LAUSR

Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Observation, meteorological, ecological, entomological, and plant pathological, etc.) and cutting edge research on pest and disease modeling to support decision making in the sustainable management of pest and disease. First, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Second, we developed an automatic system based on web GIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. Finally, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (Puccinia striiformis) and national pest oriental migratory locust (Locusta migratoria manilensis (Meyen)) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China, in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China.