A Novel Neural Network Ensemble Method and Its Application in Precision Fertilization

In order to solve the problem of precise fertilization rate determination, a novel neural network ensemble method is introduced in this paper. In this method, the method of sampling with replacement is used to produce neural network individual set. A novel formula measuring the network similarity is given and Freys clustering algorithm AP is used to select the networks with high precision and greater diversity. Then by the Lagrange multiplier ensemble(LME) and forecasting effective measure mensemble(FEME) method, these selected networks are combined. The experiment on the standard dataset shows that, LME algorithm has higher accuracy and stronger generalization than the single neural network. Furthermore, as a linear weighted ensemble method, LME is better than FEME. Generally, the root mean squared error (RMSE) decreases when the subnets number and cluster number increases, but when the number reaches a level, the decreasing trend of RMSE becomes slow. Then according to the fertilizer effect data on maize field in black soil, LM ensemble is used to build the precision fertilization model, where soil nutrient and target yield are taken as inputs and fertilization rate is taken as output. The practice shows that LM ensemble based fertilization model is better than traditional fertilization models and the existing neural network based fertilization models.