Datasets:

Project-AgML
/

tuta_absoluta_tomato_leaf_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': healthy
            '1': infected
    - name: farm
      dtype: string
  splits:
    - name: train
      num_bytes: 71865486
      num_examples: 326
  download_size: 71870228
  dataset_size: 71865486
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - image-classification
size_categories:
  - n<1K

Tuta Absoluta Tomato Leaf Classification

TomatoEbola: 326 field photographs of tomato leaves from three farms in Yobe State, Nigeria (Dikumari, Kasaisa and Kukareta), labelled healthy or infected by the Tuta absoluta leaf miner, collected for a study on early tomato disease detection with vision transformers. The farm is kept as a column. The dataset contains 326 images across 2 classes: healthy, infected.
Images per class:

healthy: 152
infected: 174

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

Citation

@article{shehu2025early,
  title={Early detection of tomato leaf diseases using transformers and transfer learning},
  author={Shehu, Harisu Abdullahi and Ackley, Aniebietabasi and Marvellous, Mark and Eteng, Ofem Ebriba},
  journal={European Journal of Agronomy},
  volume={168},
  pages={127625},
  year={2025},
  publisher={Elsevier}
}

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