*In The New phytologist *

^{2}). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles, and connectivity of veins. Multi-scale statistics then enabled identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multi-scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures.

*Xu Hao, Blonder Benjamin, Jodra Miguel, Malhi Yadvinder, Fricker Mark*

*2020-Sep-10*

**Biological network analysis, Convolutional neural network, Deep learning, Hierarchical loop decomposition, Leaf trait, Leaf venation network, Network scaling, Spatial transportation network**