Deep Learning Analysis of Rice Blast Disease Using Remote Sensing Images

Abstract

Large-scale agricultural production systems require disease monitoring and pest management on a real-time basis. Monitoring disease phenology is one of the possible ways to save agricultural products from huge yield loss incurred due to diseases. Rice is one of the major food crops across the globe. Leaf blast disease in rice affects its productivity all over the world. Monitoring of leaf blast is essential for strategic and tactical disease management decisions. Conventional methods of large-scale disease monitoring are laborious, time taking, and above all, suffer from inaccuracy. Remote sensing parameters are useful for monitoring diseases and crop health on a large scale. Spectral indices derived from remote sensing data provide characteristic features to distinguish areas between healthy and infected crops facilitating strategic application. Assessment of leaf blast incidence based on land surface temperature moderate resolution imaging spectroradiometer (MODIS) and spectral indices normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), soil adjusted vegetation index (SAVI), and moisture stress (Sentinel-2) have been used to predict disease patterns. A deep learning-based model is developed to assess the condition of rice blast disease at field scale. The model provided 90.02% training accuracy and 85.33% validation accuracy. The deep learning model on remote sensing images could assess leaf blast occurrence in real time.