Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

<div class="floats-full-page floats-full-page-figure"><h2>Figure 1</h2><div class="floats-full-page-caption"><div>Overview of the harmonization process conducted. Images were first rescaled using bilinear interpolation up- or downsampling techniques. Then, the rescaled images were split into <svg height="8.69875pt" id="M19" style="vertical-align:-0.3499298pt" version="1.1" viewbox="-0.0498162 -8.34882 63.6774 8.69875" width="63.6774pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,12.48,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"></path></g><g transform="matrix(.013,0,0,-0.013,18.72,0)"><path d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z"></path></g><g transform="matrix(.013,0,0,-0.013,27.866,0)"><path d="M528 54L331 254L528 455L492 493L294 291L96 493L60 455L257 254L60 54L96 16L294 217L492 16L528 54Z"></path></g><g transform="matrix(.013,0,0,-0.013,38.402,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,44.642,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,50.882,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"></path></g><g transform="matrix(.013,0,0,-0.013,57.122,0)"><path d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z"></path></g></svg> squared patches.</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-2020/3521852/figures/3521852.fig.001.svgz" name="3521852.fig.001" type="image/svg+xml"></object></td></tr></table></div></div>

Figure 1 | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

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Figure 1: Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

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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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<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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<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>
