AGDAformer: Agent-Guidance Dual Attention Transformer for Climate-Aware Crop Yield Prediction

Zhongyan Yi, Zhihua Fang, Ruifeng Xu, Jintai Wang, Liang He

Abstract: As climate change becomes widespread, rapid, and intensifying, accurate crop yield predictions are crucial for formulating effective agricultural development strategies. However, crop yield prediction is fairly challenging, as it is increasingly influenced by the intensifying climate change. In this paper, we propose the Agent-Guidance Dual Attention Transformer (AGDAformer), which aims to enhance the accuracy of yield predictions for climate-sensitive crops by integrating spatial attention mechanisms with multi-scale meteorological data. On the one hand, the Cross-Spatial Attention (CSA) mechanism optimizes the acquisition of broad-scale meteorological information by adaptively identifying and focusing on deep features closely related to crop yield prediction, thus improving the effectiveness of feature selection. On the other hand, the Agent-Enhanced Transformer (AET) introduces an agent matrix to facilitate the fusion of multiscale meteorological data, embedding meteorological knowledge into Agent Attention to effectively integrate information from different scales and better capture complex climatic factors. Extensive experiments on the CropNet dataset demonstrate that AGDAformer achieves superior performance across four crop types, significantly improving prediction accuracy and providing robust support for future agricultural decision-making.

External IDs:dblp:conf/ijcnn/YiFXWH25