---
dataset_info:
- config_name: augmented
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Black Spot
          '1': Downy mildew
          '2': Fresh Leaf
  splits:
  - name: train
    num_bytes: 1988446946
    num_examples: 4342
  download_size: 1884098252
  dataset_size: 1988446946
- config_name: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Black Spot
          '1': Downy Mildew
          '2': Fresh Leaf
  splits:
  - name: train
    num_bytes: 530370256
    num_examples: 917
  download_size: 530409292
  dataset_size: 530370256
configs:
- config_name: augmented
  data_files:
  - split: train
    path: augmented/train-*
- config_name: raw
  default: true
  data_files:
  - split: train
    path: raw/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# RoseNet Leaf Disease Classification

A dataset for disease classification of rose leaves.  The dataset contains raw and augmented versions.  
The raw dataset contains 917 images.  
Images per class:
- Black Spot: 313
- Downy Mildew: 200
- Fresh Leaf: 404

The augmented dataset contains 4,342 images.  
Images per class:
- Black Spot: 1,434
- Downy mildew: 1,478
- Fresh Leaf: 1,430


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

## Citation

```bibtex
@article{sazzad2022rosenet,
  title={RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose},
  author={Sazzad, Sadia and Rajbongshi, Aditya and Shakil, Rashiduzzaman and Akter, Bonna and Kaiser, M Shamim},
  journal={Data in Brief},
  volume={44},
  pages={108497},
  year={2022},
  publisher={Elsevier}
}
```

Rajbongshi, Aditya; Sazzad, Sadia ; Shakil, Rashiduzzaman ; Akter, Bonna ; Kaiser, M Shamim (2022), “FlowerNet: An extensive rose leaves dataset for disease recognition applying machine learning and deep learning models”, Mendeley Data, V2, doi: 10.17632/7z67nyc57w.2