The Prediction Model of Total Nitrogen Content in Leaves of Korla Fragrant Pear Was Established Based on Near Infrared Spectroscopy

文献类型： 外文期刊

第一作者： Yu, Mingyang

作者： Yu, Mingyang;Bao, Jianping;Wang, Zengheng;Yu, Mingyang;Bao, Jianping;Wang, Zengheng;Bai, Xinlu;Zhi, Jinhu;Bai, Xinlu;Zhi, Jinhu;Tang, Zhihui;Zheng, Qiangqing

作者机构：

关键词： Korla fragrant pear; leaves; total nitrogen content; near infrared spectroscopy

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 6 期

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收录情况： SCI

摘要： In order to efficiently detect total nitrogen content in Korla fragrant pear leaves, near-infrared spectroscopy technology was utilized to develop a detection model. The collected spectra underwent various preprocessing techniques including first-order derivative, second-order derivative, Savitzky-Golay + second-order derivative, multivariate scattering correction, multivariate scattering correction + first-order derivative, and standard normal variable transformation + second-order derivative. A competitive adaptive reweighted sampling algorithm was employed to extract characteristic wavelengths, and a prediction model for the total nitrogen content of fragrant pear leaves was established by combining the random forest algorithm, genetic algorithm-based random forest algorithm, radial basis neural network algorithm, and extreme learning machine algorithm. The study found that spectral preprocessing of SNV + SD along with the radial basis neural network algorithm yielded better predictions for total nitrogen content of fragrant pear leaves. The validation set results showed an R2 of 0.8547, RMSE of 0.291%, and RPD of 2.699. Therefore, the SNV + SD + CARS + RBF algorithm combination model proved to offer optimal comprehensive performance in predicting the total nitrogen content of fragrant pear leaves.

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