Datasets:
File size: 1,418 Bytes
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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 |