Global Wheat Challenge 2020: Analysis of the competition design and winning models
This work addresses the problem of improving generalization in plant phenotyping competitions for researchers and practitioners, but it is incremental as it focuses on analysis and recommendations rather than new methods.
The authors analyzed the Global Wheat Challenge 2020 to assess if wheat head detection models generalize across regions, finding that competition design influenced winning solutions and offering recommendations for more robust outcomes.
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.