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Project-AgML/medicinal_plant_classification_bd

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… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/medicinal_plant_classification_bd.

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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

bibtex
@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.