Integrating ecological niche and epidemiological models to predict wheat fusarium head blight using remote sensing and meteorological data

Fusarium head blight (FHB) is a major wheat disease worldwide, significantly affecting yield and quality. Disease risk assessment and spatiotemporal dynamic prediction are crucial for effective FHB management and control. Although ecological niche models (ENMs) and epidemiological models (EMs) have been widely applied to assess the potential distribution of diseases and simulate their progression, studies integrating these models with satellite remote sensing and meteorological data for crop disease prediction remain limited. To fill this gap, our study developed an integrated prediction framework based on susceptible-exposed-infected (SEI) model. First, remote sensing data extracted host factors, including wheat spatial distribution, key phenological (KPh) stages defined by Day of Year (DOY), and early physiological changes. This information, along with meteorological features, topographic factors, and sampling coordinates, was utilized to construct an ENM based on Maximum Entropy (MaxEnt) algorithm. MaxEnt evaluation results guided input adjustments, ensuring high AUC output to characterize initial infection levels for SEI model. Next, transition rates in SEI model were determined by the coupling of the parameterized response functions of daily temperature, relative humidity, and DOY for KPh stages to mechanize the EM. The mechanistic model (MM), with optimal parameter values derived from sensitivity analysis and optimization, provided a robust prediction of disease occurrence on the sampling day and enabled spatiotemporal dynamic simulation of wheat FHB. The final MM achieved a coefficient of determination of 0.83, mean absolute error of 0.06, root mean square error of 0.072, and classification F1-score of 0.88. The simulated disease progression curve was consistent with the epidemiological characteristics of FHB, exhibiting an S-shaped pattern. These results suggest that integrating remote sensing and meteorological data with MaxEnt and SEI models for FHB prediction holds significant application potential.