| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Dried |
| '1': Fresh |
| '2': Spoiled |
| '3': Sunlight |
| splits: |
| - name: train |
| num_bytes: 258952183 |
| num_examples: 5384 |
| download_size: 281862595 |
| dataset_size: 258952183 |
| 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 |
| --- |
| |
| # Mint Leaf Classification |
|
|
| A dataset for health classification of mint leaves. The dataset contains 5,384 images across 4 classes: Dried, Fresh, Spoiled, Sunlight. |
| Images per class: |
| - Dried: 1,881 |
| - Fresh: 1,773 |
| - Spoiled: 1,669 |
| - Sunlight: 61 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{jadhav2023mint, |
| title={Mint leaves: dried, fresh, and spoiled dataset for condition analysis and machine learning applications}, |
| author={Jadhav, Rohini and Suryawanshi, Yogesh and Bedmutha, Yashashree and Patil, Kailas and Chumchu, Prawit}, |
| journal={Data in Brief}, |
| volume={51}, |
| pages={109717}, |
| year={2023}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| Bedmutha, Yashashree; Suryawanshi, Yogesh; PATIL, Kailas; chumchu, prawit (2023), “Pudina Leaf Dataset: Freshness Analysis”, Mendeley Data, V1, doi: 10.17632/nvbpydc3fs.1 |
