Project-AgML/cotton_leaf_disease_classification_bangladesh_2

Cotton Leaf Disease Classification Bangladesh 2

A dataset for disease classification of cotton leaves. The dataset contains raw and augmented versions.
The raw dataset contains 2,137 images.
Images per class:

• Bacterial Blight: 250
• Curl Virus: 431
• Healthy Leaf: 257
• Herbicide Growth Damage: 280
• Leaf Hopper Jassids: 225
• Leaf Redding: 578
• Leaf Variegation: 116

The augmented dataset contains 7,000 images.
Images per class:

• Bacterial Blight: 1,000
• Curl Virus: 1,000
• Healthy Leaf: 1,000
• Herbicide Growth Damage: 1,000
• Leaf Hopper Jassids: 1,000
• Leaf Redding: 1,000
• Leaf Variegation: 1,000

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

Citation

@article{bishshash2024comprehensive,
  title={A comprehensive cotton leaf disease dataset for enhanced detection and classification},
  author={Bishshash, Prayma and Nirob, Asraful Sharker and Shikder, Habibur and Sarower, Afjal Hossan and Bhuiyan, Touhid and Noori, Sheak Rashed Haider},
  journal={Data in Brief},
  volume={57},
  pages={110913},
  year={2024},
  publisher={Elsevier}
}

Bishshash, Prayma; Nirob, Md Asraful Sharker; Shikder, Md. Habibur; Sarower, Afjal (2024), “SAR-CLD-2024: A Comprehensive Dataset for Cotton Leaf Disease Detection”, Mendeley Data, V2, doi: 10.17632/b3jy2p6k8w.2