Datasets:

Project-AgML
/

wheat_mosaic_classification_multispectral

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': diseased
            '1': early
    - name: source
      dtype: string
    - name: cultivar
      dtype: string
  splits:
    - name: train
      num_bytes: 2135684930
      num_examples: 424
  download_size: 2135737421
  dataset_size: 2135684930
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - n<1K

Wheat Mosaic Classification Multispectral

This dataset provides real multispectral images of wheat plants in field environments across South Africa, collected during August and October 2023. Captured using a specially adapted Canon EOS 800D DSLR, the images focus on early detection of Wheat Stripe Mosaic Virus, depicting plants at various stages of disease progression. The dataset contains 424 images across 2 classes: diseased, early.
Images per class:

diseased: 252
early: 172

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{desilva2025early,
  title={Early detection of Wheat Stripe Mosaic Virus using multispectral imaging with deep-learning},
  author={De Silva, Malithi and Brown, Dane},
  journal={Ecological Informatics},
  volume={87},
  pages={103088},
  year={2025},
  publisher={Elsevier}
}

De Silva, Malithi (2025), “Wheat_dataset”, Mendeley Data, V1, doi: 10.17632/kgmcb5w5tk.1

This dataset was reformatted from its original format to match HuggingFace standards.