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| Hyperspectral Estimation of Selenium Content in Selenium-Rich Tea Based on Feature Selection and Machine Learning |
| WEN Zhu1, GUO Song1, SHU Tian1, ZHAO Long-cai2, 3 |
1. Guizhou Agricultural Science and Technology Information Institute, Guiyang 550006, China
2. College of Natural Resources and Environment, Key Laboratory of Plant Nutrition and Agro-Environment in Northwest China, Ministry of Agriculture and Rural Affairs, Yangling 712100, China
3. College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
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Abstract Selenium is one of the important nutrient indices in selenium-rich tea, and its content determines the economic and nutritional value of selenium-rich tea. Hyperspectral remote sensing inversion technology has the characteristics of non-destructive, real-time, and rapid monitoring. This study utilizes the selenium content in selenium-rich tea from the Nangong River tea garden in Kaiyang County, Guizhou Province, and corresponding canopy non-imaging hyperspectral data as source data. The Savitzky-Golay second-order smoothing filter was used to preprocess the primary spectrum, and the potential of the primary spectral data was explored through first-order derivative transformation and continuum removal transformation. The independent variables for the modeling were obtained using a band elimination combination and various feature selection algorithms. Multiple inversion models of selenium content in tea were constructed using different algorithms. The results showed that: (1) the combination of spectral transformation and spectral index could enhance the ability of retrieving selenium content from the primary spectrum. (2) SPA was better than UVE overall; Continuum removal spectrum was superior to the primary spectrum and the first derivative spectrum. (3) The accuracy of the multi-factor model was better than that of the factor model, and the performance of ELMR in the multi-factor model was the best. Among all the models, the SPA-ELMR model under the continuum removal spectrum had the highest accuracy. The coefficient of determination (R2) and normalized root mean square error (nRMSE) of this model were 0.689 and 18.869%, respectively, and the corresponding verification R2 and nRMSE were 0.627 and 20.429%, respectively. In this study, the response relationship between selenium content in tea and spectral reflectance at specific growth stages was discussed. A single-factor inversion model and a multi-factor inversion model with appropriate accuracy were constructed, providing a theoretical basis for the rapid and non-destructive monitoring of selenium content in tea. Also, they provided some technical support for the digital construction of tea gardens.
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Received: 2024-11-26
Accepted: 2025-05-13
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