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To improve precision phenotyping under field conditions by assembling large, diverse and well annotated image data.

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#Welcome To Global wheat website!

What is Global Wheat Dataset?

Global wheat Dataset consortium aims to improve precision phenotyping under field conditions by assembling large, diverse and well annotated image data.

Different organ class labels
Large range of cultivars and environmental condition
Different developmental stages

Projects:

Global Wheat head detection dataset (GWHD)
Global wheat full semantic segmentation (GWFSS)

#Past projects (GWHD)

Global Wheat Head Detection Dataset

Global wheat head detection Dataset is the first large-scale dataset for wheat head detection from field optical images. It included a very large range of cultivars from differents continents. Wheat is a staple crop grown all over the world and consequently interest in wheat phenotyping spans the globe. Therefore, it is important that models developed for wheat phenotyping, such as wheat head detection networks, generalize between different growing environments around the world.

Free to use for any purpose
Large range of cultivars and environmental condition
Images taken by different platforms

GWHD 2020

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.This is the official version of the Global Wheat Head Dataset presented in David et al. (2020) . It's a corrected version of the dataset published on Kaggle, and the one used for the Codalab challenge.

version 4 DOI Download

GWHD 2021

From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. This is the official version of the Global Wheat Head Dataset presented in David et al. (2021).Labels are included in csv. The dataset is composed of more than 6000 images of 1024x1024 pixels containing 300k+ unique wheat heads, with the corresponding bounding boxes.

version 1.0 DOI Download

#Current projects (GWFSS)

Global wheat full semantic segmentation

Aim to train a semantic segmentation of wheat organs (stems, leaves, flowers) … under field conditions to enable precision breeding…

Deep learning methods for image processing are rapidly advancing and imaging techniques have become a standard for classification and quantification in agriculture. However, agricultural datasets assembled by domain experts are still comparably small. The global wheat consortium brings together this domain knowledge to assemble a large training set. We aim is to define a balanced dataset for training and validation containing the most relevant features observable for field-grown wheat. Image information will be enhanced by metadata, such as the developmental stage, genotype or agricultural treatment.

Contributed images fulfil the following conditions:

Red-green-blue (RGB) images
0° and 45°, viewing angles
Spatial resolution <= 0.5 mm / pixel
Patches of [512 x 512] pixels, up to [1024 x 1024].
At least 7 Classes: wheat spikes, stems, awns, leaves, soil ...

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CVAT Annotation Platform Website User Manual

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#Our Sponsors

Sponsors for GWHD project. Call for sponsors for GWFSS project!

Also want supporting us to become a sponsor?

Contact us

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Platinum sponsor
Global Institude For Food Security
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Gold sponsor
Kubota
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Gold sponsor
Digit Agriculture
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Gold sponsor
Hiphen
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Silver sponsor
Plant Phenomics
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Silver sponsor
Quantomics

They also support us

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Organisers and Collaborators

Name Role Organization
Marc LABADIE HIPHEN
Alexis Comar HIPHEN
DE SOLAN Benoît Arvalis
Raul Lopez-Lozano INRAe
Malcolm Hawkesford Rothamsted
Latifa Greche Rothamsted
Marie Weiss INRAE
Stavness Ian Competition University of Saskatchewan
Scott Chapman University of Queensland
Wei Guo Website manager University of Tokyo
Haozhou Wang Website manager University of Tokyo
Etienne David INRAE
Simon MADEC INRAE
Frederic Baret INRAE
Kirchgessner Norbert ETHZ
Andreas Hund Project leader ETHZ
PINTO ESPINOSA Francisco (CIMMYT)
Visioni, Andrea ICARDA-Morocco
Zenkl Radek CVAT administrator / hosting ETH

#Past Events

Global Wheat Competitions

Oct 2020

Global Wheat Head Detection

Competition

Powered by Kaggle platform

2,245 teams participated

$15,000 Prize Money

Check Details

Nov 2021

Global Wheat Challenge 2021

Competition

Powered by AIcrowd platform

563 teams participated

$4,000 Prize Money

Check Details