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Data competitions have become a popular approach to crowdsource new dataanalysis methods for general and specialized data science problems. In plantphenotyping, data competitions have a rich history, and new outdoor fielddatasets have potential for new data competitions. We developed the GlobalWheat Challenge as a generalization competition to see if solutions for wheathead detection from field images would work in different regions around theworld. In this paper, we analyze the winning challenge solutions in terms oftheir robustness and the relative importance of model and data augmentationdesign decisions. We found that the design of the competition influence theselection of winning solutions and provide recommendations for futurecompetitions in an attempt to garner more robust winning solutions.

Global Wheat Challenge 2020: Analysis of the competition design and winning models