---
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