Extended application of deep learning combined with 2DCOS: Study on origin identification in the medicinal plant of Paris polyphylla var. yunnanensis

2021 ◽  
Author(s):  
Jia Qi Yue ◽  
Heng Yu Huang ◽  
Yuan Zhong Wang
Keyword(s):  
Deep Learning ◽  
Medicinal Plant ◽  
Extended Application ◽  
Paris Polyphylla ◽  
Origin Identification

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

2021 ◽  
Vol 12 ◽  
Author(s):  
JiaQi Yue ◽  
WanYi Li ◽  
YuanZhong Wang
Keyword(s):  
Deep Learning ◽  
Medicinal Plants ◽  
Medicinal Plant ◽  
Sample Size ◽  
Discrimination Performance ◽  
Modern Technology ◽  
New Drugs ◽  
Support Vector ◽  
Paris Polyphylla ◽  
Identification Techniques

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.

Function of leafy sepals in Paris polyphylla: photosynthate allocation and partitioning to the fruit and rhizome

2013 ◽  
Vol 40 (4) ◽  
pp. 393 ◽  
Author(s):  
Kun Yu ◽  
Qilong Fan ◽  
Yan Wang ◽  
Jianrong Wei ◽  
Qing Ma ◽  
...  
Keyword(s):  
Medicinal Plant ◽  
Photosynthetic Pigment ◽  
Fruit Size ◽  
Seed Mass ◽  
Fruit Removal ◽  
Perennial Plant ◽  
Yield And Quality ◽  
Saponin Content ◽  
Paris Polyphylla ◽  
Rhizome Biomass

Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz. is a rhizomatous, herbaceous, perennial plant that is used as a medicinal plant with a variety of pharmacological activities. However, the functions of the green, leafy sepal of this plant are poorly understood. The main objectives of this study were to: (a) test the hypothesis that sepals make measurable contributions to fruit development and rhizome growth; and (b) investigate the allocation and partitioning of photosynthates produced by sepals and leaves to fruit and rhizome. Net photosynthetic rate, photosynthetic pigment composition and δ13C values were similar for sepals and leaves. Sepal-darkening and sepal-removal treatments resulted in smaller fruit size and decreased rhizome biomass, whereas fruit removal led to a decrease in calyx size and an increase in rhizome yield and saponin content. Fruit and seed mass were positively and linearly related to calyx size. These results indicate that photosynthates produced by sepals are involved in the fruit growth and seed development and that developing fruit and rhizomes compete for the photosynthates exported by leaves. We propose that the sepals of P. polyphylla function partly as leaves to compensate for reproductive costs. Fruit removal increased carbon partitioning to the rhizome and improved rhizome yield and quality, offering a useful strategy for the domestication of this valuable medicinal plant.

scholarly journals Glycomyces paridis sp. nov., isolated from the medicinal plant Paris polyphylla

2018 ◽  
Vol 68 (5) ◽  
pp. 1578-1583 ◽  
Author(s):  
Xiao-Mei Fang ◽  
Jing-Lin Bai ◽  
Jing Su ◽  
Li-Li Zhao ◽  
Hong-Yu Liu ◽  
...  
Keyword(s):  
Medicinal Plant ◽  
Paris Polyphylla

scholarly journals Medicinal Plant Identification Using Deep Learning

2021 ◽  
Vol 3 (Special Issue ICITCA-2021 5S) ◽  
pp. 48-53
Author(s):  
Geerthana R. ◽  
Nandhini P. ◽  
Suriyakala R.
Keyword(s):  
Deep Learning ◽  
Medicinal Plant ◽  
Plant Identification

scholarly journals Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

2021 ◽  
Vol 17 (12) ◽  
pp. 1210-1221
Author(s):  
Stephen Opoku Oppong ◽  
Frimpong Twum ◽  
James Ben Hayfron-Acquah ◽  
Yaw Marfo Missah
Keyword(s):  
Deep Learning ◽  
Medicinal Plant ◽  
Gabor Filters ◽  
Plant Identification ◽  
Learning Techniques ◽  
Paper Review

scholarly journals First Report of Fusarium concentricum Causing Stem Rot Disease on the Medicinal Plant Paris polyphylla var. chinensis in China

Plant Disease ◽  
2019 ◽  
Vol 103 (6) ◽  
pp. 1418-1418 ◽  
Author(s):  
R. F. Xiao ◽  
J. P. Wang ◽  
M. X. Zheng ◽  
H. L. Su ◽  
Y. J. Zhu ◽  
...  
Keyword(s):  
Medicinal Plant ◽  
Stem Rot ◽  
First Report ◽  
Paris Polyphylla ◽  
Rot Disease
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