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.

翻译：层级叶脉分割是农业科学中一项关键但尚未充分探索的任务，对植物叶脉层级结构的分析有助于植物育种。尽管当前分割技术依赖于数据驱动模型，但目前尚无公开数据集专门针对层级叶脉分割。为填补这一空白，我们提出了层级叶脉分割（HierArchical Leaf Vein Segmentation, HALVS）数据集——首个公开的层级叶脉分割数据集。HALVS包含从三种植物物种（大豆、甜樱桃和伦敦悬铃木）中采集的5,057张高分辨率扫描叶片图像，并包含人工标注的三个叶脉层级真值，总标注工作量达83.8人天。基于HALVS，我们进一步开发了一种利用部分标签信息（即三级叶脉缺失标注）的高效标注学习范式。在HALVS上开展的实证研究揭示了叶脉分割领域的新发现、挑战及研究方向。