---
configs:
- config_name: raw
  default: true
  data_dir: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Black Spot
          '1': Healthy Leaf
          '2': Insect  Hole
          '3': Yellow Mosaic Virus
- config_name: preprocessed
  data_dir: preprocessed
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Black Spot
          '1': Healthy Leaf
          '2': Insect Hole
          '3': Yellow Mosaic Virus
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# Rose Leaf Disease Classification

A dataset for classification of Rose leaf disease. The dataset contains raw and preprocessed versions.  
The dataset contains 3,228 images.  
Images per class:
- Black Spot: 409
- Healthy Leaf: 1,686
- Insect  Hole: 453
- Yellow Mosaic Virus: 680

The preprocessed version has had images scaled down to 3000x3000, and backgrounds standardized.


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

## Citation

```bibtex
@article{shacha2025roseleafinsight,
  title={RoseLeafInsight: A high-resolution image dataset for rose leaf disease recognition},
  author={Shacha, Arnob Das and Durjoy, Sabbir Hossain and ShikderKamal, Md Emon Mostafa and Shoib, Md Mehedi Hasan and Bijoy, Md Hasan Imam},
  journal={Data in Brief},
  pages={111968},
  year={2025},
  publisher={Elsevier}
}```

Shacha, Arnob Das; Durjoy, Sabbir Hossain; Shikder, Md Emon; Kamal, MD Mostafa; Shoib, Md Mehedi Hasan; Bijoy, Md Hasan Imam (2025), “RoseLeafInsight: A High-Resolution Image Dataset for Rose Leaf Disease Recognition”, Mendeley Data, V1, doi: 10.17632/8chrjdxn79.1