Application of Mutual Information to Variable Selection in Diagnosis of Phosphorus Nutrition in Rice
Application of Mutual Information to Variable Selection in Diagnosis of Phosphorus Nutrition in Rice
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DOI：
10.3964/j.issn.1000-0593(2009)09-2467-04
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发表时间：
2009-09-01
影响因子：
0.7
通讯作者：
Shen Zhang-quan
中科院分区：
文献类型：
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作者：
Lin Fen-fang;Ding Xiao-dong;Shen Zhang-quan
本研究通过溶液实验获得了不同供磷量下水稻冠层光谱、典型生育期磷和叶绿素含量的数据。采用LSD以0.05的概率进行多重比较，统计分析磷处理对叶片磷和叶绿素含量的影响；通过互信息（MI）变量选择程序，在536、630、1040、551和656 nm处确定了最佳光谱变量，其相应的互信息值分别为1.057 5、1.103 9、1.135 3、1.141 7和1.149 4；基于这些敏感波段，建立的前馈人工神经网络模型（ANN）比多元线性回归模型（MLR）具有更高的P含量估计精度。校准数据集的交叉验证和 R 的 RMSE 分别为 0.038 8 和 0.988 2，测试数据集的预测和 R 的 RMSE 分别为 0.050 5 和 0.989 2。因此，建议鼓励利用可见光/近红外高光谱信息定量预测水稻叶片磷含量，而不需要假设自变量和因变量之间的关系。但还需要做更多的工作来解释为什么这些条带对水稻叶片磷含量敏感。
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.