Datasets:

Project-AgML
/

cotton_disease_classification

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

@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.