基于叶绿素荧光光谱分析的黄瓜霜霉病害预测模型  

Cucumber Downy Mildew Prediction Model Based on Analysis of Chlorophyll Fluorescence Spectrum

作　　者：隋媛媛[1] 于海业[1] 张蕾[1] 曲剑巍[1] 武海巍[1,2] 罗瀚[1] 

机构地区：[1]吉林大学生物与农业工程学院,仿生工程教育部重点实验室,吉林长春130022 [2]北华大学电气信息工程学院,吉林吉林市132021

出　　处：《光谱学与光谱分析》2011年第11期2987-2990,共4页Spectroscopy and Spectral Analysis

基　　金：国家高技术研究发展计划(863计划)项目(2007AA10Z203)资助

摘　　要：为了实现对黄瓜病害的快速无损准确预测,基于激光诱导叶绿素荧光光谱分析技术,建立了温室黄瓜霜霉病害的预测模型。通过测定健康叶片、病菌接种3d叶片和接种6d叶片的光谱曲线,采用一阶导数光谱预处理方法,结合主成分分析数据降维方法对三组光谱数据进行特征信息提取后,建立主成分得分散点图,依据累积贡献率选取10个主成分代替导数光谱曲线,再利用最小二乘支持向量机技术进行分类和预测。通过对三组光谱数据105个样本的训练,对44个样本进行分类预测,并对比了四种核函数的支持向量机的分类能力,结果表明,径向基核函数对黄瓜霜霉病害的分类预测能力达到了97.73%,具有很好的分类和鉴别效果。In order to achieve quick and nondestructive prediction of cucumber disease,a prediction model of greenhouse cucumber downy mildew has been established and it is based on analysis technology of laser-induced chlorophyll fluorescence spectrum.By assaying the spectrum curve of healthy leaves,leaves inoculated with bacteria for three days and six days and after feature information extraction of those three groups of spectrum data using first-order derivative spectrum preprocessing with principal components and data reduction,principal components score scatter diagram has been built,and according to accumulation contribution rate,ten principal components have been selected to replace derivative spectrum curve,and then classification and prediction has been done by support vector machine.According to the training of 105 samples from the three groups,classification and prediction of 44 samples and comparing the classification capacities of four kernel function support vector machines,the consequence is that RBF has high quality in classification and identification and the accuracy rate in classification and prediction of cucumber downy mildew reaches 97.73%.

关 键 词：荧光光谱 主成分分析 支持向量机 黄瓜霜霉病 

分 类 号：S123[农业科学—农业基础科学]