Evidence map›Paper›PMID 40634310›Full record

ArticleScientific data2025

TCMP-300: A Comprehensive Traditional Chinese Medicinal Plant Dataset for Plant Recognition.

Yanling Zhang, Wanhui Sun, Chuanguang Yang, Libo Huang, Zhulin An, Weilun Feng, Wenjing Tang, Yongjun Xu

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

8 authors.

Yanling ZhangSchool of pharmacy, Xinyang Agriculture and Forestry University, Xinyang, 464000, China.
Wanhui SunSchool of pharmacy, Xinyang Agriculture and Forestry University, Xinyang, 464000, China.
Chuanguang YangInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. yangchuanguang@ict.ac.cn.
Libo HuangInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. huanglibo@ict.ac.cn.
Zhulin AnInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. anzhulin@ict.ac.cn.
Weilun FengInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
Wenjing TangInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
Yongjun XuInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62406312National Natural Science Foundation of China (National Science Foundation of China) 62476264
6 · The paper itself

Abstract

Traditional Chinese Medicinal Plants (TCMPs) are often used to prevent and treat diseases for the human body. Since various medicinal plants have different therapeutic effects, plant recognition has become an important topic. Traditional identification of medicinal plants mainly relies on human experts, which does not meet the increased requirements in clinical practice. Artificial Intelligence (AI) research for plant recognition faces challenges due to the lack of a comprehensive medicinal plant dataset. Therefore, we present a TCMP dataset that includes 52,089 images in 300 categories. Compared to the existing medicinal plant datasets, our dataset has more categories and fine-grained plant parts to facilitate comprehensive plant recognition. The plant images were collected through the Bing search engine and cleaned by a pretrained vision foundation model with human verification. We conduct technical validation by training several state-of-the-art image classification models with advanced data augmentation on the dataset, and achieve 89.64% accuracy. Our dataset promotes the development and validation of advanced AI models for robust and accurate plant recognition.

Indexed as

Plants, MedicinalArtificial IntelligenceDatabases, FactualHumansMedicine, Chinese Traditional

Identifiers

PMID40634310
PMCPMC12241305

What Socratic holds

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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 Socratic graph.