Leaf curl, a whitefly-borne begomovirus disease, is the cause of frequent epidemic in chili. In the present study, transmission parameters involved in tripartite interaction are estimated to simulate disease dynamics in a population dynamics model framework. Epidemic is characterized by a rapid conversion rate of healthy host population into infectious type. Infection rate as basic reproduction number, R0 = 13.54, has indicated a high rate of virus transmission. Equilibrium population of infectious host and viruliferous vector are observed to be sensitive to the immigration parameter. A small increase in immigration rate of viruliferous vector increased the population of both infectious host and viruliferous vector. Migrant viruliferous vectors, acquisition, and transmission rates as major parameters in the model indicate leaf curl epidemic is predominantly a vector -mediated process. Based on underlying principles of temperature influence on vector population abundance and transmission parameters, spatio-temporal pattern of disease risk predicted is noted to correspond with leaf curl distribution pattern in India. Temperature in the range of 15–35 °C plays an important role in epidemic as both vector population and virus transmission are influenced by temperature. Assessment of leaf curl dynamics would be a useful guide to crop planning and evolution of efficient management strategies.

Remote sensing technique is useful for monitoring large crop area at a single time point, which is otherwise not possible by visual observation alone. Yellow mosaic disease (YMD) is a serious constraint in soybean production in India. However, hardly any basic information is available for monitoring YMD by remote sensing. Present study examines spectral reflectance of soybean leaves due to Mungbean yellow mosaic India virus (MYMIV) infection in order to identify YMD sensitive spectral ratio or reflectance. Spectral reflectance measurement indicated significant (p \ 0.001) change in reflectance in the infected soybean canopy as compared to the healthy one. In the infected canopy, reflectance increased in visible region and decreased in near infra-red region of spectrum. Reflectance sensitivity analysis indicated wavelength *642, *686 and *750 nm were sensitive to YMD infection. Whereas, in yellow leaves induced due to nitrogen deficiency, the sensitive wavelength was *589 nm. Due to viral infection, a shift occurred in red and infra-red slope (called red edge) on the left in comparison to healthy one. Red edge shift was a good indicator to discriminate yellow mosaic as chlorophyll gets degraded due to MYMIV infection. Correlation of reflectance at 688 nm (R688) and spectral reflectance ratio at 750 and 445 nm (R750/R445) with the weighted mosaic index indicated that detection of yellow mosaic is possible based on these sensitive bands. Our study for the first time identifies the yellow mosaic sensitive band as R688 and R750/R445, which could be utilized to scan satellite data for monitoring YMD affected soybean cropping regions.

ABSTRACT:The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis) of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.) cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500nm. The partial least squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a coefficient of determination (R2)

No abstract

No abstract