---
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