Guifen Chen
email: guifchen@163.com

Yinglun Li

The monitoring system for agricultural environment based on point surface fusion with the internet of things and WebGIS

Keywords: Agricultural environment, WebGIS, Monitoring, Internet of things, Point surface fusion

Today, the technology of Internet of things in the field of agricultural environmental monitoring has been widely used, but the network monitoring technology based on the node is still faced with the monitoring points distribution design, data space, continuous expression and many other problems.According to the characteristics of agricultural environmental monitoring objects, this paper presents transference of zone simulation and evaluation method, design a set of things based on the "point" of the monitoring data and WebGIS solution based on the "face" of the space data fusion and analysis.In established networking monitoring center system platform based and optimization of WebGIS integrated graphical spatial analysis technology, in Helong and Kaian town of Nong'an County of Jilin Province in the national Spark plan experimental base fusion networking monitoring data and WebGIS spatial data points of agricultural environment monitoring system is established.System to achieve the display of data location map and transference of the regional simulation and evaluation, regional monitoring thematic map management three functions. The results ofexperimental base in maize field monitoring of growth for the case of application of the systemshow that the system can effectively realize the transference of agricultural environment dynamic monitoring of the region, corn crop disaster comprehensive monitoring ability can be improved, and can provide a more convenient application services for agricultural production management.

Introduction

Agriculture plays an important role in the national economy. The monitoring and management of crop growth environment is an important basic work to realize the sustainable development of agriculture. In recent years, with the development of electronics and communication technology, 3S, networking, cloud computing and big data is representative of a new generation of information technology, is constantly application in various fields of agriculture, promote the development of agricultural modernization pace.

Geographic information system is a kind of computer support system which uses computer to control relevant data. It is mainly used to describe the distribution and attribute of objects in the real world.

WebGIS is the product of the combination of web and GIS technology, the basic idea is based on network to provide geographic information service and WebGIS technology to the network map of this unique visual effects and geographic analysis functions and specific subject area system application and database operation is convenient and efficient integrated together. This feature promotes the GIS technology in the different fields of extensive development and application.

In the field of agricultural environment, the current domestic and foreign related networking technology and WebGIS technology optimization and integration, real time monitoring of the Internet of things andWebGIS spatial information fusion analysis of the research has been reported [START_REF] Zhongfu | Development trend of Internet of things and perspective of its application in agriculture[END_REF] . Internet of things is an extension of the application of the Internet, is the integration of sensing, transmission, intelligent decision-making and control functions of the integrated network system. The monitoring technology of the Internet of things is able to obtain the discrete points and time continuous data in a certain area in a timely manner, but there is not a continuous problem in the space. Therefore, networking and WebGIS technology combined to build things "point" of the real-time monitoring data and WebGIS spatial data based on "face" of the corresponding fusion analysis method based on to achieve expression of monitoring information in time and space, for agricultural IOT application provides more space to play. This paper based on the pre established in the corn field growth monitoring center system of Internet of things, to maize growth environment monitoring as the research object, in the areas of data analysis and further research to investigate network real-time monitoring data and WebGIS spatial data fusion analysis method and application technology, the proposed transference of regional dynamic simulation and evaluation method, established agricultural environment monitoring system based on WebGIS and Internet of things [START_REF] Kerning | Design and implementation of monitoring system for agricultural environment based on WebGIS with Internet of Things[END_REF] . Finally, to national Spark plan experimental base in maize field growth for system application case, to achieve the transference of agricultural environment dynamic monitoring of the region, and can provide a more convenient application services for agricultural production management.

System organization structure

Agricultural environmental data collection based on Internet of things

In this paper, the system is built on the basis of the establishment of the monitoring center system of maize field in the experimental base. Farmland networking monitoring center system is in Nong'an County, Jilin Province maize areas on behalf of regional deployment field monitoring sites and on maize growth environment information real-time data acquisition and remote transmission to the center of the system, realize the dynamic monitoring of Maize in the whole growth process, has maize producing areas in agricultural county deployment complete several monitoring stations, initially built in the region in the monitoring network, but also provides a basic platform for this paper based on the point to plane fusion research of agro -environmental monitoring [START_REF] Yangwen | A WebGIS-based system for rainfall-run off prediction and real-time waterresources assessment for Beijing[END_REF] .

The agricultural environmental data acquisition system based on Internet of things is designed by the three tier architecture of the Internet of things, and the 3 layer is the perception layer, network layer and application layer.

Sensing layer solved the problem of data acquisition of agricultural environmental factors. The sensor network is composed of a sensor node and a sensor network, and is deployed in the agricultural environment monitoring area. Monitoring node timing of regional monitoring environmental factors (such as location of different crops and different high in the air and soil temperature and humidity, precipitation, wind direction and wind speed) and image information such as timing acquisition and transmitted to the sink node.

The network layer solves the problem of long-distance transmission of data. The layer through the Internet and GPRS/3G mobile communication network access to achieve data remote transmission, and combined with the corresponding routing protocol and network security protocol, its security and stability of the transmission to the data center [START_REF] Niehongmiao | Research and design of Internet of things technology in precision agriculture application[END_REF] .

Application layer to solve the problem of information processing and human-computer interaction [START_REF] Tianchao | The basic framework of Internet technology overview[END_REF] .The layer composed of servers and other infrastructure platform and web applications, such as software platform, the main function is to agricultural environmental data obtained from each monitoring point for storage, distribution and analysis and other operations, the timely diagnosis and management decisions are made in order to solve the problem of the corresponding agricultural, applications and services for the user to provide. In this paper, in the application layer of graphical WebGIS spatial analysis technology of integrated optimization and establish the Internet of things based on the real-time monitoring data and WebGIS based on fusion of the "face" of the spatial data analysis, in order to realize the transference of agricultural environment dynamic monitoring of the region.

WebGIS crop growth environment data

The structure design of agricultural environmental data analysis based on WebGIS is composed of three parts: system database server, WebGIS application server, Web server and Web application program running on it. Database system, including database based on GIS, environmental factor database and expert knowledge database. Among them, geographic database for special GIS database recorded map point, line and plane space elements such as topological data and agricultural remote sensing thematic map of raster data, the data base of environment factor, surface storage monitoring sensor real-time acquisition data of environmental factors, expert knowledge database is stored crop seedling and disaster related discrimination indicator, historical experience, management measures, such as the knowledge data and indicators commonly used in statistics. WebGIS Application Server is the system realize the site map data display, regional monitoring thematic map making and other spatial data retrieval and composition analysis of the core of function and application service with a web service interface of spatial data in the form [START_REF] Jiang | Research and application of key technologies of Internet of things[END_REF] . The web server by running on a series of web application system has been realized and released to the user terminal data online, crop seedling and plant diseases and insect pests damage diagnosis, regional monitoring project management plan and to other platforms provide web service interface and other functions.

The user through a browser to the web server sends request, web server receives a browser request parsing into the corresponding operation instructions form respectively, access to the corresponding system database server and WebGIS Application Server, enquiries to the information of the monitoring data and GIS data back to the web server, after handle the web server returns to the browser, so as to realize the browser and the server dynamic interaction, and to the whole process of customer service. The structure of agricultural environmental data analysis based on WebGIS is technical route shown in Figure 1. Agricultural environmental monitoring network system in function mainly divided into: monitoring data online publishing, seedling diagnosis and monitoring of regional thematic maps of three parts [START_REF] Youguan | Discussion on the monitoring system of total cultivated land in China[END_REF] .

Application of WebGIS in the system function is mainly reflected in: data location map display, the transference of agricultural meteorological disasters in regional simulation and evaluation, crop seedling transference regional simulation and evaluation, regional monitoring thematic map management.

Location map display data, namely the networking online map location monitoring point and real-time monitoring data display; simulation and evaluation of regional growth approach, is the core part of this paper is the system function, real-time monitoring and diagnosis of crop growth based on the results of monitoring points, according to the same ecological conditions within the region relatively consistent differences and regional between the conditions of apparent principle, simulation and evaluation of dynamic monitoring region; regional monitoring thematic map management, is from the point to the area according to the simulation results, dynamic display of online dynamic generation, storage area and timing monitoring of thematic map, including the monitoring of agricultural meteorological disaster region to obtain the thematic map, thematic map, crop growth monitoring from the region other platform of agricultural remote sensing thematic map, and the information and monitoring results of the corresponding regional expert decision and forecasting and early warning function etc.

Regional data simulation and evaluation

Usually case, ecological and climatic conditions in the same ecological type region within the scope of the identical or similar, climate and ecological factors in the same ecological type region showed uniform trend, and in different ecological areas is differences between the relatively obvious [START_REF] Hayward | The Korean Seismo-Acoustic Array[END_REF] . Therefore, the collected agricultural environmental data can reflect the farmland area of the monitoring points on behalf of the state of the environment, and the same type of ecological zone representative farmland area of the state of the environment and further as a condition of the ecological types of regional agricultural environment at the beginning of further reflected. According to the characteristics of monitoring objects and different precision demand, for a wide range of ecological type region and divided into several types of ecological sub areas, or followed by progressive cycle segmentation multilevel sub regions.

According to the corn growth environment of temperature, light and water content of the main climatic factors, combined with the characteristics of topography, soil types, production conditions, a corn production area is usually divided into a number of different types of ecological regions. This study within the same region in accordance with conditions are relatively consistent and inter regional differences significantly principle, based on networking has been established for maize test field monitoring sites and in corn producing areas of different ecological zones were selected representative monitoring area of farmland, and then in the representative agricultural field monitoring area were selected representative maize monitoring points. By means of monitoring and diagnostic results with a farmland monitoring representative areas in all the selected monitoring points in the region as state of the environment; in the same ecological area in all the selected representative areas of monitoring and diagnosis results of mean as the state of environment in the ecological zones, establish "monitoring points -the representative area of farmland ecological types district -the corn producing areas" from "point" to "face" of the regional dynamic simulation and evaluation. This paper according to the selected monitoring points on Maize Seedling situation monitoring and diagnosis results of information were constructed from "point" to "side" of the regional dynamic simulation and evaluation and regional monitoring project in the form of graph to achieve regional monitoring data online map dynamic display.

System realization and result analysis

System interface using visual studio2012 integrated development tools to develop and networking sensor by multi node layered comprehensive sampling equipment, can be in different depth of the soil in the collection of temperature, humidity, air temperature, wind direction, wind speed, etc. data [START_REF] Herrin | High-Altitude Infrasound Calibaration Experiments[END_REF] . Taking the corn growing experiment in Nong'an County as an example, the monitoring results are analyzed.

system function and interface

System users can perform related browsing operations to view and manage the location information, running status and distribution of the monitoring points [START_REF] Bass | Infrasound Monitoring of Atmospheric Events[END_REF] . Can not only display, but also can be classified to choose to display a certain area or a type of monitoring site, select the user data required to display. The operation of maize growth monitoring system is shown in fig 2.

Figure. 2. The operation of maize growth monitoring system

Monitoring data real-time monitoring display, is based on the map and location information, the user through the choice of the monitoring point can query the site real-time acquisition of data and image information; can also through the selected query starting and ending time, historical data query and statistical analysis, such as: some environmental factors variation rule of different period, different years monitoring data of the same site longitudinal comparative analysis and index calculation.

To Jilin Nong'an County monitoring site, for example, in addition to HD camera installed in the field, sensor installation are: air temperature and humidity, soil temperature and humidity, wind speed, wind direction and other. The user through the system online map display interface not only can view real-time monitoring data, can also choose to query the historical monitoring data, and on maize growth, soil moisture content, irrigation and meteorological factors of online browsing and statistical analysis, and can also select the time period, data download. For ecological and climatic factors can significantly reflect conditions in the same region relatively consistent characteristics of explicit and inter regional differences, the representative of the monitoring point data in the same ecological type region has strong representation. Therefore, in crop growth monitoring design for regional scale, this study selected representative monitoring area of the Jilin Province in different soil types in the region and in the monitoring area selected a representative maize monitoring site: Nong'an county corn plots, establish "real-time monitoring -representative farmland area monitoring ecological type regional monitoring --corn producing areas in Jilin Province regional dynamic monitoring.

According to the above, on behalf of the real-time monitoring and diagnosis results of monitoring points, the system can automatically calculate the output of Jilin Province maize producing areas of crop growth situation from the point of regional simulation results. At the same time, the system according to the monitoring of soil moisture forecast and warning level, automatic transfer of expert knowledge database, to correspond to the waterlogging and drought evaluation and decision support and according to the system set respectively feedback to users in the corresponding region. Nong'an County Soil type map and regional dynamic monitoring of soil water as shown in the figure 4.

Conclusions and discussion

In this paper, agricultural environmental monitoring as the research object, the comprehensive utilization of geographic information system, Internet of things, computer network technology, based on the Internet of things and WebGIS technology of agricultural environmental monitoring system. Put forward the regional simulation and evaluation method of approach, design a set of things will be on the point of the monitoring data and WebGIS on the face of the spatial data fusion analysis solution; corn networking monitoring center system platform, the design and development of networking monitoring data and WebGIS spatial data points the integration of agricultural environmental monitoring system; simulation system realizes the monitoring and evaluation of regional data location on the map display, and based on the point to the surface of the crop growth monitoring and diagnosis results forecast and expert decision support analysis based on WebGIS graphical space function; finally in Jilin province corn growth monitoring for the case of the application of system analysis. The system can effectively realize the transference of agricultural environment dynamic monitoring of the region and to improve the diagnostic ability of agricultural environment and disaster, provide more convenient application services for the management of agricultural production.

The contribution of this paper is mainly reflected in the following two aspects:

(1)The IOT transference of zone simulation and evaluation method is proposed based on the real-time monitoring data and WebGIS spatial data based on "face" integration;

(2)Based on the above method, the system of agricultural environmental monitoring system was designed and developed, which realized the dynamic monitoring function of agricultural environment.

Figure. 1 .

 1 Figure. 1. Structure of agricultural environmental dataanalysis

4. 2

 2 real time monitoring of the Internet of things In July 2016 5 to 11, for example, in Nong'an county corn plots monitoring site, according to monitoring data shows, July 5 to July 11, the time period, maize soil temperature and moisture change trend stationary. The time period can be output diagnosis results: monitoring point crop growth environment is suitable, the crops grow well. The following figure shows the soil temperature and humidity change trend of Nong'an county corn experiment monitoring sites from July 5th to July 11th.Its shown in Figure 3.

Figure. 3 .

 3 Figure. 3. Changes of soil temperature and humidity

Figure. 4 .

 4 Figure. 4. Soil distribution map and Dynamic monitoring of soil moisture


Xin Zhao
email: zhaoxin@hzu.edu.cn

Mingwei Xu

Guoqing Sun

Yang Jiao

Haijiao Yu

Quanming Li

Guogang Zhao

The Principal Component Analysis and Cluster Analysis of Trace Elements in Gentian

Keywords: Gentian, Principal component analysis, System clustering analysis

In this paper, using principal component analysis and cluster analysis method, combining with the statistics software MATLAB. The Qingyuan rough gentian, Zuojia rough gentian, Zuojia leaf gentiana system clustering analysis of experimental data. Main ingredients swertia glycosides, Swertia bitter glycosides , gentian bitter glycosides and oleanolic acid and ursolic acid, etc. etc. Cluster analysis results showed that the three principal component contribution rate of the Qingyuan rough gentian is 89.24% , the three principal component contribution rate of the Zuojia rough gentian is 89.85% , the three principal component contribution rate of the Zuojia leaf gentian is 93.56%. By cluster analysis to Qingyuan rough gentian, Zuojia rough gentian, Zuojia leaf gentiana aristata 3 groups of data were divided into 6, 5, 4 classes, and determine the appearance characteristics of the high quality gentian, provide the basis for breeding to select high quality gentian.

Introduction

Gentian for commonly used Chinese traditional medicine, has clear heat dry wet and the efficacy of purging liver fire. Gentian including gentian bitter glycosides and swertia glycosides, Swertia bitter glycosides , and oleanolic acid and ursolic acid, etc. Commonly used in damp heat jaundice, red eyes, mouth, bitter, pain such as disease treatment. Of gentiana bitter glycosides and swertia glycosides, r swertia bitter glycosides, oleanolic acid and ursolic acid content of five elements, principal component analysis and cluster analysis in order to select high-quality gentian provided the scientific basis and theoretical basis for breeding.

Experiments and Methods

Experimental Samples

Data sources for the analysis in Jilin Agricultural University College of Chinese medicinal materials of gentian principal component extraction of experimental data, data 200 experimental data, through data cleaning, after screening the final selection of 90 data, and divided into three groups.

Experimental Methods

Principal component analysis is the use of data dimension reduction to exclude chemical information overlaps and correlation of multivariate linear statistical techniques, principal component analysis can be used to study the samples the dependencies between different data, looking for a comprehensive factor, for the comprehensive evaluation of the sample. This paper uses the MATLAB software to carry on the principal component analysis. First of all, the article to gentian bitter glycosides and swertia glycosides, swertia bitter glycosides, and oleanolic acid and ursolic acid,content of five elements principal component analysis. According to the various components of gentian, choice x1: swertia glycosides;X2: swertia bitter glycosides; X3: gentian bitter glycosides;X4: ursolic acid ; X5: oleanolic acid content for further five indexes such as principal component analysis, principal component ranking and contribution rate are obtained.

Clustering analysis is a method of mathematical statistics, suitable for sample attribution is not clear situation. Based on many properties of the sample, according to the sample of the nature of closeness degree for automatic classification of multivariate statistical analysis methods. First define the distance between the sample in each type of sample, will be the nearest two kind of merger, to calculate the distance between new class and other classes, and according to the minimum distance classification, Repeat this process, at every time reducing 1 class , until all become a kind of sample. The 200 item experimental gentian data from college of Chinese Medicine Materials of jilin agricultural university, going through data cleaning, screening, finally chooses 83 item data, and divided into three groups.

Results and Discussion

The result of principal component analysis

By gentian element characteristic value and the variance contribution rate of data table, we can get three principal component contribution rate. Three principal component contribution rate is respectively: Qingyuan rough gentian three principal component contribution rate of 89.24%, Zuojia rough gentian three principal component contribution rate of 89.85%, Zuojia leaf gentiana before gentiana aristata three principal component contribution rate 93.56%. So on the premise of less loss of information can be described with three principal components of trace element content in gentiana all of the data, it satisfies the basic demands of principal component analysis. According to the score ranking, No. 30 samples of qingyuan rough gentian, has the characteristics of green stem leaves and purple flowers is of high quality. No.26 samples of Zuojia rough gentian, has the characteristics of purple stem leaves and blue flowers is of high quality. No. 22 samples of Zuojia leaf gentian, has the characteristics of green stem leaves and blue flowers is of high quality.

Clustering analysis

According to 83 item data, through the different sections of the clustering analysis of gentiana different shape characteristics, find out the similar sex between them (i.e., the degree of close relatives). Using matlab to gentiana standardzing trace elements in the original data. According to the clustering analysis of the processes associated with select observation object clustering, using the Euclidean distance is analyzed, using Average linkage connections between every two sample, drawing in sequence. The corresponding results are shown in figure .   (a) Clustering analysis results of Qingyuan rough gentian

Cluster analysis results showed that the 30 item data of Qingyuan rough gentian can be divided into six categories. [START_REF] Zhou Libing | Qinghai region white spines of trace elements in the leaves and principal component analysis and cluster analysis[END_REF]27,22,25,11,19)

Conclusions

To sum up, both by principal component analysis and cluster analysis to find out containing trace elements of high quality gentian, and through its shape characteristics for identification. In order to further choose to breeding new varieties of gentiana provide guidance. By principal component analysis and cluster analysis methods to analyze gentian trace elements, can provide abundant information for traditional Chinese medicine formula and theoretical guidance.

  belong to the same class,[START_REF] Zhao Rui Zhi | In Gentian medicinal materials the extraction process of gentiana bitter glycosides[END_REF]15,18,[START_REF] Cao Hongcui | The trace elements in Chinese traditional medicine licorice principal component analysis and systematic cluster analysis[END_REF]13,14)belong to the same class, (3.12,21,29,30) belong to the same class,[START_REF] Wang Aiping | Different regions of sichuan dangshen clustering analysis of trace elements[J][END_REF][START_REF] Wang Huiqin | The safflower trace elements in different regions factor analysis and cluster analysis[END_REF]17,28,24,9,26) belong to the same class,[START_REF] Zhang Yifan | The trace elements in traditional Chinese medicine radix scutellariae principal component analysis and cluster analysis[END_REF]16) belong to the same class,(8,10,23,20) belong to the same class, (2, 15, 13, 14, 4, 5, 1, 27) have main component elements content is low. (b) Clustering analysis results of Zuojia rough gentian Cluster analysis results showed that the 30 item data of Qingyuan rough gentian can be divided into five categories. (1,28,2,21) belong to the same class, (3,14,10,5,12,7,24)belong to the same class, (6,30,20,18,23,26,27)belong to the same class,(8,19,13,29)belong to the same class, (9,22,11,16,15,17,25) belong to the same class, (3,14) have main component elements content is low. (c) Clustering analysis results of Zuojia leaf gentian Cluster analysis results showed that the 23 item data of Qingyuan rough gentian can be divided into four categories. (1,3,9,23,12,15) belong to the same class, (2,4,19,5,7)belong to the same class, (6,17,21,20,22) belong to the same class, (8,10,13,16,11,18,14) belong to the same class, (3,9) have main component elements content is low.

Table 1 .

 1 The contribution of Qingyuan rough gentian

	eigenvalue	feature vectors	contribution
	2.1494	(0.3407,0.2956,-0.0322,0.6109,0.6499)	0.4299
	1.3556	(0.1462,0.6471,0.7049,-0.2080,-1405)	0.2711
	0.9569	(-0.8132,-0.1215,0.4173,0.3428,0.1799)	0.1914
	0.4811	(-0.4270,0.6921,-0.5723,-0.0165,-0.1038)	0.0962
	0.0570	(0.1378,0.0104,0.0202,0.6824,-0.7175)	0.0114

Table 2 .

 2 The ranking of the principal component of Qingyuan rough gentian

	Serial number	score	rank	Serial number	score	rank	Serial number	score	rank
	1	-0.1060	27	11	0.0002	13	21	-0.0282	19
	2	-0.0365	20	12	-0.0260	18	22	0.2199	2
	3	0.0692	6	13	-0.0686	23	23	0.0684	7
	4	-0.0189	16	14	-0.0230	17	24	0.0365	10
	5	-0.0853	26	15	-0.0623	22	25	0.0742	5

Table 3 .

 3 The contribution of Zuojia rough gentian

	eigenvalue	feature vectors	contribution
	2.3406	( -0.4797, -0.4685, -0.4564, 0.4848 0.3272)	0.4681
	1.5342	( 0.1129, 0.4455, 0.3852, 0.4658, 0.6507)	0.3068
	0.6192	( 0.8198, -0.0804, -0.5333, 0.1490, 0.1218)	0.1238
	0.3096	( -0.2831, 0.7557, -0.5801, -0.0227, -0.1086)	0.0619
	0.1964	( -0.0708, -0.0672, -0.1492, -0.7248, 0.6655)	0.0393

Table 4 .

 4 The ranking of the principal component of Zuojia rough gentian

	Serial number	score	rank	Serial number	score	rank	Serial number	score	rank
	1	-0.1294	20	11	0.2448	5	21	-0.3305	29
	2	-0.1423	22	12	-0.1189	19	22	-0.1336	21
	3	-0.3906	30	13	0.1824	8	23	0.0810	11
	4	-0.2006	25	14	0.2355	7	24	0.0209	15
	5	0.1372	10	15	0.0508	13	25	-0.2495	26
	6	0.3090	3	16	-0.2509	27	26	0.3406	1
	7	0.0276	14	17	0.1688	9	27	0.0204	16
	8	-0.2790	28	18	-0.1697	23	28	0.0553	12
	9	0.2402	6	19	0.3153	2	29	-0.0346	17
	10	-0.0812	18	20	0.2645	4	30	-0.1835	24

Table 5 . The contribution of Zuojia leaf gentian

 5

	eigenvalue	feature vectors	contribution
	2.6302	(-0.0628, -0.5306, -0.3829, 0.5362, 0.5295)	0.5260
	1.0917	(-0.5542, 0.2787,0.6283,0.3264, 0.3374)	0.2183
	0.9565	(0.8263, 0.2190, 0.3368, 0.2661, 0.2915)	0.1913
	0.2172	(0.0775, -0.7698, 0.5868, -0.1877, -0.1479)	0.0434
	0.1044	(0.0130, -0.0131, 0.0293, 0.7071, -0.7063)	0.0209

Table 6 .

 6 The ranking of the principal component of Zuojia leaf gentian

	Serial number	score	rank	Serial number	score	rank	Serial number	score	rank
	1	0.0573	10	11	-0.0745	14	21	-0.0713	12
	2	0.1012	7	12	-0.1426	18	22	0.4794	1
	3	-0.2020	20	13	-0.1723	19	23	0.2077	2
	4	-0.0742	13	14	-0.1084

Acknowledgement

The authors sincerely thank many invaluable suggestions. This work was supported financially by the Students' Innovative Training Program of Jilin Agricultural University(377).