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The objective was to evaluate the ability of visible and near-infrared (NIR) spectroscopy to predict winter wheat grain yield, according to the performance of different prediction models. In situ reflectance measurements (350–1050 nm) were acquired from winter wheat flag leaves grown under nine mineral nitrogen (N) fertilization treatments (0–300 kg N ha⁻¹), during stem extension developmental stage. Linear statistical models (MLR—multiple linear regression, PLSR—partial least squares regression) and non-linear prediction (ANN—artificial neural networks) were generated to estimate grain yield, based on derived variables from hyperspectral data as input features (first derivative of reflectance in form of principal components—PCs and vegetation indices—VIs). The expected influence of variable N fertilization on agronomic and spectral variables was recorded. The red and NIR reflectance contributed …