融合Boruta-RFE与注意力机制的玉米地上生物量无人机遥感估算与单产预测方法OA

Aiming to improve the accuracy of maize aboveground biomass estimation and yield prediction under conservation tillage in the black soil region of Northeast China,the research was conducted in a spring maize experimental area in Lishu County,Jilin Province.Based on RGB,multispectral,and thermal infrared images acquired by unmanned aerial vehicles(UAVs),a modeling framework integrating Boruta-RFE feature selection,stage-wise XGBoost aboveground biomass estimation,and a lightweight attention mechanism for yield prediction was developed.The results showed that the aboveground biomass-sensitive features exhibited clear stage-specific differences,and thermal infrared temperature statistics,multispectral texture features,and RGB color indices showed high importance.The XGBoost model based on the fusion of RGB,multispectral,and thermal infrared features achieved good aboveground biomass estimation performance at all three growth stages,with the highest validation R2 at the grain-filling stage and the lowest validation NRMSE at the maturity stage.When only UAV-derived features were used for yield prediction,XGBoost showed the best overall performance,with a validation R2 of 0.700.After further incorporating the predicted aboveground biomass values from the three stages,the prediction accuracy of all models improved,among which the lightweight attention-based neural network performed best,with validation R2 and RMSE reaching 0.855 and 0.935 t/hm2,respectively.These results indicated that integrating UAV multi-source imagery with stage-wise aboveground biomass information can significantly improve maize yield prediction accuracy and provide technical support for maize growth monitoring and yield forecasting under conservation tillage conditions.