---
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

```bibtex
@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.*