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Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods
Open accessAbstract
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
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“Prior work on wheat head detection has shown that this variation can significantly degrade model performance on regions unseen during training (David et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as David et al., 2020
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“Since their 2016 publication, numerous disciplines are striving to apply FAIR Principles, including genomics, agriculture (Arnaud et al., 2020; David et al., 2020), climate science, and medicine.”
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Metadata
- Authors
- David, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M. A., Pozniak, C., de Solan, B., Hund, A., Chapman, S. C., Baret, F., Stavness, I., & Guo, W.
- Affiliations
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- ETH Zurich (CH)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Nanjing Agricultural University (CN)
- National Agriculture and Food Research Organization (JP)
- University of Saskatchewan (CA)
- The University of Tokyo (JP)
- Corresponding author
- Stavness, Ian
- Guo, Wei
- ORCID
- Venue
- Plant Phenomics
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 2020
- Pages
- 3521852
- ISSN
- 2097-0374 (ISSN-L)
- 2643-6515
- PMC
- PMC7706323
- OpenAlex
- W3064678530
- Cited by
- 277
- References
- 45
- Citations by year
- 2026: 39 · 2025: 46 · 2024: 44 · 2023: 42 · 2022: 54 · 2021: 43 · 2020: 9
- FWCI
- 23.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Plant Phenomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rothamsted Repository (Rothamsted Repository) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rothamsted Repository (Rothamsted Repository) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- FSOV “Plastix”
- CASDAR LITERAL
- ANR BREEDWHEAT
- ANR PHENOME
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- wheat head detection, RGB images, head orientation, FAIR principles, image metadata, benchmarking, plant phenotyping
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