Project-AgML/cotton_disease_classification

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.