  Data competitions have become a popular approach to crowdsource new data
analysis methods for general and specialized data science problems. In plant
phenotyping, data competitions have a rich history, and new outdoor field
datasets have potential for new data competitions. We developed the Global
Wheat Challenge as a generalization competition to see if solutions for wheat
head detection from field images would work in different regions around the
world. In this paper, we analyze the winning challenge solutions in terms of
their robustness and the relative importance of model and data augmentation
design decisions. We found that the design of the competition influence the
selection of winning solutions and provide recommendations for future
competitions in an attempt to garner more robust winning solutions.
