Datasets:

Project-AgML
/

banana_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:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': healthy
            '1': segatoka
            '2': xamthomonas
  splits:
    - name: train
      num_bytes: 6672019
      num_examples: 1288
  download_size: 6920645
  dataset_size: 6672019

Banana Leaf Disease Classification

A dataset for disease classification of Banana Leaves. The dataset contains 1,288 images across 3 classes: healthy, segatoka, xamthomonas.
Images per class:

healthy: 154
segatoka: 320
xamthomonas: 814

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

Citation

@article{genet2024sigatoka,
  title={Sigatoka and xanthomonas banana leaf disease detection via transfer learning},
  author={Genet, Yordanos Hailu and Sinshaw, Natnael Tilahun and Assefa, Beakal Gizachew and Mohapatra, Sudhir Kumar},
  journal={Scientia Iranica},
  volume={31},
  number={21},
  pages={1939--1947},
  year={2024},
  publisher={Sharif University of Technology}
}

hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1