title: [Fluorescence spectrum monitor for early warning of greenhouse cucumber aphis pests].
abstract: The infection and degree of cucumber aphis pests was studied by analyzing chlorophyllfluorescence spectrum in greenhouse. 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%).
authors.affiliation: Key Laboratory of Bionic Engineering, Ministry of Education, School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China. suiyuan0115@126.com
authors.forename: Yuan-Yuan
authors.initials: YY
authors.lastname: Sui
authors.forename: Hai-Ye
authors.initials: HY
authors.lastname: Yu
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: Han
authors.initials: H
authors.lastname: Luo
authors.forename: Shun
authors.initials: S
authors.lastname: Ren
authors.forename: Guo-Gang
authors.initials: GG
authors.lastname: Zhao
mesh_terms: D000818:Animals; D001042:Aphids; D018553:Cucumis sativus; D005453:Fluorescence; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D018515:Plant Leaves; D013057:Spectrum Analysis; D060388:Support Vector Machine