Datasets:
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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.*
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