---
configs:
- config_name: raw
  default: true
  data_dir: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Healthy Leaf
          '1': Insect Pest Disease
          '2': Leaf Spot Disease
          '3': Mosaic Virus Disease
          '4': White Mold Disease
          '5': Wilt Disease
- config_name: augmented
  data_dir: augmented
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Healthy Leaf
          '1': Insect Pest Disease
          '2': Leaf Spot Disease
          '3': Mosaic Virus Disease
          '4': White Mold Disease
          '5': Wilt Disease
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---

# Eggplant Disease Classification

A dataset for disease classification of Eggplants. The dataset contains raw and augmented versions.  
The raw dataset contains 4,089 images.  
Images per class:
- Healthy Leaf: 1,451
- Insect Pest Disease: 546
- Leaf Spot Disease: 602
- Mosaic Virus Disease: 1,362
- White Mold Disease: 63
- Wilt Disease: 65

The augmented dataset contains 20,353 images.  
Images per class:
- Healthy Leaf: 7,265
- Insect Pest Disease: 2,728
- Leaf Spot Disease: 3,010
- Mosaic Virus Disease: 6,710
- White Mold Disease: 315
- Wilt Disease: 325


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

## Citation

```bibtex
@article{howlader2025comprehensive,
  title={A comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications},
  author={Howlader, Shakib and Ahamed, Md Sabbir and Mojumdar, Mayen Uddin and Noori, Sheak Rashed Haider and Siddiquee, Shah Md Tanvir and Chakraborty, Narayan Ranjan},
  journal={Data in Brief},
  volume={59},
  pages={111353},
  year={2025},
  publisher={Elsevier}
}```

Howlader, Shakib; Ahamed, Md. Sabbir; Mojumdar, Mayen Uddin  (2025), “Eggplant Leaf Disease Detection Dataset”, Mendeley Data, V2, doi: 10.17632/d3ypkphghb.2