Comparative Evaluation of Hyperspectral Preprocessing Pipelines for Leaf-Level Nitrogen Estimation Under Controlled Conditions in Korla Fragrant Pear

Rapid and non-destructive nitrogen diagnosis in fruit orchards is critical for precision fertilization management and crop yield optimization. This study develops and evaluates a practical hyperspectral preprocessing pipeline for leaf nitrogen estimation in Korla fragrant pear (Pyrus sinkiangensis Yü), a commercially important cultivar in southern Xinjiang, China. Hyperspectral reflectance data and corresponding nitrogen measurements were collected from mature leaves of slender-spindle-trained trees. Four preprocessing strategies, comprising multiplicative scatter correction (MSC), wavelet threshold denoising, and their sequential combinations, were systematically compared to assess their effects on spectral information retention and model performance. The successive projections algorithm (SPA) was applied for characteristic wavelength selection, and four regression models, including linear regression (LR), partial least squares regression (PLSR), random forest (RF), and XGBoost, were constructed and evaluated. Results demonstrated that combined preprocessing strategies outperformed single-method approaches, and that preprocessing order significantly influenced predictive accuracy. Nonlinear models consistently outperformed linear models, confirming a pronounced nonlinear relationship between hyperspectral features and leaf nitrogen content. The MSC, followed by wavelet threshold denoising, combined with SPA and XGBoost, achieved the best predictive performance, with R2 = 0.754, RMSE = 0.179 mg/g, and RPD = 2.017 on the test set. These findings provide a methodological reference for hyperspectral nitrogen monitoring and preprocessing workflow design under controlled conditions, with potential for further validation in field applications.