基于BiPLS-CARS-PLS的哈密瓜冠层叶片SPAD值反演建模

利用光谱技术对大田哈密瓜冠层叶片叶绿素含量定量估测,可为田间水肥调控以及田间管理提供理论依据。本实验在剔除噪音后的378 nm到1 115 nm光谱的基础上采用多元散射校正、标准正态变量相交、标准化、Savitzky-Golay卷积平滑法、归一化、移动平均平滑等方法对原始光谱数据进行预处理,然后采用特征区间选择与特征波长选择相结合的方法实现数据降维和简化模型,并建立偏最小二乘和极限学习机的回归模型。结果表明,多元散射校正预处理效果最佳,在此基础上,利用反向区间偏最小二乘法(BiPLS)和竞争性自适应重加权采样算法(CARS)相结合共筛选出13个特征波长,将其作为模型的输入变量,由偏最小二乘法(PLS)建立的模型效果最优,其预测集的相关系数R_p和均方根误差RMSEP分别为0.942 4与1.006 2。因此,采用BiPLS与 CARS结合PLS建立的光谱定量分析模型,可实现对哈密瓜冠层叶片叶绿素含量的定量估测。

SPAD inversion model of cantaloupe canopy leaf based on BiPLS-CARS-PLS

The quantitative estimation of chlorophyll content in canopy leaves of Hami melon by spectral technology can provide a theoretical basis for water and fertilizer regulation and field management. On the basis of the original spectrum, this experiment used multivariate scattering correction, standard normal variable intersection, standardization, Savitzky-Golay convolution smoothing, normalization and moving average smoothing to preprocess the original spectrum data, and then combined the feature interval selection with the feature wavelength selection to achieve the purpose of data dimension reduction and model simplification. The regression models of partial least squares and extreme learning machine were established. The results showed that the preprocessing effect of multiple scattering correction was the best. On this basis, 13 characteristic wavelengths were selected by combining the inverse interval partial least squares (BiPLS) and the competitive adaptive reweighting sampling (CARS) algorithm, which were used as the input variables of the model. The model established by partial least squares (PLS) had the best effect. The correlation coefficient R_p and root mean square error (RMSEP) of the prediction set were 0.942 4 and 1.006 2, respectively. Therefore, the spectral quantitative analysis model established by BiPLS and CARS combined with PLS could realize the quantitative estimation of chlorophyll content in Hami melon canopy leaves.