基于光谱及数据挖掘技术的油菜养分及品质信息的无损检测研究
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摘要
精细农业是21世纪全球农业发展的必然趋势。它的技术核心是农田信息的获取、信息的管理与决策及变量作业三个部分。其中如何快速准确的获取作物生长信息,是开展精细农业的重要基础,也是精细农业研究的一个热点和难点。
针对国内外现状,本论文以油菜(Brassica napus)为对象,通过正交二次回归设计方法进行了油菜氮、磷、钾和硼等四种重要营养元素田间试验,采用可见-近红外光谱技术研究了油菜植株的氮、磷、钾、硼含量与油菜叶片和冠层的光谱反射特性之间的关系。还研究了油菜叶片氮含量与SPAD值,油菜冠层3CCD多光谱图像与SPAD值之间的关系。此外,还初步探讨了辐照处理对油菜籽的光谱反射特性的影响。本论文的主要研究结论如下：
(1)研究了油菜叶片的光谱反射特性与油菜叶片氮、磷、钾、硼四种含量的关系,并分别建立了全波段模型及最优波长模型。结果表明,四种元素含量的最优全波段模型均为直接正交信号校正(DOSC)结合偏最小二乘法(PLS)模型,最优模型对氮、磷、钾和硼的预测相关系数分别为0.9743、0.6971、0.9316和0.8903；利用连续投影算法(SPA)选择最优波长并建立数学模型的研究中,提出了采用DOSC结合SPA选择的氮、磷、钾和硼四种元素的最优波长的优化模型,得出的最优特征波长分别为958、540、627和686nm。结果表明,氮含量最佳模型采用DOSC-SPA结合最小二乘-支持向量机(LS-SVM)方法,其预测相关系数为0.9737；磷、钾、硼三种元素含量的最佳模型都是采用DOSC-SPA结合人工神经网络(BPNN)方法,其预测相关系数分别为0.7054、0.9380及0.8916。
(2)研究了油菜冠层光谱反射特性与油菜植株氮、磷、钾三种含量的关系,并分别建立了全波段模型及最优波长模型。通过对不同预处理方法结合PLS所建立模型的效果比较,三种元素的最佳光谱预处理方法都是DOSC.其最佳模型的预测相关系数分别为0.9440、0.8260和0.9574。利用DOSC-SPA算法选择的最优波长分别为761、994和927nm。氮和磷两种元素含量的最佳模型是DOSC-SPA结合LS-SVM方法所建立的模型,预测相关系数分别为0.9423和0.8124。钾元素含量的最佳模型采用了DOSC-SPA结合PLS方法,预测相关系数为0.9526。
(3)研究了油菜叶片SPAD值与氮含量的相关性。结果显示,在油菜生长期,油菜叶片SPAD值与氮含量呈线性相关的关系,其相关系数为0.861,对三个未知样本的预测精度分别达到78.8%,91.8%及94.12%。
(4)研究了油菜冠层多光谱图像与氮含量之间的关系。构建了一套集可见-近红外光谱仪、3CCD多光谱成像仪于一体的油菜氮含量多光谱图像测试平台。建立了基于油菜冠层多光谱反射率的植被指数NDVI、GNDVI和Ratio与SPAD值之间的校正模型。三个模型的预测相关系数分别为0.932、0.879及0.885。
(5)研究了辐照处理后油菜籽光谱反射特性的变化规律。探讨了不同预处理方法组合及神经网络隐含层节点数对所建模型的影响。所建最优模型先后采用了中值滤波平滑法、附加散射校正及二阶求导法三种预处理方法。BPNN模型建立过程中,选择对6个PLS主成分进行自然对数变化,所设定的隐含层节点数为4或9。结果表明,油菜籽经过辐照处理后,其光谱反射特性会发生较大的改变。最优模型对辐照和未辐照样本的识别率均为100%,对油菜籽所受辐照剂量的预测精度达到85.71%。说明可见-近红外光谱技术可以用于评估辐照处理对油菜籽光谱特性产生的影响。
Precision agriculture is the developing trend of global agriculture in the 21st century. The technologies of crop growth information collection, information management and variable operations are the key technologies of precision agriculture. Fast and precise collection of the crop growth information is one of the important basis, hot and difficulties of precision agriculture.
Considering the problems and deficiencies of current research of precision agriculture, this thesis was focus on rapeseed (Brassica napus). Quadratic regression orthogonal design was applied in the field experience to produce different levels of nitrogen (N), phosphorus (P), potassium (K) and boron (B) of rapeseed. Visible and near infrared spectroscopy technology was applied to study the relationship between reflectance spectra and the content of N, P, K, and B of rapeseed leaf and canopy. The relationship between N content of rapeseed leaf and SPAD value, and relationship between 3CCD multi-spectral image of rapeseed canopy and SPAD value were also investigated in this thesis. Furthermore, the influence of gamma-ray treatment on spectral characteristic of rapeseed was studied. The main research achievements were as follows:
(1) The relations between reflectance spectra and content of N, P, K, and B content of rapeseed leaf were studied. Chemometric models were built by the full waveband and optimal wavelengths. The results showed that the best models of the four elements were all developed by direct orthogonal signal correction (DOSC) method combined with partial least squares (PLS) method. The prediction coefficients of the four best models were 0.9743,0.6971,0.9316 and 0.8903 for N, P, K and B, respectively. Successive projections algorithm (SPA) method was applied to select the optimal wavelengths with least collinearity and redundancies. The optimum wavelengths selected by DOSC combined with SPA method (DOSC-SPA) were 958,540,627 and 686 nm. The best model of N content was built by DOSC-SPA combined with least squares-support vector machine (LS-SVM) method. The correlation coefficient for prediction set was 0.9737. The best models of P, K, and B content were all built by DOSC-SPA combined with back-propagation neural network (BPNN) method. The correlation coefficients for prediction set were 0.7054,0.9380 and 0.8916 for P, K and B, respectively.
(2) The relations between reflectance spectra and content of N, P, and K content of rapeseed canopy were studied. Chemometric models were built by the full waveband and optimal wavelengths. After comparing prediction results of the PLS models built by different pre-processing methods, DOSC was chosen as the best pre-processing method of these three elements. The correlation coefficients for prediction set were 0.9440,0.8260 and 0.9574 for N, P and K, respectively. The optimal wavelengths selected by DOSC-SPA were 761,994 and 927 nm, respectively. The best models of N and P content were both developed by DOSC-SPA-LS-SVM. The correlation coefficients for prediction set were 0.9423 and 0.8124 for N and P, respectively. The best model of K content was built by DOSC-SPA-PLS, and correlation coefficient was 0.9526.
(3) The relation between N content of rapeseed leaf and SPAD value was studied. The result indicated that there existed a linear relationship between the N content and SPAD value during the growth period. The coefficient was 0.861. The prediction precisions of the three unknown samples were 78.8%,91.8% and 94.12%.
(4) The relation between multi-spectral image of rapeseed canopy and N content was studied. Multi-spectral image test system which combined visible and near infrared spectroradiometer with 3CCD camera was set up. The models between SPAD value and NDVI, GNDVI and Ratio which were based on multi-spectra reflectance of rapeseed canopy were built. The correlation coefficients for prediction set were 0.932,0.918 and 0.885 for NDVI, GNDVI and Ratio, respectively.
(5) The changing rule of the spectral characteristic of rapeseed after being treated by gamma-ray was studied. The influences of different pre-processing combinations and nodes number of hidden layers to the models were discussed. As a result, the optimal model was established and the parameters of the model were shown as follows. The original spectra data were pretreated by smoothing media filter, multiplicative scatter correction and 2nd Savitzky-Golay derivatives. The 6 PLS principal components were transformed by using natural logarithm transformation method. The nodes number of hidden layers of the BPNN model was selected as 4 or 9. The results indicated that after being treated by gamma-ray, the spectral characteristic of rapeseed would change greatly. The prediction precision of the optimal model to distinguish the untreated samples from gamma-ray treated samples was 100%. The precision of predicting the dosages of gamma-ray treatment of all samples achieved 85.71%. It can be concluded that the visible and near infrared spectroscopy could be used to estimate the influence of different gamma-ray dosages on the spectral characteristic of treated rapeseed.
针对国内外现状,本论文以油菜(Brassica napus)为对象,通过正交二次回归设计方法进行了油菜氮、磷、钾和硼等四种重要营养元素田间试验,采用可见-近红外光谱技术研究了油菜植株的氮、磷、钾、硼含量与油菜叶片和冠层的光谱反射特性之间的关系。还研究了油菜叶片氮含量与SPAD值,油菜冠层3CCD多光谱图像与SPAD值之间的关系。此外,还初步探讨了辐照处理对油菜籽的光谱反射特性的影响。本论文的主要研究结论如下：
(1)研究了油菜叶片的光谱反射特性与油菜叶片氮、磷、钾、硼四种含量的关系,并分别建立了全波段模型及最优波长模型。结果表明,四种元素含量的最优全波段模型均为直接正交信号校正(DOSC)结合偏最小二乘法(PLS)模型,最优模型对氮、磷、钾和硼的预测相关系数分别为0.9743、0.6971、0.9316和0.8903；利用连续投影算法(SPA)选择最优波长并建立数学模型的研究中,提出了采用DOSC结合SPA选择的氮、磷、钾和硼四种元素的最优波长的优化模型,得出的最优特征波长分别为958、540、627和686nm。结果表明,氮含量最佳模型采用DOSC-SPA结合最小二乘-支持向量机(LS-SVM)方法,其预测相关系数为0.9737；磷、钾、硼三种元素含量的最佳模型都是采用DOSC-SPA结合人工神经网络(BPNN)方法,其预测相关系数分别为0.7054、0.9380及0.8916。
(2)研究了油菜冠层光谱反射特性与油菜植株氮、磷、钾三种含量的关系,并分别建立了全波段模型及最优波长模型。通过对不同预处理方法结合PLS所建立模型的效果比较,三种元素的最佳光谱预处理方法都是DOSC.其最佳模型的预测相关系数分别为0.9440、0.8260和0.9574。利用DOSC-SPA算法选择的最优波长分别为761、994和927nm。氮和磷两种元素含量的最佳模型是DOSC-SPA结合LS-SVM方法所建立的模型,预测相关系数分别为0.9423和0.8124。钾元素含量的最佳模型采用了DOSC-SPA结合PLS方法,预测相关系数为0.9526。
(3)研究了油菜叶片SPAD值与氮含量的相关性。结果显示,在油菜生长期,油菜叶片SPAD值与氮含量呈线性相关的关系,其相关系数为0.861,对三个未知样本的预测精度分别达到78.8%,91.8%及94.12%。
(4)研究了油菜冠层多光谱图像与氮含量之间的关系。构建了一套集可见-近红外光谱仪、3CCD多光谱成像仪于一体的油菜氮含量多光谱图像测试平台。建立了基于油菜冠层多光谱反射率的植被指数NDVI、GNDVI和Ratio与SPAD值之间的校正模型。三个模型的预测相关系数分别为0.932、0.879及0.885。
(5)研究了辐照处理后油菜籽光谱反射特性的变化规律。探讨了不同预处理方法组合及神经网络隐含层节点数对所建模型的影响。所建最优模型先后采用了中值滤波平滑法、附加散射校正及二阶求导法三种预处理方法。BPNN模型建立过程中,选择对6个PLS主成分进行自然对数变化,所设定的隐含层节点数为4或9。结果表明,油菜籽经过辐照处理后,其光谱反射特性会发生较大的改变。最优模型对辐照和未辐照样本的识别率均为100%,对油菜籽所受辐照剂量的预测精度达到85.71%。说明可见-近红外光谱技术可以用于评估辐照处理对油菜籽光谱特性产生的影响。
Precision agriculture is the developing trend of global agriculture in the 21st century. The technologies of crop growth information collection, information management and variable operations are the key technologies of precision agriculture. Fast and precise collection of the crop growth information is one of the important basis, hot and difficulties of precision agriculture.
Considering the problems and deficiencies of current research of precision agriculture, this thesis was focus on rapeseed (Brassica napus). Quadratic regression orthogonal design was applied in the field experience to produce different levels of nitrogen (N), phosphorus (P), potassium (K) and boron (B) of rapeseed. Visible and near infrared spectroscopy technology was applied to study the relationship between reflectance spectra and the content of N, P, K, and B of rapeseed leaf and canopy. The relationship between N content of rapeseed leaf and SPAD value, and relationship between 3CCD multi-spectral image of rapeseed canopy and SPAD value were also investigated in this thesis. Furthermore, the influence of gamma-ray treatment on spectral characteristic of rapeseed was studied. The main research achievements were as follows:
(1) The relations between reflectance spectra and content of N, P, K, and B content of rapeseed leaf were studied. Chemometric models were built by the full waveband and optimal wavelengths. The results showed that the best models of the four elements were all developed by direct orthogonal signal correction (DOSC) method combined with partial least squares (PLS) method. The prediction coefficients of the four best models were 0.9743,0.6971,0.9316 and 0.8903 for N, P, K and B, respectively. Successive projections algorithm (SPA) method was applied to select the optimal wavelengths with least collinearity and redundancies. The optimum wavelengths selected by DOSC combined with SPA method (DOSC-SPA) were 958,540,627 and 686 nm. The best model of N content was built by DOSC-SPA combined with least squares-support vector machine (LS-SVM) method. The correlation coefficient for prediction set was 0.9737. The best models of P, K, and B content were all built by DOSC-SPA combined with back-propagation neural network (BPNN) method. The correlation coefficients for prediction set were 0.7054,0.9380 and 0.8916 for P, K and B, respectively.
(2) The relations between reflectance spectra and content of N, P, and K content of rapeseed canopy were studied. Chemometric models were built by the full waveband and optimal wavelengths. After comparing prediction results of the PLS models built by different pre-processing methods, DOSC was chosen as the best pre-processing method of these three elements. The correlation coefficients for prediction set were 0.9440,0.8260 and 0.9574 for N, P and K, respectively. The optimal wavelengths selected by DOSC-SPA were 761,994 and 927 nm, respectively. The best models of N and P content were both developed by DOSC-SPA-LS-SVM. The correlation coefficients for prediction set were 0.9423 and 0.8124 for N and P, respectively. The best model of K content was built by DOSC-SPA-PLS, and correlation coefficient was 0.9526.
(3) The relation between N content of rapeseed leaf and SPAD value was studied. The result indicated that there existed a linear relationship between the N content and SPAD value during the growth period. The coefficient was 0.861. The prediction precisions of the three unknown samples were 78.8%,91.8% and 94.12%.
(4) The relation between multi-spectral image of rapeseed canopy and N content was studied. Multi-spectral image test system which combined visible and near infrared spectroradiometer with 3CCD camera was set up. The models between SPAD value and NDVI, GNDVI and Ratio which were based on multi-spectra reflectance of rapeseed canopy were built. The correlation coefficients for prediction set were 0.932,0.918 and 0.885 for NDVI, GNDVI and Ratio, respectively.
(5) The changing rule of the spectral characteristic of rapeseed after being treated by gamma-ray was studied. The influences of different pre-processing combinations and nodes number of hidden layers to the models were discussed. As a result, the optimal model was established and the parameters of the model were shown as follows. The original spectra data were pretreated by smoothing media filter, multiplicative scatter correction and 2nd Savitzky-Golay derivatives. The 6 PLS principal components were transformed by using natural logarithm transformation method. The nodes number of hidden layers of the BPNN model was selected as 4 or 9. The results indicated that after being treated by gamma-ray, the spectral characteristic of rapeseed would change greatly. The prediction precision of the optimal model to distinguish the untreated samples from gamma-ray treated samples was 100%. The precision of predicting the dosages of gamma-ray treatment of all samples achieved 85.71%. It can be concluded that the visible and near infrared spectroscopy could be used to estimate the influence of different gamma-ray dosages on the spectral characteristic of treated rapeseed.
引文
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