Application of Mutual Information to Variable Selection in Diagnosis of Phosphorus Nutrition in Rice

作者：Lin Fen fang; Ding Xiao dong; Fu Zhi peng; Deng Jin song; Shen Zhang quan^*

来源：Spectroscopy and Spectral Analysis, 2009, 29(9): 2467-2470.

DOI：10.3964/j.issn.1000-0593(2009)09-2467-04

• Mutual information
• Artificial neural network
• Rice
• Phosphorus nutrition
• Spectrum

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, 1040, 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.

• 出版日期2009-9
• 单位浙江大学