Advancing the prediction of nitrogen utilization efficiency in wheat by integrating high-throughput phenotyping into the WheatGrow model

Accurate prediction of nitrogen utilization efficiency (NUtE) is critical for breeding nitrogen-efficient crop cultivars and optimizing field nitrogen management. Traditional prediction methods are time-consuming and limited to resolving plant-level nitrogen dynamics, hindering effective phenotype acquisition at the field scale and understanding of nitrogen uptake and transport in crops. This study aims to couple proximal remote sensing (PRS) and a crop growth model (CGM, i.e., WheatGrow model) via high-throughput phenotyping (HTP) techniques to establish a non-destructive prediction framework for plot-level NUtE at the field scale. Firstly, the organ-level nitrogen submodule was developed and integrated into the WheatGrow model, improving the simulation accuracy of plant nitrogen accumulation dynamics. Second, proximal RGB data combined with a deep-shallow machine learning approach enabled high-precision estimation of organ-specific critical nitrogen concentrations (leaf: R2 = 0.94, RMSE = 0.21%; spike: R2 = 0.95, RMSE = 0.10%). Fitted parameters of critical nitrogen dilution curves (CNDCs) demonstrated variations between cultivar and management in both organs, with spike nitrogen dilution rates exhibiting greater sensitivity to management practices than leaves. Finally, coupling PRS-derived organ-specific CNDCs with the enhanced WheatGrow model through the ensemble Kalman filter (EnKF) algorithm, yielded precise NUtE predictions at a small spatial scale (RMSE = 4.54 kg kg−1, Bias = 0.05). Validation across multi-year, multi-cultivar trials demonstrated robust performance, reducing NUtE prediction errors below 10% (RRMSE = 9.9% ± 0.8%). This framework bridges HTP techniques with crop modeling, may catalyze a paradigm shift in CGMs from empirical parameterization to real-time sensing, and advance scalable nitrogen use efficiency phenotyping in sustainable crop improvement and smart agriculture.