Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein
  Instances
Ronja Güldenring Rasmus Eckholdt Andersen Lazaros Nalpantidis
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.