Multi-temporal wheat disease detection by multi-spectral remote sensing

Abstract

For the implementation of site-specific fungicide applications, the spatio-temporal dynamics of crop diseases must be well known. Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites. However, the identification of fungal infections at an early growth stage is essential. This study examines the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases. Within an experimental field, a 6 ha plot of winter wheat was grown, containing all possible infective stages of the powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita) pathogens. Three high-resolution remote sensing images were used to execute a spatio-temporal analysis of the infection dynamics. A decision tree, using mixture tuned matched filtering (MTMF) results and the Normalized Difference Vegetation Index (NDVI), was applied to classify the data into areas showing different levels of disease severity. Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8%, whereas the scenes from May 28th and June 20th achieved considerably higher accuracies of 65.9% and 88.6% respectively. The results showed that high-resolution multi-spectral data are generally suitable to detect in-field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections.