Spectral Variability and Prediction of Nitrogen, Phosphorus, and Potassium in Fresh Sugarcane (Saccharum officinarum) Leaves by VIS–NIR–SWIR Spectroscopy

This study evaluated the potential of VIS–NIR–SWIR leaf spectroscopy to predict N, P, and K contents in sugarcane, considering spectral variability across two growing seasons (2023/2024 and 2024/2025), phenological stages, and five varieties. Spectral signatures (350–2500 nm) were acquired using a FieldSpec 3 spectroradiometer and processed with MSC, smoothing, and the first Savitzky–Golay derivative. Diagnostic bands were identified by Spearman correlation, and prediction was performed using partial least squares regression (PLSR). PCA showed that spectral variability was driven mainly by seasonal and phenological factors, whereas varietal effects were secondary. In 2023/2024, greater spectral homogeneity was associated with lower predictive performance. In 2024/2025, greater spectral heterogeneity was associated with improved prediction for N (R2 = 0.83; RMSE = 0.78 g kg−1) and P (R2 = 0.80; RMSE = 0.10 g kg−1). Potassium remained the most challenging nutrient to predict (maximum R2 = 0.25), mainly due to its ionic nature and the resulting lack of significant correlation with specific VIS, NIR, and SWIR spectral features. These results indicate strong potential for predicting N and P in fresh sugarcane leaves, although model robustness depends on the extent of spectral variability in the dataset.