Model for predicting crop water requirements by using L-M optimization algorithm BP neural network

In this paper, a network topological structure was determined and an artificial neural network model for predicting crop water requirements was established by using L-M optimization algorithm BP neural network and correlation analysis between multi-dimension climate data and crop water requirements. The BP neural network was trained by experimental meteorological data of 100 days measured in Tennessee Plateau Experiment Station. The simulated results showed that the BP neural network can solve the uncertainty and non-linearity of multi climate factors, and the prediction precision of the model is high. At the same time, the neural network prediction precision was tested by a group of non-specimen meteorological data and crop water requirements. The tested results were good enough to meet the requirements of irrigation precision.