Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

<div class="floats-full-page floats-full-page-figure"><h2>Figure 4</h2><div class="floats-full-page-caption"><div>Diagrams of key concepts for using CNNs for plant/organ detection, counting, and localization.</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-2020/4152816/figures/4152816.fig.004.svgz" name="4152816.fig.004" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 4 | Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

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Figure 4: Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

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Figure 4

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>


Journal profile

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>


Editorial board

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>
