文献类型： 外文期刊

第一作者： Kuang Run

作者： Kuang Run;Long Teng;Wu Ji-hui;Lu Jin-sheng;Xie Zi-ran;Liu Wen-tao;Lan Yu-bin;Long Yong-bing;Zhao Jing;Kuang Run;Liu Hai-lin;Wang Zai-hua;Kuang Run;Long Teng;Wu Ji-hui;Lu Jin-sheng;Xie Zi-ran;Liu Wen-tao;Lan Yu-bin;Long Yong-bing;Zhao Jing;Kuang Run;Long Teng;Wu Ji-hui;Lu Jin-sheng;Xie Zi-ran;Liu Wen-tao;Lan Yu-bin;Long Yong-bing;Zhao Jing

作者机构：

关键词： Hyperspectral; Dendrobium nobile; Ensemble learning; Nitrogen; CARS

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2024 年 44 卷 7 期

页码：

收录情况： SCI

摘要： Dendrobium nobile leaf blades' nitrogen content is a decisive factor for precise fertilization. Traditional nitrogen content detection methods are time-consuming and can completely deplete samples. Efficiently detecting the nitrogen content of D.nobile leaf blades has become a growing concern for herbal medicine cultivation enterprises. To quickly and non-destructively obtain the nitrogen content of D.nobile leaf blades, this study used fresh D.nobile leaf blades as experimental samples. After obtaining their hyperspectral images in the range of 402.6 similar to 1 005.5 nm and nitrogen chemical detection values,the images underwent the extraction of regions of interest(ROI), followed by preprocessing of the spectral information within those regions' learner algorithms including Partial Least-Squares Regression (PLSR), Kernel Ridge Regression (KRR), and Support Vector Regression (SVR), as well as ensemble learning algorithms including Random Forest (RF), Bagging, and Adaboost, were utilized to model the nitrogen content of D.nobile . Regression prediction models were constructed based on the full-band spectral information of fresh D. nobile leaf blades and feature bands of spectral information extracted through CARS, and the prediction accuracy was compared. The results showed that when constructing the monitoring model based on the full-band spectral information, the RF model built with spectral data preprocessed by the Savitzky-Golay filtering (SG) method had the best prediction result (R-CV(2) =0.961 4, RMSECV =0.081 8, R-P(2) =0.972 6, RMSEP =0.063 3), and all models achieved R-2 values over 0.90. When constructing the regression prediction model based on feature bands extracted through CARS, the Bagging model had the highest accuracy and stability, with the best prediction result observed in the SG-CARS-Bagging model (R-CV(2) =0.938 7, RMSE (CV) =0.100 0, R-P(2) =0.953 5, RMSEP=0.082 6), while the accuracy of the individual learner models KRR and SVR was significantly lower. The CARS algorithm feature extraction removed some important bands, improving modeling efficiency but reducing model accuracy. Therefore, when optimizing the feature parameters of the regression model, it is necessary always to consider the balance between accuracy and efficiency. The research results indicate that ensemble algorithms such as RF, Bagging, and Adaboost have higher stability and prediction accuracy than individual learner algorithms such as PLSR, KRR, and SVR. They are more suitable for analyzing and processing hyperspectral data and have obvious advantages in the nitrogen nutrition monitoring of D.nobile.

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