| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Downy_mildew |
| '1': Fresh_leaf |
| '2': Gray_mold |
| '3': Leaf_scars |
| splits: |
| - name: train |
| num_bytes: 2230694991 |
| num_examples: 2358 |
| download_size: 1836636286 |
| dataset_size: 2230694991 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Sunflower Disease Classification |
|
|
| A dataset for disease classification of sunflowers. The dataset contains 2,358 images across 4 classes: Downy_mildew, Fresh_leaf, Gray_mold, Leaf_scars. |
| Images per class: |
| - Downy_mildew: 590 |
| - Fresh_leaf: 649 |
| - Gray_mold: 470 |
| - Leaf_scars: 649 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{sara2022extensive, |
| title={An extensive sunflower dataset representation for successful identification and classification of sunflower diseases}, |
| author={Sara, Umme and Rajbongshi, Aditya and Shakil, Rashiduzzaman and Akter, Bonna and Sazzad, Sadia and Uddin, Mohammad Shorif}, |
| journal={Data in brief}, |
| volume={42}, |
| pages={108043}, |
| year={2022}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Rajbongshi, Aditya; Sara, Umme ; Akter, Bonna ; Shakil, Rashiduzzaman ; Sazzad, Sadia (2022), “Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning”, Mendeley Data, V1, doi: 10.17632/b83hmrzth8.1 |
