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Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example.

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 Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

SUBMITTER: Yue J

PROVIDER: S-EPMC8493076 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The root of Paris polyphylla var. yunnanensis, a famous and endangered traditional Chinese herb, has a significant medicinal value. The aim of this study was to analyze the composition and functional characteristics of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis. The 16S rRNA gene sequencing and functional prediction of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis were conducted. The Chao and Shannon indices of the bacteria in roots were significantly higher than those in stems and leaves. The dominant endophyte phyla were Cyanobacteria, Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. The main genera detected in roots were unclassified Cyanobacteria, Rhizobium, Flavobacterium, and Sphingobium; the main genera in stems were norank_c__Cyanobacteria, Bacillus, and Pseudomonas; the main genera in leaves were norank_c__Cyanobacteria and Rhizobium. The microbiota in roots was particularly enriched in functional categories "extracellular structures" and "cytoskeleton" compared with stems and leaves (p < 0.05). Our study reveals the structural and functional characteristics of the endophytic bacteria in roots, stems, and leaves of P. polyphylla var. yunnanensis, which aids in the scientific understanding of this plant.The root of Paris polyphylla var. yunnanensis, a famous and endangered traditional Chinese herb, has a significant medicinal value. The aim of this study was to analyze the composition and functional characteristics of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis. The 16S rRNA gene sequencing and functional prediction of bacterial endophytes in roots, stems, and leaves of P. polyphylla var. yunnanensis were conducted. The Chao and Shannon indices of the bacteria in roots were significantly higher than those in stems and leaves. The dominant endophyte phyla were Cyanobacteria, Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. The main genera detected in roots were unclassified Cyanobacteria, Rhizobium, Flavobacterium, and Sphingobium; the main genera in stems were norank_c__Cyanobacteria, Bacillus, and Pseudomonas; the main genera in leaves were norank_c__Cyanobacteria and Rhizobium. The microbiota in roots was particularly enriched in functional categories “extracellular structures” and “cytoskeleton” compared with stems and leaves (p < 0.05). Our study reveals the structural and functional characteristics of the endophytic bacteria in roots, stems, and leaves of P. polyphylla var. yunnanensis, which aids in the scientific understanding of this plant.

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Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example.

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