To improve precision phenotyping under field conditions by assembling large, diverse and well annotated image data.
#Global wheat dataset
Global Wheat Dataset consortium forms ad-hoc initiatives to collect training data to solve computer vision problems.
Our aim is to improve precision phenotyping of wheat by assembling large, diverse and well annotated image data and make them publicly available. We also organize data challenges to gain the attention of the machine-learning community to plant-related computer vision problems.
The images include:
Global Wheat Head Detection (GWHD; 2020)
David et al. 2020
DOI: 10.34133/2020/3521852
Global wheat head Detection (GWHD; 2021)
David et al. 2021
DOI: 10.34133/2021/9846158
Global Wheat Full Semantic Segmentation (GWFSS_v1.0; 2025)
Wang et al. 2025
DOI: 10.1016/j.plaphe.2025.100084#Projects
Click to see more details of each dataset
#News
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affiliated conference/workshop: MLCAS, EPPS, CVPPA
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563 teams participated
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2,245 teams participated
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