BiTransformer soil moisture prediction and irrigation scheduling for peanut in North China

Accurate soil moisture prediction is essential for precision irrigation and improving water‐use efficiency in peanut ( Arachis hypogaea L.) production. This study developed a bidirectional transformer (BiTransformer)‐based soil moisture forecasting framework integrated with a growth‐stage irrigation decision model for peanut cultivation in Luanzhou, North China. Hourly meteorological and soil moisture data collected during the 2023–2025 growing seasons were used for model development and validation. The proposed model combines BiTransformer encoders, cross‐attention interaction, temporal attention, and gated feature fusion to capture complex temporal dependencies and nonlinear dynamics of soil moisture. Model performance was compared with random forest, XGBoost, recurrent neural network (RNN), long short‐term memory (LSTM), BiLSTM, and conventional Transformer models using multiple evaluation metrics. Results showed that the proposed BiTransformer achieved the highest prediction accuracy, with an R 2 of 0.9707, root mean squared error (RMSE) of 0.0144, and mean absolute error (MAE) of 0.0096, outperforming all benchmark models. Ablation experiments further demonstrated that both the bidirectional architecture and attention mechanism contributed substantially to model performance. Based on predicted soil moisture and peanut water requirement standards, a quantitative irrigation scheduling strategy was established for different growth stages. The decision model successfully identified irrigation timing and recommended irrigation amounts according to crop water demand, particularly during the podding stage when water requirements were highest. The proposed framework integrates soil moisture prediction with irrigation decision‐making and provides an effective tool for precision irrigation management, water conservation, and sustainable peanut production.