Bar-cas12a, a novel and rapid method for plant species authentication in case of Phyllanthus amarus Schumach. & Thonn

AbstractRapid and accurate species diagnosis accelerates performance in numerous biological fields and associated areas. However, morphology-based species taxonomy/identification might hinder study and lead to ambiguous results. DNA barcodes (Bar) has been employed extensively for plant species identification. Recently, CRISPR-cas system can be applied for diagnostic tool to detect pathogen’s DNA based on the collateral activity of cas12a or cas13. Here, we developed barcode-coupled with cas12a assay, “Bar-cas12a” for species authentication using Phyllanthus amarus as a model. The gRNAs were designed from trnL region, namely gRNA-A and gRNA-B. As a result, gRNA-A was highly specific to P. amarus amplified by RPA in contrast to gRNA-B even in contaminated condition. Apart from the large variation of gRNA-A binding in DNA target, cas12a- specific PAM’s gRNA-A as TTTN can be found only in P. amarus. PAM site may be recognized one of the potential regions for increasing specificity to authenticate species. In addition, the sensitivity of Bar-cas12a using both gRNAs gave the same detection limit at 0.8 fg and it was 1,000 times more sensitive compared to agarose gel electrophoresis. This approach displayed the accuracy degree of 90% for species authentication. Overall, Bar-cas12a using trnL-designed gRNA offer a highly specific, sensitive, speed, and simple approach for plant species authentication. Therefore, the current method serves as a promising tool for species determination which is likely to be implemented for onsite testing.

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Bar-cas12a, a Novel and Rapid Method for Plant Species Authentication: A Case of Phyllanthus Species

10.21203/rs.3.rs-702633/v1 ◽

2021 ◽

Author(s):

Kittisak Buddhachat ◽

Suphaporn Paenkaew ◽

Nattaporn Sripai ◽

Yash Munnalal Gupta ◽

Waranee Pradit ◽

...

Keyword(s):

Plant Species ◽

Gel Electrophoresis ◽

Point Of Care ◽

Simple Approach ◽

Agarose Gel Electrophoresis ◽

Dna Barcodes ◽

Point Of Care Testing ◽

Plant Species Identification ◽

Species Authentication ◽

Species Taxonomy

Abstract The rapid and accurate species diagnosis accelerates the performance to investigate various biology fields and its relevant, perhaps but morphology-based species taxonomy/identification hamper. DNA barcodes (Bar) has been employed extensively for plant species identification. Recently, CRISPR-cas system can be applied for diagnostic tool to detect pathogen’s DNA based on the collateral activity of cas12a or cas13. Here, we developed barcode-hyphenated with cas12a assay, “Bar-cas12a” for species authentication using Phyllanthus amarus as a model. The gRNAs were designed from trnL region, namely gRNA-A and gRNA-B. As a result, gRNA-A was highly specific to P. amarus amplified by RPA in contrast to gRNA-B even in contaminated condition. Apart from the large variation of gRNA-A binding in DNA target, cas12a- specific PAM’s gRNA-A as TTTN can be found only in P. amarus. PAM site may be recognized one of the potential regions for increasing specificity to authenticate species. In addition, the sensitivity of Bar-cas12a using both gRNAs gave the same detection limit at 0.8 fg and it was 1,000 times more sensitive compared to agarose gel electrophoresis. Overall, Bar-cas12a using trnL-designed gRNA offer a highly specific, sensitive, speed, and simple approach for plant species authentication and is likely to implement point-of-care testing.

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Periodic wavelet descriptor of plant leaf and its application in botany

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691315500435 ◽

2015 ◽

Vol 13 (06) ◽

pp. 1550043 ◽

Cited By ~ 3

Author(s):

Qing-Mao Zeng ◽

Tong-Lin Zhu ◽

Xue-Ying Zhuang ◽

Ming-Xuan Zheng

Keyword(s):

Species Identification ◽

Plant Species ◽

Shape Descriptor ◽

Plant Leaf ◽

Practical Applications ◽

Wide Range ◽

Periodic Wavelet ◽

Plant Species Identification ◽

Botanical Research ◽

Wavelet Descriptor

Leaf is one of the most important organs of plant. Leaf contour or outline, usually a closed curve, is a fundamental morphological feature of leaf in botanical research. In this paper, a novel shape descriptor based on periodic wavelet series and leaf contour is presented, which we name as Periodic Wavelet Descriptor (PWD). The PWD of a leaf actually expresses the leaf contour in a vector form. Consequently, the PWD of a leaf has a wide range in practical applications, such as leaf modeling, plant species identification and classification, etc. In this work, the plant species identification and the leaf contour reconstruction, as two practical applications, are discussed to elaborate how to employ the PWD of a plant leaf in botanical research.

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Plant Species Identification by Bi-channel Deep Convolutional Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1004/1/012015 ◽

2018 ◽

Vol 1004 ◽

pp. 012015

Author(s):

Guiqing He ◽

Zhaoqiang Xia ◽

Qiqi Zhang ◽

Haixi Zhang ◽

Jianping Fan

Keyword(s):

Species Identification ◽

Plant Species ◽

Convolutional Networks ◽

Plant Species Identification

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Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates

PLoS ONE ◽

10.1371/journal.pone.0147692 ◽

2016 ◽

Vol 11 (1) ◽

pp. e0147692 ◽

Cited By ~ 5

Author(s):

Alexandre Angers-Loustau ◽

Mauro Petrillo ◽

Valentina Paracchini ◽

Dafni M. Kagkli ◽

Patricia E. Rischitor ◽

...

Keyword(s):

Species Identification ◽

Plant Species ◽

Bioinformatics Pipeline ◽

Complex Samples ◽

Plant Species Identification

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Mobile Vision for Plant Biometric System

Environmental and Agricultural Informatics ◽

10.4018/978-1-5225-9621-9.ch034 ◽

2020 ◽

pp. 773-791

Author(s):

Shitala Prasad

Keyword(s):

Ubiquitous Computing ◽

Species Identification ◽

Plant Species ◽

Expert Knowledge ◽

Biometric System ◽

Plant Identification ◽

Significant Growth ◽

Wide Range ◽

Plant Species Identification ◽

Research Analysis

In human's life plant plays an important part to balance the nature and supply food-&-medicine. The traditional manual plant species identification method is tedious and time-consuming process and requires expert knowledge. The rapid developments of mobile and ubiquitous computing make automated plant biometric system really feasible and accessible for anyone-anywhere-anytime. More and more research are ongoing to make it a more realistic tool for common man to access the agro-information by just a click. Based on this, the chapter highlights the significant growth of plant identification and leaf disease recognition over past few years. A wide range of research analysis is shown in this chapter in this context. Finally, the chapter showed the future scope and applications of AaaS and similar systems in agro-field.

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Multipath Deep Shallow Convolutional Networks for Large Scale Plant Species Identification in Wild Image

2020 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn48605.2020.9207113 ◽

2020 ◽

Author(s):

Syeda Alleena Riaz ◽

Saeeda Naz ◽

Imran Razzak

Keyword(s):

Species Identification ◽

Plant Species ◽

Large Scale ◽

Convolutional Networks ◽

Plant Species Identification

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Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets

Biomimetics ◽

10.3390/biomimetics5010008 ◽

2020 ◽

Vol 5 (1) ◽

pp. 8 ◽

Cited By ~ 2

Author(s):

Geovanni Figueroa-Mata ◽

Erick Mata-Montero

Keyword(s):

Plant Species ◽

Euclidean Distance ◽

Threshold Value ◽

Costa Rican ◽

Similarity Function ◽

Siamese Network ◽

Learning Techniques ◽

Dataset Size ◽

Plant Species Identification ◽

Siamese Networks

The application of deep learning techniques may prove difficult when datasets are small. Recently, techniques such as one-shot learning, few-shot learning, and Siamese networks have been proposed to address this problem. In this paper, we propose the use a convolutional Siamese network (CSN) that learns a similarity metric that discriminates between plant species based on images of leaves. Once the CSN has learned the similarity function, its discriminatory power is generalized to classify not just new pictures of the species used during training but also entirely new species for which only a few images are available. This is achieved by exposing the network to pairs of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. We conducted experiments to study two different scenarios. In the first one, the CSN was trained and validated with datasets that comprise 5, 10, 15, 20, 25, and 30 pictures per species, extracted from the well-known Flavia dataset. Then, the trained model was tested with another dataset composed of 320 images (10 images per species) also from Flavia. The obtained accuracy was compared with the results of feeding the same training, validation, and testing datasets to a convolutional neural network (CNN) in order to determine if there is a threshold value t for dataset size that defines the intervals for which either the CSN or the CNN has better accuracy. In the second studied scenario, the accuracy of both the CSN and the CNN—both trained and validated with the same datasets extracted from Flavia—were compared when tested on a set of images of leaves of 20 Costa Rican tree species that are not represented in Flavia.

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