Forecasting Of Agricultural Pests And Diseases Based On WebGIS And RS Theory

China is a big agricultural country, and rice production occupies a veryimportant position. The occurrence of diseases and pests causes serious influence onrice production, therefore, timely and accurate forecasting of diseases and pestscontributes to the healthy development of agriculture. In this paper, a comprehensiveforecasting system is built up, it includes: data preprocessing, diagnose and forecastagricultural diseases and pests, auxiliary decision-making on diseases and pests, etc.The forecasting result is displayed into a clear and concise electronic map, namely,the system provides a visual platform for forecasting agricultural pests and diseases.The main research content of this paper is as follows:(1) Data preprocessing methods based on rough set theory (RS). After a lot ofanalysis and research on relevant knowledge, discretization algorithm based onattribute importance, ROUSTIDA algorithm (a kind of analysis method onincomplete data) and attribute reduction algorithm based on discernibility matrix areimproved respectively.(2) ArcGIS Server, Ajax technology, cache technology and WebGIS are studied,a WebGIS platform based on ArcGIS Server is realized. This platform can shorten theresponse time of the system and improve the user experience, besides, it can alsobalance the burden on the server side.(3) Forecasting method based on RS theory is improved, besides, severalcommon forecasting methods are introduced into the system, including the effectiveaccumulated temperature forecasting method, the development progress offorecasting method, mathematical statistics forecasting method, etc. Thus, theforecasting precision is improved, and it allows users to choose forecasting models toforecast according to their mastery of information about disease and pest.