ZHOU Ping,ZHANG Guang-cai,WANG Jiao,et al.Rapid Analysis of Rice Leaf Nitrogen Using NearInfrared Spectroscopy and Artificial Neural Network[J].HEILONGJIANG AGRICULTURAL SCIENCES,2011,(04):22-25.
近红外光谱结合ANN法快速测定水稻叶片氮含量
- Title:
- Rapid Analysis of Rice Leaf Nitrogen Using NearInfrared Spectroscopy and Artificial Neural Network
- 文章编号:
- 1002-2767（2011）04-0022-04
- 分类号:
- S511
- 文献标志码:
- A
- 摘要:
- 应用近红外(NIR)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，采用偏最小二乘回归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最佳模型的预测相关系数（RP）为0.974 7，预测标准误差（SEP）为4.005，预测相对标准差（RPD）为3.109。表明BP-ANN模型稳健可靠，可较好地用于水稻叶片氮素的快速测定。
- 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.
参考文献/References:
[1]薛利红，曹卫星，罗卫红，等.基于冠层反射光谱的水稻群体叶片氮素状况监测[J].中国农业科学，2003，36(7):807-812. [2]Tarpley L，Reddy K R，Gretchen FSC.Reflectance indices with precision and accuracy in predicting cotton leaf nitro-gen concentration[J].Crop Science，2000，40:1814-1819. [3]朱艳，李映雪，周冬琴，等.稻麦叶片氮积累量与冠层反射光谱的定量关系[J].植物生态学报，2006，30(6):983-990. [4]严衍禄.近红外光谱分析基础与应用[M].北京:中国轻工业出版社，2005. [5]Petisco C G，Cfiado B，Mediavilla S，et al.Near—infrared reflectance spectroscopy as a fast and non—destructive tool to predict foliar organic constituents of several woody spe-cies[J].Analytical and Bioanalytical Chemistry，2006，386:l823-1833. [6]Read J J，Tarpley L，Mckinion J M.Narrow-waveband reflectance rations for remote estimation of nitrogen status in cotton[J].Journal of Environmental Quality，2002，31:1442-1452. [7]王渊，黄敬峰，王福民，等.油菜叶片和冠层水平氮素含量的高光谱反射率估算模型[J].光谱与光谱分析，2008，28(2)：273-277. [8]张俊华，张佳宝.不同生育期冬小麦光谱特征对叶绿素和氮素的响应研究[J].土壤通报，2008，39（3）:586-592. [9]徐广通，袁洪福，陆婉珍.现代近红外光谱技术及应用进展[J].光谱学与光谱分析，2000，20(2):134-142. [10]Williams P C，Sobering D C.Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds[J].Journal of Near Infrared Spectroscopy，1993，1:25-32. [11]褚小立，袁洪福，陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展，2004，16(4):528. [12]许禄.化学计量学一些重要方法的原理及应用[M].北京：科学出版社，2004. [13]Stone M.Cross-validatory choice and assessment of statistical predictions[J].Journal of the Royal Statistical Society，Series B(Methodological)，1974，36(2):111-147. [14]曹干，谭中文，梁计南，等.蔗汁品质成分的傅里叶变换近红外分析数学模型[J].中国农业科学，2003，40(3):254-258. [15]彭玉魁.近红外光谱分析技术及其在农业中的应用[J].陕西农业科学，2000(3):25-27.
相似文献/References:
[1]李鑫,陈争光.基于Origin 8.0的马铃薯光谱图像处理[J].黑龙江农业科学,2015,(02):132.[doi:10.11942/j.issn1002-2767.2015.02.0132]
LI Xin,CHEN Zheng-guang.Potato Spectral Image Processing Based on Origin 8.0[J].HEILONGJIANG AGRICULTURAL SCIENCES,2015,(04):132.[doi:10.11942/j.issn1002-2767.2015.02.0132]
[2]郭贺,金兰淑,林国林.利用近红外光谱法对烟叶氮钾含量的快速测定[J].黑龙江农业科学,2008,(04):103.[doi:10.11942/j.issn1002-2767.2008.04.0103]
GUO-He,JIN Lan-shu,LIN Guo-l in.Rapid Determination of Nitrogen and Potassium Contents in TobaccoLeaves by Near Infrared Reflectance Spectroscopy[J].HEILONGJIANG AGRICULTURAL SCIENCES,2008,(04):103.[doi:10.11942/j.issn1002-2767.2008.04.0103]
备注/Memo
基金项目：教育部留学回国人员科研启动基金资助项目