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Energy consumption control for agricultural environment monitoring wireless sensor network system is of great significance. A dynamic sampling model of soil temperature sensor network based on BP neural network prediction and threshold analysis,for real-time adjustment of sampling frequency in the light of the periodic variation features of soil temperature,was constructed to reduce the redundant sampling data and power consumption of the network. Using environment temperature and air humidity as the actual measure input of the BP neural network,soil temperature as predict output of the BP neural network,we adjust the sampling periods dynamically by observing whether the output of the BP neural network is entering the threshold interval. The simulation results show that,for the periodic soil temperature,the root mean squared error(RMSE) and absolute error(AE) between predicted values and measured values were 0.83 ℃ and 0.54 ℃. Comparing to continuous sampling,dynamic sampling reduce the data collection by 30%.