---
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