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

文献类型： 外文期刊

作者： Yue, JiaQi 1 ; Li, WanYi 1 ; Wang, YuanZhong 1 ;

作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Yunnan, Peoples R China

2.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China

关键词： deep learning; identification research; medicinal plant; Paris polyphylla var; yunnanensis; superiority verification; ResNet

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.754； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2021 年 12 卷

页码：

收录情况： SCI

摘要： 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.

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