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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 |