Prevalence of Tobacco mosaic virus in Iran and Evolutionary Analyses of the Coat Protein Gene

Alishiri, Athar; Rakhshandehroo, Farshad; Zamanizadeh, Hamid Reza; Palukaitis, Peter

doi:10.5423/ppj.oa.09.2012.0145

Cited by 23 publications

(19 citation statements)

References 57 publications

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“…1) due to pest and pathogen have been identified. These diseases caused extensive damage to the selected crop under favorable conditions [19][20][21][22][23] . A dataset for these diseases have been created with the images of isolated leaf samples using different smartphone cameras in laboratory condition.…”

Section: Methodsmentioning

confidence: 99%

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Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM

Rangarajan

Raja

2020

Sci Rep

116

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Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8 th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed.The revolution of the modern technologies in the recent era has facilitated its application in agriculture to improve production. One of the applications is the diagnosis of plant diseases using a digital image from a camera which in turn will assist the farmers to control its prevalence in the fields. The availability of cheap cameras and the explosive growth on the internet have made the diagnosis relatively less complex with the availability of tools and information about the disease online 1 . But still, human diagnosis is prone to errors 2 . The scope for the automatic disease classification has improved due to the accomplishment in machine learning technologies. Traditionally shallow machine learning algorithms such as neural networks, Support Vector Machine (SVM), or other algorithms were used which is a time-consuming process as it demands feature extraction from the images manually and fed as input to the algorithm for classification. But, the deep learning approaches consist of many layers of processing elements that process images and estimate features automatically for classification. There are four major types of deep learning algorithms namely Convolutional Neural Networks (CNN), autoencoder, restricted Boltzmann machines and sparse encoding, according to a study by Guo et al. 3 Of these, CNN based architectures are most widely used for image classification problems 3 . Recent trends in the use of CNN for disease classification are on the rise and many studies have reported promising results 1-17 .Training of the CNN based deep learning models from scratch is a time consuming (difficult) process and requires a large database. It is also challenging to categorize each image to a crop disease even with an expert...

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Section: Methodsmentioning

confidence: 99%

“…The dimension of the images were resized to 224 × 224 pixels according to the input requirement of the pre-trained VGG16. The created dataset of each disease with the reported loss is shown in Table 2 [20][21][22][23][24] .…”

Section: Methodsmentioning

confidence: 99%

Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM

Rangarajan

Raja

2020

Sci Rep

116

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“…TMV has a wide host range as it can infect more that 350 plant species from different families (King et al, 2012) including tobacco, tomato, pepper, eggplant, and bean, to name a few. In Iran, TMV has been reported from eggplant (Izadpanah, 1982;Alishiri et al, 2013), pepper (Manouchehri, 1968;Alishiri et al, 2013), tomato (Izadpanah, 1982;Alishiri et al, 2013), tobacco (Manouchehri, 1968;Alishiri et al, 2013) and potato (Alishiri et al, 2013) fields. The economic loss to crops infected by TMV is considerable as in some tomato fields up to 100% loss has been reported (Izadpanah, 1982).…”

Section: Introductionmentioning

confidence: 99%

“…However, as mentioned above, the majority of studies have been concerned with human and animal pathogens. Given the economic importance of TMV and its increasing spread among different plants in Iran (Alishiri et al, 2013), developing new methods for reduction of the associated crop losses will be necessary. Considering the structural similarities between human and plant viruses in terms of genome type and coat proteins for instance, it is expected that LCE exhibits antiviral effects agatinst TMV.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Antiviral Potential of Licorice (Glycyrrhiza Glabra L.) Crude Extract Against Tobacco Mosaic Virus

2020

THE JAPS

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Tobacco mosaic virus (TMV) is a devastating plant RNA virus infecting a wide range of plants causing considerable losses to agriculture products. Various dissemination means of TMV makes it particularly difficult to control. Application of plant-derived antiviral compounds has gained attention as one of the new control measures against viral diseases. This study was conducted to examine the antiviral effects of licorice (Glycyrrhiza glabra L.) crude extract (LCE) against TMV. To this end, TMV particles were purified and treated with different LCE concentrations (0.5, 1.0, 2.5 and 5.0%). Two hosts of TMV (Nicotiana glutinosa L. and N. tabacum L.) were mechanically inoculated with the LCE-treated TMV preparations and antiviral effects were assessed by measuring different disease parameters in the plants including infection rate, number of local lesion, symptom index, and virus accumulation. These parameters were significantly reduced when LCE was used to treat the TMV preparation or it was applied 24 h before TMV inoculation. An inverse relationship was found between LCE concentrations and various studied parameters. Application of LCE 24 h after TMV inoculation had no significant effect on disease parameters suggesting that LCE directly affects TMV rather than activating plant defence system. The results showed that licorice can be a promising source of novel antiviral compounds againts TMV.

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“…TMV can be transmitted easily through mechanical means in differential hosts [2] with crop debris and contaminated equipment. [1,[3][4][5][6][7][8] Different methods including biological, serological and molecular assays have been used for the detection of TMV in plants with great ease and precision. Molecular detection through PCR variants is not only easy but also has been found to be sensitive, quick and reliable.…”

Section: Introductionmentioning

confidence: 99%

Wilting of Bean Plants from Tobacco Mosaic Virus from Smoking Tobacco in Pakistan

Iftikhar¹,

Vadamalai²,

Sajid³

et al. 2018

AJBLS

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Tobacco mosaic virus (TMV) was detected in one out of three samples of different commercial brands of cigarettes by RT-PCR. A PCR product between 650-680bp in size was observed on 1% agarose gel, which was confirmed as TMV through sequencing. Mechanical inoculation of sap from the PCR positive samples showed local lesions on bean (Phaseolus vulgaris) plants used as an indicator plant. In addition, symptoms of wilting were also observed in the bean plants. RT-PCR of extract from the inoculated bean plants reconfirmed the presence of TMV in bean plants. This study shows that TMV can be prevalent and has virulence in commercial tobacco (Nicotiana spp.). The wilting in bean plants upon mechanical inoculation of TMV was observed for the first time and can be reported as a new symptom of TMV infection in beans. This wilting associated to TMV opens the discussion for presence of different isolates of virus, symptomatic expression on differential hosts and methods of mechanical inoculation.

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Prevalence of Tobacco mosaic virus in Iran and Evolutionary Analyses of the Coat Protein Gene

Cited by 23 publications

References 57 publications

Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM

Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM

Investigation of Antiviral Potential of Licorice (Glycyrrhiza Glabra L.) Crude Extract Against Tobacco Mosaic Virus

Wilting of Bean Plants from Tobacco Mosaic Virus from Smoking Tobacco in Pakistan

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