Developing an innovative stacking ensemble machine learning and multi-source data fusion-based precision nitrogen management strategy for corn

Abstract Purpose The primary goal of this research was to develop an innovative in-season nitrogen (N) recommendation strategy for corn ( Zea mays L.) using stacking ensemble machine learning (ML) and multi-source data fusion. Methods Forty-nine site-years of N rate experiments conducted across the U.S. Corn Belt were used to evaluate the performance of five individual ML algorithms (Random Forest Regressor (RFR), Support Vector Regressor (SVR), Extreme Gradient Boosting Regressor (XGBR), CatBoo