基于高光谱的油麦菜叶片水分CARS-ABC-SVR预测模型

为了实现油麦菜生长期间更合理的灌水管理,研究一种基于高光谱技术的精确、快速、有效检测油麦菜叶片水分的新方法。以5种不同水分胁迫水平的油麦菜为研究对象,通过高光谱成像系统获取高光谱图像并利用干燥法测量叶片含水率。采用多项式平滑(Savitzky-Golay,SG)结合标准变量变换(standard normalized variable,SNV)对高光谱数据去噪平滑。利用竞争性自适应加权算法(competitive adaptive reweighted sampling,CARS)进行特征波长选择,并与逐步回归分析(stepwise regression,SR)及连续投影算法(successive projections algorithm,SPA)进行比较,利用支持向量回归机(support vector regression,SVR)分别建立油麦菜叶片全光谱数据、3种特征光谱数据与干基含水率的关系模型。结果表明,基于竞争性自适应加权算法波长选择的支持向量回归模型(CARS-SVR)效果最佳,但预测精度尚不够理想,故引入人工蜂群算法(artificial bee colony,ABC)优化模型的参数惩罚因子和核参数。最终,经人工蜂群算法优化后的模型(CARS-ABC-SVR)的预测集决定系数R2和均方根误差RMSE分别为0.9214和2.95%。因此,利用高光谱技术结合CARS-ABC-SVR模型预测油麦菜叶片水分含量是可行的。

CARS-ABC-SVR model for predicting leaf moisture of leaf-used lettuce based on hyperspectral

Abstract: In order to realize more reasonably irrigation management during the growth of leaf -used lettuce, a new method for accurately, rapidly and effectively detecting leaf-used lettuce moisture based on hyperspectral technology was investigated in this study. Leaf-used lettuces of 5 different water stress levels were adopted as experimental objects. In the first group, sufficient water irrigation was maintained during the growth period of leaf-used lettuces, and the amount of water irrigated in the second, third, fourth and fifth groups decreased in turn according to the gradient. Firstly, hyperspectral images of leaf-used lettuce samples were acquired by using the hyperspectral image acquisition system, then the water contents of all leaves were measured by the drying method and the dry-basis moisture content was calculated according to formula. Secondly, the hyperspectral data was extracted from the images by selecting the region of interest (ROI) in the ENVI software. Thirdly, a method for data pretreatment, Savitzky-Golay (SG) combined with the standard normalized variable (SNV), was applied for smoothing and denoising of the original hyperspectral data. Fourthly, the competitive adaptive reweighted sampling (CARS) algorithm was used to extract the characteristic wavelengths ranged from 965 nm to 1666 nm of leaf-used lettuce samples, simultaneously the effect of CARS algorithm was compared with that of the stepwise regression (SR) analysis and the successive projections algorithm (SPA) in order to determine the optimal method for characteristic wavelength selection. Finally, the support vector regression (SVR) machine was respectively carried out to establish the relationship models between full spectral data, three kinds of characteristic spectral data and dry-basis moisture content of leaf-used lettuce samples. And the performances of all the models were evaluated by the index of determination coefficient for calibration set (Rc2), root mean square error for calibration set (RMSEC), determination coefficient for prediction set (RP2) and root mean square error for prediction set (RMSEP). The results showed that CARS-SVR model performed better than the other model with full-SVR, SR-SVR or SPA-SVR, selecting the optimal wavelength combination (973, 993, 997, 1 050, 1 140, 1 181, 1 184, 1 188, 1 191, 1 198, 1 237, 1 240, 1 243, 1 259, 1 263, 1 285, 1 310, 1 336, 1 348, 1 354, 1 376, 1 389, 1 392, 1 395, 1 408, 1 414, 1 601, 1 662 nm), and achieving the highest accuracy with Rc2 = 0.9172, RMSEC = 2.33%, RP2 = 0.8599 and RMSEP = 3.95%. Whereas, the prediction accuracy of CARS-SVR model were not achieved the desired effect. For improving the prediction accuracy of SVR model, the artificial bee colony (ABC) algorithm was further introduced to intelligently optimize the parameters (c and g) in the SVR model to find the optimum, then the model on the basis of CARS characteristic data was reconstructed. Consequently, the optimised model, CARS-ABC-SVR, achieved the Rc2 of 0.9427, RMSEC of 1.60%, RP2 of 0.9214 and RMSEP of 2.95%, which was indeed improved significantly and proved that the method of selecting characteristic wavelengths by CARS algorithm combined with optimizing the parameters in SVR model by ABC algorithm can extremely raise the performance of prediction model for the moisture content of leaves. Hence, the method of hyperspectral technology combined with the CARS-ABC-SVR model is feasible for detecting the moisture content of leaf-used lettuces, also hopefully providing a new method and thought for water detection of other crops.