Datasets:

Project-AgML
/

REMP_plant_classification

configs:
  - config_name: default
    default: true
    features:
      - name: image
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
              '0': agnishika
              '1': anantamul
              '2': apang
              '3': ayapan
              '4': bamonhati
              '5': basok
              '6': betal
              '7': chapalish
              '8': gainura
              '9': kalochitra
              '10': kalodhutura
              '11': kalomegh
              '12': punarnava
              '13': ramtulsi
              '14': sarpagandha
              '15': shotomuli
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Remp Plant Classification

A dataset for image classification of various medicinal plants. The dataset contains 3,494 images across 16 classes: agnishika, anantamul, apang, ayapan, bamonhati, basok, betal, chapalish, gainura, kalochitra, kalodhutura, kalomegh, punarnava, ramtulsi, sarpagandha, shotomuli.
Images per class:

agnishika: 138
anantamul: 158
apang: 280
ayapan: 294
bamonhati: 134
basok: 151
betal: 194
chapalish: 238
gainura: 238
kalochitra: 201
kalodhutura: 276
kalomegh: 294
punarnava: 407
ramtulsi: 154
sarpagandha: 229
shotomuli: 108

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{islam2024remp,
  title={REMP: A unique dataset of rare and endangered medicinal plants in Bangladesh for sustainable healing and biodiversity conservation},
  author={Islam, Mohammad Manzurul and Rahman, Sanjida and Hoque, Nahida and Al Mamun, Md and Moheuddin, Md Sultan and Ali, Md Sawkat and Rashid, Mohammad Rifat Ahmmad and Masum, Saleh and Ferdaus, Md Hasanul and Niloy, Nishat Tasnim and others},
  journal={Data in Brief},
  volume={57},
  pages={110895},
  year={2024},
  publisher={Elsevier}
}

Islam, Mohammad Manzurul; Rahman, Sanjida; Hoque, Nahida; Mamun, Md. Al; Moheuddin, Md. Sultan (2024), “REMP: A Unique Dataset of Rare and Endangered Medicinal Plants in Bangladesh”, Mendeley Data, V1, doi: 10.17632/hnwrxg8zm8.1