Neural network model for profile modeling spray of chemical to fruit trees and its applications

Based on the experiment, a BP neural network model was developed to map the relationship of process variables and distributing quality coefficient of Profile Modeling Spray of the Fruit Trees. The structure and parameters of BP neural network were optimized by using the methods of orthogonal experiments, intercross evaluating and training, normalization of the network training set and principal component analysis. The result shows that the correlation coefficient R between simulating outputs of the BP network and the results of experiments is 0.99, which has wide adaptability. And the BP network can be used conveniently to carry out various quantificational calculations. For example, the distributing quality coefficient under specific process variables can be estimated, or appropriate spray variables can be determined according to given production target.