基于VMD-MSE的玉米铜污染信息提取与预测模型
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摘要
重金属污染会引起作物光谱畸变,本文通过挖掘光谱信息中微弱的畸变信息诊断玉米受污染程度。将变分模态分解(VMD)运用到高光谱弱信息探测中,并结合多尺度熵(MSE)构建VMD-MSE光谱弱信息探测模型,同时利用模型值VM进行Cu2+含量回归分析与建模。结果表明:对原始光谱数据进行3次VMD分解后,可有效提取光谱奇异特征;计算VMD结果的MSE值,可获取5个尺度的模型值。各尺度模型值VM与玉米叶片中Cu2+含量呈现显著负相关,其中第一尺度模型值(VM1)与叶片中Cu2+相关性最好。对各尺度VM构建的Cu2+含量预测模型应用结果进行比较,证明VM1线性回归模型预测效果最优。表明VMD-MSE模型可为作物污染信息提取、污染诊断及Cu2+含量预测提供思路与方法。
Spectral reflectance of crop will be changed slightly when crop is stressed by heavy metal. The changes of crop spectral reflectance have considerable significance for crop contamination diagnosis.However,vegetation photosynthetic components are complex,which means that there may be no visible symptoms in leaf spectral reflectance when the crop is stressed by heavy metal. And therefore the object was to develop a weak information extraction method to excavate the vegetative stress signals through minimizing the effects of background materials,such as those caused by non-photosynthetic components.A VMD-MSE model was built to excavate and measure the weak information in corn leaves spectrum by introducing the variational mode decomposition( VMD) into hyperspectral weak information detection and combining with multiscale entropy( MSE). The model value could be obtained after treating corn leaves spectrum by VMD-MSE model. In addition,linear regression models between model values of corn leaves spectrum under different stress concentrations and Cu2 +contents in corn leaves were established. The results showed that the spectrum singular features of the original spectrum of corn leaves can be extracted effectively after three times decomposition of variational mode decomposition. Model values of five scales were obtained by calculating the multiscale entropy of the result of three-time variational mode decomposition. And VM,the model value at five scales,had a significant negative correlation with Cu2 +contents in corn leaves,and the most significant correlation was between the first-scale model value( VM1) and Cu2 +contents in leaves. The linear regression model established based on VM1 and Cu2 +contents in corn leaves was proved to be optimal by comparing the application results of five Cu2 +contents prediction models. Therefore,the VMD-MSE model can provide a new method for pollution information extraction,crop contamination diagnosis and Cu2 +contents prediction.
Spectral reflectance of crop will be changed slightly when crop is stressed by heavy metal. The changes of crop spectral reflectance have considerable significance for crop contamination diagnosis.However,vegetation photosynthetic components are complex,which means that there may be no visible symptoms in leaf spectral reflectance when the crop is stressed by heavy metal. And therefore the object was to develop a weak information extraction method to excavate the vegetative stress signals through minimizing the effects of background materials,such as those caused by non-photosynthetic components.A VMD-MSE model was built to excavate and measure the weak information in corn leaves spectrum by introducing the variational mode decomposition( VMD) into hyperspectral weak information detection and combining with multiscale entropy( MSE). The model value could be obtained after treating corn leaves spectrum by VMD-MSE model. In addition,linear regression models between model values of corn leaves spectrum under different stress concentrations and Cu2 +contents in corn leaves were established. The results showed that the spectrum singular features of the original spectrum of corn leaves can be extracted effectively after three times decomposition of variational mode decomposition. Model values of five scales were obtained by calculating the multiscale entropy of the result of three-time variational mode decomposition. And VM,the model value at five scales,had a significant negative correlation with Cu2 +contents in corn leaves,and the most significant correlation was between the first-scale model value( VM1) and Cu2 +contents in leaves. The linear regression model established based on VM1 and Cu2 +contents in corn leaves was proved to be optimal by comparing the application results of five Cu2 +contents prediction models. Therefore,the VMD-MSE model can provide a new method for pollution information extraction,crop contamination diagnosis and Cu2 +contents prediction.
引文
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[18]钱林,康敏,傅秀清,等.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击,2017,36(3):227-233.QIAN Lin,KANG Min,FU Xiuqing,et al. Application of adaptive morphology in bearing fault diagnosis based on VMD[J].Journal of Vibration and Shock,2017,36(3):227-233.(in Chinese)
[19]陈慧,张磊,熊国良,等.滚动轴承的MSE和PNN故障诊断方法[J].噪声与振动控制,2014,34(6):169-173.CHEN Hui,ZHANG Lei,XIONG Guoliang,et al. Fault diagnosis of rolling bearings using MSE and PNN[J]. Noise and Vibration Control,2014,34(6):169-173.(in Chinese)
[20]杨松山,周灏,赵海洋,等.基于LMD多尺度熵与SVM的往复压缩机轴承故障诊断方法[J].机械传动,2015,39(2):119-123.YANG Songshan,ZHOU Hao,ZHAO Haiyang,et al. Fault diagnosis method for the bearing of reciprocating compressor based on LMD multiscale entropy and SVM[J]. Journal of Mechanical Transmission,2015,39(2):119-123.(in Chinese)
[2]潘攀,杨俊诚,邓仕槐,等.土壤-植物体系中农药和重金属污染研究现状及展望[J].农业环境科学学报,2011,30(12):2389-2398.PAN Pan,YANG Juncheng,DENG Shihuai,et al. Proceedings and prospects of pesticides and heavy metals contamination in soil-plant system[J]. Journal of Agro-Environment Science,2011,30(12):2389-2398.(in Chinese)
[3] SRIVASTAVA V,SARKAR A,SINGH S,et al. Agroecological responses of heavy metal pollution with special emphasis on soil health and plant performances[J]. Frontiers in Environmental Science,2017,5:64.
[4] RAJKUMAR M,VARA PRASAD M N,FREITAS H,et al. Biotechnological applications of serpentine soil bacteria for phytoremediation of trace metals[J]. Critical Reviews in Biotechnology,2009,29(2):120-130.
[5]朱叶青,屈永华,刘素红,等.重金属铜污染植被光谱响应特征研究[J].遥感学报,2014,18(2):335-352.ZHU Yeqing,QU Yonghua,LIU Suhong,et al. Spectral response of wheat and lettuce to copper pollution[J]. Journal of Remote Sensing,2014,18(2):335-352.(in Chinese)
[6] SRIDHAR M B B,HAN F X,DIEHL S V,et al. Monitoring the effects of arsenic and chromium accumulation in Chinese brake fern(Pteris vittata)[J]. International Journal of Remote Sensing,2007,28(5):1055-1067.
[7]王明常,应申,任福,等.基于稳健估计的白桦叶片铜元素含量反演[J].武汉大学学报(信息科学版),2017,42(5):637-642.WANG Mingchang,YING Shen,REN Fu,et al. Inversion model for the copper element in birch leaves based on robust estimation[J]. Geomatics and Information Science of Wuhan University,2017,42(5):637-642.(in Chinese)
[8]杨可明,汪国平,付萍杰,等. HHT时频分析土壤光谱的重金属铜离子污染信息提取模型[J].光谱学与光谱分析,2018,38(2):564-569.YANG Keming,WANG Guoping,FU Pingjie,et al. A model on extracting the pollution information of heavy metal copper ion based on the soil spectra analyzed by HHT in time-frequency[J]. Spectroscopy and Spectral Analysis,2018,38(2):564-569.(in Chinese)
[9]何汝艳,乔小军,蒋金豹,等.小波法反演条锈病胁迫下冬小麦冠层叶片全氮含量[J].农业工程学报,2015,31(2):141-146.HE Ruyan,QIAO Xiaojun,JIANG Jinbao,et al. Retrieving canopy leaf total nitrogen content of winter wheat by continuous wavelet transform[J]. Transactions of the CSAE,2015,31(2):141-146.(in Chinese)
[10] WANG J,WANG T,SHI T,et al. A wavelet-based area parameter for indirectly estimating copper concentration in carex leaves from canopy reflectance[J]. Remote Sensing,2015,7(11):15340-15360.
[11]杜华强,金伟,葛宏立,等.用高光谱曲线分形维数分析植被健康状况[J].光谱学与光谱分析,2009,29(8):2136-2140.DU Huaqiang,JIN Wei,GE Hongli,et al. Using fractal dimensions of hyperspectral curves to analyze the healthy status of vegetation[J]. Spectroscopy and Spectral Analysis,2009,29(8):2136-2140.(in Chinese)
[12]黄芝.水稻重金属污染胁迫遥感监测特征尺度研究[D].北京:中国地质大学(北京),2017.HUANG Zhi. Deriving the characteristic scale for heavy metal stress monitoring in rice based on the remote sensing data[D].Beijing:China University of Geosciences(Beijing),2017.(in Chinese)
[13]程龙,杨可明,王晓峰,等.作物重金属铜污染的HHT边际谱特征与污染预测模型[J].中国环境科学,2018,38(1):340-347.CHENG Long,YANG Keming,WANG Xiaofeng,et al. Characteristic changes of HHT marginal spectra and pollution predicting models on crop polluted by the heavy metal copper[J]. China Environmental Science,2018,38(1):340-347.(in Chinese)
[14] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[15]贾亚飞,朱永利,王刘旺,等.基于VMD和多尺度熵的变压器内绝缘局部放电信号特征提取及分类[J].电工技术学报,2016,31(19):208-217.JIA Yafei,ZHU Yongli,WANG Liuwang,et al. Feature extraction and classification on partial discharge signals of power transformers based on VMD and multiscale entropy[J]. Transactions of China Electrotechnical Society,2016,31(19):208-217.(in Chinese)
[16]王建国,陈帅,张超.基于VMD与多特征融合的齿轮故障诊断方法[J].机械传动,2017,41(3):160-165.WANG Jianguo,CHEN Shuai,ZHANG Chao. Fault diagnosis method of gear based on VMD and multi-feature fusion[J].Journal of Mechanical Transmission,2017,41(3):160-165.(in Chinese)
[17]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.LIU Changliang,WU Yingjie,ZHEN Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering[J]. Proceedings of the CSEE,2015,35(13):3358-3365.(in Chinese)
[18]钱林,康敏,傅秀清,等.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击,2017,36(3):227-233.QIAN Lin,KANG Min,FU Xiuqing,et al. Application of adaptive morphology in bearing fault diagnosis based on VMD[J].Journal of Vibration and Shock,2017,36(3):227-233.(in Chinese)
[19]陈慧,张磊,熊国良,等.滚动轴承的MSE和PNN故障诊断方法[J].噪声与振动控制,2014,34(6):169-173.CHEN Hui,ZHANG Lei,XIONG Guoliang,et al. Fault diagnosis of rolling bearings using MSE and PNN[J]. Noise and Vibration Control,2014,34(6):169-173.(in Chinese)
[20]杨松山,周灏,赵海洋,等.基于LMD多尺度熵与SVM的往复压缩机轴承故障诊断方法[J].机械传动,2015,39(2):119-123.YANG Songshan,ZHOU Hao,ZHAO Haiyang,et al. Fault diagnosis method for the bearing of reciprocating compressor based on LMD multiscale entropy and SVM[J]. Journal of Mechanical Transmission,2015,39(2):119-123.(in Chinese)