Research on agricultural decision support system based on rough set theory

Rough set theory as a new mathematical tool is used to deal with the imprecise, incomplete and uncertain knowledge for pattern recognition. In current information explosion era, the powerful functions of agricultural decision support system(ADSS) on assistant decision-making and rough set on information processing is becoming increasingly obviously, and have called people´s attention. According to the characteristics of the area of agriculture data, this thesis proposed the model of DSS for agriculture application based on rough set theory. In allusion to the disadvantages of traditional data analyzed methods,this thesis studied the important role of discernibility matrix in finding the core of attributes, then proposed an improved attribute reduction algorithm for incomplete information systems based on the limited tolerance relation. According to the task of knowledge acquisition in ADSS,we built the model of ADSS based on rough set theory.In this model, we firstly analyzed the pretreatment method for agricultural data, which included the discretization and generalization issues of attributes. After pretreatment, the data will form a target information system, then attribute reduction algorithm is used to get rules, then achieved the purpose of application of rough set in ADSS.