Datasets:
dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Arjun Leaf
'1': Curry Leaf
'2': Marsh Pennywort Leaf
'3': Mint Leaf
'4': Neem Leaf
'5': Rubble Leaf
splits:
- name: train
num_bytes: 134403024
num_examples: 9660
download_size: 134613715
dataset_size: 134403024
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Arjun Leaf
'1': Curry Leaf
'2': Marsh Pennywort Leaf
'3': Mint Leaf
'4': Neem Leaf
'5': Rubble Leaf
splits:
- name: train
num_bytes: 217207463
num_examples: 1380
download_size: 217238312
dataset_size: 217207463
configs:
- config_name: augmented
data_files:
- split: train
path: augmented/train-*
- config_name: raw
data_files:
- split: train
path: raw/train-*
default: true
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
Medicinal Leaf Species Classification
Leaf images of six medicinal plant species photographed in plant nurseries in Savar, Dhaka, Bangladesh: Arjun (Terminalia arjuna), curry leaf (Murraya koenigii), marsh pennywort (Hydrocotyle vulgaris), mint (Mentha spp.), neem (Azadirachta indica) and 'Rubble Leaf' (Kalanchoe pinnata). Plants were identified with expert help and photographed under natural light with an iPhone 13 Pro, and every image was resized to 512 x 512. The default raw config holds the 1,380 original photos (230 per species); the augmented config holds the authors' 9,660 augmented images (1,610 per species), produced with seven transformations including brightness changes, geometric transforms and horizontal flips. The dataset contains raw and augmented versions.
The raw dataset contains 1,380 images.
Images per class:
- Arjun Leaf: 230
- Curry Leaf: 230
- Marsh Pennywort Leaf: 230
- Mint Leaf: 230
- Neem Leaf: 230
- Rubble Leaf: 230
The augmented dataset contains 9,660 images.
Images per class:
- Arjun Leaf: 1,610
- Curry Leaf: 1,610
- Marsh Pennywort Leaf: 1,610
- Mint Leaf: 1,610
- Neem Leaf: 1,610
- Rubble Leaf: 1,610
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{matin2025extensive,
title={An extensive visual data for reliable identification of medicinal plant leaves},
author={Matin, Md. Mafiul Hasan and Sefatullah, Md. and Kabir, Md Shariar and Habib, Haifa Binte},
journal={Data in Brief},
volume={63},
pages={112129},
year={2025},
publisher={Elsevier}
}
The dataset itself can be cited as:
Mafi, M. M. H. M. (2023). Medicinal Plant Identification Dataset [Dataset]. Mendeley Data. https://doi.org/10.17632/FJ93RRFV2Y.2
This dataset was reformatted from its original format to match HuggingFace standards.