Datasets:
File size: 1,596 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Downy_mildew
'1': Fresh_leaf
'2': Gray_mold
'3': Leaf_scars
splits:
- name: train
num_bytes: 2230694991
num_examples: 2358
download_size: 1836636286
dataset_size: 2230694991
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Sunflower Disease Classification
A dataset for disease classification of sunflowers. The dataset contains 2,358 images across 4 classes: Downy_mildew, Fresh_leaf, Gray_mold, Leaf_scars.
Images per class:
- Downy_mildew: 590
- Fresh_leaf: 649
- Gray_mold: 470
- Leaf_scars: 649
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{sara2022extensive,
title={An extensive sunflower dataset representation for successful identification and classification of sunflower diseases},
author={Sara, Umme and Rajbongshi, Aditya and Shakil, Rashiduzzaman and Akter, Bonna and Sazzad, Sadia and Uddin, Mohammad Shorif},
journal={Data in brief},
volume={42},
pages={108043},
year={2022},
publisher={Elsevier}
}
```
Rajbongshi, Aditya; Sara, Umme ; Akter, Bonna ; Shakil, Rashiduzzaman ; Sazzad, Sadia (2022), “Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning”, Mendeley Data, V1, doi: 10.17632/b83hmrzth8.1 |