Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content

Abstract

The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat.

Keywords

• Winter wheat irrigation
• UAV-based hyperspectral imaging
• Filling stage optimization
• Machine learning