Estimating Total Nitrogen Content in Brown Soil of Orchard Based on Hyperspectrum

The best hyperspectral estimation model of soil total nitrogen (TN) was established, which provided the basis for rapid and accurate estimation of soil total nitrogen content, scientific and rational fertilization and soil informatization management. A total of 92 brown soil samples were collected from the orchard of Qixia County, Yantai City, Shandong Province. After drying and grinding, the hyperspectrum of the soil was measured in the laboratory using ASD FieldSpec3. The TN contents of brown soil were measured by Kjeldahl method. The sensitive wavelengths were selected by multiple linear stepwise regression method. The hyperspectral estimation model of TN was established by Random Forest (RF) and Support Vector Machines (SVM). The models were validated by independent samples. The best estimation model was obtained. The sensitive wavelengths were 956 nm, 995 nm, 1020 nm, 1410 nm, 1659 nm and 2020 nm. The coefficients of determination (R2) of the two estimation models were 0.8011 and 0.8283, the root mean square errors (RMSE) were 0.022 and 0.025, and relative errors (RE) were 0.1422 and 0.1639, respectively. Random Forest model and Support Vector Machines model are feasible in estimating TN contents, but the Support Vector Machines model is better.

Inversion of Canopy Nitrogen Content in Apple Orchard Based on GF-1 Satellite Image

The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.

Nitrogen Estimation Model of Apple Leaves Based on Imaging Spectroscopy

Imaging spectrometer was used to measure the spectral data of apple leaves.The spectral reflectance of apple leaves was extracted.The nitrogen content of apple leaves was correlated with the spectral reflectance after SG smoothing first-order differential treatment.The sensitive wavelengths were selected and nitrogen content prediction models were founded.The results showed that the spectral of apple leaves with different concentration gradients were obvious.The higher nitrogen content was,the lower spectral reflectance was.Established estimation models by using the selected SG smooth first-order differential spectral sensitive wavelengths SG-FDR403,SG-FDR469,SG-FDR525,SG-FDR566,SG-FDR650,SG-FDR696,SG-FDR781,SG-FDR851,SG-FDR933.The determined coefficient(R^2)of the partial least squares model was 0.5202.The root mean square error(RMSE)of that was 2.19 and the relative error(RE)of that was 5.89%.The R^2 of the support vector machine(SVM)model was 0.724.The RMSE of that was 1.94,and the RE of that was 5.13%.It is indicated that the SVM model can estimate the nitrogen content of apple leaves effectively.

Prediction Model of Nitrogen Content in Apple Leaves based on Ground Imaging Spectroscopy

A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves.SOC710VP hyperspectral imager was used to obtain the imaging spectral information of apple leaves,and the average spectral curve of interest region was extracted.The study is to analyze the characteristics of imaging spectral curves of apple leaves with different nitrogen content.On the basis of the SG smoothing and first derivative pretreatment of the spectral curve,the maximum sensitive band with nitrogen content is screened and the spectral parameters are constructed.Three modeling methods of BP,SVM and RF were used to establish the prediction model of nitrogen content in apple leaves.The results showed that in the visible range,the nitrogen content of apple leaves was negatively correlated with the reflectance of the spectral curve,and was most obvious in the green range.The R2 of BP,SVM and RF of apple leaf nitrogen content prediction model were 0.7283,0.8128,0.9086,RMSE were 0.9359,0.7365,0.5368,the R2 of test model were 0.6260,0.7294,0.6512,RMSE were 0.9460,0.7350,0.9024.Comparing the prediction results of the three models,the optimal prediction model is SVM model,which can well predict the nitrogen content of apple leaves.

跨文化管理:顶级管理学期刊前沿趋势的调查研究（英文）

本文对2010年到2014年发表在10本国际顶级管理学期刊的2 800篇高水平学术论文进行了编码分析,从中筛选了274篇与跨文化管理相关的文章做了进一步研究。我们从文章主题内容、研究层级、研究方法、样本来源等方面进行了统计,系统地分析了跨文化管理在主流期刊上的研究现状。