Datasets:
File size: 1,335 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Alternaria
'1': Apple_Mosaic
'2': Healthy
splits:
- name: train
num_bytes: 64844454
num_examples: 5612
download_size: 66907339
dataset_size: 64844454
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
---
# Apple Leaf Disease Classification
A dataset for image classification of Apple Leaf Disease Classification. The dataset contains 7,505 images across 3 classes: Alternaria, Apple_Mosaic, Healthy.
Images per class:
- Alternaria: 2,523
- Apple_Mosaic: 2,523
- Healthy: 2,459
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{yatoo2024indigenous,
title={An indigenous dataset for the detection and classification of apple leaf diseases},
author={Yatoo, Arshad Ahmad and Sharma, Amit},
journal={Data in Brief},
volume={53},
pages={110165},
year={2024},
publisher={Elsevier}
}
```
Yatoo, Arshad; Sharma, Amit (2024), “Indigenous Dataset for Apple Leaf Disease Detection and Classification”, Mendeley Data, V3, doi: 10.17632/9m2dcb5mmr.3 |