Early Onset Yellow Rust Detection Guided by Remote Sensing Indices

Abstract

Early warning systems help combat crop diseases and enable sustainable plant protection by optimizing the use of resources. The application of remote sensing to detect plant diseases like wheat stripe rust, commonly known as yellow rust, is based on the presumption that the presence of a disease has a direct link with the photosynthesis capability and physical structure of a plant at both canopy and tissue level. This causes changes to the solar radiation absorption capability and thus alters the reflectance spectrum. In comparison to existing methods and technologies, remote sensing offers access to near real-time information at both the field and the regional scale to build robust disease models. This study shows the capability of multispectral images along with weather, in situ and phenology data to detect the onset of yellow rust disease. Crop details and disease observation data from field trials across the globe spanning four years (2015–2018) are combined with weather data to model disease severity over time as a value between 0 and 1 with 0 being no disease and 1 being the highest infestation level. Various tree-based ensemble algorithms like CatBoost, Random Forest and XGBoost were experimented with. The XGBoost model performs best with a mean absolute error of 0.1568 and a root mean square error of 0.2081 between the measured disease severity and the predicted disease severity. Being a fast-spreading disease and having caused epidemics in the past, it is important to detect yellow rust disease early so farmers can be warned in advance and favorable management practices can be implemented. Vegetation indices like NDVI, NDRE and NDWI from remote-sensing images were used as auxiliary features along with disease severity predictions over time derived by combining weather, in situ and phenology data. A rule-based approach is presented that uses a combination of both model output and changes in vegetation indices to predict an early disease progression window. Analysis on test trials shows that in 80% of the cases, the predicted progression window was ahead of the first disease observation on the field, offering an opportunity to take timely action that could save yield.