温室黄瓜蚜虫害的荧光光谱监测与预警  

Fluorescence Spectrum Monitor for Early Warning of Greenhouse Cucumber Aphis Pests

作　　者：隋媛媛[1] 于海业[1] 张蕾[1] 罗瀚[1] 任顺[1] 赵国罡[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

出　　处：《光谱学与光谱分析》2012年第7期1834-1837,共4页Spectroscopy and Spectral Analysis

基　　金：国家高技术研究发展(863计划)项目(2012AA10A506;2007AA10Z203);吉林省科技发展计划项目(20110217)资助

摘　　要：利用叶绿素荧光光谱分析技术研究温室黄瓜蚜虫害的侵染及发生等级。从光谱形态的角度建立监测特征点,确定F632波段强度值作为健康与蚜虫害叶片的第一特征点,F512～F632光谱曲线的变化速率K值作为第二特征点,对于符合特征点的植株进行及早预警。采用最小二乘支持向量机数据挖掘方法的径向基核函数建立蚜虫害的侵染及发生等级模型,通过对比不同峰谷值所在波段对于蚜虫害的分类准确率和预测准确率,确定采用F632波段建立模型,它的预测能力达到96.34%。The infection and degree of cucumber aphis pests was studied by analyzing ehlorophytlfluorescence spectrum in green- house. Based on the configuration of the spectrum, characteristic points were established, in which the intensity of waveband F632 was the first characteristic point between healthy and aphis pests leaves. The second characteristic point was K which was the change rate of spectral curve from waveband F512 to F632. The early warning could be executed on plants depending on these two points. The models of the infection and degrees of aphis pests were established for different wavebands by the least square support vector machine classification method （LSSVMR） radial basis function（RBF）. The accuracy rate of classification and prediction of the models was compared by different peaks and valleys value in wavebands. The results indicated that the prediction accuracy of the model established by waveband F632 was the most perfect （96. 34%）.

关 键 词：荧光光谱 最小二乘支持向量机 黄瓜蚜虫害 光谱曲线变化速率 

分 类 号：S123[农业科学—农业基础科学]