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.
林芬芳，丁晓东，付志鹏，邓劲松，沈掌泉* . 基于互信息理论的水稻磷素营养高光谱诊断[J]. 光谱学与光谱分析, 2009, 29(09): 2467-2470.
LIN Fen-fang, DING Xiao-dong, FU Zhi-peng, DENG Jin-song, SHEN Zhang-quan* . Application of Mutual Information to Variable Selection in Diagnosis of Phosphorus Nutrition in Rice . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(09): 2467-2470.
[1] PAN Rui-chi(潘瑞炽). Rice Physiology(水稻生理). Beijing: Science Press(北京: 科学出版社), 1999. [2] LI Ji-yun, LIU Xiu-di, ZHOU Wei, et al(李继云，刘秀娣，周 伟，等). Science in China(Series B)(中国科学：B辑), 1995, 25(1): 41. [3] Hinsinger P. Plant and Soil, 2001, 237(2): 173. [4] Osborne S L, Schepers J S, Francis D D, et al. Agronomy Journal, 2002, 94: 1215. [5] LU Ru-kun(鲁如坤). Analytical Method of Soil and Agricultural Chemistry(土壤农业化学分析方法). Beijing: China Agricultural Science and Technology Press(北京: 中国农业科技出版社), 2000. 313. [6] LI He-sheng, SUN Qun, ZHAO Shi-jie, et al(李合生，孙 群，赵世杰，等). Theory and Technology of Experiment in Physiology and Biochemistry(植物生理生化实验原理和技术). Beijing: Higher Education Press(北京：高等教育出版社), 2000. 131. [7] Rossi F, Lendasse A, Francois D, et al. Chemometrics and Intelligent Laboratory Systems, 2006, 80(2): 215. [8] Benoudjit N, Francois D, Meurens M, et al. Chemometrics and Intelligent Laboratory Systems, 2004, 74(2): 243. [9] Durand A, Devos O, Ruckebusch C, et al. Analytica Chimica Acta, 2007, 595: 72. [10] Astakhov S A, Grassberger P, Kraskov A, et al. MILCA Algorithm, Available at http://www.klab.caltech.edu/～kraskov/MILCA/index.html. [11] XUE Li-hong, YANG Lin-zhang, SHEN Ming-xing(薛利红，杨林章，沈明星). Journal of Triticeae Crops(麦类作物学报), 2006, 26(6): 120. [12] WANG Lei, BAI You-lu, YANG Li-ping(王 磊, 白由路, 杨俐苹). Plant Nutrition and Fertilizer Science(植物营养与肥料学报), 2007, 13(5): 802.
