Project-AgML/papaya_leaf_disease_classification_bangladesh

Papaya Leaf Disease Classification Bangladesh

A dataset for disease classification of Papaya leaves. The dataset contains raw and augmented versions.
The raw dataset contains 1,400 images.
Images per class:

• Healthy Leaf: 182
• Leaf Curl: 284
• Mealybug: 233
• Mite Disease: 243
• Mosaic: 214
• Ring Spot: 244

The augmented dataset contains 6,618 images.
Images per class:

• Healthy Leaf: 879
• Leaf Curl: 1,334
• Mealybug: 1,096
• Mite Disease: 1,149
• Mosaic: 1,009
• Ring Spot: 1,151

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

Citation

@article{gani2024smartphone,
  title={Smartphone image dataset to distinguish healthy and unhealthy leaves in papaya orchards in Bangladesh},
  author={Gani, Raiyan and Rashid, Mohammad Rifat Ahmmad and Ahmed, Jubaer and Isty, Maherun Nessa and Islam, Maheen and Hasan, Mahamudul and Ferdaus, Md Hasanul and Ali, Md Sawkat},
  journal={Data in Brief},
  volume={55},
  pages={110599},
  year={2024},
  publisher={Elsevier}
}

Rashid, Mohammad Rifat Ahmmad; Gani , Raiyan ; Ahmed, Jubaer ; Isty , Maherun Nessa ; Ali, Sawkat (2024), “Healthy and Unhealthy Papaya Leaf Images from Bangladeshi Orchards”, Mendeley Data, V1, doi: 10.17632/44p8v6ywsm.1