Simulation of apparent infection rate to predict severity of soybean rust using a fuzzy logic system

ABSTRACT Few biologically based models to assess the risk of soybean rust have been developed because of difficulty in estimating variables related to infection rate of the disease. A fuzzy logic system, however, can estimate apparent infection rate by combining meteorological variables and biological criteria pertinent to soybean rust severity. In this study, a fuzzy logic apparent infection rate (FLAIR) model was developed to simulate severity of soybean rust and validated using data from field experiments on two soybean cultivars, TK 5 and G 8587. The FLAIR model estimated daily apparent infection rate of soybean rust and simulated disease severity based on population dynamics. In weekly simulation, the FLAIR model explained >85% of variation in disease severity. In simulation of an entire epidemic period, the FLAIR model was able to predict disease severity accurately once initial values of disease severity were predicted accurately. Our results suggest that a model could be developed to determine apparent infection rate and an initial value of disease severity in advance using forecasted weather data, which would provide accurate prediction of severity of soybean rust before the start of a season.