Cited by 277Smart Agriculture and AIAdvanced Neural Network ApplicationsRemote Sensing in Agriculture

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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

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David, Etienne, Madec, Simon, Sadeghi-Tehran, Pouria, Aasen, Helge, Zheng, Bangyou, Liu, Shouyang, Kirchgessner, Norbert, Ishikawa, Goro, Nagasawa, Koichi, Badhon, Minhajul A., Pozniak, Curtis, de Solan, Benoit, Hund, Andreas, Chapman, Scott C., Baret, Frédéric, Stavness, Ian, Guo, Wei·2020·Plant Phenomics

DOI 10.34133/2020/3521852PMC PMC7706323

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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. 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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Figure 1 Overview of the harmonization process conducted. Images were first rescaled using bilinear interpolation up- or downsampling techniques. Then, the rescaled images were split into 1024 × 1024 squared patches.CC BY · PubMed Central

Figure 2 Examples of wheat heads difficult to label. These examples are zoomed-in views from images contained in the dataset, with different zoom factors. It includes overlapping heads (a–c), heads at emergence (d), heads that are partly cut at the border of the image (e), and images with a low illumination (f). Note that image (d) was removed from the dataset because of the ambiguity of heads at emergence. Wheat heads in the image (e) were not labelled because less than 30% of their basal part is visible, as defined in Section 2.4.CC BY · PubMed Central

Figure 3 Distribution of the number of bounding boxes per image (a) and bounding boxes size∗ (b) in the GWHD dataset. ∗The bounding box size is defined as the square root of the bounding box area in pixel.CC BY · PubMed Central

Figure 4 Example of images from different acquisition sites after cropping and rescaling.CC BY · PubMed Central

Figure 5 A selection of bounding boxes for each sub-dataset. The same size of pixels is used across all the bounding boxes displayed.CC BY · PubMed Central

Figure 6 Comparison of GWHD dataset with other object detection datasets. Both axes are in log-scale.CC BY · PubMed Central

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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: David, Etienne
Madec, Simon
Sadeghi-Tehran, Pouria
Aasen, Helge
Zheng, Bangyou
Liu, Shouyang
Kirchgessner, Norbert
Ishikawa, Goro
Nagasawa, Koichi
Badhon, Minhajul A.
Pozniak, Curtis
de Solan, Benoit
Hund, Andreas
Chapman, Scott C.
Baret, Frédéric
Stavness, Ian
Guo, Wei
Venue: Plant Phenomics
Publisher: Elsevier BV
Published: 2020
Volume: 2020
Pages: 3521852
ISSN: 2097-0374 (ISSN-L)
2643-6515
DOI: 10.34133/2020/3521852
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: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 232 d)
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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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

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