[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.

[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.

[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.

[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.

[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.

[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.

[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.