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

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Figure 16 | TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Figure 16: TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

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Figure 16

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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>
