文献类型： 外文期刊

作者： Yu, Mingyang ¹ ; Bai, Xinlu ³ ; Bao, Jianping ¹ ; Wang, Zengheng ¹ ; Tang, Zhihui ⁵ ; Zheng, Qiangqing ⁶ ; Zhi, Jinhu ³ ;

作者机构： 1.Natl & Local Joint Engn Lab High Efficiency & High, Prod Engn Lab Characterist Fruit Trees Southern Xi, High Qual Cultivat & Deep Proc Technol Characteris, Alar 843300, Peoples R China

2.Tarim Univ, Coll Hort & Forestry Sci, Alar 843300, Peoples R China

3.Tarim Univ, Coll Agr, Alar 843300, Peoples R China

4.Tarim Univ, Minist Educ, Key Lab Tarim Oasis Agr, Alar 843300, Peoples R China

5.Inst Mech Equipment, Xinjiang Acad Agr Sci, Shihezi 832000, Peoples R China

6.Xinjiang Acad Agr Sci, Inst Forestry & Hort, Shihezi 832000, Peoples R China

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

页码：

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.