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. 2020 Apr 15;3:173. doi: 10.1038/s42003-020-0905-5

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — a, b Principal component analysis (PCA) with morphological parameters of barley seeds. Each point represents the data point of the respective seed. The colors correspond to those defined in the color legend displayed below (e). Mean PC1 and PC2 values of each cultivar are plotted as a large circle with text annotations in (a). Eigenvectors of each descriptor are drawn as arrows in (b). LWR length-to- width ratio, E eccentricity, L seed length, PL seed perimeter length, AS seed area, W seed width, S solidity, CS seed circularity. c, d PCA with elliptic Fourier descriptors (EFD). The colors and points annotated of (c) follow those of (a). Interpolation of the latent space followed by reconstruction of the contours are displayed in (d). e, f Latent space visualization of variational autoencoders (VAE). The colors and points annotated of (e) follow those of (a). Interpolation of the latent space followed by image generation using the generator of VAE are displayed in (f).