Datasets:
File size: 1,615 Bytes
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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.*
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