Research Article | Open Access

Volume 2023 |Article ID 0059 | https://doi.org/10.34133/plantphenomics.0059

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

Etienne David ,^1,2 Franklin Ogidi,³ Daniel Smith,⁴ Scott Chapman,⁴ Benoit de Solan,² Wei Guo,⁵ Frederic Baret,¹ and Ian Stavness³

¹UMR 1114 EMMAH, INRAE, Avignon, France
²Arvalis – Institut du Végétal, Paris, France
³Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
⁴School of Food and Agricultural Sciences, University of Queensland, Brisbane, Australia
⁵Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

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.