Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

<div class="floats-full-page floats-full-page-figure"><h2>Figure 6</h2><div class="floats-full-page-caption"><div>Result of LSP on the synthetic circle dataset. (a) Embedding plot. Treated samples are shown in red and control samples are shown in blue. Darker points indicate later time points. (b) Histogram of output trait values. (c) Manhattan plot. (d) Ground truth growth rate data used to generate the image dataset.</div></div><div class="floats-full-page-content"><table class="figure-group"><tr class="fig-image"><td><object data="https://s3-eu-west-1.amazonaws.com/spj-html-parsed-preprod/articles/plantphenomics/volume-2020/5801869/figures/5801869.fig.006.svgz" name="5801869.fig.006" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 6 | Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

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Figure 6: Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

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Figure 6

About this journal

<p>The open access journal <em>Plant Phenomics</em>, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.</p>


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<p><em>Plant Phenomics'</em> editorial board is led by Seishi Ninomiya (University of Tokyo),&nbsp;Frédéric Baret (French National Institute of Agricultural Research), and&nbsp;Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.</p>


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More special issue details

<p>Published Special Issues:</p>

<p><br />
<strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis">Image Analysis and Machine Learning for Cyber-Agricultural Systems</a></strong><br />
&nbsp;</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/statistics-bioinformatics/">Statistics, Bioinformatics, and Machine Learning Methods in Phenomics</a></strong></p>
