1. 塔里木大学信息工程学院，新疆 阿拉尔 843300
2. University of Liège-Gembloux Agro-Bio Tech，Gembloux 25030, Belgium
3. 塔里木大学水利与建筑工程学院，新疆 阿拉尔 843300
4. 中国农业科学院农田灌溉研究所，河南 新乡 453000
Near Infrared Spectrum Detection Method for Moisture Content of Populus Euphratica Leaf
BAI Tie-cheng1，2, WANG Ya-ming1, ZHANG Nan-nan1, YAO Na1, YU Cai-li1, WANG Xing-peng3，4*
1. College of Information Engineering，Tarim University，Alaer 843300, China
2. University of Liège-Gembloux Agro-Bio Tech，Gembloux 25030, Belgium
3. College of Water Resource and Architectural Engineering，Tarim University，Alaer 843300, China
4. Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences, Xinxiang 453000, China
Abstract：The moisture content of leaves is an important index to evaluate the health condition of the Euphrates poplar, and spectrum detection method is an effective method. But in the process of near infrared spectrum measurement, spectral data will be affected by instrument noise, morphological differences and environment interference. The paper proposed how to establish spectrum detection method of water content of populus euphratica leaf. In the first place, the influence of scattering, noise and baseline drift of spectral data are reduced by using multiple scattering correction (MSC), and increase signal to noise ratio (SNR) of spectrum data andstrengthen band features. The effective spectral information is relatively clear, so the choice of characteristic wavelength becomes easier. Then, in order to reduce the complexity of the model, to prevent overfitting and to reduce the influence of collinearity, the successive projections algorithm (SPA) is used to select the feature variables. And a multiple linear regression model is used to analysis and compare the simulated residual squared of different models, evaluate the contribution of each wavelength and eliminate the wavelengths of small contribution value. Then we obtained the optimal characteristic wavelength to improve the conditions of modeling. Finally, the partial least squares regression method is used to establish the test model. The experimental results show that the successive projections algorithm screens six effective variables on the basis of using the original spectrum data, the prediction accuracy is 90.144%, the correlation coefficient (r) is 0.674 24, the root mean square error is 0.021 434, and the successive projections algorithm screens five effective variables after using multiple scattering correction algorithm to optimize the original spectrum data, the prediction accuracy is 97.734%, the correlation coefficient (r) is 0.781 63, the root mean square error is 0.016 776. Sothe multiple scattering correction algorithm and successive projections algorithm has been successfully applied to eliminate the scattering noise, reduce the total linear interference, simplify the complexity of the model, then increase the accuracy and correlation coefficient, reduce the error. This method can be used for fast nondestructive testing of water content of the Euphrates poplar leaf, besides it also has some reference significance for moisture detection of other crops leaf.
白铁成，王亚明，张楠楠，姚 娜，喻彩丽，王兴鹏. 胡杨叶片水分含量的近红外光谱检测[J]. 光谱学与光谱分析, 2017, 37(11): 3419-3423.
BAI Tie-cheng, WANG Ya-ming, ZHANG Nan-nan, YAO Na, YU Cai-li, WANG Xing-peng. Near Infrared Spectrum Detection Method for Moisture Content of Populus Euphratica Leaf. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(11): 3419-3423.
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