Remote sensing and interpolation methods can obtain weather data for disease prediction

Abstract

The risk of the appearance or the intensification of a crop disease can be assessed using information about the weather, the pathogen or the crop. Weather data for use in disease risk prediction can be obtained from measurements at a nearby weather station. While weather measurements can represent accurate weather conditions at the site where the weather station is located, these data are representative only of a small area near the station. To obtain weather information over a larger area, spatial interpolation and remote sensing can be used to estimate the likely weather conditions in other locations. It is crucial to obtain weather data at an appropriate temporal resolution (e.g. daily or hourly) for a given disease in order to predict the disease. A weather database system is being constructed to provide high-quality climatic data (e.g. daily temperature, humidity and rainfall), which can be used to quantify the link between weather conditions and disease outbreaks.