WHEAT BLAST DETECTION AND ASSESSMENT COMBINING GROUND-BASED HYPERSPECTRAL AND SATELLITE BASED MULTISPECTRAL DATA

Abstract

Remote sensing-based large-area crop disease discrimination plays a vital role in agricultural management to assess severity level of diseases to avoid economical losses to farming community. Over the period, various biotic stresses have emerged through disease and pest infestation. Among these, blast is one of the most disastrous disease of wheat. It is also called as leaf blast, collar rot node blast, spike blast or rotten neck blast depending on the portion of wheat infected. Wheat blast is caused by the fungal pathogen Magnaporthe oryzae and first discovered in Brazil, Bolivia and Paraguay in 1985. In 2017, wheat blast was emerged in Murshidabad district of West Bengal in India. This posed a serious threat to Indian food security. In this study blast disease over wheat crop is studied using ground based hyperspectral measurements at one nm interval and Resourcesat-2 AWiFS data for year 2017. Supervised classification with maximum likelihood classifier was used to generate wheat crop mask using AWiFS data. The ground based hyperspectral bands are further used derive four RS-2 AWiFS broad spectral bands and showed significant difference in healthy and infested plants in vegetative and advance vegetative stages in green, red, NIR and SWIR spectral bands. The 2-D scatter between vegetative indices such as NDVI and LSWI showed well-marked discrimination between healthy and infested wheat crop in all sites. These results are used to derive thresholds for NDVI and LSWI and translated in RS-2 AWiFS derived indices to generate disease severity at spatial scale over Murshidabad district of West Bengal. The distributed wheat severity map showed that 0.59 percent and 99.41 percent of total wheat area is healthy and blast infested respectively. This study suggests that wheat blast can be discriminated using RS-2, AWiFS broad bands at vegetative and advance vegetative stage with support of ground measured hyperspectral data and damage area can be mapped at spatial scale before final harvest.