Ma Bo, Liu Chuanzeng, Hu Jifang, Liu Kai, Zhao Fuyang, Wang Junqiang, Zhao Xin, Guo Zhenhua, Song Lijuan, Lai Yongcai, Tan Kefei
Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China.
Northeast Branch of National Saline-Alkali-Tolerant Rice Technology Innovation Center, Harbin 150000, China.
Plants (Basel). 2022 Apr 29;11(9):1210. doi: 10.3390/plants11091210.
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.
在盐碱地种植水稻可以有效改良盐碱土壤并提高粮食产量，但传统的耐盐碱水稻品种鉴定方法需要水稻育种者基于生长指标进行繁琐且耗时的田间调查。在本研究中，利用Python机器深度学习方法分析水稻的拉曼分子光谱并辅助特征归因，以研究一种快速高效的耐盐碱水稻品种鉴定方法。共分析了四个水稻品种（两个耐盐碱水稻品种和两个盐碱敏感水稻品种）的156条拉曼光谱，通过改进的信号滤波差分法提取波峰，并利用scipy.signal.find_peaks自动提取波峰的特征信息。使用Select K Best（SKB）、递归特征消除（RFE）和Select F模型（SFM）来选择有用的分子特征。基于这些特征选择方法，建立了逻辑回归模型（LRM）和随机森林模型（RFM）进行判别分析。实验结果表明，基于RFE方法的RFM识别模型达到了较高的识别率，为89.36%。根据RFM的识别结果和特征归因材料的鉴定，淀粉是耐盐碱水稻品种鉴定中最显著的物质。因此，提出了一种基于拉曼分子光谱的耐盐碱水稻品种智能鉴定方法。
