The design and implementation of sugar-cane intelligence expert system based on EOS/MODIS data inference model

Abstract

One of the major problems in the real time decision of agricultural intelligence expert system is how to be obtained the real time information of crops growth and its close relation environment data. As result, the extraction of crops planting areas and their spatial distribution and their growth variety, especially when the natural disaster arises, such as drought, its spatial distribution and crops suffer from harmful degree have become the extraordinary important factors of the real time decision in agricultural intelligence expert system. In order to be obtained the real time information of crops growth and its close relation environment data. In the first place, this paper presents an automatic approach to the sugar-cane planting areas and its spatial distribution and growth and classification of drought extraction for mixed vegetation and hilly region, more cloud using moderate spatial resolution and high temporal resolution EOS/MODIS data around Guangxi province, south of China. Next, the framework and the method for knowledge expressing and inference mechanism of the real time decision of sugarcane intelligence expert system are proposed. Finally, the information of sugarcane planting area and sugarcane growth variety and sugar-cane drought distribution are carried out by using multi-phase EOS/MODIS data and weather forecast are used in the sugarcane intelligence expert system, the mechanism combines concern rectangle and inference based on the produce type inference, and makes good use of their advantages. © 2008 International Federation for Information Processing.