光谱学与光谱分析, 2010, 30 (11): 3018, 网络出版: 2011-01-26  

叶绿素荧光PCA-SVM分析的黄瓜病虫害诊断研究

Diagnosis of Cucumber Diseases and Insect Pests by Fluorescence Spectroscopy Technology Based on PCA-SVM
作者单位
1 吉林大学生物与农业工程学院, 工程仿生教育部重点实验室, 吉林 长春130022
2 长春税务学院应用数学系, 吉林 长春130117
摘要
为了对植物病虫害进行快速准确检测, 采用荧光光谱技术并结合支持向量机分析方法建立了黄瓜病虫害诊断模型。 通过Savitzky-Golay平滑法(SG), SG平滑法+快速傅里叶变换(FFT)和SG平滑法+一阶导数变换(FDT)三种方法对原始光谱进行降噪处理, 并利用主成分分析法(PCA)对降噪后的光谱进行降维, 根据累积贡献率选取7个主成分进行分析。 将样本数据随机分为训练集和预测集, 利用四种核函数条件下的支持向量机算法建立了预测模型, 并进行预测。 以训练集交叉验证的分类准确率最大值为指标, 对四种核函数模型进行参数优化, 并对比其分类性能, 结果表明, 经SG+FDT+PCA预处理后, 具有多项式核函数的支持向量机对黄瓜病虫害的鉴别准确率达到98.3%, 具有很好的分类和鉴别效果。
Abstract
The diagnosis model of the cucumber diseases and insect pests was established by laser-induced chlorophyll fluorescence (LICF) spectroscopy technology combined with support vector machines (SVM) algorithm in the present research. This model would be used to realize the fast and exact diagnosis of the cucumber diseases and insect pests. The noise of original spectrum was reduced by three methods, including Savitzky-Golay smoothing (SG), Savitzky-Golay smoothing combined with fast Fourier transform (FFT) and Savitzy-Golay smoothing combined with first derivative transform (FDT). According to the accumulative reliabilities (AR) seven principal components (PCs) were selected to replace the complex spectral data. The one hundred fifty samples were randomly separated into the calibration set and the validation set. Support vector machines (SVM) algorithm with four kinds of kernel functions was used to establish diagnosis models of the cucumber diseases and insect pests based on the calibration set, then these models were applied to the diagnosis of the validation set. According to the best diagnosis accuracy of cross-validation method in calibration set, the parameters of four kinds of kernel function models were optimized, and the capabilities of SVM with different kernel function were compared. Results showed that SVM with the ploy kernel function had the best identification capabilities and the accuracy was 98.3% after the original spectrum noise was reduced by SG+FDT+PCA. This research indicated that the method of PCA-SVM had a good identification effect and could realize rapid diagnosis of the cucumber diseases and insect pests as a new method.

杨昊谕, 于海业, 刘煦, 张蕾, 隋媛媛. 叶绿素荧光PCA-SVM分析的黄瓜病虫害诊断研究[J]. 光谱学与光谱分析, 2010, 30(11): 3018. YANG Hao-yu, YU Hai-ye, LIU Xu, ZHANG Lei, SUI Yuan-yuan. Diagnosis of Cucumber Diseases and Insect Pests by Fluorescence Spectroscopy Technology Based on PCA-SVM[J]. Spectroscopy and Spectral Analysis, 2010, 30(11): 3018.

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