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NIRS-based detection advances in agriculture: Data enhancement, characteristic wavelength selection and modelling techniques

文献类型： 外文期刊

作者： Wang, Yueting 1 ; Zhao, Chunjiang 1 ; Tian, Hongwu 1 ; Xing, Zhen 1 ; Yue, Xiaolong 1 ; Liu, Shuai 1 ; He, Yili 1 ; Bai, Juekun 1 ; Hao, Lianglin 1 ; Zhu, Mingyan 1 ; Dong, Daming 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Key Lab Agr Sensors, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Natl Local Engn Lab Agr Internet Things Beijing, Beijing 100097, Peoples R China

关键词： Near-infrared spectroscopy; Data enhancement; Feature selection; Artificial intelligence

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.6； 五年影响因子：4.3 ）

ISSN： 1386-1425

年卷期： 2025 年 343 卷

页码：

收录情况： SCI

摘要： Near-infrared spectroscopy (NIRS) has gained increasing attention in agricultural element detection due to its advantages such as rapid analysis, low cost, and non-destructive measurement. With the continuous advancement of chemometric techniques, NIRS has become a powerful tool for extracting meaningful information from complex spectral data. Despite its growing applications, several critical challenges remain, particularly in enhancing data quality, selecting informative features, and developing robust modelling approaches. This review provides a comprehensive overview of recent progress in these areas, offering insights into data augmentation strategies, feature selection methods, and advanced modelling techniques. While the focus is on agricultural applications, the methodologies and conclusions presented herein are broadly applicable to other domains where NIRS plays a significant role.

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