In the field of plant disease protection, many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications. In this work, a remote sensing approach for the detection of Verticillium dahliae in olive fields is presented.
A model was created that calculates a spectral index based on Sentinel-2 data and uses it to assess the levels of stress in olive trees in the region of Chalkidiki, Greece. The derived map provides an overview of the situation concerning stress levels in olive fields across a large area and in a small amount of time. Additionally, because of the constant flow of data from the Sentinel satellites, a time series of calculations can be used as an anomaly indicator for regions of interest. This model can be implemented in an e-Infrastructure as a cloud service to further enhance its usability by relevant parties, such as agricultural advisors and scientists.