---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Alternaria leaf spot
          '1': Brown spot
          '2': Frogeye leaf spot
          '3': Grey spot
          '4': Health
          '5': Mosaic
          '6': Powdery mildew
          '7': Rust
          '8': Scab
  splits:
  - name: train
    num_bytes: 1184144271
    num_examples: 14582
  download_size: 1184524325
  dataset_size: 1184144271
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Appleleaf9 Disease Classification

AppleLeaf9 is an apple leaf disease classification set of 14,582 RGB images in nine classes: healthy, Alternaria leaf spot, brown spot, frogeye leaf spot, grey spot, mosaic, powdery mildew, rust and scab. It was compiled from four public sources: the PlantVillage dataset, the apple tree leaf disease segmentation dataset (ATLDSD, about half laboratory and half field images from four apple experimental stations), and the Kaggle Plant Pathology 2020 and 2021 challenge sets (field photos). Agricultural disease experts screened every image and removed mislabelled ones, and some static-background images were dropped during fusion, so the set mixes plain-background leaf photos with leaves photographed in orchards. The dataset contains 14,582 images across 9 classes: Alternaria leaf spot, Brown spot, Frogeye leaf spot, Grey spot, Health, Mosaic, Powdery mildew, Rust, Scab.  
Images per class:
- Alternaria leaf spot: 417
- Brown spot: 411
- Frogeye leaf spot: 3,181
- Grey spot: 339
- Health: 516
- Mosaic: 371
- Powdery mildew: 1,184
- Rust: 2,753
- Scab: 5,410

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{yang2022efficient,
  title={Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks},
  author={Yang, Qing and Duan, Shukai and Wang, Lidan},
  journal={Agronomy},
  volume={12},
  pages={2784},
  year={2022},
  publisher={MDPI}
}
```


*This dataset was reformatted from its original format to match HuggingFace standards.*