Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

<div class="floats-full-page floats-full-page-other"><h2>Table 3</h2><div class="floats-full-page-caption"><div>Presentation of the different splits which can be used with the GWHD_2021.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr class="thead"><td class="align_left">Splits</td><td class="align_center">Training</td><td class="align_center">Validation</td><td class="align_center">Test</td></tr><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr><td class="align_left">Global Wheat Challenge 2021</td><td class="align_center">Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all)</td><td class="align_center">UQ_1 to UQ_6, Utokyo (all), NAU_1, Usask_1</td><td class="align_center">UQ_7 to UQ_12, Ukyoto_1, NAU_2 and NAU_3, ARC_1, CIMMYT (all), KSU (all), Terraref (all)</td></tr><tr><td class="align_left">GlobalWheat-WILDS</td><td class="align_center">Ethz_1, Rres_1, Inrae_1, Arvalis (all), NMBU (all), ULiège-GxABT (all)</td><td class="align_center">UQ (all), Utokyo (all), Ukyoto_1, NAU (all)</td><td class="align_center">CIMMYT (all), KSU (all); Terraref (all), Usask_1, ARC_1</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 3 | Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

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Table 3: Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

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Table 3

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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 Environment), 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><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><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>


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Plant Phenomics | Science Partner Journal

The open access journal Plant Phenomics, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.