Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

Abstract

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 Monitoring of Rice Diseases and Pests from Different Data Sources: A Review

“…Liu and Ahmadi et al used an ANN approach to classify and identify rice glume blight and root rot of oil palm [92,93]. Mandal et al tested and validated the classification of rice blast severity using support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) methods; the results indicate that the RF model is optimal in both calibration and validation processes, with accuracies of 0.995 and 0.606, respectively [36]. Ma et al obtained canopy images of rice blast using drones and constructed neural networks (CNN), random forest (RF), and support vector regression (SVR) inversion algorithms based on spectra and vegetation indices.…”

“…Das et al pointed out that spectral indices of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), and SAVI can be used to predict the severity of rice leaf blast in large areas based on MODIS remote sensing images [35]. Mandal et al developed two hyperspectral indices of RBI (R1148, R1301) and NDBI (R1148, R1301) for identification of rice blast, with R2 of 0.85 and 0.86, respectively [36]. Using hyperspectral data from UAVs, Qin et al, of the Chinese Academy of Agricultural Sciences, showed that visible and near-infrared spectra could be used for monitoring rice blight and proposed a series of spectral indices for predicting the extent of rice pest development [37].…”

Molecular screening for the presence of Magnaporthe oryzae resistance Pi-b gene in rice hybrids

“…One of the most popular and extensively grown grains in the world, particularly in Asia and Africa, is rice. On the other hand, some illnesses cause biotic stress to rice, which leads to notable reductions in production and quality [1]. Blast is a harmful disease of rice that causes enormous damage every year in many areas where rice is cultivated.…”

Study on Major Disease-insect Incidence of Scented Gobindabhog Rice in New Alluvial Zone of West Bengal

“…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 losses ranging from 10 to 30%. [3]. The incidence of insects and diseases in rice fields varied depending on the season, weather, variety, etc.…”