| --- |
| 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 |
|
|
| ```bibtex |
| @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.* |
| |
