Wheat Yield Prediction Based on Parallel CNN-LSTM-Attention with Transfer Learning Model

Accurate wheat yield prediction is essential for ensuring food security and supporting governmental decision-making. However, the scarcity of long-term agricultural time-series data and the complex interplay between meteorological and socio-economic factors pose significant challenges. To address these issues, this study proposes a Transfer-Learning-Based Parallel CNN–LSTM–Attention (TPCLA) model for wheat yield forecasting. A cross-regional transfer learning strategy is employed to mitigate data scarcity by leveraging temporal patterns learned from regions with similar ecological characteristics. The proposed parallel architecture integrates one-dimensional convolutional neural networks and long short-term memory networks to jointly extract spatial and temporal features, while an attention mechanism is incorporated to highlight key influencing factors and enhance feature interpretability. Unlike conventional studies that primarily focus on climatic variables, this work considers both direct factors (e.g., average temperature and precipitation) and indirect socio-economic factors (e.g., agricultural mechanization level, total agricultural output value, grain production scale, cultivated land area, and disaster-affected area). Experimental results on multivariate wheat data from 1993 to 2024 demonstrate that several indirect indicators exert a more substantial influence on yield than traditional meteorological variables—reflecting the increasing ability of modern agricultural practices to buffer climatic variability. The proposed TPCLA model achieves an RMSE of 0.394, MAE of 0.326, and an R2 of 0.904, outperforming multiple benchmark models and confirming its robustness and predictive superiority under small-sample conditions. The findings not only validate the effectiveness of integrating indirect yield-influencing factors but also provide new insights for agricultural policy formulation and climate resilience strategies.