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 fi...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.展开更多
基金support from ANRT for the CIFRE grant of E.D.,cofunded by Arvalispartly supported by several projects,including:Canada:The Canada First Research Excellence Fund and the Global Institute Food Security,University of Saskatchewan supported the organization of the competition.+2 种基金rance:PIA#Digitag Institut Convergences Agriculture Numérique,Hiphen sup-ported the organization of the competition and the Agence Nationale de la Recherche projects ANR-11-INBS-0012(Phenome)Japan:Kubota supported the organization of the competitionAustralia:Grains Research and Development Corporation(UOQ2002-008RTX Machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-A technol-ogy and analytics platform for improving variety selection)supported competition and data provision/discussions.
文摘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.
