Datasets:
| 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.* | |