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最小角回归结合核极限学习机的近红外光谱对柑橘黄龙病的鉴别
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作者单位
陈文丽,王其滨,路皓翔,杨辉华,刘彤,许定舟,杜文川 1.桂林电子科技大学计算机与信息安全学院2.桂林电子科技大学电子工程与自动化学院3.北京邮电大学自动化学院4.广州迅动网络科技有限公司 
基金项目:国家自然科学基金项目(21365008,61105004);广西自动检测技术与仪器重点实验室主任基金项目(YQ18108);广西科技计划项目(桂科AD19245202)
中文摘要:传统的柑橘黄龙病检测方法存在准确度低、稳定性差等问题,该文提出了一种基于最小角回归结合核极限学习机(Least angle regression combined with kernel extreme learning machine,LAR-KELM(RBF))的近红外柑橘黄龙病鉴别方法。该方法将光谱数据通过小波变换进行预处理,然后用最小角回归(LAR)算法进行光谱波长的筛选,最后通过核极限学习机(KELM(RBF))实现样本的分类。实验采用柑橘叶片的近红外光谱数据,验证了LAR-KELM(RBF)算法的性能,其分类准确度最高为99.91%,标准偏差为011。不同规模训练集的实验结果表明,LAR-KELM(RBF)模型较极限学习机(ELM)、波形叠加极限学习机(SWELM)、反向传播神经网络(BP(2层))、KELM(RBF)和支持向量机(SVM)模型分类准确度高、稳定性强,能够广泛应用于柑橘黄龙病的检测鉴别。
中文关键词:近红外光谱;柑橘黄龙病;变量筛选;核极限学习机  最小角回归
 
Identification of Citrus Huanglongbing by Near Infrared Spectroscopy with Least Angle Regression and Kernel Extreme Learning Machine
Abstract:A method was proposed for the identification of citrus huanglongbing by near infrared(NIR) spectroscopy based on least angle regression combined with kernel extreme learning machine(LAR-KELM(RBF)) as the traditional detection method for the disease has some defects such as low accuracy and poor stability.Firstly,the acquired spectral data were preprocessed by wavelet transform,then the least angle regression(LAR) algorithm was used to select the spectral wavelength,and finally,with the help of KELM(RBF),the filtered spectral data were managed to classify.The NIR spectral data of orange leaves were taken to verify the performance of LAR-KELM(RBF) algorithm in the experiment.The classification accuracy of the algorithm could reach up to 99.91%,and standard deviation(STD) was 0.11.The experimental results of different training sets showed that LAR-KELM(RBF) model was more accurate and stable than extreme learning machine(ELM),summation wavelet extreme learning machine(SWELM),back propagation(BP(two layers)),KELM(RBF) and support vector machine(SVM) model,which could be widely used in the detection and differentiation of citrus huanglongbing.
Key Words:near infrared spectroscopy  huanglongbing of citrus  variable screening  kernel extreme learning machine  least angle regression
引用本文:陈文丽,王其滨,路皓翔,杨辉华,刘彤,许定舟,杜文川.最小角回归结合核极限学习机的近红外光谱对柑橘黄龙病的鉴别[J].分析测试学报,2020,39(10):1267-1273.
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