---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Alternaria
          '1': Bacterial Blight
          '2': Cercospora
          '3': Grey Mildew
          '4': Healthy
  splits:
  - name: train
    num_bytes: 1294179547
    num_examples: 132
  download_size: 1294198933
  dataset_size: 1294179547
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---
# Cotton Disease Classification

This dataset comprises RGB images of cotton plants collected in field environments, featuring a mix of real agricultural observations and synthetic representations of disease conditions. It captures visual variations of cotton foliage under typical farming settings, providing a resource for developing computer vision models in agricultural disease detection. The dataset contains 132 images across 5 classes: Alternaria, Bacterial Blight, Cercospora, Grey Mildew, Healthy.  
Images per class:
- Alternaria: 25
- Bacterial Blight: 23
- Cercospora: 40
- Grey Mildew: 25
- Healthy: 19

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

## Citation

```bibtex
@article{aslam2025multi,
  title={Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm},
  author={Aslam, Afira and Usman, Syed Muhammad and Zubair, Muhammad and Yasin, Amanullah and Owais, Muhammad and Hussain, Irfan},
  journal={PLOS One},
  volume={20},
  pages={e0324293},
  year={2025},
  publisher={Public Library of Science}
}
```


The dataset itself can be cited as:

Bhagya Patil. (2021). *Cotton Leaf Dataset* [Dataset]. Mendeley. https://doi.org/10.17632/6HM6PC3Y43.2


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