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近红外光谱结合ANN法快速测定水稻叶片氮含量
引用本文:周萍,张广才,王佼,周崇俊,韩晓日.近红外光谱结合ANN法快速测定水稻叶片氮含量[J].黑龙江农业科学,2011(4):22-25.
作者姓名:周萍  张广才  王佼  周崇俊  韩晓日
作者单位:沈阳农业大学,土地与环境学院,辽宁,沈阳,110866
基金项目:教育部留学回国人员科研启动基金资助项目
摘    要:应用近红外(NlR)光谱和误差反传人工神经网络(BP-ANN)方法建立了水稻叶片氮素含量的定量分析模型.首先对近红外光谱进行Savitzky-Golay求导处理,然后通过相关系数法选择波长范围,采用偏最小二来回归PLS降维并输入BP-ANN建立校正模型,用验证样品对校正模型进行验证.结果表明:BP-ANN最佳模型的预测...

关 键 词:人工神经网络  近红外光谱  水稻叶片  氮素

Rapid Analysis of Rice Leaf Nitrogen Using Near Infrared Spectroscopy and Artificial Neural Network
ZHOU Ping,ZHANG Guang-cai,WANG Jiao,ZHOU Chong-jun,HAN Xiao-ri.Rapid Analysis of Rice Leaf Nitrogen Using Near Infrared Spectroscopy and Artificial Neural Network[J].Heilongjiang Agricultural Science,2011(4):22-25.
Authors:ZHOU Ping  ZHANG Guang-cai  WANG Jiao  ZHOU Chong-jun  HAN Xiao-ri
Institution:(Land and Environment College of Shenyang Agricultural University,Shenyang,Liaoning 110866)
Abstract:The models of quantitative analysis of nitrogen in the rice leaf were established by using near infrared spectroscopy(NIS)coupled with the back propagation-artificial neural network method(BP-ANN).Firstly,the data of original spectra were pretreated by Savitzky-Golay derivative.Secondly,the wavelength range of model was optimized by using correlation coefficient method.Finally,PLS dimension-reducing was input into BP-ANN.The calibration models were established by calibration set and validated by prediction set.The results showed that the related coefficient(RP)of the best prediction for nitrogen was 0.974 7,the standard errors of prediction(SEP)for nitrogen was 4.005,and ratio of performance deviation(RPD)was 3.109.Therefore,the method could be applied to fast and accurate determination of nitrogen in the rice leaf.
Keywords:artificial neural network  near infrared spectroscopy  rice leaf  nitrogen
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