现了约10%的平均交并比（mIoU）的 sim-to-real 差距，优于先前的先进合成生成方法。我们进一步表明，即使将少量真实图像与合成数据结合，也能提高跨领域的泛化能力，为农业感知任务中合成数据的有效使用提供了新见解。
英文摘要：
The adoption of agroecological practices in modern agriculture requires robotic systems capable of operating in highly diverse and complex field environments. Developing and evaluating such systems relies heavily on simulation, yet generating realistic and configurable 3D environments representative of agroecological diversity remains a major challenge. This paper presents CropCraft, an open-source procedural world generator built on Blender and Python, designed to produce 3D simulation environments tailored to agricultural robotics. CropCraft generates crop fields from a simple YAML configuration file, supporting a wide range of scenarios including intercropping, vineyards, and weed-infested fields. The tool includes a library of 3D plant models (crops, grasses, and weeds) at multiple growth stages, and uses stochastic placement algorithms to realistically reproduce the spatial variability observed in real fields. Generated worlds are directly importable into the Gazebo simulator and include ground-truth annotations for all placed elements, supporting both perception and navigation algorithm development. To demonstrate the practical utility of CropCraft, we apply it to the task of crop-weed semantic segmentation using deep learning. A dataset of 10,000 synthetic images of maize fields with varying weed densities, growth stages, and lighting conditions was generated and used to train several segmentation architectures. Models trained exclusively on synthetic data achieve a sim-to-real gap of approximately 10% mean Intersection over Union (mIoU) on real field images, outperforming previous state-of-the-art synthetic generation approaches. We further show that combining even a few real images with synthetic data improves generalization across domains, providing new insights into the effective use of synthetic data for agricultural perception tasks.
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