Datasets:

Project-AgML
/

sunflower_disease_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Downy_mildew
            '1': Fresh_leaf
            '2': Gray_mold
            '3': Leaf_scars
  splits:
    - name: train
      num_bytes: 2230694991
      num_examples: 2358
  download_size: 1836636286
  dataset_size: 2230694991
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

Sunflower Disease Classification

A dataset for disease classification of sunflowers. The dataset contains 2,358 images across 4 classes: Downy_mildew, Fresh_leaf, Gray_mold, Leaf_scars.
Images per class:

Downy_mildew: 590
Fresh_leaf: 649
Gray_mold: 470
Leaf_scars: 649

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

Citation

@article{sara2022extensive,
  title={An extensive sunflower dataset representation for successful identification and classification of sunflower diseases},
  author={Sara, Umme and Rajbongshi, Aditya and Shakil, Rashiduzzaman and Akter, Bonna and Sazzad, Sadia and Uddin, Mohammad Shorif},
  journal={Data in brief},
  volume={42},
  pages={108043},
  year={2022},
  publisher={Elsevier}
}

Rajbongshi, Aditya; Sara, Umme ; Akter, Bonna ; Shakil, Rashiduzzaman ; Sazzad, Sadia (2022), “Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning”, Mendeley Data, V1, doi: 10.17632/b83hmrzth8.1