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Intelligent Identification and Features Attribution of Saline-Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy

文献类型： 外文期刊

作者： Ma, Bo 1 ; Liu, Chuanzeng 1 ; Hu, Jifang 1 ; Liu, Kai 2 ; Zhao, Fuyang 1 ; Wang, Junqiang 1 ; Zhao, Xin 4 ; Guo, Zhenhua 3 ; Song, Lijuan 3 ; Lai, Yongcai 2 ; Tan, Kefei 1 ;

作者机构： 1.Heilongjiang Acad Agr Sci, Qiqihar Branch, Qiqihar 161006, Peoples R China

2.Natl Saline Alkali Tolerant Rice Technol Innovat, Northeast Branch, Harbin 150000, Peoples R China

3.Heilongjiang Acad Agr Sci, Harbin 150086, Peoples R China

4.Qiqihar Univ, Coll Comp & Control Engn, Qiqihar 161006, Peoples R China

关键词： saline-alkali-tolerant rice; Raman spectroscopy; Python; scipy.signal.filtfilt difference; identification feature information

期刊名称：PLANTS-BASEL （ 影响因子：4.658； 五年影响因子：4.827 ）

ISSN：

年卷期： 2022 年 11 卷 9 期

页码：

收录情况： SCI

摘要： Planting rice in saline-alkali land can effectively improve saline-alkali soil and increase grain yield, but traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline-alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline-alkali-tolerant rice varieties and two saline-alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline-alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline-alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed.

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