  Hierarchical leaf vein segmentation is a crucial but under-explored task in
agricultural sciences, where analysis of the hierarchical structure of plant
leaf venation can contribute to plant breeding. While current segmentation
techniques rely on data-driven models, there is no publicly available dataset
specifically designed for hierarchical leaf vein segmentation. To address this
gap, we introduce the HierArchical Leaf Vein Segmentation (HALVS) dataset, the
first public hierarchical leaf vein segmentation dataset. HALVS comprises 5,057
real-scanned high-resolution leaf images collected from three plant species:
soybean, sweet cherry, and London planetree. It also includes human-annotated
ground truth for three orders of leaf veins, with a total labeling effort of
83.8 person-days. Based on HALVS, we further develop a label-efficient learning
paradigm that leverages partial label information, i.e. missing annotations for
tertiary veins. Empirical studies are performed on HALVS, revealing new
observations, challenges, and research directions on leaf vein segmentation.
