基于叶绿素荧光光谱分析技术, 从光谱形态角度出发确定了波长685 nm作为健康与病虫害叶片分析的第一特征点, 采用简单波段自相关选择与主成分分析方法相结合实现对光谱的降维处理, 并在保持光谱信息达到99.999%的前提下将主成分因子个数由10降为5。 对比分析了偏最小二乘回归、 BP神经网络和最小二乘支持向量机回归三种建模方法, 以真实值与模型预测值的相关系数作为评价标准, 最终确定最小二乘支持向量机为温室黄瓜病虫害叶绿素荧光光谱分析的一种较为适宜的建模方法。

The present paper is based on chlorophyll fluorescence spectrum analysis. The wavelength 685 nm was determined as the primary characteristic point for the analysis of healthy or disease and insect damaged leaf by spectrum configuration. Dimensionality reduction of the spectrum was achieved by combining simple intercorrelation bands selection and principal component analysis (PCA). The principal component factor was reduced from 10 to 5 while the spectrum information was kept reaching 99.999%. By comparing and analysing three modeling methods, namely the partial least square regression (PLSR), BP neural network (BP) and least square support vector machine regression (LSSVMR), regarding correlation coefficient of true value and predicted value as evaluation criterion, eventually, LSSVMR was confirmed as the appropriate method for modeling of greenhouse cucumber disease and insect damage chlorophyll fluorescence spectrum analysis.