README.md

metadata

language:
  - en
license: cc-by-4.0
task_categories:
  - object-detection
pretty_name: Global Wheat Head
tags:
  - agriculture
  - biology
dataset_info:
  features:
    - name: image
      dtype: image
    - name: domain
      dtype: string
    - name: country
      dtype: string
    - name: location
      dtype: string
    - name: development_stage
      dtype: string
    - name: objects
      struct:
        - name: boxes
          sequence:
            sequence: int64
        - name: categories
          sequence: int64
  splits:
    - name: train
      num_bytes: 701105106.93
      num_examples: 3655
    - name: validation
      num_bytes: 264366740.324
      num_examples: 1476
    - name: test
      num_bytes: 301053063.17
      num_examples: 1381
  download_size: 1260938177
  dataset_size: 1266524910.424
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for "Global Wheat Head Dataset 2021" 😊

If you want any update on the Global Wheat Dataset Community, go on https://www.global-wheat.com/

Table of Contents

• Dataset Description
• Dataset Composition
• Usage
• Citation
• Acknowledgements

Dataset Description

• Creators: Etienne David and others
• Published: July 12, 2021 | Version 1.0
• Availability: Zenodo Link
• Keywords: Deep Learning, Wheat Counting, Plant Phenotyping

Introduction

Wheat is essential for a large part of humanity. The "Global Wheat Head Dataset 2021" aims to support the development of deep learning models for wheat head detection. This dataset addresses challenges like overlapping plants and varying conditions across global wheat fields. It's a step towards automating plant phenotyping and enhancing agricultural practices. 🌾

Dataset Composition

• Images: Over 6000, Resolution - 1024x1024 pixels
• Annotations: 300k+ unique wheat heads with bounding boxes
• Geographic Coverage: Images from 11 countries
• Domains: Various, including sensor types and locations
• Splits: Training (Europe & Canada), Test (Other regions)

Dataset Composition

Files and Structure

• Images: Folder containing all images (.png)
• CSV Files: competition_train.csv, competition_val.csv, competition_test.csv for different dataset splits
• Metadata: Metadata.csv with additional details

Labels

• Format: CSV with columns - image_name, BoxesString, domain
• BoxesString: [x_min,y_min, x_max,y_max] format for bounding boxes
• Domain: Specifies the image domain

Usage

Tutorials and Resources

• Tutorials available at AIcrowd Challenge Page

License

• Type: Creative Commons Attribution 4.0 International (cc-by-4.0)
• Details: Free to use with attribution

Citation

If you use this dataset in your research, please cite the following:

@article{david2020global,
  title={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},
  author={David, Etienne and others},
  journal={Plant Phenomics},
  volume={2020},
  year={2020},
  publisher={Science Partner Journal}
}
@misc{david2021global,
  title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods},
  author={Etienne David and others},
  year={2021},
  eprint={2105.07660},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Acknowledgements

Special thanks to all the contributors, researchers, and institutions that played a pivotal role in the creation of this dataset. Your efforts are helping to advance the field of agricultural sciences and technology. 👏