【作者】 张筱蕾；

【导师】 何勇；

【作者基本信息】 浙江大学 ， 生物系统工程， 2013， 博士

【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。更多还原

【Abstract】 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.更多还原