摘要
目前,我国设施园艺面积已突破330万公顷,居世界第一位。经常出现氮、磷、钾等主要营养元素比例失调和胁迫症状,直接影响产量和品质。因此,迫切需要在作物生长过程中对养分进行精确监测和诊断。目前国内外营养检测仅利用了光波的强度,即反射率或反射强度,采用反射光谱技术或图像技术等单一方法诊断作物的营养状况。为了克服单一技术手段无法全面获取作物营养元素亏缺引起的内部组分改变和外部形态特征变化的不足,本研究以番茄为研究对象,引入光波的偏振信息,提出基于偏振-高光谱的多维光信息番茄营养胁迫的诊断方法。
本论文的主要研究内容如下：
(1)开发了基于偏振-高光谱多维光信息的作物营养综合检测实验平台。为了对设施番茄的偏振特征进行精确提取和研究,设计开发了一种新型的偏振光谱采集分析系统,实现对不同入射天顶角的偏振光激励下的作物叶片在不同探测天顶角的偏振响应进行综合检测分析。结合高光谱图像系统构建了偏振-高光谱作物营养多维光信息综合检测实验平台。在温室大棚中以无土栽培方式培育了N、P、K营养胁迫及对照组番茄植株,分别采用凯氏定氮法、分光光度计法、火焰光度法分析了各个生长期营养元素含量分布规律。结果表明样本培育达到了预期的效果,营养元素胁迫植株的成功培育为后续研究奠定了坚实的基础。
(2)研究了基于高光谱的图像特征的氮磷钾营养胁迫模型,试验将获取的番茄叶片高光谱图像进行图像分割、滤波等预处理,经过主成分变换后有效降维,并通过前5个主成分与N、P、K之间的各波长点的权重系数曲线分析,分别找出4个敏感波长,其中三个共有波长为566.29nm,693.71nm,733.71nm;以及N、P、K特有的敏感波长依次是：464.91nm,474.85nm,762.24nm。接着将在敏感波长下采用基于灰度共生矩阵的二阶概率统计滤波提取纹理特征,通过相关性分析得出与N、P、K相关性均较高的图像特征为:VAR693.71、CON566.29、DIS693.71、ENT733.71、 ASM566.29、COR733.71; N、P、K特有的图像特征依次为：氮ASM464.91、COR464.91;磷HOM693.71、ENT474.85;钾HOM762.24、ENT762.24。在建模过程中有比较地运用了MLR.PCR以及PLS三种建模方法。从建模结果看,N元素的PCR模型性能最优,Rc=0.9630,RMSECV=0.3846%,Rp=0.9205,RMSEP=0.4486%.P元素的PLS模型最优,Rc=0.8864,RMSECV=0.5704%,Rp=0.8713,RMSEP=0.5420%.而K元素的MLR模型最优,校正集Rc=0.9109,RMSECV=0.4163%,Rp=0.8547, RMSEP=0.5047%.
(3)研究了基于反射光谱特征的氮磷钾营养胁迫模型,试验将从番茄叶片高光谱图像中获取的的反射光谱进行SNV、MSC等光谱预处理后,分别采用iPLS、 SiPLS、BiPLS和iPLS-GA特征波段筛选方法优选N、P、K元素含量对应的敏感波长并建立基于反射光谱特征的模型,其中N元素的iPLS-GA模型效果最佳,Rc=0.9156,RMSECV=0.595%,Rp=0.9048,RMSEP=0.632%.P元素的SiPLS模型最佳,Rc=0.8765,RMSECV=0.592%,Rp=0.8740,RMSEP=0.512%；K元素的SiPLS模型最佳,Rc=0.9116,RMSECV=0.598%,Rp=0.9075,RMSEP=0.835％.
(4)研究了基于偏振反射特征分析的偏振度番茄营养检测模型。针对特定研究对象,通过正交试验并结合分析各因素与偏振反射比的关系确定了优化组合角度由主到次分别为：入射天顶角60°,偏振片旋转角度45°,探测天顶角45°,方位角180°。对比了在优化组合角度下不同胁迫程度和不同生长期对番茄叶片偏振反正比的影响和差异。为了量化这种差异,利用Stocks公式提取了不同N、P、K水平番茄叶片样本的偏振度特征,采用相关分析法筛选基于偏振度特征与番茄叶片参考值含量有显著关系的敏感波长,提取出N、P、K共有的敏感波长有为655.408nm,744.482nm,850.578nm,而N、P、K特有的敏感波长依次分别为380.487nm,914.562nm,556.664nm。以敏感波长处的偏振度为特征建立了MLR、PCR、PLS模型。N元素的PLS模型最佳,Rp=0.9145,RMSEP=0.7299%;P元素的PLS模型的Rp=0.7846,RMSEP=1.1021%。K的MLR模型最佳,Rp=0.9009,RMSEP=0.7982%,为番茄养分含量情况的快速检测提供了新的思路。
(5)首次研究了基于偏振-高光谱多维光信息的设施番茄营养含量检测模型。在对图像、光谱和偏振度特征进行准确提取和充分研究的基础上,通过线性和非线性融合方法对番茄叶片的光谱、图像、偏振度特征进行特征层融合。线性融合模型均采用MLR和PLS两种方法,只有N元素含量的线性融合模型精度高于单一类型特征变量的模型。利用BP-ANN以及SVR方法建立非线性融合模型,从建模的结果看,N元素的BP-ANN方法的模型效果最佳,主成分因子数等于6时,Rp=0.9400,RMSEP=0.1995%。P元素的SVR-PSO模型最佳Rp=0.8998,RMSEP=0.1912%。K元素的SVR-GS模型最优,Rp=0.9101,RMSEP=0.1417。研究结果表明基于偏振-高光谱多信息融合的方法评判番茄营养胁迫状况的方法是可行的,融合模型的精度和稳定性较单一来源模型明显提高。
(6)首次研究了基于偏振-高光谱多维光信息的N、P、K交互作用下设施番茄营养含量诊断模型。通过尝试权重系数矩阵和交互影响系数矩阵的求解对一般线性融合方程进行修正,建立了在考虑氮、磷、钾三者交互作用下的番茄营养含量检测模型。以开花期和结果中期的数据为例计算了N、P、K的预测值,开花期N元素模型的Rp=0.9585,RMSEP=0.2609%,;P元素的Rp=0.9201,RMSEP=0.1739%；K元素的Rp=0.9194,RMSEP=0.2263%。结果中期N元素的Rp=0.9461,RMSEP=0.2452%;P元素的Rp=0.9183,RMSEP=0.2616%;K元素的Rp=0.9144,RMSEP=0.2436%。预测结果表明该方法获得了精度更高的检测模型,尤其是P元素提升幅度相对较大,证明三种元素之间存在着交互影响作用,通过对氮、磷、钾交互作用的综合解耦可以进一步提高多信息融合模型的预测精度,为交互作用下作物营养快速无损检测建模提供新的思路。
Nowadays, China has the world's largest area of facility agriculture, with a total of3.3million ha. The yield and economic profits are serious affected because of nutritional stress and imbalance, such as nitrogen(N), phosphorus(P) and potassium (K) stress. Accurate monitoring and diagnosis of nutrient state in facility crops during the growth process has very important significance. Currently nutrition examinations at home and abroad are using only the strength of lightwave, usually focus on reflectance or reflection intensity. Reflectance spectroscopy technology or imaging technology is widely used in crop nutrition status diagnosis. In order to overcome the inadequacies of models build with single feature sauce, which cannot fully obtain the internal components and external morphological changes caused by crop nutrients stress, polarization information of light is add into this study using tomato as the object. A new method of polarized spectra-hyperspectral is proposed on nutrition stress of facility crops.
The main contents of this paper are as follows:
(1) Polarized spectra-hyperspectral multidimensional information detecting platform for crop nutrition comprehensive testing was developed. In order to accurate extract and research the polarized features of facility crops, a new type of polarized spectra acquisition and analysis system was designed, which can comprehensive detect crop leaf and obtain polarized response on different incident zenith angle and different detection zenith angle. Combined polarized spectra acquisition system with hyperspectral imaging system, polarized spctrum-hyperspectral detecting platform was constructed to realize multidimensional information detection of crop nutrition. N, P, K nutrient stress and control groups tomato plants were cultivated respectively via soilless cultivation method in greenhouse. N, P, K element contents were tested adopting Kjeldahl method, spectrophotometry, flame photometric analysis respectively to analyse nutrition distribution in the different growing stage. The results showed that the sample cultivation reached the desired effect. Successful cultivation of nutrition stress samples plants had laid a solid foundation for subsequent research.
(2) Tomato N, P, K nutrient stress detection models based on hyperspectral image features were studied. Hyperspectral image of tomato leaves were obtain on experiment followed by image segmentation, filtering preprocessing, and principal component transform which effectively reduce dimensionality. Then weighting coefficient curves were drawn after analysis the correlation between the first five principal components and N, P, K nutrition contents respectively. Four sensitive wavelengths were identified, of which there were common wavelength566.29nm,693.71nm,733.71nm; and N, P, K specific sensitive wavelengths corresponding sequence is:464.91nm,474.85nm,762.24nm. Adopting the method of second-order probabilistic statistical filtering based on GLCM to extract texture feature under sensitive wavelengths. Though correlation analysis to find that the following image features showed high correlation with N, P, K, that were VAR.693.71,CON566.29,DIS693.71,ENT733.71,ASM566.29,COR733.71; as well as unique high correlation image features of N, P, K, that were:nitrogen ASM464.91,COR464.91; phosphorus HOM693.71,ENT474.85; potassium HOM762.24.ENT762.24.In the process of establishing a quantitative model, three modeling methods MLR, PCR and PLS were comparatively used. The results showing that PCR model of N elements was best, RMSECV=0.3846%, Re=0.9630, Rp=0.9205, RMSEP=0.4486%, PLS model of P element get optimal performance, RMSECV=0.5704%, Re=0.8864, Rp=0.8713, RMSEP=0.5420%. MLR model of K elements performance best, Re=0.9109, RMSECV=0.4163%, RMSEP=0.5047%, Rp=0.8547.
(3) Tomato N, P, K nutrient stress detection models based on reflectance spectrum features were studied. Reflectance spectra were collected from hyperspectral of tomato leaves followed by SNV, MSC preprocessing, etc., and feature band screening methods such as iPLS, SiPLS, BiPLS and iPLS-GA were using to select sensitive wavelength of N, P, K respectively to build detection models. iPLS-GA model of N elements is best, Re=0.9156, RMSECV=0.595%, Rp=0.9048, RMSEP=0.632%. SiPLS model of P elements is best, Re=0.8765, RMSECV=0.592%, Rp prediction set=0.8740, RMSEP =0.512%; SiPLS model of K elements is best, Rc=0.9116, RMSECV=0.598%, Rp=0.9075, RMSEP=0.835%.
(4) Tomato N, P, K nutrient stress detection models based on degree of polarization features were studied after polarized reflectance characteristics analysis. For a specific object of study, identify the optimal combination of detection angle:incident zenith angle60°, polarizer rotation angle45°, Detect polarize zenith45°, azimuth180°. Difference between different nutrient stress levels and tomato growing stage was compared after optimal combination of detection angle. To quantify the difference, degree of polarization features of different N, P, K nutrient levels of tomato leaf samples were calculated by Stocks formula, the sensitive wavelengths were extracted after analysis the correlation between degree of polarization features and the reference measurement of N, P, K respectively. Though correlation analysis to find that the following sensitive wavelength showed high correlation with N, P, K degree of polarization features, that were655.408nm,744.482nm,850.578nm, and N, P, K specific sensitive wavelengths that N, P, K were380.487nm,914.562nm,556.664nm successively. Degree of polarization sensitive wavelength characteristics as independent variables, the value of the chemical reference measurement as dependent variable to established MLR, PCR and PLS models. PLS model of N elements is best, Rp=0.9145, RMSEP=0.7299%; PLS model of P element is best, Rp=0.7846, RMSEP=1.1021%. MLR model of K is best, Rp=0.9009, RMSEP=0.7982%. This study provides a new idea for the rapid detection of tomato nutrient content.
(5) Tomato N, P, K nutrient stress detection models based on polarized spectra-hyperspectral multidimensional information were first studied. The image features, spectral features and the degree of polarization features were full and accurate extracted as well as feature level fusion based on linear and nonlinear methods after normalized of the three sources of data information. Only the accuracy of N elements is higher than each single source feature model among the linear fusion models of N, P, K with MLR and PLS methods. Then BP-ANN and SVR methods were using to establish the nonlinear fusion models. The results showing that, the effect of BP-ANN model of N elements is best when principal component factor equal to6, Rp=0.9400, RMSEP=0.1995%. SVR-PSO Model of P element is best, Rp=0.8998, RMSEP=0.1912%; the effect of SVR-GS Model of K element get optimal accuracy, Rp=0.9101, RMSEP=0.1417. The results show that the polarized spectrum-hyperspectral multidimensional information detecting method to judge the tomato nutrient stress conditions is feasible.
(6) Tomato N, P, K nutrient stress detection models considering interaction effect of the three elements based on polarized spectra-hyperspectral multidimensional information were first studied. The general linear fusion equation were modified via calculation of weight coefficient matrix and interaction effect coefficient matrix. Then tomato N, P, K nutrient stress detection models were established which considering interaction effect of the three elements. Take flowering and mid-fruiting stage data for example to calculate the predicted values of N, P and K nutrition content. On flowering stage, the results of N model were Rp=0.9585, RMSEP=0.2609%; the results of P model were Rp=0.9201, RMSEP=0.1739%; the results of K model were Rp=0.9194, RMSEP=0.2263%. On mid-fruiting stage, the results of N model were Rp=0.9461, RMSEP=0.2452%; the results of P model were Rp=0.9183, RMSEP=0.2616%; the results of K model were Rp=0.9144, RMSEP=0.2436%. The results showed that this method obtain higher accuracy models, especially the accuracy of P element was enhanced relatively large, which proved the existence of the interaction effect between the three elements. Decoupling of interaction effect of N, P and K can further improve prediction accuracy of the multi-information fusion models, and provides a new idea for the rapid detection of crop nutrient content in the case of considering interaction effect of nutrients.
引文
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