Wheat rust, a major threat to global agricultural production, necessitates accurate modeling of its spatiotemporal dynamics for effective disease management. This study proposes a novel Physics-Informed Neural Network (PINN) model to predict wheat rust spread, achieving enhanced accuracy through innovative model design and network construction. The model incorporates a dynamic weighted loss function and stratified sampling strategy, effectively balancing initial conditions and physical constraints while improving modeling accuracy near infection foci and domain boundaries. Additionally, a seasonal environmental factor is introduced to enhance biological realism. In network construction, a deep five-layer PINN architecture is employed, optimized with boundary condition-enhanced training and L2 regularization to accurately capture nonlinear dynamics. Experimental results demonstrate high consistency with numerical solutions over a 200-day simulation period, with a mean absolute error below 0.013500 and PDE residuals tightly distributed around zero, validating the model’s physical consistency. This study provides a high-precision predictive tool for the spatiotemporal spread of wheat rust, offering scientific guidance for disease control strategies and opening new avenues for agricultural disease modeling.
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Xiong, H., Li, X., Yuan, D., Shen, J. (2025) Modeling Wheat Rust Spread Using Physics-informed Neural Networks for Improved Agricultural Management. Scientific Research Bulletin, 2(6), 159-169. https://doi.org/10.71052/srb2024/IGPA5268