Published March 8, 2021
| Version 2.01.00
Software
Open
Leaf Vein Network CNN Analysis Software v2
Authors/Creators
- 1. University of Oxford
- 2. University of California, Berkeley
Description
Software to extract and analyse leaf vein networks using convolutional neural networks.
- LeafVeinCNN.mlappinstall - installs version 2 of the software as a matlab app. The package includes the original set of trained CNN models (at 1.68 µm resolution). Requires Matlab 2020b or later, and the Deep Learning Toolbox, Image Processing Toolbox, Parallel Computing Toolbox, and Mapping Toolbox for full functionality. In addition, the support package 'Deep Learning Toolbox Converter for TensorFlow Models' needs to be installed using the Add-On Explorer
- LeafVeinCNN.exe - installs version 2 of 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. .
- LeafVeinCNN_Manual v2.pdf - A user manual describing installation and use of the software.
Notes
Files
LeafVein_Manual v2.pdf
Additional details
Funding
- UK Research and Innovation
- 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
- European Commission
- 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
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 229, 631-648. Doi: 10.1111/nph.16923.
- 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. 228, 1796-1810. Doi: 10.1111/nph.16830.