A hybrid deep learning approach for winter wheat yield prediction: evidence from leveraging multi-source data

Accurate district-level wheat yield forecasts are critical for food security planning, supply-chain management, and agricultural policy in India, the world's second-largest wheat producer. We benchmark nine model classes for this task on a 23-year (2001-2023) dataset of 275 districts across India's seven largest wheat-producing states, which together account for ∼95% of national production. The benchmark covers Random Forest, XGBoost, LightGBM, a 1D-CNN, an LSTM, a BiLSTM, a single-stream Transformer encoder, the recently proposed Parallel CNN-LSTM-Attention design, and our hybrid CNN-BiLSTM-Attention with modality-specific routing (a 1D-CNN over the vertically structured soil profile and a BiLSTM with self-attention over the meteorological and remote-sensing time series). The proposed model is the best entry, achieving a Mean Absolute Error (MAE) of 273.2 kg/ha and an [Formula: see text] of 0.795 on the held-out test set - a 43.8% MAE reduction over the Random Forest baseline, a ∼28% reduction over the gradient-boosted baselines, and a ∼4% reduction over the next-best deep model. A simple persistence forecast ([Formula: see text]) however, achieves an MAE of 274.9 kg/ha, essentially tying the proposed model on average. We show that the architectural value-add concentrates in anomalous years: in the dry 2023 sowing season the model improves MAE by 7.2% and RMSE by 11.2% over persistence, and SHAP attribution localises the temporal contribution to the February-March grain-filling window led by EVI and NDVI signal - consistent with the well-documented sensitivity of wheat grain-filling to moisture and temperature stress in that window. Together, these results position persistence-aware, modality-specific deep learning as a practical framework for stress-sensitive yield forecasting in data-scarce agricultural regions.