Cultivar,Nitrogen and Irrigation Influence on Grain Quality and Its Forecasting Methods by In situ Reflected Spectrum of Winter Wheat

Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, etc. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significantly correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established to forecast grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.

Study on the echinococcosis blood serum detection based on Raman spectroscopy combined with neural network

A Raman spectroscopy method combined with neural network is used for the invasive and rapid detection of echinococcosis. The Raman spectroscopy measurements are performed on two groups of blood serum samples,which are from 28 echinococcosis patients and 38 healthy persons,respectively. The normalized Raman reflection spectra show that the reflectivity of the echinococcosis blood serum is higher than that of the normal human blood serum in the wavelength ranges of 101—175 nm and 1 801—2 701 nm. Then the principal component analysis(PCA) and back propagation neural network(BPNN) model are used to obtain the diagnosis results. The diagnosis rates for healthy persons and echinococcosis persons are 93.333 3% and 90.909 1%,respectively,so the average final diagnosis rate is 92.121 2%. The results demonstrate that the Raman spectroscopy analysis of blood serum combined with PCA-BPNN has considerable potential for the non-invasive and rapid detection of echinococcosis.

Influence of color coatings on aircraft surface ice detection based on multi-wavelength imaging

In this paper, a simple aircraft surface ice detection system is proposed based on multi-wavelength imaging. Its feasibility is proved by the experimental results. The influence of color coatings of aircraft surface is investigated. The results show that the ice area can be clearly distinguished from the red, white, gray and blue coatings painted aluminum plates. Due to the strong absorption, not enough signals can be detected for the black coatings. Thus, a deep research is needed. Even though, the results of this paper are helpful to the development of aircraft surface ice detection.