Datasets:
image imagewidth (px) 643 4.16k | label class label 8
classes | split stringclasses 1
value |
|---|---|---|
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train | |
2Haritoki | Train |
Medicinal Plant Classification Bd
This dataset contains real RGB images of medicinal plants native to Bangladesh, captured in a controlled laboratory environment. Images were collected using handheld smartphones during the summer months (July to August), providing a diverse and standardized representation of plant specimens under consistent lighting and background conditions. The dataset contains 5,000 images across 10 classes: Bohera, Devilbackbone, Haritoki, Lemongrass, Nayontara, Neem, Pathorkuchi, Thankuni, Tulsi, Zenora.
Images per class:
- Bohera: 500
- Devilbackbone: 500
- Haritoki: 500
- Lemongrass: 500
- Nayontara: 500
- Neem: 500
- Pathorkuchi: 500
- Thankuni: 500
- Tulsi: 500
- Zenora: 500
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
@article{borkatulla2023bangladeshi,
title={Bangladeshi medicinal plant dataset},
author={Borkatulla, Bijly and Ferdous, Jannatul and Uddin, Abdul Hasib and Mahmud, Prince},
journal={Data in Brief},
volume={48},
pages={109211},
year={2023},
publisher={Elsevier}
}
Bijly Borkatullah, Jannatul Ferdous, Abdul Hasib Uddin, and Prince Mahmud. (2022). Medicinal Plant Raw [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4510170
This dataset was reformatted from its original format to match HuggingFace standards.
- Downloads last month
- 108