[Determination of Wine Varieties with NIR and Fusion of Multiple Classifiers].

The conventional qualitative analysis of near infrared spectroscopy (NIR) commonly uses one single classification model. This paper focused on the fusion of multiple classifiers based on different single classifiers by using the fused classifier to determine different varieties of red-wines. NIR spectra of 170 red-wine samples were collected by using Fourier transform near-infrared spectrometer.

[Study on the Determination of the Maturity Level of Tobacco Leaf Based on In-Situ Spectral Measurement].

Discriminating the maturity levels of tobacco leaf with in-situ measurement can effectively reduce loss rate and quality decline due to misjudgment of the maturity levels of tobacco leaf. In the meantime, the regular way we use to determine the maturity levels of tobacco, which is depend on tobacco leaf age and judgment of tobacco grower, lacks of objectivity. So this paper proposed a method to identify maturity levels of tobacco leaf by using spectral feature parameters combined with the method of support vector machine (SVM).

[The Study of the Spectral Model for Estimating Pigment Contents of Tobacco Leaves in Field].

Fast and non-destructive measurements of tobacco leaf pigment contents by spectroscopy in situ in the field has great significance in production guidance for nutrient diagnosis and growth monitoring of tobacco in vegetative growth stage, and it is also very important for the quality evaluation of tobacco leaves in mature stage. The purpose of this study is to estimate the chlorophyll and carotenoid contents of tobacco leaves using tobacco leaf spectrum collected in the field. Reflectance spectrum of tobacco leaves in vegetative growth stage and mature stage were collected in situ in the field and the pigment contents of tobacco leaf samples were measured in this study, taking the tobacco leaf samples collected in each and both stages as modeling sets respectively, and using the methods of support vector machine (SVM) and spectral indice to establish the pigment content estimation models, and then compare the prediction performance of the models built by different methods.

[Applying local neural network and visible/near-infrared spectroscopy to estimating available nitrogen, phosphorus and potassium in soil].

To establish the quantitative relationship between soil spectrum and the concentration of available nitrogen, phosphorus and potassium in soil, the critical procedures of a new analysis method were examined, involving spectral preprocessing, wavebands selection and adoption of regression methods. As a result, a soil spectral analysis model was built using VIS/NIRS bands, with multiplicative scatter correction and first-derivative for spectral preprocessing, and local nonlinear regression method (Local regression method of BP neural network). The coefficients of correlation between the chemically determined and the modeled available nitrogen, phosphorus and potassium for predicted samples were 0.

[Inversion of winter wheat foliage vertical distribution based on canopy reflected spectrum by partial least squares regression method].

With the widespread application of remote sensing (RS) in agriculture, monitoring and prediction of crop nutrition condition attracts attention of many scientists. Foliar nitrogen content (N) is one of the most important nutrients for plant growth, and vertical leaf N gradient is an important indicator of crop nutrition situation. Investigations have been made on N vertical distribution to describe the growth status of winter wheat.