scholarly journals Vietnamese Herbal Plant Recognition Using Deep Convolutional Features

2019 ◽  
Vol 9 (3) ◽  
pp. 363-367 ◽  
Author(s):  
Anh H. Vo ◽  
◽  
Hoa T. Dang ◽  
Bao T. Nguyen ◽  
Van-Huy Pham
2020 ◽  
Author(s):  
Priya Pinder Kaur ◽  
Sukhdev Singh ◽  
Monika Pathak

2020 ◽  
Vol 9 (5) ◽  
pp. 2198-2205
Author(s):  
Izwan Asraf Md Zin ◽  
Zaidah Ibrahim ◽  
Dino Isa ◽  
Sharifah Aliman ◽  
Nurbaity Sabri ◽  
...  

This paper investigates the application of deep Convolutional Neural Network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy


Author(s):  
Yusmayasari Yusmayasari ◽  
◽  
Noor Pramono ◽  
Imam Djamaluddin ◽  
◽  
...  

Author(s):  
Florian P. Schiestl ◽  
Erika A. Wallin ◽  
John J. Beck ◽  
Magne Friberg ◽  
John N. Thompson

AbstractVolatiles are of key importance for host-plant recognition in insects. In the pollination system of Lithophragma flowers and Greya moths, moths are highly specialized on Lithophragma, in which they oviposit and thereby pollinate the flowers. Floral volatiles in Lithophragma are highly variable between species and populations, and moths prefer to oviposit into Lithophragma flowers from populations of the local host species. Here we used gas chromatography coupled with electroantennographic detection (GC-EAD) to test whether Greya moths detect specific key volatiles or respond broadly to many volatiles of Lithophragma flowers. We also addressed whether olfactory detection in Greya moths varies across populations, consistent with a co-evolutionary scenario. We analyzed flower volatile samples from three different species and five populations of Lithophragma occurring across a 1400 km range in the Western USA, and their sympatric female Greya politella moths. We showed that Greya politella detect a broad range of Lithophragma volatiles, with a total of 23 compounds being EAD active. We chemically identified 15 of these, including the chiral 6, 10, 14-trimethylpentadecan-2-one (hexahydrofarnesyl acetone), which was not previously detected in Lithophragma. All investigated Lithophragma species produced the (6R, 10R)-enantiomer of this compound. We showed that Greya moths detected not only volatiles of their local Lithophragma plants, but also those from allopatric populations/species that they not encounter in local populations. In conclusion, the generalized detection of volatiles and a lack of co-divergence between volatiles and olfactory detection may be of selective advantage for moths in tracking hosts with rapidly evolving, chemically diverse floral volatiles.


Separations ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 22
Author(s):  
Pei Chen ◽  
Xiaoman Li ◽  
Xuemin Yan ◽  
Minglei Tian

(1) Background: ZIF-67 is one of the most intriguing metal–organic frameworks already applied in liquid adsorption. To increase its adsorption performance, dual ionic liquids were immobilized on ZIF-67 in this research; (2) Methods: The obtained sorbent was used to adsorb aristolochic acid I (AAI) in standard solutions. Then, the sorbent was applied in solid-phase extraction to remove AAI from Fibraurea Recisa Pierre extracted solution. (3) Results: By analyzing the adsorption models, the highest adsorption capacity of immobilized sorbent (50.9 mg/g) was obtained at 25 °C within 120 min. In the SPE process, 0.02 mg of AAI was removed per gram of herbal plant, the adequate recoveries were in the range of 96.2–100.0%, and RSDs were 3.5–4.0%; (4) Conclusions: The provided experimental data revealed that ZIF-67@EIM-MIM was an excellent potential sorbent to adsorb and remove AAI from herbal plant extract, and the successful separation indicated that this sorbent could be an ideal material for the pretreatment of herbal plants containing AAI.


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