基于流形学习算法的柑橘叶片氮含量光谱估测模型

提出了一种基于流形学习算法的柑橘叶片氮含量光谱快速检测方法。分别在萌芽期、稳果期、壮果促梢期和采果期,使用ASD Field Spec 3光谱仪采集了柑橘叶片的反射光谱,并同步采用凯式定氮法测定叶片的氮含量。首先采用正交试验确定各个生长期小波去噪的最佳参数组合,然后分别采用主成分分析、多维尺度变换、局部线性嵌入、等距映射和拉普拉斯特征映射5种流形学习算法对原始光谱和经小波去噪后的光谱数据进行特征提取,将特征数据导入支持向量机回归建立柑橘叶片氮含量预测模型,4个生长期的最佳验证集模型决定系数依次为0.901 4、0.934 4、0.895 4和0.877 9。试验结果表明,这5种流形学习算法都能有效地用于柑橘叶片氮含量预测,为柑橘叶片氮含量快速无损检测、生长态势监测和变量施肥提供了理论依据。

Estimation Model of Nitrogen Content for Citrus Leaves by SpectralTechnology Based on Manifold Learning Algorithm

Traditional methods of obtaining nitrogen content of citrus leaves are time-consuming, and the process is cumbersome and harmful to citrus leaves, which need proficient experiment techniques and amounts of instruments, equipment and chemical reagents. According to the high dimensionality and redundancy of origin spectral reflectance, a nitrogen content obtaining method of citrus leaves was provided based on manifold learning algorithm which was applied to the high-dimensional spectral vectors for dimension reduction and feature extraction. During four different growth stages, corresponding to germination, stability, bloom and picking stages, spectral reflectance of citrus leaves were measured by the ASD FieldSpec 3 spectrometer, respectively, and at the same time, nitrogen content of citrus leaves was obtained by using Kjeldahl method. For data processing, firstly the parameter combination of wavelet denoising which was used to the high-frequency noise removal was optimized through orthogonal test, and then the principal component analysis (PCA), multidimensional scaling (MDS), locally-linear embedding (LLE), isometric mapping (Isomap) and laplacian eigenmaps (LE) manifold learning algorithms were applied to extract features of original spectrum and denoised spectrum. Finally, the five corresponding support vector regression (SVR) prediction models of nitrogen content for citrus leaves were established based on their features. Experiment results reveal that the five manifold learning algorithms can be effectively used to predict nitrogen content of citrus leaves, which provides theoretical basis for obtaining nitrogen content of citrus leaves rapidly and non-destructively, as well as in growth monitoring and variable-rate fertilization.