[Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology].

The aim was to find a nondestructive way to improve the accuracy of detecting the winter wheat aboveground fresh biomass(AGFB). In this study, data fusion technology of the spectroscopy technology and the machine vision technology were used to analyze the AGFB and solve the problem that the accuracy of the prediction model of a single technology is not high. In this experiment, canopy spectra and canopy pictures of 93 samples at seeding stage were collected. Canopy spectra and side images of 200 samples at medium and later growth stage were collected. Spectral reflectance as the spectral absorption parameter was used to construct the AGFB prediction models based on the spectra technology at different stages; The wheat coverage were extracted from canopy pictures and side images by using image processing technology to build the AGFB prediction models. Multivariate regression analysis (MRA) and Partial least-squares regression analysis(PLS) were implemented on the feature variables from the spectral information and image information. The results showed that, compared with the individual image model and spectral model, the AGFB prediction models of PLS based on multi-information at different stages shows better performance. At the seeding stage, the determination coefficient (R2) of PLS models based on multi-information was 0.881，and the RMSE was 0.015 kg. The R2 of PLS models based on multi-information was 0.791, the RMSE was 0.059 kg at middle and final stages. It demonstrated that the precision of model based on multi-information fusion technology, which increased utilization of image and spectral information, was improved for AGFB detecting, which is than the individual image model and spectral model.

[The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion].

Hyperspectral imaging technology has great potential in the identification of crop varieties because it contains both image information and spectral information for the object. But so far most studies only used the spectral information, the image information has not been effectively utilized. In this study, hyperspectral images of single seed of three types including strong gluten wheat, medium gluten wheat, and weak gluten wheat were collected by near infrared hyperspectra imager, 12 morphological characteristics such as length, width, rectangularity, circularity and eccentricity were extracted, the average spectra of endosperm and embryo were acquired by the mask which was created by image segmentation. Partial least squares discriminant analysis (PLADA) and least squares support vector machine (LSSVM) were used to construct the classification model with image information, results showed that the binary classification accuracy between strong gluten wheat and weak gluten wheat could achieve 98%, for strong gluten wheat and medium gluten wheat, it was only 74.22%, which indicated that hyperspectral images could reflect the differences of varieties, but the accuracy might be poor when recognizing the varieties just by image information. Soft independent modeling of class analogy (SIMCA), PLSDA and LSSVM were used to established the classification model with spectral information, the classification effect of endosperm is slightly better than the embryo, it demonstrated that the grain shape could influence the classification accuracy. Then, we fused the spectral and image information, SIMCA, PLSDA and LSSVM were used to established the identification model, the fusion model showed better performance than the individual image model and spectral model, the classification accuracy which used the PLSDA raise from 96.67% to 98.89%, it showed that digging the morphological and spectral characteristics of the hyperspectral image could effectively improve the classification effect.

[An integrated on-line processing method for spectrometric data based on wavelet transform and Gaussian fitting].

Miniature mobile field spectrometry is pivotal equipment for qualitative and quantitative in-situ analysis of chemical substances. To solve the problem of spectrum signal interfered by complicated noise, overlapped and irregular peak shape recognition, and quick monitoring, an integrated on-line processing method for spectrometric data based on wavelet transform and Gaussian fitting was developed. In this way, toluene and perfluorotributylamine were processed, and the results shows that the integrated method can powerfully and effectively eliminate the noise, retain the original feature, and correct the overlapped and asymmetrical peaks, which can improve the analysis accuracy of instrument, and also achieve data compression. In addition, the method satisfies the requirement of on-site analysis for mobile field spectrometry. For the processing of mass spectra of toluene, at the characteristic peaks of 91 and 92, the SNR increased 1.3 times compared to that of moving average smoothing method, while the error between original peaks and theoretic peaks decreased 3.6 times. In addition, Gaussian fitting described the multipoint mass spectra data by three Gaussian parameters, and achieved data compression. For the processing of mass spectrogram of perfluorotributylamine, the ratio of compression was 197 : 1.

[Advance of the imaging spectral technique in diagnosis of the information of crop].

Labor intensive, time consuming, high technical requirements in operation and much affected by human factors is the limitation of diagnosing the crop information with conventional method, which could not make diagnosis real-time and rapid. Imaging spectral technique could simultaneously obtain the image and spectral information of crops. It could diagnose the growth and insects information of crop rapidly and non-destructively. In recent years, imaging spectroscopy has been widely used in diagnosis of the information of crop, so it provides technical support for agricultural informatization. In the present study, the principle of imaging spectroscopy was presented. The application progress of imaging spectroscopy technique in crop detection was investigated, including seed component detection, seed variety discrimination, seed disease and insect pest detection, field crop growth monitoring and field crop disease and insects monitoring. Then the paper analyzed difficulty of imaging spectroscopy for crop measurement, and the prospect of this technique was also discussed.

[A field-based pushbroom imaging spectrometer for estimating chlorophyll content of maize].

As an image-spectrum merging technology, the field-hperspectral imaging technology is a need for dynamic monitoring and real-time management of crop growth information acquiring at field scale in modern digital agriculture, and it is also an effective approach to promoting the development of quantitative remote sensing on agriculture. In the present study, the hyperspectral images of maize in potted trial and in field were acquired by a self-development push broom imaging spectrometer (PIS). The reflectance spectra of maize leaves in different layers were accurately extracted and then used to calculate the spectral vegetation indices, such as TCARI, OSAVI, CARI and NDVI. The spectral vegetation indices were used to construct the prediction model for measuring chlorophyll content. The results showed that the prediction model constructed by spectral index of MCARI/OSAVI had high accuracy. The coefficient of determination for the validation samples was R2 = 0.887, and RMSE was 1.8. The study indicated that PIS had extensive application potentiality on detecting spectral information of crop components in the micro-scale.

[FTIR microspectroscopy and its progress in application].

FTIR microspectroscopy technique was born in the mid-nineties. The research on this technique has just began abroad, and this technology has not yet been widely recognized in China. It is a rapid, nondestructive testing technology, has the advantages of microdomain, visualization, high precision and high sensitivity. In the present study, the composition, operational principle and working mode of FTIR microspectroscopy were summarized. The progress in application of FTIR microspectroscopy technique was investigated in some fields, including biomedicine, microbiology, forensic science, materials science, nutrition and feed science and agricultural products. The difficulty of FTIR microspectroscopy research and the prospects of this technique were also discussed.

[Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.

[Using extraction of red edge position to validate consistency of hyperspectral imaging and non-imaging data].

Using Pushbroom imaging spectrometer (PIS) and FieldSpec ProFR2500 (ASD), spectral reflectances of winter wheat and maize at different stages were collected synchronously. In order to validate the reliability of imaging spectral data, the red edge position of hyperspectral data for PIS and ASD were extracted by different algorithms, respectively. The following results were obtained: (1) The original spectrum of both instruments had high inosculation in red light region (670-740 nm); (2) With the spectra collected under laboratory condition (maize leaf), the extracted red edge position was is concentrated between 700 and 720 nm for the two instruments; (3) With the spectra collected undre field condition (wheat leaf), the extracted red edge position for PIS and ASD were different, the red edge position of PIS data was in 760 nm, while it was in 720 nm for ASD data. The main reason might be that the imaging spectral data were influenced by oxygen absorbtion; (4) the red edge rangeability of PIS and ASD were different, but the trends were the same. The above results could provide some references for hyperspectral imaging data's extensive application.

[The NIR spectra based variety discrimination for single soybean seed].

With the development of soybean producing and processing, the quality breeding becomes more and more important for soybean breeders. Traditional sampling detection methods for soybean quality need to destroy the seed, and does not satisfy the requirement of earlier generation materials sieving for breeding. Near infrared (NIR) spectroscopy has been widely used for soybean quality detection. However, all these applications were referred to mass samples, and they were not suitable for little or single seed detection in breeding procedure. In the present study, the acousto--optic tunable filter (AOTF) NIR spectroscopy was used to measure the single soybean seed. Two varieties of soybean were measured, which contained 60 KENJIANDOU43 seeds and 60 ZHONGHUANG13 seeds. The results showed that NIR spectra combined with soft independent modeling of class analogy (SIMCA) could accurately discriminate the soybean varieties. The classification accuracy for KENJIANDOU43 seeds and ZHONGHUANG13 was 100%. The spectra of single soybean seed were measured at different positions, and it showed that the seed shape has significant influence on the measurement of spectra, therefore, the key point for single seed measurement was how to accurately acquire the spectra and keep their representativeness. The spectra for soybeans with glossy surface had high repeatability, while the spectra of seeds with external defects had significant difference for several measurements. For the fast sieving of earlier generation materials in breeding, one could firstly eliminate the seeds with external defects, then apply NIR spectra for internal quality detection, and in this way the influence of seed shape and external defects could be reduced.

[Principal component analysis of nutrients in five varieties of velvet antler (Cornu Cervi Pantotrichum)].

Studies were carried out by basically analyzing the contents of conventional ingredients, inorganic elements and amino acids of five varieties of velvet antler. The contents of inorganic elements in velvet, antler samples were determined by an atomic absorption spectrophotometer. The contents of amino acids in velvet antler samples were determined by spectrophotometry after being separated by an amino acid analyzer. The principal component analysis was applied to the study of characteristic elements in velvet antler. The results showed that crude protein, Ca, P, Na, Ba, Sr, glutamate and glycine are the characteristic elements in velvet antler. Velvet antlers (Cervus nippon Temminck and Elaphodus davidianus) were differentiated from others by the score plot of inorganic elements for five varieties of velvet antler samples. However, according to the score plots of conventional ingredients and amino acids, no differences were found among the five varieties of velvet antler samples. The similarities and differences of nutrients in velvet antlers were revealed by principal component analysis. All these data would provide important evidence for further exploitation of velvet antler.

[Progress in quality analysis of honey by infrared spectroscopy].

The detection of the quality of honey and the differentiation of adulteration are very important for quality and safety assurance. Traditionally used chemical methods were expensive and complicated, therefore they are not suitable for the requirement of wide-scale detection. In the past decade, the detection technology of honey developed with a trend of fast and high throughput detection. Spectroscopy has the fast and non-contact characteristic, and was widely used in petrifaction. This technology also has the potential for application in honey analysis. In the present study, the progress in quantitative and qualitative analysis of honey by near infrared spectroscopy (NIR) and mid infrared spectroscopy (MIR) is reviewed. The application of this two spectroscopy methods to honey detection refers to several aspects, such as quality control analysis, determination of botanical origin, determination of geographical origin and detection of adulteration. The detailed information of the detection of honey by NIR and MIR spectroscopy was analyzed, containing detection principle, technology path, accuracy, influence factors, and the development trend.

[Using GA-dOSC method to eliminate interference of peel with prediction of apple firmness based on near infrared diffuse reflection spectra].

In the present work, "Fuji" apples from Shandong Yantai were used to take the diffuse reflection spectra by FT-NIR PLS components (i.e., factors) were computed by nonlinear iterative partial least squares (NIPALS) and the number of latent factors (LV) was optimized by a leave-one-out cross-validation procedure on the calibration set. On the basis of partial least square (PLS) regression, the models for apples' firmness before and after peeling were compared. In order to eliminate the effect of apple peel on prediction, spectral pretreatments such as multiplicative scatter correction (MSC), derivative, direct orthogonal signal correction (DOSC) and wavelengths selection based on genetic algorithms (GA) were used. Finally, the results of different spectral treatments were compared. In conclusion, the RSDp of models for apples before and after peeling was 16.71% and 12.36%, respectively, suggesting that the apple peel played a negative role in constructing good predictive models. Moreover, the traditional spectral pretreatments (such as MSC, derivative) can hardly resolve the problem. In this research, GA-DOSC played an important role in reducing the interference of apple peel. It not only reduced the wavelength variables from 1480 to 36, but also reduced the latent variables from 5 to 1. The correlation coefficient (r) was improved from 0.753 to 0.805, and the RMSECV and RMESP were reduced from 1.019 kgf x cm(-2) and 1.197 kgf x cm(-2) to 0.919 kgf x cm(-2) and 0.924 kgf x cm(-2), respectively. Especially, the RSDp was decreased remarkably from 16.71% to 12.89%. The performance of the model after GA-DOSC treatment was similar to the model using spectra of apple flesh (12.36%). It was concluded that the prediction precision based on GA-DOSC satisfied the requirement of NIR non-destruction determination of apples firmness.

[Possibilities of near-infrared spectroscopy for the assessment of principle components in honey].

The potential of near infrared spectroscopy (NIR) as a nondestructive method for determining the principle components of honeys was studied for 153 unifloral honeys and multifloral honey samples. Fourier transform near-infrared spectroscopy (FT-NIR), CCD near-infrared spectroscopy and PDA near-infrared spectroscopy were evaluated to quantitatively determine water content, fructose content and glucose content in honey. On the basis of partial-least square (PLS) regression, the models of honey were compared. The best calibration model gives the correlation coefficients of 0.978 5, 0.931 1 and 0.90 7 for water, fructose and glucose, respectively, with the root mean square error of prediction (RMSEP) of 0.410 8(%), 1.914 48(%) and 2.531 9(%) respectively. The results demonstrated that near-infrared spectrometry is a valuable, rapid and nondestructive tool for the quantitative analysis of the principle components in honey.

[Analysis of NIR characteristic wavelengths for apple flesh firmness based on GA and iPLS].

In the present study, the fruit flesh firmness of apple was analyzed by near infrared (NIR) spectroscopy using an FT-NIR spectrometer. The sensitive spectral regions that provide the lowest prediction error were analyzed by different well-known variable selection methods, including dynamic backward interval partial least-squares (dynamic biPLS), sequential application of backward interval partial least-squares and genetic algorithm(dynamic biPLS & GA-PLS), and iterative genetic algorithm partial least-squares (iterative GA-PLS). Iterative GA-PLS, dynamic biPLS & GA-PLS led to a distinct reduction in the number of spectral data points with better predictive quality. Furthermore, the majority of selected wavelengths were content with the characteristic of the sorption bands of fruit flesh firmness. Pectin constituents, complex non-starch polysaccharides, which are related to texture change in apple, play an important role in their harvest maturity, ripening and storage. Comparing NIR characteristic wavelengths of apple flesh firmness and typical absorption bands for pectin, it was found that characteristic wavelengths of apple flesh firmness were consistent with the pectins relevant spectral regions. Therefore, the NIR characteristic wavelengths of apple firmness based on GA and iPLS reflected the chemical component of apple and the results were reasonable.

[Feasibility of using NIR spectroscopy to detect melamine in milk].

In the present study, 22 certified milk samples without melamine were collected, then 50 adulterated milk samples with added different content of melamine (0.1-1 500 mg x kg(-1)) were prepared. The near-infrared (NIR) spectra of these milk samples were measured. The possibility of using NIR spectra to detect melamine in milk was studied. Partial least square regression (PLSR) was applied to construct the calibration model between NIR spectra and the content of melamine. The results showed that NIR spectroscopy can not accurately predict the content of melamine because of its poor detection limit. However, the combination of NIR spectra and partial least square-discriminate analysis (PLS-DA) was applied to differentiate the certified milk samples and the adulterated milk sample. The classification accuracy was 100%. Therefore, NIR spectra could be used to preliminarily detect whether the milk was adulterated with melamine. As a complementary detecting method to the high performance liquid chromatography (HPLC), NIR spectra could improve the detecting efficiency of milk

[Improving apple fruit quality predictions by effective correction of Vis-NIR laser diffuse reflecting images].

In the present study, improved laser-induced light backscattering imaging was studied regarding its potential for analyzing apple SSC and fruit flesh firmness. Images of the diffuse reflection of light on the fruit surface were obtained from Fuji apples using laser diodes emitting at five wavelength bands (680, 780, 880, 940 and 980 nm). Image processing algorithms were tested to correct for dissimilar equator and shape of fruit, and partial least squares (PLS) regression analysis was applied to calibrate on the fruit quality parameter. In comparison to the calibration based on corrected frequency with the models built by raw data, the former improved r from 0. 78 to 0.80 and from 0.87 to 0.89 for predicting SSC and firmness, respectively. Comparing models based on mean value of intensities with results obtained by frequency of intensities, the latter gave higher performance for predicting Fuji SSC and firmness. Comparing calibration for predicting SSC based on the corrected frequency of intensities and the results obtained from raw data set, the former improved root mean of standard error of prediction (RMSEP) from 1.28 degrees to 0.84 degrees Brix. On the other hand, in comparison to models for analyzing flesh firmness built by means of corrected frequency of intensities with the calibrations based on raw data, the former gave the improvement in RMSEP from 8.23 to 6.17 N x cm(-2).