A century progress of research on phytoplasma diseases in India

Rao, G. P.; Madhupriya,; Thorat, Vipool; Manimekalai, R.; Tiwari, Ajay; Yadav, Amit

doi:10.5958/2249-4677.2017.00001.9

Cited by 85 publications

(53 citation statements)

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“…To date, two tree species, Dendrocalamus strictus (Poaceae) from Pune and Zamia furfuracea (Zamiaceae) from Delhi, were reported to be associated with the 16SrII phytoplasma group in India (Rao et al, ). The results described here confirmed the association of 16SrII‐C subgroup phytoplasma with Acacia mangium in Tripura, in the Northeast region of India.…”

Section: Resultsmentioning

confidence: 99%

“…mangium leaf yellowing in world. This phytoplasma subgroup is known to have a wide host range, infecting a range of plant species including carrot, sesame, fennel and several weeds in different regions of India (Rao et al, ). Since the 16SrII group appears to be widely distributed in India, further investigations are required on the epidemiology of the disease, in order to formulate strategic management protocols.…”

Section: Resultsmentioning

confidence: 99%

“…These pathogens are transmitted by phloem‐sucking leafhopper species. Approximately 129 plant species, including vegetables, legumes, spices, medicinal plants, trees, ornamentals, cash and oil crops, palms, fruit trees and weeds, have been reported to be associated with ten phytoplasma groups in India (Rao, Madhupriya, Thorat, Manimekalai, Tiwari & Yadav, ).…”

Section: Introductionmentioning

confidence: 99%

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First report of 16Sr II‐C subgroup phytoplasma association with Acacia mangium in Tripura, India

et al. 2019

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Leaf yellowing symptoms were observed on Acacia mangium in the Sipahijala district of Tripura, India, during June 2017. Symptomatic and asymptomatic leaf samples (three of each) were collected from roadside trees of A. mangium for DNA extraction using the CTAB method. Amplicons of ~1.25 kb and ~480 bp were detected in all the symptomatic samples using the phytoplasma‐specific universal 16S rRNA and secA gene primers. Pair wise sequence analysis of 16S rRNA gene sequences, virtual RFLP and phylogenetic analysis revealed that the phytoplasma strain associated with A. mangium belonged to phytoplasma subgroup 16SrII‐C. This is the first report of an association between the 16SrII‐C subgroup and A. mangium leaf yellowing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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First report of 16Sr II‐C subgroup phytoplasma association with Acacia mangium in Tripura, India

et al. 2019

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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%

“…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

141

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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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