Datasets:

Project-AgML
/

grapevine_disease_classification

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

@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.