采用灰色理论对冬枣叶片氮素含量和光谱反射率之间进行了灰度关联分析, 分析结果显示波长560, 678以及786 nm处的光谱反射率（G560, R678, NIR786）与冬枣叶片氮素含量之间的灰色关联度最高。 利用上述三个特征波段光谱反射率计算得到的植被指数共计9个。 进一步运用灰色系统理论分析了九种植被指数与叶片氮素含量的灰色关联度, 结果显示: 归一化植被指数（NDVI）、 绿色比值植被指数（GRVI）、 归一化差异绿度植被指数（NDGI）、 绿色归一化植被指数（GNDVI）和组合归一化植被指数（CNDVI）等5个指数与叶片氮素含量的灰色关联度较高。 利用3个特征波段的光谱反射率和5个关联度较高的植被指数, 分别采用最小二乘支持向量机（LS-SVM）以及GM（1, N）模型建立了冬枣叶片氮素含量预测模型。 结果表明, 采用特征波段光谱反射率（G560, R678, NIR786）建立的冬枣叶片氮素含量GM(1, N)模型的精度最高, 预测R2达0.928, 验证R2达0.896。

Jujube was chosen as the object in the present research. Spectra data of jujube leaves were collected during the period of budding, branch leaf, flowering and coloring. The nitrogen contents of jujube leaf samples were determined by Kjeldahl analysis method. Grey relation analysis between spectral reflectance and nitrogen content of jujube leaves was done based on Grey theory. It was found that the gray relation between spectral reflectance and nitrogen content of jujube leaves at 560, 678 and 786 nm was high. Nine kinds of vegetation index based on spectra data of NIR786, R678 and G570 were calculated. The gray relation of nine kinds of vegetation index was NDVI>GRVI>NDGI>GNDVI>CNDVI>RVI>GDVI>DVI>SAVI. NDVI, GRVI, NDGI, GNDVI and CNDVI were chosen to build prediction models of nitrogen content of jujube leaves. Spectra data of 560, 678 and 786 nm were also used to build prediction models of nitrogen content of jujube leaves. LS-SVM and GM(1, N) were used to build prediction module. The prediction R2 and verification R2 of LS-SVM module were 0.805 and 0.704 respectively when five kinds of vegetation index were used as input of prediction module. When when Spectra data of 560, 678 and 786 nm were used as input, the prediction R2 and verification R2 of LS-SVM prediction model were 0.772 and 0.685 respectively. The prediction R2 and verification R2 of GM(1, N) module were 0.927 7 and 0.895 8 respectively when spectra data of 560, 678 and 786 nm were used as input. The results of prediction GM(1, N) module which used five kinds of vegetation index as input were 0.547 6 and 0.489 7. From those results it was observed that grey theory only needed little information to build prediction module with high precision, so that it could be used in precision management of jujube plants.