| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Black Scurf |
| '1': Blackleg |
| '2': Blackspot Bruising |
| '3': Brown Rot |
| '4': Common Scab |
| '5': Dry Rot |
| '6': Healthy Potatoes |
| '7': Miscellaneous |
| '8': Pink Rot |
| '9': Soft Rot |
| splits: |
| - name: train |
| num_bytes: 92972501 |
| num_examples: 3905 |
| download_size: 93064969 |
| dataset_size: 92972501 |
| 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 |
| --- |
| # Potato Disease Classification |
|
|
| This dataset contains real RGB images of potato tubers captured in field environments, depicting various disease symptoms affecting potato crops. The natural agricultural context of the image collection provides a realistic foundation for developing and evaluating computer vision models focused on potato disease detection. The dataset contains 3,905 images across 10 classes: Black Scurf, Blackleg, Blackspot Bruising, Brown Rot, Common Scab, Dry Rot, Healthy Potatoes, Miscellaneous, Pink Rot, Soft Rot. |
| Images per class: |
| - Black Scurf: 49 |
| - Blackleg: 60 |
| - Blackspot Bruising: 770 |
| - Brown Rot: 105 |
| - Common Scab: 60 |
| - Dry Rot: 1,355 |
| - Healthy Potatoes: 815 |
| - Miscellaneous: 74 |
| - Pink Rot: 57 |
| - Soft Rot: 560 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{gkountakos2026potato, |
| title={Potato-Free: Eliminating Model Training for Disease Detection and Localization in Potato Tubers}, |
| author={Gkountakos, Konstantinos and Pasios, Stefanos and Ioannidis, Konstantinos and Demestichas, Konstantinos and Vrochidis, Stefanos and Kompatsiaris, Ioannis}, |
| journal={Agriculture}, |
| volume={16}, |
| pages={1408}, |
| year={2026}, |
| publisher={MDPI} |
| } |
| ``` |
|
|
|
|
| The dataset itself can be cited as: |
|
|
| Islam, S., & Afrin, T. (2025). *PotatoCare: Deep learning based potato disease dataset* [Dataset]. Mendeley Data. https://doi.org/10.17632/7VM7XSKFG4.2 |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
