偏振-高光谱多维光信息的番茄叶片营养诊断

以Venlo型温室中无土栽培模式下自行培育的25%，50%，75%，100%，150%五个梯度水平的氮、磷、钾营养胁迫样本为研究对象，利用高光谱成像系统以及课题组自行研发的偏振反射光谱测量分析系统分别采集不同氮磷钾营养水平番茄叶片的偏振光谱和高光谱数据。通过扫描电镜分析阐明营养胁迫叶片非光滑表面的凹凸和质地发生的一系列变化与偏振反射辐射之间具有一定的联系。由斯托克斯公式将偏振光谱换算成偏振度后，提取偏振度与氮磷钾实测值之间的各4个偏振度特征;同时将高光谱数据经过主成分分析降维并确定氮磷钾各4个特征波长，再通过相关分析法提取这4个特征波长下的各8个高光谱图像纹理特征。偏振度特征与高光谱纹理特征相加累计氮磷钾各12个特征作为支持向量回归(SVR)的输入变量。对这12个特征变量进行最大—最小值归一化后，采用SVR建立番茄氮磷钾营养水平的定量诊断模型，求得氮的相关系数r=0.961 8，均方根误差RMSE=0.451;磷的相关系数r=0.916 3，均方根误差RMSE=0.620;钾的相关系数r=0.940 6，均方根误差RMSE=0.494。研究结果表明采用偏振反射光谱结合高光谱的多维光信息融合技术能够建立精度较高的番茄营养水平预测模型，具有较好的诊断作用，对于提高模型的精度和专用仪器的开发具有一定的指导意义，为番茄养分含量的快速检测提供了新的思路。

Study on the Polarized Reflectance-Hyperspectral Information Fusion Technology of Tomato Leaves Nutrient Diagnoses

With 25%, 50%, 75%, 100% and 150%, five levels of, nitrogen(N), phosphorus(P) and potassium(K) nutrition stress samples cultivated in Venlo type greenhouse soilless cultivation mode as the research object, polarized reflectance spectra and hyperspectral images of different nutrient deficiency greenhouse tomato leaves were acquired by using polarized reflectance spectroscopy system developed by our own research group and hyperspectral imaging system respectively. The relationship between a certain number of changes in the bump and texture of non-smooth surface of the nutrient stress leaf and the level of polarization reflected radiation was clarified by scanning electron microscopy (SEM). On the one hand, the polarization spectrum was converted into the degree of polarization through Stokes equation, and the four polarization characteristics between the polarization spectroscopy and reference measurement values of N, P and K respectively were extracted. On the other hand, the four characteristic wavelengths of N, P, K hyperspectral image data were determined respectively through the principal component analysis, followed by eight hyperspectral texture features extracted corresponding to the four characteristic wavelengths through correlation analysis. Polarization characteristics and hyperspectral texture features combined with each characteristics of N, P, K were extracted. These 12 characteristic variables were normalized by maximum-minimum value method. N, P, K nutrient levels quantitative diagnostic models were established by SVR. Results of models are as follows: the correlation coefficient of nitrogen r=0.961 8, root mean square error RMSE=0.451; correlation coefficient of phosphorus ｒ=0.916 3, root mean square error RMSE=0.620; correlation coefficient of potassium ｒ=0.940 6, root mean square error RMSE=0.494. The results show that high precision tomato leaves nutrition prediction model could be built by using polarized reflectance spectroscopy combined with high spectral information fusion technology and achieve good diagnoses effect. It has a great significance for the improvement of model accuracy and the development of special instruments. The research provides a new idea for the rapid detection of tomato nutrient content.