基于高光谱成像技术的油菜养分及产量信息快速获取技术和方法研究
|
摘要
农业物联网技术作为当代农业信息化发展的新方向,是提高资源利用率和生产力水平,加快我国农业由传统农业向现代农业转型的关键和核心技术。农业物联网的应用主要体现在建立对农作物田间及温室环境控制和信息反馈的农业精准控制系统。这种精细化管理要求对作物生长状况的快速准确获取,以实现田间信息的实时动态监测。然而传统的实验室化学测量分析和农田信息监测方法耗时费力,不适合农业物联网技术的发展需要。本论文针对农作物生长信息快速无损检测技术的需要,应用高光谱成像技术,结合多种光谱和图像处理技术及化学计量学方法,以油菜为研究对象,研究油菜生长过程中养分信息的快速获取方法、养分分布可视化以及在较早生长阶段对油菜籽产量的快速预测方法。为制定油菜大田变量作业处方提供主要数据源和参数,为油菜生长的实时监测系统提供技术支撑。本研究主要内容和结论如下：
(1)探讨了基于高光谱成像技术对油菜苗期、花期、角果期和苗。花-角果生命期叶片氮含量的快速检测和氮素在叶片中分布的可视化方法。对每个时期叶片高光谱数据提取可见/近红外波段(380-1030nm)光谱信息,在经过最优预处理后,通过分析比较利用RC和SPA算法提取的特征波长所建立的PLS和LS-SVM模型,得到在油菜苗期、花期、角果期和苗-花-角果生命期对叶片氮含量的预测结果相关系数Rp分别为0.793、0.891、0.772和0.852；基于特征波长图像,利用二阶概率统计滤波方法提取图像纹理特征和特征光谱信息一并作为模型自变量,苗期、花期和角果期的叶片氮含量分别作为模型因变量,建立预测模型,在各时期得到0.752、0.863和0.747的Rp；利用苗-花-角果生命期叶片光谱信息提取的12个特征波长和建立的SPA-PLS模型,对油菜三个不同生长期的叶片氮含量情况进行可视化表达,得到直观具体的氮素营养信息分布图。
(2)研究建立了油菜叶片磷含量快速检测模型和实现磷在叶片中的可视化表达。可见/近红外波段光谱经过SNV预处理后,基于5个特征波长的SPA-BPNN模型获得了最优预测效果(Rp为0.762,RMSEP为0.030)；利用二阶概率统计滤波算法分别提取特征波长图像和主成分图像的纹理特征,并与特征波长信息结合,建立PLS、LS-SVM和BPNN模型,基于特征波长图像的最优模型BPNN预测结果Rp=0.740, RMSEP=0.032,基于主成分图像的最优模型BPNN预测结果Rp=0.757,RMSEP=0.032;利用SPA提出的5个特征波长和SPA-PLS模型回归系数,对叶片高光谱图像中的每个像素点进行磷含量预测,将采自不同施肥梯度的叶片样本磷素含量差异进行了可视化表达。
(3)研究建立了油菜叶片钾含量快速检测模型和实现钾在叶片中的可视化表达。在可见/近红外波段,利用GA.RC和SPA三种方法对原始光谱数据进行特征波长提取,通过PLS、LS-SVM和BPNN三种预测模型的比较得出RC-BPNN模型预测性能最优,对预测集样本预测的Rp为0.759,RMSEP为0.158；分别使用概率统计滤波和二阶概率统计滤波方法对特征波长图像提取纹理特征值,将纹理特征融合光谱特征建模分析比较,得到基于概率统计滤波纹理特征提取方法建立的最优BPNN模型,其中Rp0.730,RMSEP=0.171;基于主成分灰度图像提取纹理特征,结合光谱特征信息后建立不同模型,其中最优模型BPNN模型的Rp为0.726,RMSEP为0.179；基于RC提取的5个特征波长和对应RC-PLS模型,对高光谱图像中叶片区域内每个像素点进行钾含量的预测,从而获得叶片钾含量的可视化分布图,实现同一样本内或不同样本间钾素水平差异的可视化。
(4)应用高光谱成像技术,实现了油菜籽产量的早期快速预测。在油菜较早生长阶段(苗期、抽薹期、花期和角果期)获取叶片高光谱图像数据,通过比较基于各时期光谱数据建立的PLS模型预测效果,确定在初花期(3月份)获得的光谱数据最适宜准确预测油菜籽产量；利用基于载荷系数法提取的6个产量预测敏感波段分别建立线性模型(PLS和MLR)与非线性模型(LS-SVM和BPNN),结果表明LS-SVM模型(RP=0.887,RMSEP=22.303)对油菜籽产量的早期预测具有更好的稳定性和一致性。
Internet of things (IoT) in agriculture as a new direction of the development of modern agriculture is the key and kernel technology to improve resource utilization and productivity level, and accelerate the transition from traditional agriculture to modern agriculture. IoT in agriculture is mainly applied in the establishment of agricultural precise control and feedback systems for field and greenhouse environment. In order to achieve real-time and dynamic monitoring of the field, the precise management requires the information acquisition of the crop growth information rapidly and accurately. However, the traditional lab chemical measurements and field information monitoring methods are time-consuming and destructive, which cannot meet the needs of the development of IoT in agriculture. In this study, hyperspectral imaging technique combined with image process algorithms and chemometrics was used to detect the crop growth condition in situ in living plant samples for Brassica napus L. The content of nitrogen (N), phosphorus (P) and potassium (K) in oilseed rape leaves were determined rapidly and non-invasively. Image processing algorithms were developed for the visualization of macronutrients status of rape leaves in all pixels within an image to generate distribution maps of N, P and K content. A new method and system were also developed for rapid rapeseed yield estimation in an early growing stage. These results could provide information characterising plant growth to develop a programme of plant-specific application of fertiliser to improve agricultural profitability and minimize the impact on the environment. The main research contents and achievements are shown as follows:
(1) Hyperspectral imaging technique was applied to determine and display the distribution maps of N content in rape leaves at different growing stages, including seedling, flowering and pod stage. Hyperspectral images of leaf samples were acquired in the Vis/NIR region (380-1030nm) and their spectral data were extracted from each stage. After the optimal spectral preprocessing and analysis of the PLS and LS-SVM models only based on the effective wavelengths (EWs) selected by regression coefficient (RC) and Successive projections algorithm (SPA), the best results were obtained with Rp of0.793,0.891,0.772and0.852for seedling, flowering, pod stage and the whole lifetime of seedling-flowering-pod, respectively. Besides, second-order statistic filtering algorithm was applied to extract the texture features (TFs) from hyperspectral images. Calibration models for N content detection were established by partial least squares (PLS) and least squares-support vector machine (LS-SVM) based on the combination of spectral features (SFs) and TFs. The optimal models got the best results with Rp of0.752,0.863and0.747for seedling, flowering and pod stage, respectively. Based on10EWs extracted by SPA and the developed SPA-PLS model of seeding-flowering-pod stage, the distribution maps were generated to visualize N content of rape leaves in three different stages.
(2) The rapid and non-invasively detection models were developed and the visualized distribution maps were generated for P content in rape leaves. A complete comparison was first performed among raw spectra and different spectral preprocessing methods. RC, SPA and x-Loading Weights (x-LW) were proposed to select EWs. By comparison different modeling algorithms, SPA-BPNN model based on5EWs obtained the best prediction results (Rp=0.762, RMSEP=0.030). On the other hand, image TFs based on second-order statistic filtering algorithm were combined with SFs as the inputs of PLS, LS-SVM and BPNN models. The performance of BPNN model based on images at EWs got the best results with Rp of0.740and RMSEP of0.032. BPNN model also achieved the best results based on images of principal components (PCs) with Rp of0.757and RMSEP of0.032. The SPA-PLS model executed via5EWs was transferred to each pixel of hyperspectral image to predict P content in all spots of the leaf sample. The visualization of P distribution facilitated discovering the differences of P content within one sample as well as among the samples from different fertilized plots.
(3) The rapid and non-invasively determination models of K content of rape leaf were established and the visualization of K content within a leaf was realized. Genetic algorithm (GA), RC and SPA were applied to acquire the EWs based on raw spectra in Vis/NIR region. By the comparison of PLS, LS-SVM and BPNN prediction models, RC-BPNN model obtained the optimal performance with Rp of0.759and RMSEP of0.158. Probability statistics filter and second-order probability statistic filtering algorithms were respectively conducted to extract the texture features from images at effective wavelengths. The prediction models for K content in leaves were developed by PLS, LS-SVM and BPNN based on the TFs combined with SFs. BPNN model was the best one with the TFs extracted by probability statistical filtering (Rp=0.730, RMSEP=0.171). Different models were also established using the combination of TFs from principal component images and SFs as input variables. The prediction accuracy achieved in BPNN model (Rp=0.726, RMSEP=0.179) were the most satisfactory one. Furthermore, the RC-PLS model built by using5EWs was applied to produce the distribution map at the EWs in each pixel of the reduced image. The final visualized maps of K content distribution could demonstrate how the nutrient content varied from sample to sample.
(4) An early and rapid estimation of rapeseed yield was realized using hyperspectral imaging technique. The hyperspectral images of leaves were acquired in early growth periods (seedling, bolting, flowering and pod stage). By comparison of the performance of PLS models with the spectra extracted from different stages, the spectra data obtained from early flowering (on March25,2011) were considered as the optimal time for rapeseed yield prediction. The linear models (PLS and MLR) and nonlinear models (LS-SVM and BPNN) were developed using only6effective wavelengths selected by RC. The results showed that LS-SVM model (Rp=0.887, RMSEP=22.303) obtained a much more stable and consistent results for rapeseed yield estimation.
(1)探讨了基于高光谱成像技术对油菜苗期、花期、角果期和苗。花-角果生命期叶片氮含量的快速检测和氮素在叶片中分布的可视化方法。对每个时期叶片高光谱数据提取可见/近红外波段(380-1030nm)光谱信息,在经过最优预处理后,通过分析比较利用RC和SPA算法提取的特征波长所建立的PLS和LS-SVM模型,得到在油菜苗期、花期、角果期和苗-花-角果生命期对叶片氮含量的预测结果相关系数Rp分别为0.793、0.891、0.772和0.852；基于特征波长图像,利用二阶概率统计滤波方法提取图像纹理特征和特征光谱信息一并作为模型自变量,苗期、花期和角果期的叶片氮含量分别作为模型因变量,建立预测模型,在各时期得到0.752、0.863和0.747的Rp；利用苗-花-角果生命期叶片光谱信息提取的12个特征波长和建立的SPA-PLS模型,对油菜三个不同生长期的叶片氮含量情况进行可视化表达,得到直观具体的氮素营养信息分布图。
(2)研究建立了油菜叶片磷含量快速检测模型和实现磷在叶片中的可视化表达。可见/近红外波段光谱经过SNV预处理后,基于5个特征波长的SPA-BPNN模型获得了最优预测效果(Rp为0.762,RMSEP为0.030)；利用二阶概率统计滤波算法分别提取特征波长图像和主成分图像的纹理特征,并与特征波长信息结合,建立PLS、LS-SVM和BPNN模型,基于特征波长图像的最优模型BPNN预测结果Rp=0.740, RMSEP=0.032,基于主成分图像的最优模型BPNN预测结果Rp=0.757,RMSEP=0.032;利用SPA提出的5个特征波长和SPA-PLS模型回归系数,对叶片高光谱图像中的每个像素点进行磷含量预测,将采自不同施肥梯度的叶片样本磷素含量差异进行了可视化表达。
(3)研究建立了油菜叶片钾含量快速检测模型和实现钾在叶片中的可视化表达。在可见/近红外波段,利用GA.RC和SPA三种方法对原始光谱数据进行特征波长提取,通过PLS、LS-SVM和BPNN三种预测模型的比较得出RC-BPNN模型预测性能最优,对预测集样本预测的Rp为0.759,RMSEP为0.158；分别使用概率统计滤波和二阶概率统计滤波方法对特征波长图像提取纹理特征值,将纹理特征融合光谱特征建模分析比较,得到基于概率统计滤波纹理特征提取方法建立的最优BPNN模型,其中Rp0.730,RMSEP=0.171;基于主成分灰度图像提取纹理特征,结合光谱特征信息后建立不同模型,其中最优模型BPNN模型的Rp为0.726,RMSEP为0.179；基于RC提取的5个特征波长和对应RC-PLS模型,对高光谱图像中叶片区域内每个像素点进行钾含量的预测,从而获得叶片钾含量的可视化分布图,实现同一样本内或不同样本间钾素水平差异的可视化。
(4)应用高光谱成像技术,实现了油菜籽产量的早期快速预测。在油菜较早生长阶段(苗期、抽薹期、花期和角果期)获取叶片高光谱图像数据,通过比较基于各时期光谱数据建立的PLS模型预测效果,确定在初花期(3月份)获得的光谱数据最适宜准确预测油菜籽产量；利用基于载荷系数法提取的6个产量预测敏感波段分别建立线性模型(PLS和MLR)与非线性模型(LS-SVM和BPNN),结果表明LS-SVM模型(RP=0.887,RMSEP=22.303)对油菜籽产量的早期预测具有更好的稳定性和一致性。
Internet of things (IoT) in agriculture as a new direction of the development of modern agriculture is the key and kernel technology to improve resource utilization and productivity level, and accelerate the transition from traditional agriculture to modern agriculture. IoT in agriculture is mainly applied in the establishment of agricultural precise control and feedback systems for field and greenhouse environment. In order to achieve real-time and dynamic monitoring of the field, the precise management requires the information acquisition of the crop growth information rapidly and accurately. However, the traditional lab chemical measurements and field information monitoring methods are time-consuming and destructive, which cannot meet the needs of the development of IoT in agriculture. In this study, hyperspectral imaging technique combined with image process algorithms and chemometrics was used to detect the crop growth condition in situ in living plant samples for Brassica napus L. The content of nitrogen (N), phosphorus (P) and potassium (K) in oilseed rape leaves were determined rapidly and non-invasively. Image processing algorithms were developed for the visualization of macronutrients status of rape leaves in all pixels within an image to generate distribution maps of N, P and K content. A new method and system were also developed for rapid rapeseed yield estimation in an early growing stage. These results could provide information characterising plant growth to develop a programme of plant-specific application of fertiliser to improve agricultural profitability and minimize the impact on the environment. The main research contents and achievements are shown as follows:
(1) Hyperspectral imaging technique was applied to determine and display the distribution maps of N content in rape leaves at different growing stages, including seedling, flowering and pod stage. Hyperspectral images of leaf samples were acquired in the Vis/NIR region (380-1030nm) and their spectral data were extracted from each stage. After the optimal spectral preprocessing and analysis of the PLS and LS-SVM models only based on the effective wavelengths (EWs) selected by regression coefficient (RC) and Successive projections algorithm (SPA), the best results were obtained with Rp of0.793,0.891,0.772and0.852for seedling, flowering, pod stage and the whole lifetime of seedling-flowering-pod, respectively. Besides, second-order statistic filtering algorithm was applied to extract the texture features (TFs) from hyperspectral images. Calibration models for N content detection were established by partial least squares (PLS) and least squares-support vector machine (LS-SVM) based on the combination of spectral features (SFs) and TFs. The optimal models got the best results with Rp of0.752,0.863and0.747for seedling, flowering and pod stage, respectively. Based on10EWs extracted by SPA and the developed SPA-PLS model of seeding-flowering-pod stage, the distribution maps were generated to visualize N content of rape leaves in three different stages.
(2) The rapid and non-invasively detection models were developed and the visualized distribution maps were generated for P content in rape leaves. A complete comparison was first performed among raw spectra and different spectral preprocessing methods. RC, SPA and x-Loading Weights (x-LW) were proposed to select EWs. By comparison different modeling algorithms, SPA-BPNN model based on5EWs obtained the best prediction results (Rp=0.762, RMSEP=0.030). On the other hand, image TFs based on second-order statistic filtering algorithm were combined with SFs as the inputs of PLS, LS-SVM and BPNN models. The performance of BPNN model based on images at EWs got the best results with Rp of0.740and RMSEP of0.032. BPNN model also achieved the best results based on images of principal components (PCs) with Rp of0.757and RMSEP of0.032. The SPA-PLS model executed via5EWs was transferred to each pixel of hyperspectral image to predict P content in all spots of the leaf sample. The visualization of P distribution facilitated discovering the differences of P content within one sample as well as among the samples from different fertilized plots.
(3) The rapid and non-invasively determination models of K content of rape leaf were established and the visualization of K content within a leaf was realized. Genetic algorithm (GA), RC and SPA were applied to acquire the EWs based on raw spectra in Vis/NIR region. By the comparison of PLS, LS-SVM and BPNN prediction models, RC-BPNN model obtained the optimal performance with Rp of0.759and RMSEP of0.158. Probability statistics filter and second-order probability statistic filtering algorithms were respectively conducted to extract the texture features from images at effective wavelengths. The prediction models for K content in leaves were developed by PLS, LS-SVM and BPNN based on the TFs combined with SFs. BPNN model was the best one with the TFs extracted by probability statistical filtering (Rp=0.730, RMSEP=0.171). Different models were also established using the combination of TFs from principal component images and SFs as input variables. The prediction accuracy achieved in BPNN model (Rp=0.726, RMSEP=0.179) were the most satisfactory one. Furthermore, the RC-PLS model built by using5EWs was applied to produce the distribution map at the EWs in each pixel of the reduced image. The final visualized maps of K content distribution could demonstrate how the nutrient content varied from sample to sample.
(4) An early and rapid estimation of rapeseed yield was realized using hyperspectral imaging technique. The hyperspectral images of leaves were acquired in early growth periods (seedling, bolting, flowering and pod stage). By comparison of the performance of PLS models with the spectra extracted from different stages, the spectra data obtained from early flowering (on March25,2011) were considered as the optimal time for rapeseed yield prediction. The linear models (PLS and MLR) and nonlinear models (LS-SVM and BPNN) were developed using only6effective wavelengths selected by RC. The results showed that LS-SVM model (Rp=0.887, RMSEP=22.303) obtained a much more stable and consistent results for rapeseed yield estimation.
引文
Adams M.L., Philpot W.D., Norvell W.A. Yellowness index:an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing,1999,20(18):3663-3675.
Allen E.J., Morgen D.G., Ridgman W.J. A physiological analysis of the growth of oilseed rape. The Journal of Agricultural Science,1971,77,339-341.
Aouidi F., Dupuy N., Artaud J., et al. Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses. Industrial Crops and Products,2012,37:292-297.
Araujo, M.C.U., Saldanha, T.C.B., Galvao, R.K.H., et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems,2001,57:65-73.
Borhan M.S., Panigrahi S., Lorenzen J. H., et al. Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment. Transactions of the ASAE,2004,47(2):599-608.
Bosoon P., Michio K., William R., et al. Textural analysis of hyperspectral images for improving contaminant detection accuracy. Sensing and Instrumentation for Food Quality and Safety,2008,2,208-214.
Brar C., Thies W. Contribution of leaves, stem, siliques and seeds to dry matter accumulation in ripening seeds of rapeseed, Brassica napus L. Z. Pflanzen physiol,1977,82,1-13.
Bullock D.G., Anderson D.S. Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition,1998,21:741-755.
Carlos C., Lianne M.D., Pierre D. Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes. Journal of plant nutrition,2001,24(8): 1173-1194.
Cen H., He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology,2007,18:72-83.
Cen Y.P., Bornman J.F. The response of bean plants to UV-B radiation under different irradiances of background visible light. Journal of Experimental Botany,1990,41(11), 1489-1495.
Chen J.Y., Iyo C., Terada F., et al. Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. Journal of Near Infrared Spectroscopy,2002,10:301-307.
Cheng T., Rivard B., Sanchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment,2011,115(12): 659-670.
Chong I.G., Jun C.H. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems,2005, 78(1-2):103-112.
Clarke J.M. The Effects of Leaf Removal on Yield and Yield Components of Brassica Napus. Canadian Journal of Plant Science,1978,58 (4),1103-1105.
Dhanoa M.S., Lister S.J., Sandersona R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy,1994,2:43-47.
Durand A., Devos O., Ruckebusch C., et al. Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles. Analytica Chimica Acta, 2007,595 (1-2):72-79.
ElMasry G., Wang N., ElSayed A., et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 2007,81,98-107.
Esbensen K.H. Multivariate Data Analysis in Practice. Oslo:CAMO Process As,2002.
Evans J.R., Seemann J.R. Difference between wheat genotypes in specific activity of ribulose-1,5-bisphosphate carboxylase and the relationship to photosynthesis. Plant physiology,1984,74:759-765.
Feret J.B., Francois C., Gitelson A., et al. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment,2011,115(10):2742-2750.
Filella I., Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing,1994,15: 1459-1470.
Flusser J. Moment invariants in image analysis. World Academy Science, Engineering and Technology,2005,11:376-381.
Freyman S., Charnetski W.A., Crookston R.K. Role of Leaves in the Formation of Seed in Rape. Canadian Journal of Plant Science,1973,53(3),693-694.
Gabrielle B., Denoroy P., Gosse G., et al. Development and evaluation of a CERES-type model for winter oilseed rape. Field Crops Research,1998,57,95-111.
Geladi P., Kowalski B.R. Partial least-squares regression:a tutorial. Analtica Chimica Acta 1986,185:1-17.
Gillon D., Dauriac F., Deshayes M. Estimation of foliage moisture content using near infrared reflectance spectroscopy. Agricultural and Forest Meteorology,2004,124: 51-62.
Goel P.K., Prasher S.O., Landry J.A., et al. Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture,2003,38:99-124.
Gomez N.V., Miralles D.J. Factors that modify early and late reproductive phases in oilseed rape (Brassica napus L.):Its impact on seed yield and oil content. Industrial Crops and Products,2011,34,1277-1285.
Gorry P.A. General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry,1990,62:570-573.
Gowen A.A., O'Donnell C.P., Cullen P.J., et al. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology,2007,18:590-598.
Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. Tritici) and take-all disease (Gaeumannomyces graminis sp. Tritici) in wheat (Triticum aestivum 1.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1(2):275-288.
Haralick R.M., Shanmugam K., Dinstein I.H. Textural Features for Image Classification. IEEE Transactions on Systems, Man, Cybernetics,1973,3(6):610-621.
He Y., Song H.Y., Pereira A.G., et al. A new approach to predict N, P, K and OM content in a loamy mixed soil by using near infrared reflectance spectroscopy. Lecture Notes in Computer Scinece,2005,3644:859-867.
Hedin L.O. Global organization of terrestrial plant-nutrient interactions. Proceedings of the National Academy of Sciences of the United States of America,2004,101(30): 10849-10850.
Helland I.S. Some theoretical aspects of partial least squares regression. Chemometrics and Intelligent Laboratory Systems,2001,58:97-107.
Hinzman L.D., Bauer M.E., Daughtry C.S.T. Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote Sensing of Environment,1986,19: 47-61.
Hogya P., Franzaring J., Schwadorf K., et al. Effects of free-air CO2 enrichment on energy traits and seed quality of oilseed rape. Agriculture, Ecosystems and Environment,2010, 139:239-244.
Holland J.H. Adaptation in natural and artificial systems. USA:A Bradford Book,1992.
Huang W., Lamb D.W., Niu Z., et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture,2007,8(4-5):187-197.
Inoue Y., Penuelas J. An AOTE-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. International Journal of Remote Sensing, 2001,22 (18):3883-3888.
Jago R.A., Cutler M.E.J., Curran P.J. Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment,1999,68(3):217-224.
Jia X.P., Richards J.A. Segmented Principal Components Transformation for Efficient Hyperspectral Remote-Sensing Image Display and Classification. IEEE Transactions on Geoscience and Remote Sensing,1999,37(1):538-542.
Jiang H., Wei W., Song X.D., et al. Hyperspectral studies of transgenic oilseed rape. International Journal of Remote Sensing,2011,32(4):1095-1103.
Ju C.H., Tian Y.C., Yao X., et al. Estimating leaf chlorophyll content using red edge parameters. Pedosphere,2010,20(5):633-644.
Kim Y, Glenn D.M., Park J., et al. Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture,2011,77(2):155-160.
Kobayashi T., Kanda E., Kitada K., et al. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 2001,91(3):316-323.
Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. Journal of Chemometrics,2000,14:643-655.
Leardi R., Boggia R., Terrile M. Genetic algorithms as a strategy for feature selection. Journal of Chemometrics,1992,6(5):267-281.
Leardi R., Gonzalez A.L. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems,1998, 41,195-207.
Lee K.S., Cohen W.B., Kennedy R.E., et al. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 2004,91(3-4):508-520.
Liu F., He Y., Wang L. Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. Analytica Chimica Acta,2008b,610:196-204.
Liu F., He Y., Wang L., et al. Feasibility of the use of visible and near infrared spectroscopy to assess soluble solids content and pH of rice wines. Journal of Food Engineering, 2007,83(3):430-435.
Liu F., Jin Z.L., Naeem M.S., et al. Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves. Food and Bioprocess Technology,2011,4:1314-1321.
Liu F., Zhang F., Jin Z.L., et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. Analytica Chimica Acta,2008a,629:56-65.
Major D.J., Bole J.B., Charnetski W.A. Distribution of photosynthates after 14CO2 assimilation by stem, leaves, and pods of rape plants. Canadian Journal of Plant Science, 1978,58,783-787.
Michio S., Tsuyoshi A. Seasonal visible, near-infrared and mid-infrared spectra of rice spectra canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment,1989,27(2):119-127.
Min M., Lee W.S. Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASABE,2005,48:455-461.
Moron A., Cozzolino D. Measurement of phosphorus in soils by near infrared reflectance spectroscopy:Effect of reference method on calibration. Communications in soil science and plant analysis,2007,38(15-16):1965-1974.
Muller K., Bottcher U., Meyer-Schatz F., et al. Analysis of vegetation indices derived from hyperspectral reflection measurements for estimating crop canopy parameters of oilseed rape (Brassica napus L.). Biosystems Engineering,2008,101(2):172-182.
Osborne S.L., Schepers J.S., Francis D.D., et al. Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal,2002,94: 1215-1221.
Pacumbaba R.O., Beyl C.A. Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research,2011,48: 32-42.
Penuelas J., Filella I. Technical focus:visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science,1998,3(4),151-156.
Penuelas J., Pinol J., Ogaya R., et al. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997,18,2869-2875.
Petisco C., Garcia-Criado B., Vazquez-de-Aldana B.R., et al. Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy. Industrial Crops and Products,2010,32:139-146.
Pinar A., Curran P. J. Grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing,1996,17(2):351-357.
Pontius J., Hallett R., Martin M. Assessing hemlock decline using visible and near-infrared spectroscopy:Indices comparison and algorithm development. Applied Spectroscopy, 2005,59(6):836-843.
Renner G., Ekart A. Genetic algorithms in computer aided design. Computer-Aided Design, 2003,35(8),709-726.
Rood S.B., Major D.J., Charnetski W.A. Seasonal changes in 14CO2 assimilation and 14C translocation in oilseed rape. Field Crops Research,1984,8:341-348.
Savitzky A, Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry,1964,36(8):1627-1639.
Serrano L., Filella I., Penuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science,2000,40:723-731.
Shi J.Y., Zou X.B., Zhao J.W., et al. Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Scientia Horticulturae,2012,138(1):190-197.
Spinelli F., Noferini M., Costa G. Near infrared spectroscopy (NIRS):Perspective of fire blight detection in asymptomatic plant material. Proceeding of 10th International Workshop on Fire Blight. Acta Horticulturae,2006,704:87-90.
Stone M.L., Soile J.B., Raun W.R., et al. Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of ASAE,1996,39: 1623-1631.
Suykens J.A.K., Van Gestel T., De Brabanter J., et al. Least Squares Support Vector Machines. World Scientific:Singapore,2002.
Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9:293-300.
Thenkabail, P.S., Smith, R.B., De Pauw, E. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics. Remote Sensing of Environment, 2000,71:158-182.
Thomas J.R., Oerther D.F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal,1972,64:11-13.
Thoren D., Schmidhalter U. Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. European Journal of Agronomy,2009,30: 238-242.
Tumbo S.D., Wagner D.G., Heinemann P.H. Hyperspectral-based neural network for predicting chlorophyll status in corn. Transactions of the ASAE,2002,45(3):825-832.
Vigneau N., Ecarnot M., Rabatel G., et al. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in Wheat. Field Crops Research, 2011,122:25-31.
Walburg G., Bauer M.E., Drughtry C.S.T., et al. Effects of nitrogen on the growth, yield, and reflectance characteristics of corn canopie. Agronomy Journal,1982,74:677-683.
Weber V.S., Arausb J.L., Cairnsc J.E., et al. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Research,2012,128,82-90.
Weiss E.A. Oilseed crops. New York:Longman Inc,1983.
Wouwer V. D., Scheunders P., VanDyck D. Statistical Texture Characterization from Discrete Wavelet Representation. IEEE Trans, on Image Processing,1999,8(4):592-598.
Wu D., Cao F., Feng S.J., et al. Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines. Spectroscopy and Spectral Analysis,2008a,28(5):1071-1075.
Wu D., Yang H.Q., Chen X.J., et al. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine. Journal of Food Engineering,2008b,88(4):474-483.
Yang C.M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11(1):61-81.
Ye X.J., Sakai K., Garciano L.O., et al. Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological modelling,2006,198: 426-432.
Zou X.B., Zhao J.W., Huang X.Y., et al. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of'Fuji'apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用.分析测试学报,2007,26(1)：66-69.
陈永明,林萍,何勇等.基于遗传算法的近红外光谱橄榄油产地鉴别方法研究.光谱学与光谱分析,2009,29(3)：671-674.
程一松,胡春胜,王成,等.养分胁迫下的夏玉米生理反应与光谱特征.资源科学,2001,23(6)：54-58.
褚小立.化学计量学方法与分子光谱分析技术.北京：化学工业出版社,2011.
丁小霞,李培武,李光明等.傅里叶变换近红外光谱技术测定完整油菜籽中芥酸和硫甙含量.中国油料作物学报,2004,26(3)：76-79.
杜培军,陈云浩,方涛等.高光谱遥感数据光谱特征的提取与应用.中国矿业大学学报,2003(5)：500-504.
渡边和彦.作物营养元素缺乏与过剩症的诊断与对策.罗小勇.日本：日本种苗株式会社,1999,58.
方慧,宋海燕,曹芳等.油菜叶片的光谱特征与叶绿素含量之间的关系研究.光谱学与光谱分析,2007,27(9)：1731-1734.
冯雷,方慧,周伟军等.基于多光谱视觉传感技术的油菜氮含量诊断方法研究.光谱学与光谱分析,2006,26(9),1749-1752.
冯新沪,史永刚.近红外光谱及其在石油产品分析中的应用.北京：中国石油化工出版社,2002.
何飞,张效信,陈波等.遗传算法反演地球等离子体层离子密度分布.地球物理学报,2012,55(1)：29-35.
何勇,冯雷.地球空间信息学基础.杭州：浙江大学出版社,2010.
何勇,赵春江.精细农业.杭州：浙江大学出版社,2010.
何勇.精细农业.杭州：浙江大学出版社,2003.
贺启川,蒙大庆,李芝凡等.近红外光谱仪快速检测油菜硫苷、芥酸及油份含量数学模
Allen E.J., Morgen D.G., Ridgman W.J. A physiological analysis of the growth of oilseed rape. The Journal of Agricultural Science,1971,77,339-341.
Aouidi F., Dupuy N., Artaud J., et al. Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses. Industrial Crops and Products,2012,37:292-297.
Araujo, M.C.U., Saldanha, T.C.B., Galvao, R.K.H., et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems,2001,57:65-73.
Borhan M.S., Panigrahi S., Lorenzen J. H., et al. Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment. Transactions of the ASAE,2004,47(2):599-608.
Bosoon P., Michio K., William R., et al. Textural analysis of hyperspectral images for improving contaminant detection accuracy. Sensing and Instrumentation for Food Quality and Safety,2008,2,208-214.
Brar C., Thies W. Contribution of leaves, stem, siliques and seeds to dry matter accumulation in ripening seeds of rapeseed, Brassica napus L. Z. Pflanzen physiol,1977,82,1-13.
Bullock D.G., Anderson D.S. Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition,1998,21:741-755.
Carlos C., Lianne M.D., Pierre D. Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes. Journal of plant nutrition,2001,24(8): 1173-1194.
Cen H., He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology,2007,18:72-83.
Cen Y.P., Bornman J.F. The response of bean plants to UV-B radiation under different irradiances of background visible light. Journal of Experimental Botany,1990,41(11), 1489-1495.
Chen J.Y., Iyo C., Terada F., et al. Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. Journal of Near Infrared Spectroscopy,2002,10:301-307.
Cheng T., Rivard B., Sanchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment,2011,115(12): 659-670.
Chong I.G., Jun C.H. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems,2005, 78(1-2):103-112.
Clarke J.M. The Effects of Leaf Removal on Yield and Yield Components of Brassica Napus. Canadian Journal of Plant Science,1978,58 (4),1103-1105.
Dhanoa M.S., Lister S.J., Sandersona R., et al. The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. Journal of Near Infrared Spectroscopy,1994,2:43-47.
Durand A., Devos O., Ruckebusch C., et al. Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles. Analytica Chimica Acta, 2007,595 (1-2):72-79.
ElMasry G., Wang N., ElSayed A., et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 2007,81,98-107.
Esbensen K.H. Multivariate Data Analysis in Practice. Oslo:CAMO Process As,2002.
Evans J.R., Seemann J.R. Difference between wheat genotypes in specific activity of ribulose-1,5-bisphosphate carboxylase and the relationship to photosynthesis. Plant physiology,1984,74:759-765.
Feret J.B., Francois C., Gitelson A., et al. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment,2011,115(10):2742-2750.
Filella I., Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing,1994,15: 1459-1470.
Flusser J. Moment invariants in image analysis. World Academy Science, Engineering and Technology,2005,11:376-381.
Freyman S., Charnetski W.A., Crookston R.K. Role of Leaves in the Formation of Seed in Rape. Canadian Journal of Plant Science,1973,53(3),693-694.
Gabrielle B., Denoroy P., Gosse G., et al. Development and evaluation of a CERES-type model for winter oilseed rape. Field Crops Research,1998,57,95-111.
Geladi P., Kowalski B.R. Partial least-squares regression:a tutorial. Analtica Chimica Acta 1986,185:1-17.
Gillon D., Dauriac F., Deshayes M. Estimation of foliage moisture content using near infrared reflectance spectroscopy. Agricultural and Forest Meteorology,2004,124: 51-62.
Goel P.K., Prasher S.O., Landry J.A., et al. Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture,2003,38:99-124.
Gomez N.V., Miralles D.J. Factors that modify early and late reproductive phases in oilseed rape (Brassica napus L.):Its impact on seed yield and oil content. Industrial Crops and Products,2011,34,1277-1285.
Gorry P.A. General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry,1990,62:570-573.
Gowen A.A., O'Donnell C.P., Cullen P.J., et al. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology,2007,18:590-598.
Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. Tritici) and take-all disease (Gaeumannomyces graminis sp. Tritici) in wheat (Triticum aestivum 1.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1(2):275-288.
Haralick R.M., Shanmugam K., Dinstein I.H. Textural Features for Image Classification. IEEE Transactions on Systems, Man, Cybernetics,1973,3(6):610-621.
He Y., Song H.Y., Pereira A.G., et al. A new approach to predict N, P, K and OM content in a loamy mixed soil by using near infrared reflectance spectroscopy. Lecture Notes in Computer Scinece,2005,3644:859-867.
Hedin L.O. Global organization of terrestrial plant-nutrient interactions. Proceedings of the National Academy of Sciences of the United States of America,2004,101(30): 10849-10850.
Helland I.S. Some theoretical aspects of partial least squares regression. Chemometrics and Intelligent Laboratory Systems,2001,58:97-107.
Hinzman L.D., Bauer M.E., Daughtry C.S.T. Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote Sensing of Environment,1986,19: 47-61.
Hogya P., Franzaring J., Schwadorf K., et al. Effects of free-air CO2 enrichment on energy traits and seed quality of oilseed rape. Agriculture, Ecosystems and Environment,2010, 139:239-244.
Holland J.H. Adaptation in natural and artificial systems. USA:A Bradford Book,1992.
Huang W., Lamb D.W., Niu Z., et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture,2007,8(4-5):187-197.
Inoue Y., Penuelas J. An AOTE-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. International Journal of Remote Sensing, 2001,22 (18):3883-3888.
Jago R.A., Cutler M.E.J., Curran P.J. Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment,1999,68(3):217-224.
Jia X.P., Richards J.A. Segmented Principal Components Transformation for Efficient Hyperspectral Remote-Sensing Image Display and Classification. IEEE Transactions on Geoscience and Remote Sensing,1999,37(1):538-542.
Jiang H., Wei W., Song X.D., et al. Hyperspectral studies of transgenic oilseed rape. International Journal of Remote Sensing,2011,32(4):1095-1103.
Ju C.H., Tian Y.C., Yao X., et al. Estimating leaf chlorophyll content using red edge parameters. Pedosphere,2010,20(5):633-644.
Kim Y, Glenn D.M., Park J., et al. Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture,2011,77(2):155-160.
Kobayashi T., Kanda E., Kitada K., et al. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 2001,91(3):316-323.
Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. Journal of Chemometrics,2000,14:643-655.
Leardi R., Boggia R., Terrile M. Genetic algorithms as a strategy for feature selection. Journal of Chemometrics,1992,6(5):267-281.
Leardi R., Gonzalez A.L. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometrics and Intelligent Laboratory Systems,1998, 41,195-207.
Lee K.S., Cohen W.B., Kennedy R.E., et al. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 2004,91(3-4):508-520.
Liu F., He Y., Wang L. Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy. Analytica Chimica Acta,2008b,610:196-204.
Liu F., He Y., Wang L., et al. Feasibility of the use of visible and near infrared spectroscopy to assess soluble solids content and pH of rice wines. Journal of Food Engineering, 2007,83(3):430-435.
Liu F., Jin Z.L., Naeem M.S., et al. Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves. Food and Bioprocess Technology,2011,4:1314-1321.
Liu F., Zhang F., Jin Z.L., et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. Analytica Chimica Acta,2008a,629:56-65.
Major D.J., Bole J.B., Charnetski W.A. Distribution of photosynthates after 14CO2 assimilation by stem, leaves, and pods of rape plants. Canadian Journal of Plant Science, 1978,58,783-787.
Michio S., Tsuyoshi A. Seasonal visible, near-infrared and mid-infrared spectra of rice spectra canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment,1989,27(2):119-127.
Min M., Lee W.S. Determination of significant wavelengths and prediction of nitrogen content for citrus. Transactions of the ASABE,2005,48:455-461.
Moron A., Cozzolino D. Measurement of phosphorus in soils by near infrared reflectance spectroscopy:Effect of reference method on calibration. Communications in soil science and plant analysis,2007,38(15-16):1965-1974.
Muller K., Bottcher U., Meyer-Schatz F., et al. Analysis of vegetation indices derived from hyperspectral reflection measurements for estimating crop canopy parameters of oilseed rape (Brassica napus L.). Biosystems Engineering,2008,101(2):172-182.
Osborne S.L., Schepers J.S., Francis D.D., et al. Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal,2002,94: 1215-1221.
Pacumbaba R.O., Beyl C.A. Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research,2011,48: 32-42.
Penuelas J., Filella I. Technical focus:visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science,1998,3(4),151-156.
Penuelas J., Pinol J., Ogaya R., et al. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997,18,2869-2875.
Petisco C., Garcia-Criado B., Vazquez-de-Aldana B.R., et al. Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy. Industrial Crops and Products,2010,32:139-146.
Pinar A., Curran P. J. Grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing,1996,17(2):351-357.
Pontius J., Hallett R., Martin M. Assessing hemlock decline using visible and near-infrared spectroscopy:Indices comparison and algorithm development. Applied Spectroscopy, 2005,59(6):836-843.
Renner G., Ekart A. Genetic algorithms in computer aided design. Computer-Aided Design, 2003,35(8),709-726.
Rood S.B., Major D.J., Charnetski W.A. Seasonal changes in 14CO2 assimilation and 14C translocation in oilseed rape. Field Crops Research,1984,8:341-348.
Savitzky A, Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry,1964,36(8):1627-1639.
Serrano L., Filella I., Penuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science,2000,40:723-731.
Shi J.Y., Zou X.B., Zhao J.W., et al. Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Scientia Horticulturae,2012,138(1):190-197.
Spinelli F., Noferini M., Costa G. Near infrared spectroscopy (NIRS):Perspective of fire blight detection in asymptomatic plant material. Proceeding of 10th International Workshop on Fire Blight. Acta Horticulturae,2006,704:87-90.
Stone M.L., Soile J.B., Raun W.R., et al. Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of ASAE,1996,39: 1623-1631.
Suykens J.A.K., Van Gestel T., De Brabanter J., et al. Least Squares Support Vector Machines. World Scientific:Singapore,2002.
Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9:293-300.
Thenkabail, P.S., Smith, R.B., De Pauw, E. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics. Remote Sensing of Environment, 2000,71:158-182.
Thomas J.R., Oerther D.F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agronomy Journal,1972,64:11-13.
Thoren D., Schmidhalter U. Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. European Journal of Agronomy,2009,30: 238-242.
Tumbo S.D., Wagner D.G., Heinemann P.H. Hyperspectral-based neural network for predicting chlorophyll status in corn. Transactions of the ASAE,2002,45(3):825-832.
Vigneau N., Ecarnot M., Rabatel G., et al. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in Wheat. Field Crops Research, 2011,122:25-31.
Walburg G., Bauer M.E., Drughtry C.S.T., et al. Effects of nitrogen on the growth, yield, and reflectance characteristics of corn canopie. Agronomy Journal,1982,74:677-683.
Weber V.S., Arausb J.L., Cairnsc J.E., et al. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Research,2012,128,82-90.
Weiss E.A. Oilseed crops. New York:Longman Inc,1983.
Wouwer V. D., Scheunders P., VanDyck D. Statistical Texture Characterization from Discrete Wavelet Representation. IEEE Trans, on Image Processing,1999,8(4):592-598.
Wu D., Cao F., Feng S.J., et al. Application of infrared spectroscopy technique to protein content fast measurement in milk powder based on support vector machines. Spectroscopy and Spectral Analysis,2008a,28(5):1071-1075.
Wu D., Yang H.Q., Chen X.J., et al. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine. Journal of Food Engineering,2008b,88(4):474-483.
Yang C.M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11(1):61-81.
Ye X.J., Sakai K., Garciano L.O., et al. Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological modelling,2006,198: 426-432.
Zou X.B., Zhao J.W., Huang X.Y., et al. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of'Fuji'apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems,2007,87(1):43-51.
陈斌,孟祥龙,王豪.连续投影算法在近红外光谱校正模型优化中的应用.分析测试学报,2007,26(1)：66-69.
陈永明,林萍,何勇等.基于遗传算法的近红外光谱橄榄油产地鉴别方法研究.光谱学与光谱分析,2009,29(3)：671-674.
程一松,胡春胜,王成,等.养分胁迫下的夏玉米生理反应与光谱特征.资源科学,2001,23(6)：54-58.
褚小立.化学计量学方法与分子光谱分析技术.北京：化学工业出版社,2011.
丁小霞,李培武,李光明等.傅里叶变换近红外光谱技术测定完整油菜籽中芥酸和硫甙含量.中国油料作物学报,2004,26(3)：76-79.
杜培军,陈云浩,方涛等.高光谱遥感数据光谱特征的提取与应用.中国矿业大学学报,2003(5)：500-504.
渡边和彦.作物营养元素缺乏与过剩症的诊断与对策.罗小勇.日本：日本种苗株式会社,1999,58.
方慧,宋海燕,曹芳等.油菜叶片的光谱特征与叶绿素含量之间的关系研究.光谱学与光谱分析,2007,27(9)：1731-1734.
冯雷,方慧,周伟军等.基于多光谱视觉传感技术的油菜氮含量诊断方法研究.光谱学与光谱分析,2006,26(9),1749-1752.
冯新沪,史永刚.近红外光谱及其在石油产品分析中的应用.北京：中国石油化工出版社,2002.
何飞,张效信,陈波等.遗传算法反演地球等离子体层离子密度分布.地球物理学报,2012,55(1)：29-35.
何勇,冯雷.地球空间信息学基础.杭州：浙江大学出版社,2010.
何勇,赵春江.精细农业.杭州：浙江大学出版社,2010.
何勇.精细农业.杭州：浙江大学出版社,2003.
贺启川,蒙大庆,李芝凡等.近红外光谱仪快速检测油菜硫苷、芥酸及油份含量数学模