LettuceVisSim：一种为基于视觉的强化学习生成生菜图像时间序列的模拟器
LettuceVisSim: A Simulator That Generates Lettuce Image Time-series for Vision-Based Reinforcement Learning
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中文总结 AI 辅助
LettuceVisSim模拟器通过过程模型、冠层布局算法和Unity渲染生成生菜图像时间序列，实现基于视觉的强化学习在受控环境农业中的概念验证。
中文摘要 AI 辅助
基于视觉的强化学习在受控环境农业（CEA）的决策中具有巨大潜力。然而，其发展受到标记作物图像稀缺的阻碍。为弥补这一空白，开发并验证了LettuceVisSim——一种生成标记作物图像时间序列的生菜生长模拟器。该模拟器包含一个基于过程的模型（PBM）用于茎干重动态模拟、一个冠层布局算法用于从茎干重推导冠层布局表示，以及一个Unity渲染引擎用于图像生成。五项发现支持该模拟器。第一，PBM在动态植株密度管理下重现了茎干重，R²=0.84。第二，分段三次回归将茎干重映射到潜在投影面积，R²=0.94。第三，冠层布局表示使用12个具有不同动态环境和间距条件的实验数据集进行了验证。它重现了测量图像中观察到的地面覆盖率动态，在由实测茎干重驱动时达到R²=0.84，在由PBM模拟值驱动时达到R²=0.40（排除一个异常值后为0.76）。第四，Unity渲染引擎在不到10毫秒内将冠层布局表示转换为RGB和分割图像。第五，一项演示表明，仅通过观察由LettuceVisSim生成的作物图像即可学习和应用光照控制策略，为在CEA中使用LettuceVisSim进行基于视觉的强化学习提供了概念验证。
英文摘要
Vision-based reinforcement learning holds strong potential for decision-making in controlled environment agriculture (CEA). However, its development is hindered by the scarcity of labelled crop images. To address this gap, LettuceVisSim, a lettuce growth simulator that generates labelled time series of crop images, was developed and validated. The simulator contains a process-based model (PBM) for shoot dry weight dynamics, a canopy layout algorithm for deriving canopy layout representations from shoot dry weight, and a Unity rendering engine for image generation. Five findings support the simulator. First, the PBM reproduced shoot dry weight under dynamic plant-density management with $\mathrm{R}^{2}=0.84$. Second, a piecewise cubic regression mapped shoot dry weight to potential projected area with $\mathrm{R}^{2}=0.94$. Third, the canopy layout representation was validated using 12 experimental datasets each having different dynamic environmental and spacing conditions. It reproduced the ground coverage ratio dynamics observed in measured images, achieving $\mathrm{R}^{2}=0.84$ when driven by measured shoot dry weight and $\mathrm{R}^{2}=0.40$ (0.76 excluding one outlier) when driven by PBM-simulated values. Fourth, the Unity rendering engine converted canopy layout representations into RGB and segmentation images at less than 10~ms. Fifth, a demonstration showed that a lighting-control policy can be learned and applied by observing only crop images that were generated with LettuceVisSim, providing a proof of concept of vision-based reinforcement learning in CEA using LettuceVisSim.
发表机构
- Wageningen University & Research（瓦赫宁根大学及研究中心）
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