杂交水稻2026，Vol.41Issue(4)：53-60,8.DOI:10.16267/j.cnki.1005-3956.20250618.129
近红外光谱技术结合反向区间偏最小二乘法预测水稻植株氮含量
Near Infrared Spectroscopy Combined with Backward Interval Partial Least Squares for Predicting Nitrogen Content in Rice Plants
摘要
Abstract
To realize real-time monitoring of nitrogen(N)content and fertilization regulation during the process of rice growth,a method for rapidly determining N content in rice plants based on near infrared spectroscopy was proposed.A near infrared spectrometer was used to collect the near infrared spectra of samples,and N content was determined by the Kjeldahl method.Raw spectral data were pretreated by standard normal variate(SNV),first derivative(FD),and their combined method.Backward interval partial least squares(BiPLS)was employed to select characteristic intervals,thus achieving data dimensionality reduction and model simplification,and a partial least squares(PLS)prediction model was established.The results showed that the model established with 402 characteristic wavelengths screened by SNV+FD pretreatment combined with BiPLS performed the best.The root mean square error of cross validation(RMSECV)and correlation coefficient(Rc2)were 0.197 9 and 0.952 5,respectively.The root mean square error of prediction(RMSEP)and correlation coefficient(Rp2)were 0.201 3 and 0.950 6,respectively,and the residual predictive deviation(RPD)was 3.22.The study demonstrated that the near infrared quantitative analysis model established by the SNV+FD pretreatment and BiPLS exhibited excellent accuracy and stability.It is suitable for the rapid monitoring of N content in rice plants and provides a technical reference for the prevention of rice diseases and pests and scientific field management.关键词
水稻/氮/特征区间选择/反向区间偏最小二乘法/近红外光谱技术Key words
rice/nitrogen/characteristic interval selection/backward interval partial least squares/near infrared spectroscopy分类
农业科技引用本文复制引用
刘登彪,黎妮,王伟平,Rajaonera Tahina Ernest,苗雪雪,刘洋..近红外光谱技术结合反向区间偏最小二乘法预测水稻植株氮含量[J].杂交水稻,2026,41(4):53-60,8.基金项目
国家重点研发计划(2021YFD1401100) （2021YFD1401100）
