China is a large agricultural country. In the process of agricultural production, it is of great significance to accurately predict the soil moisture. In view of the local minimization and slow convergence in the prediction process of the traditional back propagation (BP) neural network, an improved genetic algorithm is applied to the traditional BP neural network model in this study. A soil moisture prediction method is proposed that optimizes the BP neural network by the adaptive genetic algorithm. A prediction model of the BP neural network optimized by the improved genetic algorithm is established by the Matlab simulation software and experimented on the soil moisture of corn fields in Harbin. The results show that the accuracy of the model is higher than that of the unoptimized BP neural network model. This model can greatly reduce the use of moisture sensor and thus reduce the agricultural production cost.