[Measurement of Soil Total Nitrogen Using Near Infrared Spectroscopy Combined with RCA and SPA].

Visible near spectra tecnnology was adopted to detect soil total nitrogen content. 394 soil samples were collected from Wencheng, Zhejiang province to be used for calibration model (n=263) and independent prediction set (n=131). Raw spectra and wavelength-reduced spectra with five different pretreatment methods (SG smoothing, SNV, MSC, 1st-D and 2nd-D) were compared to determine the optimal wavelength range and pretreatment method for analysis. The results with 5 different pretreatment methods were not improved compared to that both of full spectra PLS model and wavelength reduction spectra model. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the wavelength variables selected by two different variable selection methods (i. e. regression coefficient analysis (RCA) and successive projections algorithm (SPA) were proposed to be the inputs of calibration methods of PLS, MLR and LS-SVM models separately. These calibration models were also compared to select the best model to predict soil TN. In total, 9 different models were built ahd the best results indicated that PLS, MLR and LS-SVM obtained the highest precision with determination coefficient of prediction R2(pre) =0. 81, RMSEP=0. 0031 and RPD=2. 26 based on wavelength variables selected by RCA (0. 0002) and SPA as inputs of models. SPA-MLR model and other three models based on 7 sensitive variables selected by RC using 0. 0002 regression coefficient threshold value obtained the best result with R2(pre), RMSEP and RPD as 0. 81, 0. 0031 and 2. 26. This prediction accuracy is classied to be very good. For all the models, it could be concluded that RCA and SPA could be very useful ways to selected sensitive wavelengths, and the selected wavelengths were effective to estimate soil TN. It is recommended to adopt SPA variable selection or RCA variable selection method with both linear and nonlinear calibration models for measurement of the soil TN using Vis-NIR spectroscopy technology, and wavelengths selection could be very useful to reduce collinearity and redundancies of spectra.

[Review of Crop Canopy Spectral Information Detection Technology and Methods].

Compared with the traditional chemical methods and the subjective visual ways for measuring plant physiology information indicators, the assessments of crop canopy information through spectral radiometer are more simple, rapid and accurate. The applications of different types of spectral radiometer, especially for international general used Cropscan multispectral radiometer, for predicting crop canopy leaf area index under different growth stage, biomass, nitrogen, chlorophyll and yield, and monitoring plant diseases and insect pests were summarized based on crop group information acquisition methods in recent years. The varity of vegetation indices (VIs) were concluded after comparing regression coefficients of related models among different crops. In general, the correlation coefficients of mathematical models were high and it can realize the crop detection of various kinds of physiological information. Besides, the combination of multispectral radiometer and other sensors can provide useful information to evaluate the status of crops growth, which is very important in practice.

[Research on the sugar content measurement of grape and berries by using Vis/NIR spectroscopy technique].

Aiming at the nonlinear correlation characteristic of Vis/NIR spectra and the corresponding sugar content of grape and berries, the Vis/NIR spectra of grape and berries were obtained by diffusion reflectance. A mixed algorithm was presented to predict sugar content of grape and berries. The original spectral data were processed using partial least squares (PLS), and three best principal factors were selected based on the reliabilities. The scores of these 3 principal factors would be taken as the input of the three-layer back-propagation artificial neural network (BP-ANN). Trained with the samples in calibration collection, the BP-ANN predicted the samples in prediction collection. The values of decision coefficient (r2), the root mean squared error of prediction (RMSEP), and bias were used to estimate the mixed model. The observed results using PLS-ANN (r2 = 0.908, RMSEP = 0.112 and Bias = 0.013) were better than those obtained by PLS (r2 = 0.863, RMSEP = 0.171, Bias = 0.024). The result indicted that the detection of internal quality of grape and berries such as sugar content by nondestructive determination method was very feasible and laid a solid foundation for setting up the sugar content forecasting model for grape and berries.