Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment. Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) and breeding of novel and more resilient crop varieties are helpful to address these challenges. The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor. With this paper, we address the problem of automatic fine-grained organ-level geometric analysis needed for precision phenotyping. As the availability of real-world data in this domain is relatively scarce, we propose a novel dataset that was acquired using UAVs capturing high-resolution images of a real breeding trial containing 48 plant varieties and therefore covering great morphological and appearance diversity. This enables the development of approaches for autonomous phenotyping that generalize well to different varieties. Based on overlapping high-resolution images from multiple viewing angles, we compute photogrammetric dense point clouds and provide detailed and accurate point-wise labels for plants, leaves, and salient points as the tip and the base. Additionally, we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office on the real plants, allowing the evaluation of new approaches not only on segmentation and keypoint detection but also directly on the downstream tasks. The provided labeled point clouds enable fine-grained plant analysis and support further progress in the development of automatic phenotyping approaches, but also enable further research in surface reconstruction, point cloud completion, and semantic interpretation of point clouds.

翻译：农业生产正面临气候变化及可持续发展需求带来的严峻挑战，亟需减少对环境的负面影响。通过机器人非化学除草实现田间管理升级，结合自主无人机（UAV）作物监测，以及培育新型抗逆作物品种，有助于应对这些挑战。植物性状分析（即表型鉴定）是育种工作的核心环节，但目前仍依赖大量人工操作。本文针对精准表型分析所需的自动细粒度器官级几何分析问题展开研究。鉴于该领域真实场景数据相对稀缺，我们提出一个基于无人机采集的新数据集，涵盖包含48个品种的真实育种试验田高分辨率图像，因此具有显著的形态与外观多样性，可支持开发具有跨品种泛化能力的自主表型分析方法。基于多视角重叠高分辨率图像，我们通过摄影测量生成密集点云，并为植株、叶片及叶尖、叶基等关键点提供精确的逐点标注。此外，数据集还包含德国联邦植物品种办公室专家对真实植株进行的表型性状测量结果，使得新方法不仅能评估分割与关键点检测性能，还可直接用于下游任务验证。所标注的点云数据支持细粒度植物分析，助力自动表型分析方法的发展，同时为表面重建、点云补全及点云语义理解等方向的研究提供数据支撑。