Use of an empirical model to estimate leaf wetness duration for operation of a disease warning system under a shade in a ginseng field

Abstract

Ginseng foliar diseases are typically controlled by spray application using periodic schedules. Few disease warning systems have been used for effective control of ginseng foliar diseases because ginseng is grown under shade nettings,whichmakes it difficult to obtain weather data for operation of the disease warning system. Using weather data measured outside the shade as inputs to an empirical leaf wetness duration (LWD) model, LWD was estimated to examine if operation of a disease warning system would be feasible for control of ginseng foliar diseases. An empirical model based on a fuzzy logic system (fuzzy model) was used to estimate LWD at two commercial ginseng fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012. Accuracy of LWD estimates was assessed in terms of mean error (ME) and mean absolute error (MAE). The fuzzy model tended to overestimate LWD during dew eligible days whereas it tended to underestimateLWD during rainfall eligible days. Still, daily disease risk ratings of the TOM-CAST disease warning system, which are derived from estimates of wetness duration and temperature, had a tendency to coincidewith that derived frommeasurements ofweather variables. As a result, spray advisory dates for the TOM-CAST disease warning system were predicted within ±3 days for about 78% of time windows during which the action threshold for spray application was reached. This result suggested that estimates of LWD using an empirical model would be helpful in control of a foliar disease in a ginseng field. It was also found that a spray application time model using meteorological observationsmay prove successful without the requirement of leaf wetness sensors within the field. Development of empirical correction schemes to the fuzzy model and a physical model for LWD estimation in a ginseng field could improve accuracy of LWD estimates and, as a result, spray advisory date prediction, which merits further studies.