---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': conf
          '1': conf+
          '2': esca
          '3': fd
  - name: grape_variety
    dtype: string
  splits:
  - name: train
    num_bytes: 792632284
    num_examples: 611
  download_size: 792664214
  dataset_size: 792632284
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---
# Grapevine Disease Classification

A dataset for disease classification of grapevines. The dataset contains 611 images across 4 classes and 4 grape varieties: 
Images per class:
- conf: 199
- conf+: 77
- esca: 107
- fd: 228

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

## Citation

```bibtex
@article{tardif2023expertized,
  title={An expertized grapevine disease image database including five grape varieties focused on Flavescence dor{\'e}e and its confounding diseases, biotic and abiotic stresses},
  author={Tardif, Malo and Amri, Ahmed and Deshayes, Aymeric and Greven, Marc and Keresztes, Barna and Fontaine, Ga{\"e}l and Sicaud, Laetitia and Paulhac, Laetitia and Bentejac, Sophie and Da Costa, Jean-Pierre},
  journal={Data in Brief},
  volume={48},
  pages={109230},
  year={2023},
  publisher={Elsevier}
}
```

Tardif, Malo (2023), “An expertized grapevine disease image database focused on Flavescence dorée and its confounding diseases”, Mendeley Data, V2, doi: 10.17632/3dr9r3w3jn.2

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