GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

<div class="floats-full-page floats-full-page-figure"><h2>Figure 1</h2><div class="floats-full-page-caption"><div>Results of our volumetric regression network. Output volumetric banana models resting on 2D input images.</div></div><div class="floats-full-page-content"><table class="figure-group"><tr class="fig-image"><td><object data="https://d31iiq90dwlpfy.cloudfront.net/articles/plantphenomics/volume-2021/9874597/figures/9874597.fig.001.svgz" name="9874597.fig.001" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 1 | GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

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Figure 1: GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

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Figure 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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Meet the editorial board

<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>

<p>&nbsp;</p>

<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>

<p>&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>
