Application of a Multi-factor Model in the Grain Yield Prediction

A multi-factor model for the grain yield prediction based on the previous data and relevant impacting factors is reported. In this model, the weight coefficient of each individual factor that affected the grain yield historically is analyzed by the variation coefficient method and the conversion degree function in the attribute theory. The effects of various factors on the predicted grain yield are then used as standard vectors and subjected to a similarity-based search in the matrix of historical values. The predicted total grain yield is then determined by multiplying the sown area by the unit grain yield which is obtained by the highest similarity between the historical data and those predicted. Compared to the results obtained by the BP nerve network method, this method is simpler, more flexible, less time-consuming, and more accurate.