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Research Article | Open Access

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting

Etienne David1,2( ), Franklin Ogidi3, Daniel Smith4, Scott Chapman4, Benoit de Solan2, Wei Guo5, Frederic Baret1, Ian Stavness3
UMR 1114 EMMAH, INRAE, Avignon, France
Arvalis – Institut du Végétal, Paris, France
Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
School of Food and Agricultural Sciences, University of Queensland, Brisbane, Australia
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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Abstract

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

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Plant Phenomics
Volume 5,
2023
Article number: 0059
DOI: 10.34133/plantphenomics.0059
Cite this article:
David E, Ogidi F, Smith D, et al. Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting. Plant Phenomics, 2023, 5: 0059. https://doi.org/10.34133/plantphenomics.0059

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Received: 29 June 2022
Accepted: 01 June 2023
Published: 26 June 2023
© 2023 Etienne David et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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