Near-infrared prediction of total phosphorus in leaves content in korla fragrant pear with growth period specificity via spectral modeling.

Leaf total phosphorus content (LTP) is a key indicator for assessing fruit nutrition status. As a rapid non-destructive inspection method, Near-infrared spectroscopy technology is susceptible to the influence of changes in plant growth periods and spectral noise on its prediction accuracy. At present, how to synergistically utilize growth period information and Spectral pre - processing methods to optimize the LTP Prediction model remains to be further studied. The study systematically collected Leaf sample and their near-infrared Spectral data during three key growth periods of Korla fragrant pear (fruit-setting period, fruit swelling period, and Maturity period). In the Spectral pre-processing stage, multiple scattering correction, Savitzky-Golay Smooth, First Derivative (FD), Second Derivative (SD) and their combined algorithms were comprehensively applied. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was used for characteristic wavelength selection, and based on this, Growth period specificity BP neural network model and cross-growth period general prediction models were constructed respectively to evaluate the performance of different Modeling strategies. Results The study showed that LTP content exhibited a significant differential distribution across different growing stage. In the characteristic wavelength bands, after processing with Combined pre-processing method (e.g., MSC+ FD), the correlation coefficient between the spectrum and LTP content significantly increased to approximately 0.90. The predictive performance of the Growth-period-specific model was comprehensively superior to that of the general model, with the Validation set coefficient of determination remaining above 0.83. Compared with the general model, the Coefficient of determination (R