#Welcome To Global wheat website!
Global wheat Dataset consortium aims to improve precision phenotyping under field conditions by assembling large, diverse and well annotated image data.
#Past projects (GWHD)
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.
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
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)
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:
CVAT Annotation Platform Website User Manual
Dataset download link comming soon
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|DE SOLAN Benoît||Arvalis|
|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|
|Andreas Hund||Project leader||ETHZ|
|PINTO ESPINOSA||Francisco (CIMMYT)|
|Zenkl Radek||CVAT administrator / hosting||ETH|
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