TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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      </button><div class="floats-full-page-caption"><div>Selected feature maps of discriminator and output images of a generator in the final GAN model: (a) feature maps of the second Conv layer, (b) generated images (the feature maps are scaled up for visualization purpose).</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-2020/8309605/figures/8309605.fig.0014.svgz" name="8309605.fig.0014" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 14 | TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Figure 14: TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Figure 14

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<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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<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 />
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<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>
