---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Anthracnose
          '1': Bacterial_Wilt
          '2': Belly_Rot
          '3': Downy_Mildew
          '4': Fresh_Cucumber
          '5': Fresh_Leaf
          '6': Gummy_Stem_Blight
          '7': Pythium_Fruit_Rot
  splits:
  - name: train
    num_bytes: 2883647720
    num_examples: 7689
  download_size: 2791082481
  dataset_size: 2883647720
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


# Cucumber Disease Classification

A dataset for disease classification of cucumber. The dataset contains 7,689 images across 8 classes: Anthracnose, Bacterial_Wilt, Belly_Rot, Downy_Mildew, Fresh_Cucumber, Fresh_Leaf, Gummy_Stem_Blight, Pythium_Fruit_Rot.  
Images per class:
- Anthracnose: 960
- Bacterial_Wilt: 960
- Belly_Rot: 960
- Downy_Mildew: 960
- Fresh_Cucumber: 960
- Fresh_Leaf: 960
- Gummy_Stem_Blight: 960
- Pythium_Fruit_Rot: 969

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

## Citation

```bibtex
@article{sultana2023dataset,
  title={A dataset for successful recognition of cucumber diseases},
  author={Sultana, Nusrat and Shorif, Sumaita Binte and Akter, Morium and Uddin, Mohammad Shorif},
  journal={Data in Brief},
  volume={49},
  pages={109320},
  year={2023},
  publisher={Elsevier}
}
```

Sultana, Nusrat; Shorif, Sumaita Binte ; Akter, Morium ; Uddin, Mohammad Shorif  (2022), “Cucumber Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/y6d3z6f8z9.1