Crop precision fertilization model based on improved BP neural network ensemble

There exists obvious nonlinear relation between the optimal fertilization rate and soil and yield. In order to simulate this relation more accurately, a novel neural network ensemble method was proposed, where the K-means clustering was used to select better network individuals and Lagrange multiplier was used to compute the weight of network individuals. Based on the fertilizer effect data in the experimental field, taking soil nutrient and fertilization rate as inputs and taking yield as output, a crop precision fertilization model was constructed. By solving a nonlinear programming problem, both the maximum yield and the optimal fertilization rate were achieved. The results showed that the simulation error of the fertilization model based on neural network ensemble (root mean square error was 64.54) was much less than that of the fertilization model based on individual neural network (root mean square error was 169.74). Also, as a quantitative model, it is better than the traditional fertilization models and can be used to guide precision fertilization effectively.