Datasets:
File size: 1,586 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Damaged
'1': Dried
'2': Old
'3': Ripe
'4': Unripe
- name: crop_type
dtype:
class_label:
names:
'0': Bell Pepper
'1': Chile Pepper
'2': New Mexico Green Chile
'3': Tomato
splits:
- name: train
num_bytes: 144209551
num_examples: 6150
download_size: 131841250
dataset_size: 144209551
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
---
# VegNet Quality Classification
A dataset for quality classification of various crops. The dataset contains 6,150 images across 5 classes: Damaged, Dried, Old, Ripe, Unripe.
Images per class:
- Damaged: 317
- Dried: 1,389
- Old: 2,044
- Ripe: 1,787
- Unripe: 613
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{suryawanshi2022vegnet,
title={VegNet: dataset of vegetable quality images for machine learning applications},
author={Suryawanshi, Yogesh and Patil, Kailas and Chumchu, Prawit},
journal={Data in Brief},
volume={45},
pages={108657},
year={2022},
publisher={Elsevier}
}
```
Suryawanshi, Yogesh; PATIL, Kailas; Chumchu, Prawit (2022), “VegNet: Vegetable Dataset with quality (Unripe, Ripe, Old, Dried and Damaged)”, Mendeley Data, V1, doi: 10.17632/6nxnjbn9w6.1 |