Irrigation Prediction Model with BP Neural Network Improved by Genetic Algorithm in Orchards

Abstract

The orchard irrigation is susceptible significantly to various environmental factor but the approach to predict water demand of irrigation remains an outstanding challenge up to now. In this paper, a prediction model of irrigation based on GA-BP neural network has been proposed in orchards, which selects three environmental factors including air temperature, soil moisture content and light intensity as the input of back. propagation neural network. In order to overcome BP’s disadvantage of being easily stuck in a local minimum, genetic algorithm is used to optimize the weight and threshold of neural network. The results showed that the GA-BP neural network model can express the nonlinear relationship between the water demand of litchi and the main environmental factors more accurately. The mean absolute percentage error (MAPE) is only 0.0283, and the correlation coefficient of the target and output value is 0.9799. Hence, the model can provide a theoretical basis for the further development of the intelligent irrigation decision system of litchi orchards.