---
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