---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Bacteria
          '1': Fungi
          '2': Healthy
          '3': Nematode
          '4': Pest
          '5': Phytopthora
          '6': Virus
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# Potato Leaf Disease Classification

A dataset for disease classification of potato leaves. The dataset contains 3,076 images across 7 classes: Bacteria, Fungi, Healthy, Nematode, Pest, Phytopthora, Virus.  
Images per class:
- Bacteria: 569
- Fungi: 748
- Healthy: 201
- Nematode: 68
- Pest: 611
- Phytopthora: 347
- Virus: 532

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

## Citation

```bibtex
@article{shabrina2024novel,
  title={A novel dataset of potato leaf disease in uncontrolled environment},
  author={Shabrina, Nabila Husna and Indarti, Siwi and Maharani, Rina and Kristiyanti, Dinar Ajeng and Prastomo, Niki and others},
  journal={Data in brief},
  volume={52},
  pages={109955},
  year={2024},
  publisher={Elsevier}
}
```

Shabrina, Nabila Husna; Indarti, Siwi; Maharani, Rina; Kristiyanti, Dinar Ajeng; Irmawati, Irmawati; Prastomo, Niki; M, Tika Adillah (2023), “Potato Leaf Disease Dataset in Uncontrolled Environment”, Mendeley Data, V1, doi: 10.17632/ptz377bwb8.1