Datasets:

Project-AgML
/

mint_leaf_classification

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

@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