Datasets:

Project-AgML
/

arabica_coffee_leaf_disease_classification

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
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Cerscospora
            '1': Healthy
            '2': Leaf_rust
            '3': Miner
            '4': Phoma
  splits:
    - name: train
      num_bytes: 1923533109
      num_examples: 58549
  download_size: 1840319580
  dataset_size: 1923533109

Arabica Coffee Leaf Disease Classification

A dataset for disease classification of Arabica Coffee Leaf. The dataset contains 58,549 images across 5 classes: Cerscospora, Healthy, Leaf_rust, Miner, Phoma.
Images per class:

Cerscospora: 7,681
Healthy: 18,983
Leaf_rust: 8,336
Miner: 16,978
Phoma: 6,571

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

Citation

@article{jepkoech2021arabica,
  title={Arabica coffee leaf images dataset for coffee leaf disease detection and classification},
  author={Jepkoech, Jennifer and Mugo, David Muchangi and Kenduiywo, Benson K and Too, Edna Chebet},
  journal={Data in brief},
  volume={36},
  pages={107142},
  year={2021},
  publisher={Elsevier}
}

Jepkoech, jennifer; Kenduiywo, Benson; Mugo, David; Chebet, Edna (2021), “JMuBEN”, Mendeley Data, V1, doi: 10.17632/t2r6rszp5c.1
Jepkoech, Jennifer; Mugo, David; Kenduiywo, Benson; Chebet, Edna (2021), “JMuBEN2”, Mendeley Data, V1, doi: 10.17632/tgv3zb82nd.1