Datasetpublic

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… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/cotton_leaf_disease_classification_bangladesh_2.

sourceHugging Facecc-by-4.0updated 3mo agoView on Hugging Face

0likes55downloads

Dataset Card

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

bibtex

@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