不同生长期柑橘叶片磷含量的高光谱预测模型  

Prediction model of phosphorus content for citrus leaves during different growth periods based on hyperspectrum

作　　者：岳学军[1] 全东平[1] 洪添胜[1] Wei Xiang 刘永鑫[1] 王健[1] 

机构地区：[1]华南农业大学工程学院,广州510642 [2]南昆士兰大学工程与测绘学院

出　　处：《农业工程学报》2015年第8期207-213,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金(30871450);广东省自然科学基金项目(S2012010009856);广州市科技计划项目(7414558112697)

摘　　要：针对传统柑橘叶片磷含量检测耗时费力、操作繁琐且损伤叶片等弊端,该研究引入高光谱信息探索柑橘叶片磷含量快速无损检测与预测模型,选ASD Field Spec 3光谱仪采集柑橘4个重要生长期的叶片反射光谱,同步采用硫酸-双氧水消煮-钼锑抗比色法测定叶片的磷含量;先用正交试验确定小波去噪的最佳去噪参数组合,再分别选拉普拉斯特征映射(laplacian eigenmaps,LE)、局部线性嵌入(locally-linear embedding,LLE)、局部切空间对齐(local tangent space alignment,LTSA)、等距映射(isometric mapping,Isomap)和最大方差展开(maximum variance unfolding,MVU)5种典型的流形学习算法对去噪后的光谱数据进行降维和特征提取,进而建立基于支持向量机回归(support vector regression,SVR)的柑橘叶片磷含量预测模型。结果表明,基于一阶导数谱的Isomap-SVR建模结果最佳,全生长期校正集和验证集模型决定系数分别为0.9430和0.8949。试验表明,5种流形学习算法皆适用于对柑橘叶片磷含量的预测,为高光谱检测技术用于柑橘树长势监测和营养诊断提供了参考。Traditional methods of obtaining phosphorus content of citrus leaves are time-consuming procedures with complex operations which can be harmful to citrus trees. More over, traditional methods can not meet the demand of rapid and non-destructive monitoring of phosphorus content in large-scale citrus orchards. In this paper, we presented several models suitable for phosphorus content prediction in 4 growth periods using hyperspectral information. The experiments were conducted in the Crab Village of Luogang District, Guangzhou City, Guangdong Province, and the samples were 195 citrus trees planted. During 4 growth periods, i.e. germination, stability, bloom and picking period, hyperspectral reflectance of citrus leaves was respectively measured by spectrometer （ASD FieldSpec 3）, and at the same time, phosphorus content of citrus leaves was obtained by using traditional chemical method. Owing to the high dimensionality and redundancy of raw data, an enhanced algorithm was provided based on manifold learning to deal with the high-dimensional spectral vectors for dimension reduction and feature extraction. First of all, the parameters of wavelet de-noising, which was applied to reduce the high-frequency noise, was determined through orthogonal test, and then 5 manifold learning algorithms, i.e. laplacian eigenmaps （LE）, locally-linear embedding （LLE）, local tangent space alignment （LTSA）, isometric mapping （Isomap） and maximum variance unfolding （MVU） were applied to reduce dimension and extract features for de-noising spectrum. The 5 corresponding prediction models of support vector regression （SVR） for phosphorus content of citrus leaves were established based on their features. Besides, we compared the modeling results of different spectral forms. Some critical conclusions were obtained. First, the optimized parameter combination of wavelet de-noising through orthogonal test was： “coif2” as wavelet basis function, the number of decomposition layer being 7 and “heursure” as the

关 键 词：模型 光谱分析 监测 高光谱 流形学习算法 柑橘叶片 磷含量 

分 类 号：S127[农业科学—农业基础科学]