---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Faulty
          '1': Fresh
  splits:
  - name: train
    num_bytes: 359681976
    num_examples: 343
  download_size: 359700000
  dataset_size: 359681976
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
---
# Luffa Quality Classification

This dataset provides real RGB images of luffa plants captured in a field environment in Bangladesh using a handheld smartphone. Collected during October 2023, the images depict natural variations in luffa quality relevant to agricultural disease classification. It serves as a practical resource for developing computer vision models in agricultural quality assessment under real-world field conditions. The dataset contains 343 images across 2 classes: Faulty, Fresh.  
Images per class:
- Faulty: 160
- Fresh: 183

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.*