| --- |
| 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 |
