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 https://project-agml.github.io/ as part of the AgML python library.

Citation

@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.