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

Abstract

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 2 ) increased by 0.05-0.16, and the root mean square error decreased by 0.0029-0.0079. This study successfully constructed a technical system of "Growth period-Preprocessing-Model". The results indicated that the Modeling strategy considering the characteristics of crop growing stage could significantly improve the predictive ability of near-infrared spectroscopy models. This study provides a reliable technical framework for Precision nutrient management in orchard, and the established methodology can also serve as a reference for nutrient Surveillance of other fruit tree plants.

Plant phenotyping relevance

近赤外分光法、前処理、波長選択、ニューラルネットワークを組み合わせ、ナシ葉のリン含量という植物生理形質を非破壊推定する技術体系を構築・検証しており、表現型取得法が研究の中心である。

Code and data availability

The paper describes NIR spectral data collection and modeling of leaf total phosphorus in Korla fragrant pear, but no public phenotype/spectral dataset, images, code, or trained model deposit is mentioned. The only supplementary material is referenced for model evaluation tables, not raw data or code, and no authors'公开

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