Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting
Étienne David, Franklin Ogidi, Daniel Smith, Scott Chapman, Benoît de Solan, Wei Guo, Frédéric Baret, Ian Stavness
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.