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Published August 29, 2020 | Version 1.0.7
Software Open

Leaf Vein Network CNN Analysis Software

Creators

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

Description

Software to extract and analyse leaf vein networks using convolutional neural networks. Version 1.0.7 is the original version uploaded to coincide with publication of the paper (Xu et al. 2020). This has been updated and users are recommended to use the latest version by following the links in the Versions panel.

  • LeafVeinCNN.mlappinstall - installs the software and trained CNN models as a matlab app (requires Matlab 2020a or later). Note the Deep Learning Toolbox and Image Processing Toolbox are required. In addition, the support package importKerasNetwork needs to be installed using the Add-On Explorer
  • LeafVeinCNN.exe - installs the software and trained CNN models as a standalone package for Windows 10. This will automatically download the Matlab runtime library from the web during installation. (Jan 2021 - Please note we are aware of installation problems with this release on some systems and are currently working on a workaround).
  • LeafVeinCNN_Manual.pdf - A user manual describing installation and use of the software.

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

LeafVeinCNN_Manual.pdf

Files (2.3 GB)

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md5:1c5a6ae01372ba0d0661719d9c7b9146
1.1 GB Download
md5:fe3a959951663fec92f88700fd4848b4
1.1 GB Download
md5:568ffab8439a7a37df965311f8508f4c
72.1 MB Preview Download

Additional details

Funding

Towards a more predictive community ecology: integrating functional traits and disequilibrium NE/M019160/1
UK Research and Innovation
BIODIVERSITY AND LAND-USE IMPACTS ON TROPICAL ECOSYSTEM FUNCTION (BALI) NE/K016253/1
UK Research and Innovation
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

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 Phytologist (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).