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Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires–Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires–Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers’ fields for developing better disease management options.

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region.