Abstract: A deep learning prediction model for soil moisture content （transformer LSTM） was constructed, which integrated transformer and LSTM, to address the difficulties in solving nonlinear and complex features, as well as the tendency to fall into local minima in the soil moisture prediction model. Soil and meteorological data from the blueberry（Vaccinium spp.） root zone of two stations, cold shed and outdoor, in the blueberry production area of Dingjiazhai Village, Huangdao District, Qingdao City, Shandong Province, were collected as modeling data,based on Pearson correlation and partial autocorrelation analysis, the data input characteristics and input length of the selected model were compared and analyzed with a single transformer model and LSTM model to evaluate the predictive performance of the model on soil moisture content. The results showed that the transformer LSTM model outperformed both the single transformer model and the LSTM model in prediction accuracy. The mean absolute error （MAE）, root mean square error （RMSE）, mean absolute percentage error （MAPE）, and coefficient of determination （R²） of the transformer LSTM model were 0.245 9, 0.572 0, 0.012 1, and 0.960 6, respectively. The transformer LSTM model could more comprehensively extract feature information from the input sequence of blueberry planting environmental factors, effectively improving the accuracy and level of soil moisture factor prediction.

Key words: blueberry（Vaccinium spp.）, root zone soil, moisture content, transformer, LSTM, prediction model