README.md

metadata

configs:
  - config_name: augmented
    data_files:
      - split: train
        path: augmented/train-*
  - config_name: raw
    data_dir: raw
    default: true
license: cc-by-nc-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K
dataset_info:
  - config_name: augmented
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': Alternaria_Leaf
              '1': Bacterial_Blight
              '2': Fusarium_Wilt
              '3': Healthy_Leaf
              '4': Verticillium_Wilt
    splits:
      - name: train
        num_bytes: 355269281
        num_examples: 4963
    download_size: 359213105
    dataset_size: 355269281
  - config_name: raw
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': Alternaria_Leaf
              '1': Bacterial_Blight
              '2': Fusarium_Wilt
              '3': Healthy_Leaf
              '4': Verticillium_Wilt
    splits:
      - name: train
        num_bytes: 60642991
        num_examples: 1373
    download_size: 61250807
    dataset_size: 60642991

Cotton Leaf Disease Classification

A dataset for disease classification of cotton leaves. The dataset contains both the raw data and augmented version.
The raw data contains 1373 images distributed between the classes as follows:

• Alternaria Leaf Spot: 173 images
• Bacterial Blight: 218 images
• Fusarium Wilt: 337 images
• Healthy Leaf: 333 images
• Verticillium Wilt: 312 images

The augmented data contains 4963 images, again distributed as follows:

• Alternaria_Leaf: 987 images
• Bacterial_Blight: 1027 images
• Fusarium_Wilt: 957 images
• Healthy_Leaf: 1015 images
• Verticillium_Wilt: 977 images

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

Citation

@article{ripon2025cotton,
  title={Cotton leaf image dataset for disease classification and health monitoring},
  author={Ripon, Shamim and Gani, Raiyan and Niha, Nazratan Mazumder and Rahat, Wasimul Bari and Toufiq, Shafaeat Hasan and Maisha, Mushfida Ferdous and Ahmed, Jubaer},
  journal={Data in Brief},
  pages={112142},
  year={2025},
  publisher={Elsevier}
}

Ripon, Shamim; Gani , Raiyan ; Niha, Nazratan Mazumder ; Rahat, Wasimul Bari ; Toufiq, Shafaeat Hasan ; Maisha, Mushfida Ferdous ; Ahmed, Jubaer (2025), “Cotton Leaf Image Dataset for Disease Classification ”, Mendeley Data, V1, doi: 10.17632/t9hgvk2h9p.1