Datasets:

Project-AgML
/

VegNet_quality_classification

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

@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