Agrometeorological information used in agriculture field depends heavily on productivity in accordance with analysis and forecasting accuracy. In this study, we used necessary weather information for potato crops during the growing season and developed a decision support system to help farmers' management activities. The core of the solution is to utilize the real-time weather data observed in my field. By using application weather information in agricultural field rather than simple weather information transmission, we understood control effect through various pest information services. These ICT (Information & Communication Technologies) solutions in the field of weather-based agriculture provide appropriate intention information for growing crops, thus it is expected that effective control will reduce the risk of insect pests and reduce direct costs of farmers. In addition, it is expected that it will contribute not only to reduce soil pollution but also to safe food production by controlling indiscriminate use of pesticides properly.

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.