The coat protein (CP) sequences of eleven Papaya ringspot virus (PRSV) isolates originating from different locations in India were determined, analysed and compared with the sequences of other isolates of PRSV. The virus isolates from India exhibited considerable heterogeneity in the CP sequences. The CP-coding region varied in size from 840-858 nucleotides, encoding protein of 280-286 amino acids. Comparative sequence analysis revealed that the PRSV isolates originating from India were divergent up to 11%. Though the PRSV isolates were differentiated in to two clusters, yet the sequence variation could not be correlated with the geographical origin of the isolates. Implication of the sequence variation in the coat protein derived transgenic resistance in papaya is discussed.
This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction is done with discrete wavelet transform (DWT) and features are further reduced by using Principal component analysis (PCA). Reduced features are then used as inputs to classifiers and tests are performed to classify image samples. We have used statistical based Mahalanobis distance and Probabilistic neural network (PNN) classifiers. The average classification accuracies using Mahalanobis distance classifier are 83.17% and using PNN classifier are 86.48%.
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