| 光谱学与光谱分析 |
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| Research on Modeling Method for Chlorophyll Content Fine Measurement Based on Neural Network |
| ZHOU Chun-yan1, 2, HUA Deng-xin1*, LE Jing1, WAN Wen-bo1, JIANG Peng1，MAO Jian-dong2 |
| 1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China2. School of Electrical and Information Engineering, North University of Nationalities, Yinchuan 750021, China |
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Abstract Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD values and water index(WI) of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650nm, 940nm, 1450nm respectively. Meanwhile, the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method. To modeling samples, the single parameter model between SPAD values and chlorophyll content was built and the nonlinear model between WI, thickness, SPAD values and chlorophyll content was established based on BP neural network. The predicted value of chlorophyll content of test samples were calculated separately by two models, and the correlation and relative errors were analyzed between predicted values and actual values. 340 samples of three different plant leaves were tested by the method described above in experiment. The results showed that compared with single parameter model, the prediction accuracy of three different plant samples were improved in different degrees, the average absolute relative error of chlorophyll content of all pooled samples predicted by BP neural network model reduced from 7.55% to 5.22%. the fitting determination coefficient is increased from 0.83 to 0.93. The feasibility were verified in this paper that the prediction accuracy of living plant chlorophyll content can improved effectively using multiple parameter BP neural network model.
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Received: 2014-10-13
Accepted: 2015-01-20
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Corresponding Authors:
HUA Deng-xin
E-mail: xauthdx@163.com
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