| --- |
| 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 |
