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Dataset . 2020
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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Leaf Vein Network CNN Results

Authors: Mark Fricker;

Leaf Vein Network CNN Results

Abstract

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.

{"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."]}

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

Related Organizations
Keywords

Network analysis, Convolutional Neural Network, Leaf venation, Functional traits

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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