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

Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances