Application of Artificial Neural Network on Relationship Analysis between Wheat Yield and Soil Nutrients

As a type of nonlinear dynamic parallelism system, artificial neutral network has a capacity of identifying complex causal relationships between variables, such as those between the winter wheat yield and the soil spatial distribution nutrients, including water content, organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus and potassium. By training 50 tested soil samples in the back-propagation neutral network of topological structure 6:9:1, the model of analyzing the relation between the crop yield and those 6 soil characteristics was established to validate the remaining 13 samples. The results show that the soil water content and alkali-hydrolysable nitrogen are linear to the crop yield, the total nitrogen, organic matter and rapidly available potassium are respectively multinomial to it and that the rapidly available phosphorous is of the exponential relationship with the crop yield.