Wheat Yield Prediction Based on Multi-Source Heterogeneous Data and Attention Gate Mechanism
To solve the problem of low precision of traditional single-mode data for wheat yield prediction,we proposed a new method combining multi-source heterogeneous data and attention gating mechanism.Firstly,a feature-level gating strategy was introduced to capture the information variation within each modality.Then,a neural network is used to evaluate the confidence scores within each modality and construct a module for obtaining effective information between modalities.Finally,a space and channel attention gating mechanism module based on Transformer is designed to fully integrate effective information between different modes,so as to obtain the best prediction feature representation.The comparative experimental results show that the proposed method has higher prediction accuracy compared to traditional methods,with RMSE and MAE only reaching 809 kg/hm2 and 522 kg/hm2,respectively,and R2 reaching 0.806.The three evaluation indicators obtained by predicting the wheat yield in Henan province over the past 10 years are relatively stable and demonstrate strong robustness.The ablation experiment also verified that different components in our method can effectively improve the prediction accuracy of wheat yield,and can provide strong data support for relevant departments to make decisions to ensure food security management.
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