Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances
This work addresses the need for precise phenotypic analysis in agriculture, but it is incremental as it builds on existing keypoint and polyline methods.
The paper tackles the problem of fine-grained plant understanding for agricultural robots by developing a model to extract leaf, stem, and vein instances, and introduces a new dataset RumexLeaves and metric POKS, with baseline results showing benefits over OKS.
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.