Crossformer-Based Model for Predicting and Interpreting Crop Yield Variations Under Diverse Climatic and Agricultural Conditions

Ruolei Zeng, Jialu Li (), Zihan Li and Qingchuan Zhang
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Ruolei Zeng: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Jialu Li: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 Fucheng Road, Beijing 100048, China
Zihan Li: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 Fucheng Road, Beijing 100048, China
Qingchuan Zhang: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 Fucheng Road, Beijing 100048, China

Agriculture, 2025, vol. 15, issue 9, 1-26

Abstract: Crop yield prediction is critical for agricultural decision making and food security. Traditional models struggle to capture the complex interactions among meteorological, soil, and agricultural factors. This study introduces Crossformer, a Transformer-based model with a Local Perception Unit (LPU) for local dependencies and a Cross-Window Attention Mechanism for global dependencies. Experiments on winter wheat, rice, and corn datasets show that Crossformer outperforms CNN, LSTM, and Transformer in Test Loss, R 2 , MSE, and MAE. For instance, on the corn dataset, Crossformer achieves a Test Loss of 0.0271 and an R 2 of 0.9863, compared to 0.7999 and 0.1634 for LSTM, respectively, demonstrating a substantial improvement in predictive performance. Interpretability analysis highlights the importance of temperature and precipitation in yield prediction, aligning with agricultural insights. The results demonstrate Crossformer’s potential for precision agriculture.

Keywords: crop yield prediction; deep learning; Crossformer; interpretability analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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