Research Group for Pest Prediction has hitherto developed decision support system for control of diseases such as rice false smut and sheath blight using Information and Communication Technology (ICT), and disease management strategy against soil-borne diseases of vegetables by evaluating disease potential of fields. From these research backgrounds, our group aims to develop pest prediction systems to support pest control, which lead to sustainable agriculture by decreasing input of costs and environmental stresses. Currently, we are developing new decision support systems to timely control rice blast and rice stripe diseases, as well as rice stink bugs, using such as meteorological data, AI (Artificial Intelligence) and ICT technologies. In addition, we have developed assessment system for soil-borne diseases potential of fields against clubroot of Chinese cabbage and foot rot of sweet potato, by integrating data mining approaches through AI technology. We believe collaboration with many stakeholders (national and local governments, extension workers, farmers, business companies) is significant to promote these researches and implement the research output efficiently, so that we also make efforts on the outreach works to build strong relationships with the stakeholders and finally achieve our goal.
