---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Alternaria
          '1': Angular Spot
          '2': Fresh
          '3': Holed
          '4': Mosaic Virus
  splits:
  - name: train
    num_bytes: 1007582708
    num_examples: 1227
  download_size: 990926240
  dataset_size: 1007582708
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
---
# Luffa Disease Classification

This dataset provides real field images of luffa plants with disease symptoms, collected in Bangladesh using a handheld smartphone during October 2023. Captured in natural agricultural environments, the images offer a practical resource for developing computer vision models focused on crop disease classification. The dataset contains 1,227 images across 5 classes: Alternaria, Angular Spot, Fresh, Holed, Mosaic Virus.  
Images per class:
- Alternaria: 270
- Angular Spot: 238
- Fresh: 256
- Holed: 222
- Mosaic Virus: 241

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

## Citation

```bibtex
@article{sheikh2024luffafolio,
  title={LuffaFolio: A Multidimensional Image Dataset of Smooth Luffa},
  author={Sheikh, Md Ripon and Islam, Md. Masudul and Himel, Galib Muhammad Shahriar},
  journal={Data in Brief},
  volume={53},
  pages={110149},
  year={2024},
  publisher={Elsevier}
}
```


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