Evidence map›Paper›PMID 40727026›Full record

ArticleData in brief2025

BDHerbalPlants: augmented and curated herbal plants image dataset for classification.

Sunzil Khandaker, Md Mizanur Rahman

Abstract read
In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sunzil KhandakerFaculty of Science & Information Technology, Department of CSE, Daffodil International University, Bangladesh.
Md Mizanur RahmanFaculty of Science & Information Technology, Department of CSE, Daffodil International University, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This extensive dataset of herbal plants can be highly beneficial for the potential development of agricultural research and practical plant identification tasks. This article introduces a dataset named "BDHerbalPlants" with 1792 raw, high-quality images and 14,336 augmented data images of herbal plants. It contains images of eight distinct herbal plants from different regions, including Dhaka and Tangail. The eight plants are Eclipta prostrata, Ocimum tenuiflorum, Centella asiatica, Mentha arvensis, Kalanchoe pinnata, Azadirachta indica, Coriandrum sativum, Datura stramonium. Each image is carefully captured and labeled after verifying by experts. The significance of this dataset is showcased using popular pre-trained models such as Xception, DenseNet201, and RegNetY032 DL models. This herbal plant data has immense potential to be seamlessly integrated into Deep learning (DL) tasks and play a significant role in the healthcare and pharmaceutical industry. It serves as valuable data for future research for agricultural Informatics and classifying herbal plants that can be found in woods but are challenging to identify without field knowledge.

Indexed as

Agricultural informaticComputer visionData augmentationDeep learningHerbal plantImage classification

Identifiers

PMID40727026
PMCPMC12301944

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.