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

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.

Plant phenotyping relevance

圃場画像からコムギ穂を検出・計数するモデルのチャレンジ設計と、異なるデータセットへの頑健性評価を中心に扱うため、画像ベースの表現型解析手法・ベンチマーク研究に該当します。

Code and data availability

The paper's wheat head detection analysis relies on two paper-specific public assets: the Global Wheat Head Dataset 2021 (annotated field RGB images used for training/testing the challenge solutions) openly deposited on Zenodo, and the winning challenge solutions' code made open-source on GitHub. Both have explicit, in