PlantShade：预测植物阴影以实现光照感知的机器人农业操作
PlantShade: Predicting Plant Shadows for Lighting-Aware Robotic Agricultural Operation
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中文总结 AI 辅助
本文提出PlantShade，通过构建涵盖四种作物的阴影数据集和基于扩散模型的生成式阴影模拟，实现光照感知的机器人农业操作，支持感知、光照控制与视角规划。
中文摘要 AI 辅助
植物生长和农业生产构成一个国家可持续发展的基础，并直接影响人类生计。近期前沿人工智能的进展使得科学农业成为可能，并具有提高作物生产力的巨大潜力。在本文中，我们指出了植物阴影模拟的重要性和固有复杂性，因为遮荫是影响植物生长的关键因素。为了推进这一领域并促进更广泛的社会效益，我们聚焦于两个主要贡献。首先，我们引入了一个全面的植物生长和阴影数据集，涵盖四种植物物种，包括大豆、番茄、甜菜和草莓。该数据集包含带有补充光源沿圆形轨迹的俯视视角，在多个生长阶段和多样的观测复杂性下投射动态阴影。其次，我们提出了基于扩散模型的生成式阴影模拟，能够为未见过的植物生成逼真的阴影，并支持下游机器人任务，如感知、光照控制和视角规划。该模型结合了时间条件以促进不同时间阶段的灵活阴影模拟。我们进行了定量和定性评估以衡量模型性能。这项工作为植物感知的阴影建模提供了基础性研究，并对更广泛的农业和机器人应用具有深远意义。
英文摘要
Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent advances in frontier artificial intelligence have enabled scientific agriculture with strong potential to improve crop productivity. In this paper, we identify the importance and inherent complexity of plant shade simulation, as shading is a critical factor influencing plant growth. To advance this field and promote broader societal benefits, we focus on two main contributions. First, we introduce a comprehensive plant growth and shade dataset covering four plant species, including soybean, tomato, sugarbeet, and strawberry. The dataset includes top-down viewpoints with a supplementary light along a circular trajectory, casting dynamic shadows across multiple growth stages and diverse observation complexities. Second, we propose generative shade simulation based on diffusion models, enabling realistic shade generation for unseen plants and supporting downstream robotic tasks such as perception, lighting control, and view planning. The model incorporates temporal conditioning to facilitate flexible shade simulation across different time stages. We conduct both quantitative and qualitative evaluations to assess model performance. This work provides a foundational study for plant-aware shade modeling and has meaningful implications for broader agricultural and robotic applications.
发表机构
- Arizona State University（亚利桑那州立大学）
- Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。
