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Published August 30, 2020 | Version 1
Dataset Open

Leaf Vein Network CNN Results

  • 1. University of Oxford

Contributors

  • 1. University of Oxford
  • 2. University of California, Berkeley

Description

Results for leaf vein networks extracted using the LeafVeinCNN software package. The original image data set is available from Blonder et al. (2019) https://doi.org/10.1002/ecy.2844. The LeafVeinCNN software used in the analysis is available at  https://doi.org/10.5281/zenodo.4007730

  • The Results_xxx.zip files contain all the Excel results spreadsheets separated by the code for each field site.
  • results.xls provides a summary of all the network metrics for each file that was analysable
  • Results_figures.pdf provides a summary image of the processing steps and results for each leaf segment
  • Network_images.pdf contains a colour-coded image of each network superimposed on the leaf segment
  • HLD_plots shows the binary tree following Hierarchical Network Decomposition
  • PR_results.zip contains the Excel spreadsheets for evaluation of different enhancement methods for each leaf segment.
  • PR_summary.xls provides a summary of the performance of each enhancement method.
  • PR_F1_images.pdf and PR_FBeta2_images.pdf show the pixel classification for each enhancement and segmentation method compared to the manual ground-truth using two different optimum criteria (F1 and FBeta2).
  • PR_fullwidth_plots show the full Precision-Recall plots for the full-width binary image compared to the manual ground-truth using the FBeta2 metric.
  • PR_skeleton_plots show the full Precision-Recall plots for the skeletonised binary image compared to the manual ground-truth using the FBeta2 metric.
  • PR_threshold_plots.pdf show how a set of network metrics vary with the segmentation threshold for each enhancement method.

Notes

Additional funding from: Human Frontier Science Program (RGP0053/2012), Leverhulme Trust (RPG-2015-437), NSF: DEB-2025282 and RoL:FELS:RAISE DEB-1840209

Files

HLD_plots.pdf

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Additional details

Funding

GEM-TRAIT – GEM-TRAIT: The Global Ecosystems Monitoring and Trait Study: a novel approach to quantifying the role of biodiversity in the functioning and future of tropical forests. 321121
European Commission
BIODIVERSITY AND LAND-USE IMPACTS ON TROPICAL ECOSYSTEM FUNCTION (BALI) NE/K016253/1
UK Research and Innovation
Towards a more predictive community ecology: integrating functional traits and disequilibrium NE/M019160/1
UK Research and Innovation

References

  • Xu, H., Blonder, B., Jodra, M., Malhi, Y. and Fricker, M.D. (2020) Automated and accurate segmentation of leaf venation networks via deep learning. . New Phytol. (In press).
  • Blonder, B., S. Both, M. Jodra, H. Xu, M. Fricker, I. S. Matos, N. Majalap, D. F. R. P. Burslem, Y. Teh and Y. Malhi (2020) Linking functional traits to multiscale statistics of leaf venation networks. New Phytol. (In press).
  • Blonder, B., S. Both, M. Jodra, N. Majalap, D. Burslem, Y. A. Teh and Y. Malhi (2019). "Leaf venation networks of Bornean trees: images and hand-traced segmentations." Ecology 100: e02844.