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

<div class="floats-full-page floats-full-page-other"><h2>Table 1</h2><div class="floats-full-page-caption"><div>Results: real vs. generated image identification.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr /></td></tr><tr class="thead"><td class="align_left"></td><td class="align_center">True real</td><td class="align_center">True generated</td></tr><tr><td class="thead-hr" colspan="3"><hr /></td></tr><tr><td class="align_left">Perceived real</td><td class="align_center">72.36%</td><td class="align_center">39.38%</td></tr><tr><td class="align_left">Perceived generated</td><td class="align_center">27.64%</td><td class="align_center">60.62%</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 1 | TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Table 1: TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Table 1

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>Open for submission:</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/time-series-phenotyping/">Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping</a></strong></p>

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<p>Published Special Issues:</p>

<p><strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis">Image Analysis and Machine Learning for Cyber-Agricultural Systems</a></strong></p>

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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>
