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Project-AgML/papaya_leaf_disease_classification_bd

Papaya Leaf Disease Classification Bd A dataset for image classification of Papaya Leaf Disease Classification Bd. The dataset contains 11,074 images across 8 classes: Antracnose, Bacterial Spot, Healthy Leaf, Leaf Curl, Mealybug, Mite Disease, Mosaic, Ring Spot.Images per class: Antracnose: 1,261 Bacterial Spot: 1,235 Healthy Leaf: 1,210 Leaf Curl: 1,982 Mealybug: 1,390 Mite Disease: 1,588 Mosaic: 1,118 Ring Spot: 1,290 This dataset is indexed on… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/papaya_leaf_disease_classification_bd.

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Dataset Card

Papaya Leaf Disease Classification Bd

A dataset for image classification of Papaya Leaf Disease Classification Bd. The dataset contains 11,074 images across 8 classes: Antracnose, Bacterial Spot, Healthy Leaf, Leaf Curl, Mealybug, Mite Disease, Mosaic, Ring Spot. Images per class:

—Antracnose: 1,261
—Bacterial Spot: 1,235
—Healthy Leaf: 1,210
—Leaf Curl: 1,982
—Mealybug: 1,390
—Mite Disease: 1,588
—Mosaic: 1,118
—Ring Spot: 1,290

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

Citation

bibtex

@article{dey2026swinghost,
  title={SwinGhost-ClustNet: An explainable deep ensemble model for papaya leaf disease detection and field deployment in Bangladeshi agriculture},
  author={Dey, Shourav and Hasan, Mohammad Kamrul and Adhikary, Apurba and Akter, Sanjida and Ejaz, Md Sabbir},
  journal={Smart Agricultural Technology},
  volume={13},
  pages={101824},
  year={2026},
  publisher={Elsevier}
}

https://www.kaggle.com/datasets/shourav123/enlarged-data

This dataset was reformatted from its original format to match HuggingFace standards.