用仿真环境生成真实感农田图像，解决农业数据少难题
Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery
- 用Unity模拟葡萄园，剪贴合成带几何一致性的逼真图像
- 在表葡萄检测任务中显著提升先进检测模型性能
- 方法可自动运行，适合实际农业场景落地
在精准农业中，标注数据稀缺与显著的协变量偏移给机器学习模型训练带来独特挑战。这一问题尤为突出，因环境动态变化且作物作为活体其外观持续演变。本文提出一种新型合成数据生成系统以应对上述挑战。基于Unity引擎的葡萄园模拟器，该系统采用考虑几何一致性的剪贴技术，从合成环境中生成准确的逼真图像与标签，用于训练检测算法。该方法可生成多样化的样本，涵盖不同视角与光照条件。实验表明，将该方法应用于表葡萄种植任务，能显著提升当前最先进检测器的性能。所提技术组合易于自动化，这对农业实践中的推广应用日益重要。
原文摘要 · Abstract (English)
In precision agriculture, the scarcity of labeled data and significant covariate shifts pose unique challenges for training machine learning models. This scarcity is particularly problematic due to the dynamic nature of the environment and the evolving appearance of agricultural subjects as living things. We propose a novel system for generating realistic synthetic data to address these challenges. Utilizing a vineyard simulator based on the Unity engine, our system employs a cut-and-paste technique with geometrical consistency considerations to produce accurate photo-realistic images and labels from synthetic environments to train detection algorithms. This approach generates diverse data samples across various viewpoints and lighting conditions. We demonstrate considerable performance improvements in training a state-of-the-art detector by applying our method to table grapes cultivation. The combination of techniques can be easily automated, an increasingly important consideration for adoption in agricultural practice.
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