Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines. In our experimental evaluation, we provide baseline results for our newly introduced dataset while showcasing the benefits of POKS over OKS.

翻译：机器人感知远不及人类能力。人类不仅具备复杂的语义场景理解能力，还能针对显著对象提取其内部细粒度属性。当人类观察植物时，会自然感知其由独立叶片与分支系统构成的植株结构。本研究旨在提升农业精准机器人对植物理解的粒度水平。我们开发了一种从叶片、茎干到叶脉实例中提取细粒度表型信息的模型。基础数据集RumexLeaves已公开，作为首个包含关键点引导多段线标注（沿茎干最低点经叶片基部至叶尖路径）的数据集，具有开创性意义。此外，我们引入了一种适配关键点引导多段线概念的改进度量指标POKS。在实验评估中，我们不仅展示了POKS相较于OKS的优势，还为新数据集提供了基准测试结果。