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

A - Papers appearing in refereed journals

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. and Guo, W. 2020. 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. Plant Phenomics. 2020 (3521852), pp. 1-16. https://doi.org/10.34133/2020/3521852

AuthorsDavid, 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. and Guo, W.
Abstract

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.

KeywordsDataset; Wheat spike/ear/head; Detection; Deep learning; RGB
Year of Publication2020
JournalPlant Phenomics
Journal citation2020 (3521852), pp. 1-16
Digital Object Identifier (DOI)https://doi.org/10.34133/2020/3521852
Web address (URL)https://spj.sciencemag.org/journals/plantphenomics/2020/3521852/
Open accessPublished as bronze (free) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeDesigning Future Wheat (DFW) [ISPG]
Accepted author manuscript
Output statusPublished
Publication dates
Online20 Aug 2020
Publication process dates
Accepted01 Jul 2020
PublisherAmerican Association for the Advancement of Science (AAAS)
ISSN2643-6515
Other fileGlobal%20Wheat%20Head%20Detection%20(GWHD)%20Dataset%3A%20A%20Large%20and%20Diverse%20Dataset%20of%20High-Resolution%20RGB-Labelled%20Images%20to%20Develop%20and%20Benchmark%20Wheat%20Head%20Detection%20Methods.pdf

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