作 者：
杨宝华;陈建林;陈林海;曹卫星;姚霞;朱艳
关键词：
氮;光谱分析;算法;小麦冠层;检测;敏感波段;竞争性自适应重加权算法;高光谱数据
摘 要：
为提高小麦冠层叶片氮素含量检测精度,在不同生育时期对5种不同氮素水平的小麦试验田进行光谱采集,获取了234个范围为350~2 500 nm的高光谱数据。在比较蒙特卡洛-无信息变量消除(monte carlo-uninformative variable elimination,MC-UVE)、随机青蛙(random frog)、竞争自适应重加权采样(competitive adaptive reweighted sampling,CARS)及移动窗口偏最小二乘法的波段选择等方法的基础上,提出一种竞争性自适应重加权算法与相关系数法相结合的敏感波段选择方法,并从2151个原始波段中选出了30个敏感波段。用筛选后的30个波段数据建立非线性回归模型,得到了径向基神经网络模型校正集均方根误差为0.3699,预测集均方根误差为1.074e-009,校正决定系数为0.9832,预测决定系数为0.9982。试验结果表明:经过竞争自适应重加权采样的相关分析后所建立的径向基神经网络预测模型,无论是预测精度还是建模精度,比误差后向传播(back propagation,BP)神经网络和支持向量回归模型相比都有了显著提高,该方法在小麦氮含量预测过程中具有明显的优势,可在实际生产中应用。
译 名：
Estimation model of wheat canopy nitrogen content based on sensitive bands
作 者：
Yang Baohua;Chen Jianlin;Chen Linhai;Cao Weixing;Yao Xia;Zhu Yan;Nanjing Agriculture University /National Engineering and Technology Center for Information Agriculture;College of Information and Computer, Anhui Agricultural University;Information Research Institute of Sciences and Technology, Shanghai Academy of Agricultural Science;School of Continuing Education, Nanjing Agricultural University;
关键词：
nitrogen;;spectrum analysis;;algorithms;;wheat canopy;;detection;;sensitive bands;;competitive adaptive reweighted sampling algorithm;;hyperspectral data
摘 要：
Nitrogen is an important nutrition for wheat production and it affects significantly growth, yield and quality of wheat. Leaf non-destructive monitoring is the technique which is reported to provide accurate information of nutrition status of wheat. In this study, the raw hyperspectral reflectance of wheat leaf samples was measured by the standard procedure with an ASD Field Spec3 instrument equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, physical and chemical properties of these wheat leaf samples were analyzed. One hundred and ninety of 234 samples were used for building hyperspectral estimation models and the other 44 samples were used for model validation. In order to improve precision determination of wheat leaf canopy nitrogen content accuracy by hyperspectral technology, firstly, we compared the four different variables screening methods: MC-UVE,Random Frog,CARS and MWPLS. The selected sensitive bands by using partial least squares(PLS) model, and compared with full bands modeling. The number of variables and the variables selected by four methods were different. In the Random Frog and CARS methods, the variables were reduced to 10 and 39. Three hundred and eighty seven variables were selected by MC-UVE method and 425 variables were selected by MWPLS for calibration set and validation set. Based on the above analysis, CARS method and coefficient of determination error of the wavelength variables were optimal, and significantly improved the quality of modeling. For the nitrogen content estimation model based on sensitive bands selected by CARS, the coefficient of determination and error were optimal, resulting a significant improvement in the quality of modeling. Secondly, these collinear variables could contain a large number of redundant information. So, a combinatorial method named CARS-CC was proposed to select variables from 39 wavelength variables.As such, the number of wavelength variables was reduced to 30.It showed the method was effective.Finally, the BP, SVR and RBF models were developed with the selected variables by CARS-CC for leaf nitrogen of wheat. The selected wavelengths were used as the inputs of the models. The results showed that the BP model prediction coefficient of determination was 0.8247, root mean square error was 1.24; the SVR model prediction coefficient of determination and root mean square error were 0.847 and 1.248, and the RBF model prediction coefficient of determination and root mean square error were 0.9982 and 1.074e-009. They had an adequate precision and can quickly predict wheat leaf nitrogen content. For the prediction results of RBF neural network model of the optimal RBF model, the root mean square error of calibration set was 0.3699, the root mean square error of prediction was 1.074e-009, and the correction coefficient of determination and predictive correlation coefficient were 0.9832 and 0.9982. The experimental results showed that CARS-CC was a feasible and efficient algorithm for the spectral sensitive bands selection provided a theoretical basis for the applications of high spectral reflectance in non-destructive nitrogen level detection. At last, it could be concluded that the CARS-CC-RBF model for leaf nitrogen of wheat was better than CARS-CC-BP, CARS-CC-SVR models not only in full bands but also in significant bands. In the future, the CARS-CC-RBF model can be used as a reference for aerospace hyperspectral remote sensing of leaf nitrogen of wheat.
