Flawless Detection of Herbal Plant Leaf by Machine Learning Classifier Through Two Stage Authentication Procedure

Samuel Manoharan J

Herbal plants are crucial to human existence for medical reasons, and they can also provide free oxygen to the environment. Many herbal plants are rich in therapeutic goods and also it includes the active elements that will benefit future generations. Many valuable plant species are being extinguished and destroyed as a result of factors such as global warming, population growth, occupational secrecy, a lack of government support for research, and a lack of knowledge about therapeutic plants. Due to the lag of dimensional factors such as length and width, many existing algorithms fail to recognize herbal leaf in all seasons with the maximum accuracy. Henceforth, the proposed algorithm focuses on the incomplete problems in the datasets in order to improve the detection rate for herbal leaf identification. The inclusions of dimension factors in the datasets are performing good results in the image segmentation process. The obtained result has been validated with a machine learning classifier when combined with ex-or gate operation is called deep knowledge-based identification. This two-stage authentication (TSA) procedure is improving the recognition rate required for the detection of herbal leaf. This fusion of image segmentation with machine learning is providing good robustness for the proposed architecture. Besides, intelligent selection of image segmentation techniques to segment the leaf from the image is improving the detection accuracy. This procedure is addressing and answering the drawbacks associated with the detection of the herbal leaf by using many Machine Learning (ML) approaches. Also, it improves the rate of detection and minimizes the classification error. From the results, it is evident that the proposed method has obtained better accuracy and other performance measures.

Ultrasonics ◽  
2019 ◽  
Vol 91 ◽  
pp. 1-9 ◽  
Yuan Xu ◽  
Yuxin Wang ◽  
Jie Yuan ◽  
Qian Cheng ◽  
Xueding Wang ◽  

2019 ◽  
Vol 9 (1) ◽  
Jiali Sun ◽  
Qingtai Wu ◽  
Dafeng Shen ◽  
Yangjun Wen ◽  
Fengrong Liu ◽  

AbstractOne of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population structure and polygenic effects, then selected the SNPs that were potentially related to target traits by using least angle regression (LARS), furtherly analyzed this variable subset using random forest (RF) to detect quantitative trait nucleotides (QTNs) associated with target traits. The new method has more powerful detection in simulation experiments and real data analyses. The results of simulation experiments showed that, compared with the existing approaches, the new method effectively improved the detection ability of QTNs and model fitting degree, and required less calculation time. In addition, the new method significantly distinguished QTNs and other SNPs. Subsequently, the new method was applied to analyze five flowering-related traits in Arabidopsis. The results showed that, the distinction between QTNs and unrelated SNPs was more significant than the other methods. The new method detected 60 genes confirmed to be related to the target trait, which was significantly higher than the other methods, and simultaneously detected multiple gene clusters associated with the target trait.

2020 ◽  
Vol 67 (12) ◽  
pp. 1072-1077
Marina M. Melek ◽  
David Yevick

Prem Timsina ◽  
Himanshu N. Joshi ◽  
Fu-Yuan Cheng ◽  
Ilana Kersch ◽  
Sara Wilson ◽  

2019 ◽  
Vol 25 (S2) ◽  
pp. 188-189
Matthew Guay ◽  
Zeyad Emam ◽  
Richard Leapman

2018 ◽  
Vol 24 (6) ◽  
pp. 667-675 ◽  
Charline Lormand ◽  
Georg F. Zellmer ◽  
Károly Németh ◽  
Geoff Kilgour ◽  
Stuart Mead ◽  

AbstractCrystals within volcanic rocks record geochemical and textural signatures during magmatic evolution before eruption. Clues to this magmatic history can be examined using crystal size distribution (CSD) studies. The analysis of CSDs is a standard petrological tool, but laborious due to manual hand-drawing of crystal margins. The trainable Weka segmentation (TWS) plugin in ImageJ is a promising alternative. It uses machine learning and image segmentation to classify an image. We recorded back-scattered electron (BSE) images of three volcanic samples with different crystallinity (35, 50 and ≥85 vol. %), using scanning electron microscopes (SEM) of variable image resolutions, which we then tested using TWS. Crystal measurements obtained from the automatically segmented images are compared with those of the manual segmentation. Samples up to 50 vol. % crystallinity are successfully segmented using TWS. Segmentation at significantly higher crystallinities fails, as crystal boundaries cannot be distinguished. Accuracy performance tests for the TWS classifiers yield high F-scores (>0.930), hence, TWS is a successful and fast computing tool for outlining crystals from BSE images of glassy rocks. Finally, reliable CSD’s can be derived using a low-cost desktop SEM, paving the way for a wide range of research to take advantage of this new petrological method.

2017 ◽  
Vol 13 (S337) ◽  
pp. 9-12
C. M. Tan ◽  

AbstractThe LOFAR Tied Array All-Sky Survey (LOTAAS) is an ongoing all northern sky survey for pulsars and transients. It is one of the first large scale pulsar surveys conducted at an observing frequency below 200 MHz. The unique set-up of the survey is the simultaneous formation of 222 beams for each survey pointing by coherently adding signals from the central 6 LOFAR stations. This represents the first SKA-like pulsar survey. As of 12 September 2017, the survey has completed 1456 pointings, more than two-thirds of the total. The survey has discovered 61 new pulsars via Fourier-based periodicity searches and a further 5 via single pulse searches. I present the survey approach and distinctive features including a discussion of an improved machine learning classifier used to identify the best candidates produced by the pipeline for further investigation. I present a summary of the discoveries so far including the first binary pulsar and the pulsar with the longest spin period of 23.5 s.

2020 ◽  
Vol Publish Ahead of Print ◽  
Jasbir Dhaliwal ◽  
Lauren Erdman ◽  
Erik Drysdal ◽  
Firas Rinawi ◽  
Jennifer Muir ◽  

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