In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy...In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.展开更多
The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using s...The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.展开更多
基金This study was supported by the Chinese High Technology Research and Development Research Fund(2016YFD0300600-2016YFD0300606,2016YFD0300600-2016YFD0300610)NSFC program(31501219)+1 种基金the Fundamental Research Funds for the Central Universities(2018TC020,2018XD003)Industry Research Project(QingPu 2017-12).
文摘In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.
基金supported by the National Key Research and Development Program(2016YFD0200600-2016YFD0200602)National Natural Science Fund(Grant No.31501219)the graduate training project of China agricultural university(ZYXW037,HJ2019029,YW2019018).
文摘The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares(MWPLS)and genetic algorithm(GA)for the chlorophyll content detection of winter wheat canopy using spectroscopy technology.Firstly,the original spectral dataset was pre-processed by wavelet denosing,multiple scatter correction.Then,abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY.Finally,the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares(PLS)models were established for chlorophyll content prediction.For the model established by using all the wavebands in the region of 400-900 nm,its R_(c)^(2) and R_(v)^(2) were 0.4468 and 0.3821 respectively;its modeling root mean square error(RMSEM)and verification root mean square error(RMSEV)were 2.9057 and 1.7589 respectively.For the model established by using 151 wavebands selected by MWPLS,its R_(c)^(2) and R_(v)^(2) were 0.6210 and 0.5901 respectively;its RMSEM and RMSEV were 2.4007 and 1.6408 respectively.For the model established by using 36 wavebands selected by MWPLS+GA,its R_(c)^(2) and R_(v)^(2) were 0.7805 and 0.7497 respectively;its RMSEM and RMSEV were 1.8504 and 1.1315 respectively.The results show that wavelength selection can remove redundant information and improve model performance.The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction.
