ArticleFrontiers in plant science2021
Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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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.
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Who cites it
8 citing papers in PubMed.
- Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genusJournal of pharmaceutical analysis · 2025Review
- Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review.Frontiers in pharmacology · 2024Review
- ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability ofFoods (Basel, Switzerland) · 2022Article
- Potential Anti-Tumor Activity of Nardoguaianone L Isolated fromMolecules (Basel, Switzerland) · 2022Article
- Article
- ResNet and MaxEnt modeling for quality assessment ofFrontiers in plant science · 2022Article
- Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts ofFrontiers in microbiology · 2022Article
- Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant
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Registered trials
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