Incomplete Information System In The Application Of Agricultural Biological Hazard Prediction

China has a territory of9.6million square kilometers and a population of14million.How to feed1/4of the world’s population within the9.6million square kilometers of landhas become our main problem. Despite China’s vast territory and rich species, we also havemany kinds of agricultural biological disasters occurred frequently. Agriculturalbiotechnology disaster is a natural disaster. It mainly refers to the natural mutation processcaused by the pests, such as serious harm to crops, disease, insects, grass and mice. Undercertain environmental conditions, they break out and caused huge losses of crops and theirproducts. Basically it can be divided into the following categories, crop diseases,agricultural pests, weeds in farmland and farmland Rodents.With the time being, more and more attentions are paid to agricultural biologicaldisasters. Various prevention methods are constantly used in agriculture. But in spite of this,insect damage still broke out last summer in most parts of northern China, which causednearly a huge property damage of20billion.42,300hectares of land crops have no harvest.This shows how to prevent the agricultural biotechnology disaster is imminent.The rough set theory is a theory of data analysis raised by the Polish mathematicianZ.Pawlak in1982. This theory is successfully applied in decision-making and analysis ofthe data, the theory of pattern recognition, machine learning and knowledge discovery, andgradually aroused extensive attention of scholars from various countries in the world. Inrecent years, with the deeper research of the rough set theory, how incomplete informationsystem containing unknown attribute values excavated more useful knowledge andinformation has become a hot issue of concern to the researchers.In this paper, we apply the decision making in incomplete information system of roughset theory to the agricultural biological disasters forecasting. We analyze variousenvironmental factors that affect agricultural biological disasters outbreak; analyze the datacollected over the years to predict the time and the area of the outbreak of agriculturalbiological disasters, so as to achieve the effects of prevention.