| 光谱学与光谱分析 |
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| Application of Mutual Information to Variable Selection in Diagnosis of Phosphorus Nutrition in Rice |
| LIN Fen-fang, DING Xiao-dong, FU Zhi-peng, DENG Jin-song, SHEN Zhang-quan* |
| Institute of Agriculture Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029, China |
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Abstract The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD’s multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1 040, 551 and 656 nm, and their corresponding mutual information values were 1.057 5, 1.103 9, 1.135 3, 1.141 7 and 1.149 4 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.988 2, respectively, for the calibration data set, and the RMSE of prediction and R were 0.050 5 and 0.989 2, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.
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Received: 2008-07-18
Accepted: 2008-10-22
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Corresponding Authors:
SHEN Zhang-quan
E-mail: zhqshen@zju.edu.cn
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