Journal article
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
- David, Etienne ORCID UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l’Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Madec, Simon ORCID UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l’Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Sadeghi-Tehran, Pouria ORCID Plant Sciences Department, Rothamsted Research, Harpenden, UK
- Aasen, Helge ORCID Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
- Zheng, Bangyou ORCID CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
- Liu, Shouyang ORCID Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Kirchgessner, Norbert ORCID Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
- Ishikawa, Goro ORCID Institute of Crop Science, National Agriculture and Food Research Organization, Japan
- Nagasawa, Koichi ORCID Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Japan
- Badhon, Minhajul A. ORCID Department of Computer Science, University of Saskatchewan, Canada
- Pozniak, Curtis ORCID Department of Plant Sciences, University of Saskatchewan, Canada
- de Solan, Benoit ORCID Arvalis, Institut du végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- Hund, Andreas ORCID Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
- Chapman, Scott C. ORCID School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
- Baret, Frédéric ORCID Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Stavness, Ian ORCID Department of Computer Science, University of Saskatchewan, Canada
- Guo, Wei ORCID Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
Published in:
- Plant Phenomics. - American Association for the Advancement of Science (AAAS). - 2020, vol. 2020, p. 1-12
English
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.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.34133/2020/3521852
- ISSN 2643-6515
- Persistent URL
- https://sonar.ch/global/documents/112899
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