Enze Xiao

Guifen Chen
email: guifchen@163.com.通讯地址:吉林省长春市新城大街2888号

Shan Zhao

Siwei Fu

Three -dimensional Visualization of Soil Nutrient Evolution in Maize Precision Operation Area Based on ArcGIS

Keywords: Soil nutrient, 3D GIS, variable fertilization, Kriging interpolation method * corresponding author, professor, doctoral tutor, engaged in artificial intelligence,

With the development of 3D GIS technology, the application of 3D GIS in agriculture has become a hotspot in agricultural information technology research. A total of 109 soil samples were collected from the soil of Jilin Province Yushu City Gongpeng Town No. 13 Village No. 7 test area.

Three -dimensional visualization of soil nutrient evolution in maize precise operation area was carried out by using ArcGIS technology. Firstly, the Kriging optimal interpolation method was used to calculate the sampling points of soil nutrient space in the field of maize test field. Then threedimensional spatial map of soil available phosphorus, available potassium available nitrogen and other nutrient contents during the period from 2005 to 2009 were established by using the spatial analysis technique of 3D GIS. By comparing its three-dimensional thematic map, analyze trends in the evolution of its soil fertility characteristics. The results showed that the difference of soil fertility was gentle after four years of variable fertilization, and the effect of precision fertilization was verified.

Introduction

With the development of computer science and 3D simulation technology, the development and application of 3D GIS are becoming more and more mature. 3D GIS can truly perceive the objective world, present the spatial geography phenomenon to the user, and carry on the three-dimensional spatial analysis operation to the space object. However, the current three-dimensional GIS in the field of agricultural applications less [START_REF] Xiaohui | Study on Dynamic Monitoring of Basic Farmland Based on GPS and GIS Technology [J][END_REF] . Therefore, this study in the national "863" project demonstration base in Yushu City Gongpeng Town No. 13 Village No. 7 test area by 40m * 40m grid sampling. The soil nutrient content was measured and the characteristics of soil nutrient evolution were discussed by using the threedimensional GIS spatial analysis technique. The spatial variation of soil nutrient after precision fertilization was analyzed and analyzed, which provided a reliable basis for the division of farmland precision management area and Implementation of Precise Operation of Corn [START_REF] Haijiang | Spatial and temporal variability of soil nutrients in farmland based on GIS[END_REF] .

Materials and Methods

1.1Overview of the study area

The study area is the experimental field of the NO.13th village of Gongpeng Town, Yushu City, Jilin Province. It is located in the eastern part of Jilin Province and is a semi-humid temperate continental monsoon climate. It is characterized by four distinct seasons, winter long summer short, annual precipitation in the 500-600mm, the vast majority of concentrated in the warm season, accounting for about 90% of annual precipitation, the annual average temperature of 4.6-5.6 ℃. Soil type is a typical black soil, the main crop is corn and soybeans, etc. It is an important commodity grain base in Jilin Province [START_REF] Zhao Yue-Ling | Study on Spatial Variability of Soil Nutrients Based on GIS[END_REF][START_REF] Jian | Based on the spatial fuzzy clustering of visual variable fertilization decisionmaking system[END_REF] .

In the national "863" project " Research and Application of Corn Precision Operation

System " and the national Spark plan " Integration and Demonstration of Precision Training Technology of Corn Based on Internet of Things and demonstration of" the strong support of the project in Yushu City Gongpeng Town No.13 Village continuous four years of variable fertilization operations, the accumulation of a large number of soil nutrient space data. Therefore, this article selected the Yushu City Gongpeng Town No.13 Village No. 7 plots were experimented and research. The total area of the test field is about 375 mu, and the grid size is set to A1~L11 as the sampling point [START_REF] Zhao Yue-Ling | Spatial Variation of Soil Nutrients in Typical Black Soil Region of Jilin Province[END_REF] . We selected the three kinds of nutrient data of available phosphorus, available nitrogen and available potassium respectively, and the application of the algorithm for four years of continuous variable fertilization from 2005 (before variable fertilization) to 2009 on the Yushu City Gongpeng Town No.

13 Village No. 7 plots.

Data collection

Application of GPS (Global Satellite Differential Positioning System) device for precise positioning, the use of ArcGIS software to produce the soil grid sampling map, sample distribution shown in Figure 1.

Fig. 1. sampling grid diagram

Research methods

In this paper, the Kriging optimal interpolation method was used to calculate the sampling points of soil nutrient space in the field of maize experiment field. On this basis, the three-dimensional visualization of soil nutrient multidimensional spatial data was realized by regular body element model. Through the three-dimensional Kriging interpolation algorithm, the original data contained in the spatial distribution of features without significant loss of the situation first passed to the estimated grid points other than the unknown data, so that the structure of the three-dimensional space model more realistic real soil environment. The three-dimensional Kriging interpolation method is essentially an improved score for the inverse distance weighting method, but it is still a linear interpolation method [START_REF] Jian | Study and Realization of Three-dimensional Visualization of Soil Nutrients Based on VTK[END_REF] . The principle of Kriging interpolation is that the attribute Z(x) at the point Xi∈A (i = 1,2, ..., n) is Z(Xi), the interpolated point X0∈A(X0), the Kriging interpolation result Z*(X0) is the weighted sum of the known sampling point attribute values Z(Xi

) (i = 1, 2, ..., n). Z * (X 0 ) =  i Z(X i ) (1)
(1) where λi is the weight coefficient of the attribute value to be determined. There is a certain correlation between Z(Xi), which is related to the distance, but also to its relative direction change. Thus, the three-dimensional Kriging method refers to the object of study as a regionally controllable amount of change. we obtain the matrix of coefficient coefficients of the attribute value Kriging by using the spherical model, and then determine the augmented matrix for the spatial position of each unknown point. We obtain the weight coefficient value by solving the Kriging equation group, and then we can get each. The estimated value of the attribute value and the estimated variance of the attribute value.

3D GIS spatial analysis technique 1.4.1 Information acquisition

The spatial data of sampling points in 2005, 2007 and 2009 were obtained by GPS respectively and the data of the soil samples of the sampling points were obtained as shown in Table 1. (1) First open the ArcScene module, the ArcMap in the two-dimensional data (Figure 2) into the ArcScene, while loading DEM data (Figure 3). (3) In the "Elevation from Surface", select "Float on a custom surface" (the surface here is itself). You can find click "OK", DEM did some changes, but this is not very obvious.

(4) Then click on the "Scene layer", select the "scene properties" option (Figure 5), opened a "scene properties" menu. Here you can adjust the value of "vertical exaggerated" and set it to 5. Now you can see, the DEM's three-dimensional sense has been very strong. the color is set to eye-catching color (Figure 6). So that the completion of the threedimensional model [START_REF] Li | ArcScene campus three-dimensional scene of the establishment[END_REF] . From Fig. 7, Fig. 8 and Fig. 9, it is the three-dimensional spatial distribution of available nitrogen, available phosphorus and available potassium in soil nutrients of groundwater No. 7 and the historical evolution of soil nutrient content can be analyzed according to these historical data [START_REF] Lu Xiao-Nan | Spatial variability of soil fertility in agricultural science and technology demonstration park supported by GIS technology[END_REF] .

The results and analysis

It can be seen from Fig. 7, Fig. 8 and Fig. 9 that the nutrient content of soil is gradually increased with the implementation of variable fertilization [START_REF] Hang | Study and application of temporal and spatial variability of soil fertility[END_REF][START_REF] Shengwu | Three-dimensional GIS key technology research[END_REF]

Conclusion

(1) 3D GIS(Fig. 7, Fig. 8, Fig. 9)can show more information than the twodimensional spatial variation map(Fig. 2), and the spatial variation of soil available nutrient is more clear, Intuitive and true.

(2) The high degree of data change in the three-dimensional spatial variation map can fully prove the significant effect of variable fertilization in 2005 and 2009, which is beneficial to the correct evaluation of soil fertility change.

(3) Three-dimensional visualization technology can not only be applied to the threedimensional distribution of soil nutrient spatial distribution, which can let users experience the spatial distribution characteristics of real soil nutrient, which is beneficial to soil fertility analysis and evaluation, but also the soil nutrient threedimensional spatial variation map for any mobile, rotation, split and extraction operations. The process and results of variable fertilization are tested and forecasted, which provides an objective, image and reliable auxiliary decision tool.

1. 4 . 2

 42 ArcScene three-dimensional model of the establishment Firstly, the attribute information of available phosphorus, available nitrogen and available potassium in the soil of Gongpeng Town in Yushu City of Jilin Province in 2005, 2007 and 2009 were transformed into spatial information. Then, the Kriging interpolation method is used to calculate the element values of the points in the threedimensional space in the region. On the basis of this, the 3D visualization of soil element data is realized by ArcScene.

Fig. 2 .Fig. 3 .

 23 Fig. 2 . 2D spatial variation of available phosphorus in 2005

Fig. 4 .Fig. 5 .

 45 Fig. 4 . Layer Properties

Fig. 6 .Fig. 7 .( 5 )

 675 Fig. 6 . Stretch

Fig. 8 .Fig. 9 .

 89 Fig. 8 . Spatial variability of available Potassium in 2005-2009

Table 1 .

 1 Data about YuShu city and GongPeng town in the year of 2005

	Soil
		N	P2O5	K	X	Y
	number
	7-A1	186.82	16.44	138.00	126.316244	45.0033416
	7-A2	166.66	10.07	130.00	126.316128	45.0036914
	7-A3	182.11	11.77	120.00	126.316013	45.0040413

Table 2 .

 2 Data about YuShu city and GongPeng town in the year of 2007

	Soil
		N	P2O5	K	X	Y
	number
	7-A1	124.85	8.04	64	126.316244	45.0033416
	7-A2	137.34	9.99	80	126.316128	45.0036914
	7-A3	131.09	10.23	75	126.316013	45.0040413
	7-B1	115.49	13.16	100	126.316738	45.0034264
	7-B2	106.12	10.72	90	126.316622	45.0037763
	7-B3	115.49	14.86	80	126.316506	45.0041262
	7-C1	121.73	9.50	62	126.317232	45.0035113
	7-C2	109.25	11.70	64	126.317116	45.0038612
	7-C3	127.97	8.28	99	126.317	45.004211
	7-D1	152.94	26.32	72	126.317726	45.0035962
	7-D2	127.97	9.50	80	126.31761	45.0039461
	7-D3	118.61	30.95	70	126.317494	45.0042959

Table 3 .

 3 Data about YuShu city and GongPeng town in the year of 2009

	Soil
		N	P2O5	K	X	Y
	number
	7-A1	139.55	9.93	110	126.316244	45.0033416
	7-A2	137.06	14.54	101	126.316128	45.0036914
	7-A3	127.72	18.71	110	126.316013	45.0040413
	7-B1	138.31	14.76	130	126.316738	45.0034264
	7-B2	139.31	17.48	134	126.316622	45.0037763
	7-B3	133.95	34.31	130	126.316506	45.0041262
	7-C1	152.64	41.12	101	126.317232	45.0035113
	7-C2	132.08	38.26	125	126.317116	45.0038612
	7-C3	149.52	41.12	110	126.317	45.004211
	7-D1	137.06	21.57	132	126.317726	45.0035962
	7-D2	130.21	11.90	148	126.31761	45.0039461
	7-D3	133.95	20.47	150	126.317494	45.0042959

Acknowledgments

This work was funded by the China Spark Program.2015GA66004."Integration and demonstration of corn precise operation technology based on Internet of things".


Siwei Fu

Guifen Chen
email: guifchen@163.com

Shan Zhao

Enze Xiao

Research and Application of Spark Platform on Big Data Processing in Intelligent Agriculture of Jilin Province

Keywords: Spark, big data processing, MapReduce, intelligent agriculture in Jilin province

Aiming at the demand of real-time massive data processing of Intelligent Agriculture in Jilin Province, this paper studies the big data processing of Intelligent Agriculture in Jilin Province based on Spark platform by acquiring real-time data through monitoring platform. This study first conducted the performance comparison experiment of Hadoop and Spark data processing platform,then used the Spark distributed cluster computing platform,real-time processing the big data of monitoring area. The experimental results show that the Spark platform speeds up 11.4 times faster than the Hadoop platform in the case of 100 million data sizes; and based on the Spark platform for real-time processing of big data intelligent agricultural monitoring network, not only provides memory calculations to reduce IO overhead, but also the results are faster and more accurate. The research results provide strong support for the implementation of precision agriculture technology in intelligent agriculture.

Introduction

China is a large agricultural country,facing the ever changing agricultural production technology,Especially the introduction of remote sensing, geographic information technology, precision agriculture and expert system in the field of farmland management. This condition leads to the original variety of agricultural ecosystems turning more complex. Among them, the agricultural data gradually evolved from single quantitative transformation into qualitative transformation. The characteristics of data are massive,dynamic, spatio-temporal,etc, accompanied by structural changes. Thus, if only relying on the existing data mining technology and relational database, it has been unable to meet the current data storage and data analysis needs. It is particular important how to effectively analyze and deal with massive data, and according to the needs of the results back to the user's hands is particular important. One of the starting points in the study of big agricultural data is how to dig out valuable knowledge from complicated agricultural data. Therefore, it is urgent to use a method to classify and extract large amounts of data to find out the correlation and underlying pattern between them [START_REF] Xueqi | Large Data System and Analysis Technology[END_REF] ,establish the platform for analysis and preprocessing data, monitor agricultural production process, and combine with data mining technology to obtain real-time information,formulate appropriate coordination measures,and finally realize the precise operation of the farmland management area.Improving the quality of agricultural products under the premise of sustainable development provides a good development environment and material basis for other areas and even the whole national economic development [START_REF] Reyes-Ortiz1 | Big Data Analytics in the Cloud:Spark on Hadoop vs MPI/OpenMP on Beowulf[END_REF] . Aiming at demand for real-time processing of massive intelligent agricultural data, this study firstly accesses the soil air temperature and humidity data from intelligent monitoring network of the National Spark Program " Integration and demonstration of corn precise operation technology based on Internet of things " demonstration area at Nong'an county in real time through the intelligent agricultural monitoring platform,proposes the Spark distributed cluster computing platform. Secongdly, conducted the performance comparison experiment of Hadoop and Spark data processing platform, then used the Spark distributed cluster computing platform with extended MapReduce computational models, real-time processing the big data of monitoring area. Spark distributing cluster computing platform has efficient supported for multiple computing modes. Spark with the main characteristics of computing in memory can be applied to a wide variety of distributed platform scenarios, including batch processing, iterative algorithms, interactive queries, stream processing, and so on [START_REF] Feng | Based on Hadoop's massive agricultural data resource management platform[END_REF][START_REF] Shyam | Apache Spark a Big Data Analytics Platform for Smart Grid[END_REF] .These different calculations are supported by a unified framework. Spark allows us to integrate various processing processes simply and at a low cost, combined Spark streaming ,SQL,MLlib,Graph X and other modules and data mining technology to make appropriate analysis of data processing [START_REF] Rongzhi | Based on Spark's parallel combination test case set generation method[END_REF] ,it provides the basis for the analysis of mass data in the future. The research results provide strong support for the processing of agricultural big data and the implementation of precision agriculture technology in intelligent agriculture.Thus,it contributes to the sustainable development of agriculture,and realize the modern development of intelligent agriculture.

2

Data Sources and Research Methods

Data Sources

The study area is Nong'an ,the total area is 5400 square kilometers.In 2013, the county planting area of agricultural land reached 366,000 hectares,the coverage rate of fine varieties is above 98%,it is one of the important commodity grain bases in Jilin Province.Nong'an County annual average temperature of 4.7 degrees, frost free period of 145 days, rainfall of 507.7 mm, the effective accumulated temperature of 2800 degrees.Flat terrain, four distinct seasons, is a temperate continental climate.The soil in Nongan county is divided into 10 soil types and 20 sub types, 50 genera and 111 species of soil .The zonal soil is black soil, chernozem [START_REF] Czerwinski | Digital Filter Implementation in Hadoop Data Mining System[END_REF] .This data comes from the seven real-time data monitoring stations located in Nong'an County Kai'an Town,Hualong Town Chenjiadian Village,real-time monitoring network for intelligent agriculture,monitoring and collection of crop production environment information using wireless sensor networks,thus to realize the real-time processing and analysis of the big data of the intelligent agriculture.

2.2

Research Methods

Spark cluster computing platform

Spark was originally born at the APM laboratory at the University of Berkeley.It is a fast, general purpose engine that can be used in large-scale data processing [START_REF] He | Parallel sampling from big data with uncertainty distribution[END_REF] ,today is one of the top open source projects under the Apache Software Foundation. Just as its name Spark,such as lightning fast cluster computing platform,the original design goal of Spark was to make data analysis faster-Not only is it fast, but it also has to be able to write programs quickly and easily.In order to make the program run faster,Spark provides memory computing, reducing the IO overhead in iterative computation.In order to make the program run easier,Spark is written in a concise, elegant Scala language [START_REF] Zhiwei | Adaptive task scheduling strategy under heterogeneous Spark cluster[END_REF] ,Scala provides an interactive programming experience.From enterprise, medical treatment, transportation to retail trade,the big data solutions offered by Spark are pushing ahead with the insights of business that have never been seen before,and thus accelerated decision-making. The Spark project contains more than one tightly integrated component,the core of Spark is a computing engine that consists of scheduling,distributing,and monitoring applications that are composed of many computing tasks, running on multiple work machines, or a computing cluster.As shown in Figure 1,Spark is a large and unified software stack,including Spark SQL,Spark Streaming,MLlib,GraphX,Spark Core and Independent scheduler,YARN,Mesos Modules [START_REF] Chen | Variable-Rata Fertilization Decision-Making System Based on Visualization Toolkit and Spatial Fuzzy Clustering[END_REF] .Spark Core implements the basic functions of Spark,including task scheduling,memory mangement,error recovery,storage system interaction and other modules [START_REF] Zhang Fan | Visualization of Large Data Heat Map Based on Spark[END_REF] ; Spark SQL is a program package that Spark used to manipulate structured data ; Spark Streaming is a component that Spark provides for streaming computations of real-time data [START_REF] Chen | Analysis and Research of K-means Algorithm in Soil Fertility Based on Hadoop Platform[END_REF] ; MLlib provides a variety of machine learning algorithms, all of which are designed to be easily scalable on a cluster [START_REF] Cai Lixia | Based on large data processing technology Hadoop platform maize precision fertilization intelligent decision system research[END_REF] ;GraphX is a library for operating diagrams, which can be computed in parallel

[ 14 ] .

Spark Streaming Spark SQL

Independent scheduler YARN Mesos

Spark Core (task scheduling,memory management,error recovery,storage system interaction and other modules) In a distributed environment, the Spark cluster uses a master / slave architecture.In a Spark cluster, one node is responsible for central coordination, and each distributed work node is scheduled.The central coordination node is called the drive node, and the corresponding work node is called the actuator node.Drive nodes can communicate with a large number of actuator nodes, and they also operate as independent Java processes The Spark relies on the cluster manager to start the executor node, but in some special cases it also depends on the cluster manager to start the drive node.The cluster manager is a pluggable component in Spark [START_REF] Qingfu | Distributed Hash Learning Method for Approximate Nearest Neighbor Query[END_REF] .In this way, in addition to Spark's own standalone cluster manager, Spark can also run on other external cluster managers, such as YARN and Mesos [START_REF] Wanhua | Parallel limit learning machine based on improved particle swarm optimization[END_REF] .

GraghX Mlib

The characteristics and advantages of Spark platform implementation

Although Hadoop has become the defacto standard of big data, there are still many defects in its MapReduce distributed computing model,Spark draws lessons from the advantages of Hadoop and MapReduce, and solves the problems faced by MapReduce.As shown in Figure 3, comparing the execution flow of Hadoop and Spark can be seen, The biggest feature of Spark is the introduction of the concept of an elastic distributed data set(Resilient Distributed Dataset,RDD) [START_REF] Ziyu | Big data technology principle and application[END_REF] ,this allows Spark to cache data set in each node memory in cluster computing, eliminating the need to load multiple times into memory and disk storage [START_REF] Tianqing | A Data Processing Mechanism for High-efficiency Large-scale Graphs in Spark[END_REF] ,and greatly speed up the processing speed. About the advantages of Spark and Hadoop in executing engine, technology stack and so on,As shown in Table 1 [START_REF] Heng | A Spark-based Distributed Semantic Data Distributed Reasoning Framework[END_REF][START_REF] Xinxiang | HSSM: A method of maximizing hierarchical data for streaming data[END_REF][START_REF] Zhongfu | Research and Application of Large Data in Wisdom Agriculture[END_REF] .

Table 1. Comparison table of Hadoop and Spark performance

3

Experimental results and analysis

Comparison of efficiency test between Spark and Hadoop

In performance, the efficiency test compares the time differences between Hadoop and Spark processing large amounts of data when performing logical regressions.This experiment from 2015-2017 intelligent monitoring network in Nong'an County 230 million soil, air temperature and humidity data in the selected 5 groups,respectively 10 thousand, 100 thousand, 1 million, 10 million, 100 million data for execution logistic regression time contrast,the efficiency test results are shown in Table 2, Figure 4.

Table 2. Comparison of run time between Hadoop and Spark

The number in Table 2 is the run time,units are seconds.In Figure 4, "W" stands for "ten thousand bars".

Real time processing of big data in Intelligent Agriculture

(1)Build a stream processing framework for Spark platform The big data analysis platform is designed to have tremendous capabilities and flexibility to meet all these requirements.The major types of processing used in big data analysis are batch processing, stream processing, and iterative processing.Therefore, we need such a platform to store such huge distributed data and perform all of these types of analysis.

Before implementing the Spark ecosystem,you need to configure the HDFS distributed file system,build Block data blocks, Namenode master nodes and Datanode slave nodes;based on the HDFS distributed processing model,create HBase database to achieve high performance, real-time read and write, column storage, scalable functions ; with the Thrift software framework, we build seamless and efficient service with Java, Python, C/C++, Ruby and other programming languages.

For the intelligent agriculture monitoring system in Jilin, which requires millisecond real-time response,the processing data is characterized by online, small, dynamic,relative to off-line data processing,because of the high time requirements, it is more suitable for dealing with small amounts of data and running relatively simple algorithms;for the high fault tolerance and high reliability requirement of the system,Spark uses record update to create a record RDD transform sequence,in order to facilitate the recovery of file partitions.

According to the above requirements analysis,the system uses the Spark Streaming stream computing framework with good fault tolerance and easy combination with machine learning and graph computing in the Jilin intelligent agriculture monitoring platform,real-time reception, calculation and delivery of data streams using Spark Streaming;Based on the MLib machine learning framework,combining the improved particle swarm optimization and the limit learning machine ELM,batch processing the data for the training model;transferred processing data to the distributed file system HDFS and HBase database to store calls;the final results are fed back to the Jilin provincial wisdom agriculture monitoring platform in the form of analytical charts,as shown in Figure 5. 3),and import the data into the HBase system . (3)Processing of Soil Moisture Monitoring Data According to Table 3 data,soil moisture data in different depths were obtained in real time,then to dynamic monitoring and multi angle data processing analysis.The processing results are shown in Figure 6.

Results and analysis

(1)Comparing the efficiency test of Hadoop and Spark platform By comparing the efficiency test of Hadoop and Spark platform, the experimental results are obtained(table 2 and figure 4):When the amount of monitoring data is 10 thousand,Hadoop running time is lower than Spark platform,at this point, the Hadoop is processing faster than the Spark.When the amount of data reaches 100 thousand or more,Spark takes advantage of its fast memory, computing, and distributed frameworks,In time, it is much better than Hadoop's multiple iteration algorithm.When the test content reaches 100 million,the Hadoop platform is 11.4 times more expensive than Spark.At the same time, it can be seen by the contrast chart:Under the Spark distributed framework, as the amount of data increases, the time difference between the time spent and the total time is getting smaller and smaller,reflects the stability and reliability of the Spark platform.

(2)Real-time monitoring and processing of soil moisture data As can be seen from Figure 6, in 2015 and 2016 in May to December year on year,the change of soil temperature at different levels is not obvious.But the soil moisture change is obvious at different levels;soil moisture content was lowest in late seedling stage (at the end of June), and it was the most in early mature (September 4th);soil temperature was lowest in the middle of seedling (June 10th), and highest in late maturing (at the end of September).At the same time, the trend of moisture change of 20cm and 60cm in 2015 was compared with that in 2016,affected by climate factors such as rainfall and light in different years,there were some differences, but the soil temperature did not change significantly in previous years.The analysis results show that the intelligent agriculture monitoring network based on Spark platform can deal with soil moisture data in real-time and effectively.It provides support for precision agricultural production such as timely sowing of crops and water-saving irrigation and so on.

Conclusion and Prospect

Through accessing the soil air temperature and humidity data from intelligent monitoring network of the National Spark Program " Integration and demonstration of corn precise operation technology based on Internet of things " demonstration area at Nong'an county in real time through the intelligent agricultural monitoring platform, research on Spark platform for big data processing of Intelligent Agriculture in Jilin province found that: (1)Spark and Hadoop platform efficiency comparison test results show that,the Spark platform has the advantage of reducing IO overhead with its memory computing,Hadoop is more suitable for dealing with real time big data of intelligent agriculture monitoring network in Jilin province.

(2)The agricultural big data processing method of intelligent agriculture in Jilin Province based on Spark platform,using the machine learning algorithm characterized by dynamic and rapid expansion,combining the Spark streaming flow calculation framework,able to real-time analyze continuous and rapid changes in the massive data.Moreover, the calculation results are faster and more accurate.The implementation of precision agriculture and the wisdom of agricultural big data processing have a certain role in promoting.

(3)For this study,the problem of the data quantity is small, but the monitoring network real-time data processing needs large, in the future, we will further adopt the Spark platform to combine the better clustering algorithm, make the sensor transmission data to be timely and effective treatment,utilize the advantages of large data processing,combine soil moisture content with big data information,constructing perfect intelligent monitoring system of agriculture.

Fig. 1 .

 1 Fig.1. Spark software stack

[ 15 ]

 15 .The drive node, together with all of the actuator nodes, is called a Spark application,as shown in Figure2:

Fig. 2 .

 2 Fig.2. Calculation framework of Spark cluster

Fig. 3 .

 3 Fig.3. Comparison of Hadoop and Spark in execution flow

Fig. 4 .

 4 Fig.4. Comparison of Hadoop and Spark in efficiency test

Fig. 5 .

 5 Fig. 5. Stream processing framework of intelligent agriculture monitoring platform in Jilin province (2)Collecting intelligent monitoring network data Based on the data comes from seven intelligent monitoring network of the National Spark Program "Integration and demonstration of corn precise operation technology based on Internet of things " demonstration area at Nong'an county, collected from May 1, 2015 to December 31, 2016 crop different dimensions (0-20cm, 20-40cm, 40-60cm, 60-80cm) soil temperature and humidity data(as shown in Table3),and import the data into the HBase system .

Fig. 6 .

 6 Fig.6. Analysis of massive soil temperature and humidity data of intelligent agricultural monitoring network in Jilin province A.Moisture trend of 0 ~ 80 cm soil in 2015;B.Temperature trend of 0 ~ 80 cm soil in 2015; C.Moisture trend of 0 ~ 80 cm soil in 2016;D.Temperature trend of 0 ~ 80 cm soil in 2016; E.Moisture trend comparison of 0 ~ 20 cm soil between 2015 and 2016;F.Temperature trend comparison of 0 ~ 20 cm soil between 2015and2016;G.Moisture trend comparison of 40~60 cm soil between 2015 and 2016;H.Temperature trend comparison of 40~60 cm soil between 2015 and 2016.

  County of Jilin Province, located in the hinterland of Songliao plain, and it is the central part of Jilin Province,attached to the city of

	Changchun, located 60 kilometers northwest of Changchun City,east
	longitude124 degrees 31′~125 degrees 45′,north latitude 43 degrees 55′~44
	degrees 55′,the south is adjacent to the suburbs of Changchun,the east borders on
	Dehui,northeast across the riverand to Fuyu,north of the former Guoer Ross
	Mongolian Autonomous County,west of Changling,southwest border with
	Gongzhuling City.County 114.7 kilometers long from north to south,east and
	west 97.7 km wide [ 6 ]

Table 3 .

 3 Soil moisture and temperature data of intelligent agricultural monitoring network in Jilin province

			Water content			Soil temperature(℃ )
	Time	0~	20~	40~	60~	0~	20~	40~	60~
		20cm	40cm	60cm	80cm	20cm	40cm	60cm	80cm
	2015.5	8.75%	15.31%	19.47%	17.43%	14.51	12.63	11.18	10.11
	2015.6	20.62	20.62%	29.38%	28.85%	20.46	18.55	17.06	15.94
	2015.7	% 12.04	20.59%	30.73%	29.81%	24.67	23.07	21.75	20.70
	2015.8	% 14.16	20.91%	31.41%	30.27%	23.79	23.31	22.79	22.24
	2015.9	% 20.69	20.69%	30.09%	29.43%	19.02	19.34	19.47	19.41
	2015.1	% 12.61	19.72%	28.66%	28.13%	9.87	11.36	12.44	13.12
	0 2015.1	% 5.69%	15.78%	25.63%	26.05%	2.22	4.33	5.93	7.04
	1 2015.1	3.51%	9.35%	11.63%	21.92%	-2.51	-0.30	1.38	2.48
	2 2016.1	2.70%	7.47%	6.24%	9.91%	-8.06	-5.20	-2.70	-0.99
	2016.2	4.70%	7.76%	6.15%	8.50%	-6.35	-5.31	-4.14	-3.17
	2016.3	15.33	13.83%	11.73%	11.12%	-0.75	-1.04	-0.98	-0.80
	2016.4	% 15.85	20.74%	25.51%	19.71%	3.96	1.39	0.09	-0.02
	2016.5	% 15.05	20.98%	30.96%	28.80%	12.75	10.86	7.98	8.36
	2016.6	% 14.10	20.92%	31.23%	29.75%	19.73	18.10	16.80	15.81
	2016.7	% 13.05	20.60%	31.57%	30.33%	24.08	22.73	21.58	20.68
	2016.8	% 13.91	22.07%	33.36%	31.36%	24.73	24.25	23.67	23.08
	2016.9	% 18.43	23.13%	34.19%	31.66%	18.82	19.29	19.61	19.74
	2016.1	% 16.76	21.51%	31.16%	29.17%	9.77	11.95	12.65	13.56
	0 2016.1	% 9.18%	18.21%	27.27%	26.54%	1.83	3.79	5.51	6.78
	1 2016.1	7.35%	9.55%	14.36%	21.40%	-2.12	0.18	1.88	3.03
	2

Acknowledgments

This work was funded by the China Spark Program.2015GA660004. "Integration and demonstration of corn precise operation technology based on Internet of things".


Yang Li

Guowei Wang

Yu Chen

Yang Jiao

Haijiao Yu

Guogang Zhao

Application of DBSCAN Algorithm in Precision Fertilization Decision of Maize

Keywords: Data mining, Cluster analysis, Soil classification, DBSCAN algorithm

In the current era of big data, information technology is developing quite rapidly, the most important data mining technology in information technology is also widely used, and now it is applied to the field of agricultural production, what can solve many problems such as agricultural production, fertilization and so on. In this paper, data mining technology is applied to the process of corn fertilization, because in corn production, effective and reasonable amount of fertilizer can make corn grow better, however, if there is no specific fertilization according to the soil properties of the corn, it will lead to the soil which needs fertility can not be with enough fertility, and the soil without fertility will be added more and more. In view of this problem, the soil planted with corn was graded and treated with different levels of soil, so as to achieve the purpose of effective utilization of soil fertility. In this paper, the DBSCAN algorithm in clustering analysis is used to classify the soil, the DBSCAN algorithm to this field have not been reported so far. By applying the nutrient balance method, the amount of soil fertilizer was calculated at each level, and the fertilizer was targeted according to the amount of fertilizer. Through the pilot application in Nong'an County of Jilin province Chen hometown, compared with the traditional fertilization results, Fertilizer reduced by 25%, corn production increased by about 15%, effectively reducing the input of chemical fertilizer and increasing the output of crops.

Introduction

Precision agriculture developed in the early 1980, and precision agriculture is a subject jumping. Precision agriculture is the main system of modern agricultural production operation according to the spatial location, timing, spatial variability and quantitative to achieve, it is mainly the information technology to support, most of the data mining technology [START_REF] Miao Yubin | Research and application of intelligent agricultural production measurement system[END_REF] .

Precision agriculture is mainly composed of intelligent decision-making technology, field information collection technology, and intelligent equipment technology. Through the collection of farmland information for digital analysis and processing, intelligent decision-making, mechanization of farmland information and application of information, can increase crop production, increase efficiency and increase the income of farmers [START_REF] Guifen | Data mining and precision agriculture intelligent decision system[END_REF]. The main idea of precision agriculture is that agricultural production can be adjusted according to local conditions. It can be reasonably invested, not wasted, and managed scientifically. Precision agriculture has achieved orderly management in space and time, and improved the efficiency of agricultural production. Through the use of modern agricultural machinery operation scale, improve the agricultural production efficiency and the efficiency of agricultural production, greatly changed the peasant workers will always work hard in the fields only in traditional agriculture, backward production mode and the "inspiration" extensive industry management [START_REF]Precision Agriculture in the 21st Century, Geospatial and Information Technologies in Crop Management[END_REF] .

The productivity of the soil is mainly composed of soil fertility, soil fertility mainly refers to organic matter, nutrient content, soil texture, soil thickness and other factors in the soil, these factors have different effects on soil fertility, and have different constraints on soil fertility.

For the study of soil fertility classification, there are several situations that appear below: Zhang Yulong et al [START_REF] Yulong | Preliminary study on soil fertility classification using numerical analysis method[END_REF] .applied the mathematical method of principal component analysis and discriminant function discriminant to classify soil fertility. In addition to the mathematical model approach, quantification of soil fertility has also been studied, So far, however, quantitative indicators have not appeared, so that the size of soil fertility cannot be calculated. Liu Changmei and Liu Longchao [START_REF] Changmei | Study on quantitative classification of soil fertility[END_REF] proposed that the soil per unit area under general conditions should be used to measure the amount of soil formed by plants to measure soil fertility. Because of the different types of soil use, the annual crop yield of the farming soil; the annual growth of timber for the forest soil; the grassland soil is measured by the annual grass growth, from an ecological point of view, soils of all types of use can be measured by solar energy (Joules), which is fixed by plants in the unit area throughout the year.

DBSCAN Algorithm

DBSCAN algorithm concepts

DBSCAN algorithm is a density based clustering analysis method. The algorithm defines clusters as the largest set of points connected by density, and divides the regions with high density into clusters. The kernel idea of clustering is to measure the density of the space of the point with the neighbor number in the neighborhood of a point [START_REF] Yiling | Improved OPTICS algorithm and its application in text clustering[END_REF] .For example, the object p is the center, and epsilon is the radius of the region, that is, within the epsilon neighborhood of p, including at least one positive integer Minpts objects, and p is the core object, fields containing objects are clusters, otherwise p is on the boundary of a cluster and is called boundary point [7~8] .The following explains the relevant definitions:

Directly density-reachable

If the p is the core point, q in the -neighborhood of p, the p direct density of up to q.

Density-linked

If an object o exists in the object set D, which causes the object p and q to be reachable from the object o about epsilon and Minpts, then the object p and q are connected to epsilon and Minpts density [START_REF] Kriegel | A density-based algorithm for discovering clusters in large spatial database[C][END_REF] .

The definitions are parsed using Figure 2 below, In Figure 2, given the radius of the field epsilon, the minimum number is Minpts=3.According to the above definition, point m, o, p, and r core objects because there are at least 3 objects in their respective epsilon fields. In addition, it is also observed that m is directly accessible from p and vice versa. q is direct density reachable from m, but m is not directly reachable from q, because q is not the core object. The density from q to m is up to m, and the direct density of p from p can reach up to q so that the density is reachable. Similarly, p and s are density reachable from o, and o is density accessible from r.

As a result, o, r, and s are density-reachable between each other. Output: all clusters that reach the density requirement. Algorithm processing flow:

Step1 Extracts an unprocessed point from the data object;

Step2 IF points out is the core point, THEN finds all objects reachable from the point density, forming a cluster;

Step3 ELSE The point that you take is the edge point (non core object). Jump out of this loop and look for the next point;

Step4 Loop Step1 to Step3 until all points are processed [START_REF] Ester | A Density Based Algorithm for Discovering Clusters in Large Spatial Data-bases with Noise[END_REF] .

Maize precision fertilization

Data Acquisition

In Nong'an County of Jilin province Chen hometown pilot application, access to land information using the 3S technology, grid according to the distance of 40m*40m.Soil sampling was carried out in the divided mesh, and 152 sampling points were obtained, which were collected for soil nutrient content, including four values of organic matter, available phosphorus, available nitrogen and available potassium.

Data Standardization

Since different data has different dimensions, it needs a unified dimension to compare the data, that is to standardize the data. normalization formula is:

g = min max min   G (1)

Soil Fertility Grading

The DBSCAN algorithm is used to cluster the processed data, that is, to classify the soil fertility, and each soil grade is called the cluster. In this paper, WAKA data mining tools are used to download DBSCAN data management package, set (field radius) 0.5, Minpts (epsilon field minimum point) is 10 of the cluster, the soil is divided into six cluster classes, that is, six levels.

According to the number of soil plots at each level, the four attributes of all soils in different levels are calculated, the average value is summed, and sorted according to the size of the sum, and the bigger one is ranked, and so on.

According to the sum of the average values of the attributes in the six clusters, the soil classification results are shown in table 1.

Table 1. Soil classification result

Soil level Included sample points

Class A a140 a83 a80 a3 a22 a54 a21 a131 a51 a90 a19 a20 a57

Class B a40 a31 a11 a6 a149 a28 a314 a119 a64 a13 a151 a16

Class C a70 a142 a100 a148 a109 a121 a47 a95 a96 a62 a116 a44 a39 a146 a10 a33 a38 a88 a7 a9 a147 a86 a130 a138 a14 a8 a139 a108 a144 a45

Class D a35 a17 a145 a120 a23 a97 a71 a41 a143 a123

Class E a1 a63 a133 a18 a89 a69 a84 a15 a50 a106 a60 a4

Class F a55 a53 a81 a43 a59 a103 a105 a120 a78 a124 a75

Calculation of Fertilizer Application

A fertilizer calculation model was established, and the nutrient balance method was used to calculate the amount of fertilizer needed for the nutrient content in different soil levels. We calculate the average of four attributes in different soil levels and substitute them into the formula of nutrient balance method, the required values here are the raw values before data processing, and the data values are shown in table 2. According to the nutrient balance method, the data of Table 2 are replaced by (2), and the specific amount of soil fertility at each level of soil as shown in Table 3 can be obtained.

Conclusions

In this paper, the DBSCAN algorithm is used to classify the soil, and the classification results are applied to the corn precision fertilization decision,The experimental results of demonstration and application in Nong'an County of Jilin province Chen hometown. The average amount of fertilizer is 560 kg/hm2 (the average amount of fertilizer is calculated by the total amount of fertilizer divided by the total area), compared with the traditional fertilizer 608.42kg/hm 2 , saving fertilizer 48.42kg/hm 2 ;The average yield of the experiment is 8313kg/hm 2 , which is 930kg/hm 2 higher than that of the traditional output (the traditional values are derived from the statistical yearbook of China).Indeed, the purpose of reducing chemical fertilizer input, improving soil environment, increasing production and increasing income has been achieved.

Figure 1 .

 1 Figure 1.Directly density-reachable

Figure 2 .

 2 Figure 2.Density-reachable and density-linked

Table 2 .

 2 Average properties of different soil classification

	Soil level	Organic mean	Available phosphorus mean	available nitrogen mean	K mean
	Class A	32.295	19.042	132.538	314.846
	Class B	30.785	23.004	116.083	241.583
	Class C	27.912	18.092	115.500	157.333
	Class D	28.765	16.480	111.700	142.400

Table 3 .

 3 Soil nutrient fertilizer rate

	Soil level	N amount of fertilizer(kg/hm 2 )	P amount of fertilizer(kg/hm 2 )	K amount of fertilizer(kg/hm 2 )
	Class A	440.063	565.009	327.032
	Class B	440.158	564.844	327.171
	Class C	440.161	565.053	327.398
	Class D	440.185	565.135	327.451
	Class E	441.160	565.200	327.465
	Class F	440.276	565.287	327.471

Acknowledgment

The study was conducted by 2016 jilin province rural special project supported by the modern agricultural development《 Demonstration and Application of Traceable System of Quinoa Products Based on Internet of Things and 3S Technology》,Jilin science and technology development plan project,《Research on precision control and control technology for high quality and high efficiency production of major grain crops》(20170204020NY).


Hang Chen
email: chenhang0811@163.com

Guifen Chen
email: guifchen@163.com

Ying Zhang

Hongjun Gu

Research on Agricultural Scientific and Technological Information Dissemination System Based on Complex Network Technology

Keywords: Complex network, Dissemination of scientific and technological information, Centrality, Relationship strength

In view of the complexity of information dissemination in agriculture and the uncertainty of farmers' demand for scientific and technological information, this paper makes a comprehensive and accurate analysis for the dissemination network of scientific and technological information of agriculture based on complex network technology. Firstly, this paper researches the characteristics of the peak degree distribution, average path length and clustering coefficient of a complex network established by the agricultural information dissemination network. Through the research on the three basic characteristics, this paper confirms that the agriculture information dissemination network has the characteristics of scale-free network and small-world network. The method of node research is analyzed by using a complex network. That is that this paper makes a comprehensive analysis for the information propagation speed, scope, analysis and application of dissemination network of agricultural scientific and technological information from degree centrality, betweenness centrality and relationship strength theory. The research results show that the degree centrality can accelerate the information dissemination and be conducive to the accuracy of the information; the betweenness centrality can quickly expand the information dissemination and accurately grasp the degree of control for information resources; the relationship strength theory can reduce the cost of information dissemination and improve the degree of information adoption. This conclusion proves that the application of the analysis method of complex network can effectively improve the speed and quality of agricultural information dissemination network and better serve the agricultural production and farmers' life.

Introduction

In the study of the dissemination of information content, people frequently discussed the spread of infectious viruses in the crowd, the spread of computer viruses in the network and viral marketing activities. China is a big agricultural country, but the study of information dissemination in agriculture is comparatively scarce [1] . The basic position of agriculture in the development of our national economy can not be ignored. It is the source of people's food and clothing and the basis condition for survival. In real life, the majority of the population in China is in the rural areas [2][3][4] . The development of agriculture is directly related to the improvement of the living standards of farmers and the stability of the rural society. Every year, our country will accumulate a large amount of agricultural information. The specific agricultural information data include crop emergence information, soil information, fertilizer information, irrigation information, insects information, weather information, various disaster information, etc [5] . However, in rural areas, the information is often blocked and the farmers can't understand various information timely, which will seriously restrict the development of rural social economy [6] . Therefore, now people should urgently study and solve the problems that how to improve the farmers' ability to obtain information and how to quickly and locally transmit the information to farmers to promote the rapid development of agriculture.

The Complex Network Characteristic of the Dissemination of Scientific and

Technological Information of Agriculture

Distribution of the Degree of Network

The degree distribution of network nodes is one of the important statistical characteristics of complex networks. The degree of a node is usually defined as the number of adjacent nodes and the number of edges connected to the node. The average degree of a network refers to the average value of the degree of all nodes in the whole network, which is usually referred to as <K>. When the nodes with the same degree are counted separately, a distribution diagram of the degree of network nodes can be obtained. We can use the distribution function P(k) to describe the distribution situation of the degree [7] . It represents that when any node was selected in network, the degree of it is exactly the probability of K.

Through the practice of a large number of researchers, it is proved that many networks in real life have power-law distribution, such as Internet network, scientific collaboration networks, protein interaction networks, etc. Such networks usually have the scale-free properties, also known as scale-free networks.

Researches show that the interpersonal relationship network has two statistical characteristics in real life, namely small world characteristics and scale-free characteristics. In rural areas, the rural information dissemination network also has scale-free network characteristics like the complex network [8] .

The average path length

The average path length is the another important statistical characteristic in a complex network. The distance between two nodes in a network has many different definitions. The most commonly used statistical method is to compute the number of edges in the shortest path that connects the two nodes. As shown in Fig. 1, the node A is directly connected to the node B and the distance between the node A and B is 1. As shown in Fig. 2, if node A wants to connect to the node B, it must pass through the node C, and the distance between node A and B is 2. As shown in Fig. 3, there are two paths A-C-B and A-C-D-B to connect node A and node B. If we want to calculate the distance between node A and B, we should adopt the path of A-C-B, so the distance between the node A and B is 2. Network diameter is defined as the maximum distance between two selected nodes in the network. The average path length of a network is defined as the average minimum distance between all node pairs in a network. The average path length and diameter measure the transmission performance and efficiency of the network. In different network structures, the average path length is given different meanings. For example, in urban traffic network, the average path length is often defined as the distance between traffic stations.

Clustering Coefficient

In a complex network, the clustering coefficient of a node is defined as the ratio between the number of edges between all nodes which are adjacent to this node and the maximum number of edges between these adjacent nodes. Suppose that there is a node i, and there are Ki nodes connected with it, then the clustering coefficient of the node(i) is:

(1) In the formula, Ei is the actual number of edges that exist between the ki nodes. The average clustering coefficient of a network is defined as the average value of the clustering coefficients of all nodes contained in the network. In the case of (1), the maximum number of connected edges between ki nodes is ki (Ki-1) /2, then the average clustering coefficient of the network is (2) The average clustering coefficient of the network reflects the aggregation of the network. For example, in a friend relationship network, a person's two friends are likely to be friends with each other. The aggregation coefficient of a network is essentially the study of the probability that the two adjacent nodes of the same node are still the adjacent nodes in the network. It clearly reflects the local characteristics of the complex network.

The Role of Complex Network Technology in the Dissemination of Scientific and

Technological Information of Agriculture

Degree centrality

For a node, the more the number of nodes which are directly associated with it, the stronger the central position of it, the higher the degree centrality of it. In complex networks, most of the nodes which are less directly related to other nodes are in the edge position. Such nodes have very little informal power and influence in the network. The behavior of such actors is usually limited by the actors in the central position [9] . Survey shows that rural big-specialized-households of crop and animal productions have the strongest demand for information resources, followed by the intermediary organization, marketing businesses, wholesale market personnel, agricultural entrepreneurs. The general farmers have the lowest demand for information. People such as the big-specialized-households of crop and animal productions and marketing businesses have higher knowledge level than the general farmers, and can accurately interpret and apply the acquired information. In general, ordinary villagers will have a process to re-read the information they receive. The information transmitted by the mass media is broad and the dissemination content lacks the pertinence, moreover has the strong specialty. For the ordinary farmers in low cultural level, it is difficult to achieve the understanding of the same level. Therefore, these big-specialized-households of crop and animal productions in high cultural level play a crucial role, they can translate the information they receive into the language that ordinary farmers can understand.

They have a high educational level, have a strong ability to receive and acquire information and their horizon is broadened, so they can deliver valuable information to ordinary farmers. Therefore, they have high prestige among the farmers. They can be regarded as "opinion leaders" in the eyes of ordinary farmers, and have a high influence on the attitude and behavior of farmers to accept information. In rural areas, people who can be regarded as "opinion leaders" are locally recognized as the people who have a high cultural level and have a certain prestige among the masses.

In the rural information dissemination network, the general farmers and "opinion leaders" are regarded as nodes in the network, the connection between the general farmers and general farmers, between the general farmers and "opinion leaders" and between the "opinion leaders" and "opinion leaders" are the edges between nodes in the network. In this complex network, the majority of farmers occupy the marginal position, but the "opinion leaders" occupy the central position. The opinion leaders have a high degree centrality in the whole complex network. Therefore, in the process of information dissemination, new information can be first transmitted to the local "opinion leaders" and then transmitted by "opinion leaders" to the ordinary farmers affected by them. By using this transmission mode from the node with a high degree centrality to the node with a low degree centrality, the speed of information dissemination can be accelerated and the scope of information dissemination also can be increased. It is also conducive to the accurate interpretation and application of information.

Betweenness Centrality

In a complex network, the degree centrality of nodes refers to the degree of difficulty and ease of the resource from one node to another. The betweenness centrality of nodes measures the control degree of this node for the resource in the network. The betweenness centrality means that in a network, if the two nodes want to have an association, they must establish a connection through another node. If a node is on the shortest path between the two nodes, then the node is considered to have a high betweenness centrality. The actor with high betweenness centrality can use more opportunities to speed up the dissemination of information. He has a strong ability to control the flow of information. This also shows that he has an important position in the network, because he decides whether or not the other two actors could communicate with each other and also affects whether information resources in the network can spread a large area. Therefore, nodes with high betweenness centrality have the obvious media role in the network transmission.

The rural interpersonal network is a common phenomenon. Fig. 4 shows a typical model of rural interpersonal network. Through the connection among nodes, we can analyze the characteristics of information dissemination in rural areas and the path of information dissemination. According to the nodes and the content of connection, the network can be divided into several different sub networks, such as the village head network, well-informed man network and home core personnel network. The dotted portion in Fig. 4 is a small network combined by four nodes which are connected with each other. The people in this network usually have the common interests and hobbies and share information with each other. In this typical rural interpersonal network, it can be expanded according to the blood relationship, geographical relationship and occupation relationship to form a more complicated rural interpersonal interaction network.

Fig. 4. Rural interpersonal network model

The people who promote the expansion of the network may be the village chief, well-informed people, agricultural demonstration households, or the general villagers in the network. In the network, he may be at the center or on the edge. Such villagers are very important, because they have an important place in the information dissemination network. Such nodes are called the intermediate nodes in complex networks because they directly affect the change of network size. The villagers who are similar to intermediary nodes should be paid enough attention in the process of information dissemination. The approval degree of such villagers for information and their willingly attitude of transmitting information directly determine the stability and scale of the information dissemination network. In the rural information dissemination network, the higher the betweenness centrality of farmers, the more important his position, the greater the impact on the scope of information dissemination. Therefore, the application of the analysis strategy of betweenness centrality of a complex network in the information dissemination of farmers can better control the diffusion range and accurately master the degree of control of information resources.

Relationship Strength

Strong relationship and weak relationship have different functions in the process of formation and operation of an information dissemination network [10] . Strong relationship will usually form a closed ring in the information dissemination network. The members in the ring network generally contact with each other directly. The information obtained by them is often what they have already known. In that way, information will be transmitted repeatedly and shared deeply. The weak relationship will form a non-closed linear relation. The redundancy of information and the cohesiveness of the structure in such network are very low. Therefore, the main role of the strong relationship is to maintain the stability of information dissemination network, improve the cohesion within the organization and deepen the trust and sense of belonging of the organization members to the organization. The weak relationship exists among the people with different interests, so the information shared among them can't be repeated. Therefore, the weak relationship plays a "bridge" role in the dissemination of information in the network. It can transfer the information resources to another person who is not familiar with this information resources group.

As a ubiquitous group phenomenon, relationship strength theory also exists in the complex network of rural information dissemination. Due to the specific rural blood relationship, geographical relationship and occupation relationship, a small closed group with strong relationship will be formed easily among villagers. They have a common recognition, high sense of understanding and comply with the arrangement of the same system. Under the guidance of the high cohesion and behavior coordination of villagers, the cost of information dissemination can be reduced to a greater extent. Through the loose indirect contact, weak connection can integrate more villagers in other groups into the relationship network that it exists. In that way, it can not only be conductive to the villagers to acquire the information with high quality and low repetition rate in a larger scope, but also expand the scope of the dissemination of information.

The relationship strength theory should be applied to the rural information dissemination network. The villagers in strong relationship group should contact new villagers to form the weak relationship, and in this relationship, they should increase interaction frequency, deepen the emotion and enhance the intimacy, so as to strengthen the weak relationship and transform it into the strong relationship. Farmers are busy in spring and autumn. In summer and winter, they are mostly idle, so they have more leisure time to participate in various activities, create the opportunities to communicate with each other and deepen their feelings. Because of the unique lifestyle in rural areas, it is very beneficial to develop new relationships, and the cycle of transforming the weak relationship into the strong ones is also very short. Therefore, the application of the analysis method of relationship strength theory in rural information dissemination network can reduce the cost of information dissemination, accelerate the speed of information dissemination, expand the scope of information dissemination and improve the degree of information adoption.

Conclusion

From the original agriculture to the ancient traditional agriculture, and then to the modern agriculture today, the agricultural development of our country has lasted for thousands of years. Rural information dissemination involves the information flow at different levels and in many fields, such as agriculture, farmers and rural areas. According to the above research results, we can analyze that:

The first one is that because of the rural special geographical relationship, blood relationship and occupation relationship, rural information dissemination network has a small average path length and a large clustering coefficient. The information dissemination network of agriculture has scale-free network characteristics and small world network characteristics.

The second one is that the application of degree centrality theory in rural information dissemination network, the attention of "opinion leaders" effect and the adoption of mode of information dissemination from the villagers with high degree centrality to the villagers with low degree centrality can accelerate the speed of information dissemination, and be conducive to the accurate interpretation and application for the information.

The third one is that the analysis theory of betweenness centrality should be applied to the rural information dissemination network to analyze the villagers with intermediary nature. These villagers should be paid highly attention, their recognition for the information should be enhanced and their enthusiasm of information dissemination should be mobilized. In that way, the scope of information dissemination can be expanded quickly.

The fourth one is that by using the theory of relationship strength, the relationship between villagers can be divided into strong relationship and weak relationship. The strong relation can be used to develop new relation, that is the weak relation, and then the weak relation can be transformed into the strong relation. Because of the unique work and rest rules in rural areas, this transformation can be completed quickly. The application of relationship strength theory can reduce the cost of information dissemination cost, accelerate the speed of information dissemination, expand the scope of information dissemination and improve the adoption of information.

Fig. 1 . 2 .

 12 Fig. 1. Direct connection Fig. 2. Connection with a midpoint Fig. 3. Multipath connection

Acknowledgements.The authors wish to express their gratitude to the national spark program project: Precise operation technology integration and demonstration of corn (No. 2015GA660004) for its generous support of this work.


Alain Portavoce
email: alain.portavoce@im2np.fr

Anthony De Luca
email: banthony.deluca@northwestern.edu

Nelly Burle
email: cnelly.burle@im2np.fr

Michaël Texier
email: dmichael.texier@im2np.fr

Redistribution of Metallic Impurities in Si during Annealing and Oxidation: W and Fe

Keywords: Silicon, Diffusion, Oxidation, Tungsten, Iron

Atomic redistribution of W and Fe in Si were studied using secondary ion mass spectrometry and transmission electron microscopy. W diffusion experiments performed during isothermal annealing and during Si oxidation show that W atoms should use at least two different diffusion mechanisms. Experimental diffusion profiles can be well simulated by considering the simultaneous use of three different W diffusion mechanisms: the dissociative and the kick-out mechanisms, as well as an original mechanism based on the formation of a W-Si self-interstitial pair located on the interstitial Si sub-lattice. Fe redistribution was studied during the oxidation of a Fe-contaminated Si wafer. Fe is shown to be first pushed-out in Si by the mobile SiO 2 /Si interface, and thus to form Fe silicides at this interface. The silicide precipitates, which can exhibit a coreshell structure, appear to move with the SiO 2 /Si interface thanks to an oxidation/dissolution mechanism in the SiO 2 and a nucleation/growth mechanism in the Si matrix. Furthermore, the rate difference between Si and Fe silicide precipitate oxidation leads to the formation of Si pyramidal defects at the SiO 2 /Si interface.

Introduction

It is well known that metallic contamination of silicon (Si) lead to strong device performance decay. They create deep levels in Si band gap behaving as acceptor or donor levels, decreasing minority carrier lifetime and minority carrier diffusion length. Furthermore, contaminated Si devices show significant leakage current. Due to microelectronic device size reductions, the current tolerable contamination concentration levels are particularly low (~ 10 10 cm -2 ). These very low levels cannot be detected with conventional surface analysis techniques such as Auger electron spectroscopy, X-ray photoelectron spectroscopy, and ultraviolet photoelectron spectroscopy, and are also too low to be detected with usual bulk analysis tools such as secondary ion mass spectrometry, complicating device failure analyses. It is thus necessary to identify contaminant possible effects on fabrication processes, and to be able to simulate contamination redistribution at levels lower than detection limits during device fabrication, in order to potentially identify their influence on device electrical properties [1][2][3].

Tungsten (W) contamination leads to significant device performance degradations [START_REF] Davis | Characterization of the effects of metallic impurities on silicon solar cell performance[END_REF][START_REF] Gui | IEEE International Conference on Semiconductor Electronics[END_REF][START_REF] Fujisaki | [END_REF], leading to the introduction of three different levels in Si gap at E c -0.59 eV, E v + 0.33 eV and E v + 0.22 eV [7]. W solubility in Si is expected to be high compared to other metals, with a maximum solubility limit higher than 10 20 cm -3 at 1100 °C [START_REF] Fujisaki | [END_REF]. However, W diffusion kinetic is unknown, the few measurements performed to date suggesting diffusion kinetic similar to usual Si dopants [START_REF] Graff | Metal impurities in silicon-device fabrication[END_REF]. Furthermore, these measurements suggest that W atoms use complex diffusion mechanisms different from the mechanisms of known metals [START_REF] Fujisaki | [END_REF][7]9]. Iron (Fe) diffusion in Si is well known, since Fe is one of the most common contaminants in Si wafer due to multiple possible sources during microelectronic fabrication processes. Fe is a fast-diffusing specie in Si due to the use of the direct interstitial diffusion mechanism [START_REF] Graff | Metal impurities in silicon-device fabrication[END_REF]10]. Fe solubility limit in Si is quite low, with a maximum close to 3 × 10 17 cm -3 at 1300 °C [10][11].

In the present work, the redistributions of W and Fe atoms in Si are investigated during thermal annealing and during Si thermal oxidation. In a first part, a detailed study of W diffusion is presented, showing that W probably uses an original diffusion mechanism, involving interstitial W-Si self-interstitial pair diffusion [12]. In the second part, the mechanisms involved with Fe redistribution at the mobile SiO 2 /Si interface during Si oxidation are presented, allowing Si pyramidal defect formation at this interface [13] to be understood [14].

Experiments

Commercial n-type phosphorus-doped Si(001) wafers were implanted with W or Fe atoms using the industrial implanter IMC200 developed by the company IBS. Ions were implanted at an angle of 7° compared to the normal of the sample surface, with an ion beam energy of 165 keV and 65 keV for W and Fe, respectively. Two types of samples were prepared with two different ion doses: 1 × 10 13 and 1 × 10 15 ions cm -2 .

For diffusion experiments, the samples were pre-annealed at 776 °C for 1 h under vacuum (pressure < 10 -7 Torr) in order to cure the samples from implantation-induced defects, creating an initial diffusion source that could be used as a reference state for following diffusion annealing. For oxidation experiments, the implanted samples were annealed at 900 or 1100 °C for various times under an O 2 flux of 4-5 L min -1 .

Secondary ion mass spectrometry (SIMS) measurements were performed using a Cameca IMS 7F apparatus with a primary ion O + energy of 3 keV. High resolution (HR) transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) high angle annular dark field (HAADF) analyses were performed using a FEI Titan 80-300 Cs-corrected microscope operating at 200 kV and 300 kV.

Results and Discussion

W diffusion. Only samples implanted with the 1 × 10 13 W cm -2 dose were used for diffusion studies, as no defect could be detected by TEM in these samples after pre-annealing. In these samples, the W concentration is lower than the W solubility limit in Si in the temperature range investigated. The implantation of the 1 × 10 15 W dose in Si leads to ~ 100 nm-deep Si amorphization, and W-Si clusters form during subsequent annealing, degrading SIMS measurement accuracy [12]. Fig. 1 present SIMS profiles measured in the sample implanted with 10 13 W at cm -2 after pre-annealing and different subsequent diffusion annealing. Annealing at 776 °C for 48 hours did not allow W diffusion, the profile after annealing (solid squares) being similar to the profile measured after pre-annealing (solid line). However, W diffusion occurred for annealing at higher temperatures for the same time. In this case, all the W atoms were mobile, but several singular behaviors can be noted on W diffusion profiles, which cannot be reproduced by classical Fickian diffusion models or usual models for dopant or metallic impurity diffusion in Si: i) the initial Gaussian distribution does not change into a flatter Gaussian distribution after diffusion as expected for classical Fickian diffusion, ii) the maximum concentration of the W distribution increases and shifts to the surface during annealing, iii) leading to a W diffusion profile exhibiting a quasi-linear shape. The observations related to the maximum concentration of the profiles could be explained by the well-known dissociative or kick-out diffusion mechanisms used by some metallic impurities in Si such as Au or Pt, promoting a 'U-shape' diffusion profile between the two surfaces of a Si wafer

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Diffusion in Materials DIMAT-2017 [15][16]. However, the linear shape of W diffusion profile could not be reproduced by these diffusion models. In order to better understand W atoms interactions with point defects, the samples implanted with a W dose of 1 × 10 15 ions cm -2 were oxidized at 900 and 1100 °C. Fig. 2 presents TEM measurements performed on two pieces of a same sample after annealing at 900 °C for 3 hours with an O 2 flux allowing for Si oxidation (Fig. 2a) or without oxygen (Fig. 2b). Si oxidation is known for injecting Si self-interstitials (I) in Si [START_REF] Fahey | Point defects and dopant diffusion in silicon[END_REF][START_REF] Pichler | Intrinsic Point Defects, Impurities, and Their Diffusion in Silicon[END_REF]. In Fig. 2a we observe that in this condition W silicide clusters are formed in the Si substrate, while it is not observed after similar annealing without selfinterstitial injection (Fig. 2b), suggesting attractive chemical interactions between W atoms and Si self-interstitials. Furthermore, Fig. 3 shows that W diffusion is enhanced for a small fraction of W atoms (concentration lower than ~ 2 × 10 17 cm -3 ) compared to without oxidation, suggesting that a fraction of W atoms uses a self-interstitial-mediated diffusion mechanism. Thus, we proposed that substitutional W atoms (W s ) use simultaneously two different diffusion mechanisms based on three point-defect reactions in order to move in Si [START_REF] Fujisaki | [END_REF][7]: i) the dissociative mechanism (W i + V  W s ) using vacancies (V), ii) the kick-out mechanism (W i  W s + I), and iii) a mechanism based on the formation of an atomic pair WI, from an interstitial W atom (W i ) and a Si self-interstitial (W i + I  WI). In this case, the experimental diffusion profiles can be reproduced as shown in Fig. 1 (cross line) [12]. W solubility limit was found to be close to 0.15%-0.2%, and the diffusion coefficient of W atoms on interstitial sites was found to be D Wi = 5.59 × 10 -5 exp (-1.35 eV/kT) cm 2 s -1 , while the diffusion coefficient of the WI pair was found to be D WI = 2.68 × 10 -5 exp(-2.39 eV/kT) cm 2 s -1 . Fe redistribution. The study of oxidation of Fe-implanted Si substrates allowed six steps in the Fe redistribution process to be determined [14]. Fig. 4 presents Fe SIMS profiles measured after implantation (solid line) and after subsequent oxidation performed at 900 °C for 5 (Fig. 4a) and 592 minutes (Fig. 4b). As soon as Si oxidation starts (Fig. 4a, consumption of 6.6 nm of Si) Fe atoms are pushed back into the Si substrate, leading to Fe accumulation at the SiO 2 /Si interface, corresponding to the first step of redistribution schematized in Fig. 5. The comparison between the profiles measured before and after oxidation in Fig. 4a shows that Fe atoms diffused into the substrate at concentrations significantly higher than the Fe solubility limit in Si. This effect could be related to Si self-interstitial injection resulting from the Si oxidation interfacial reaction. In the second step, due to the increase of the Fe concentration at the SiO 2 /Si interface during oxidation, Fe silicide precipitates form at this interface and grow in the Si substrate. Fig. 6-1 presents a TEM image of one of the Fe silicide precipitates formed at the SiO 2 /Si interface. The precipitates exhibit facets in the Si substrate, while their interface with the SiO 2 layer is flat. Their orthorhombic structure corresponds to the phase β-FeSi 2 . In a third step, while Si oxidation progresses, the Fe-Si precipitates stay located on the mobile SiO 2 /Si interface, while part of their volume get incorporated in the SiO 2 layer. Fig. 6-2 shows a TEM image of a β-FeSi 2 precipitate at this stage (step 3 in fig. 5). Almost half of the precipitate is locate in SiO 2 and half is located in Si. It can be noted that the precipitate exhibits a spherical shape at the interface with amorphous SiO 2 , while its interface with monocrystalline Si is facetted. At that

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Diffusion in Materials DIMAT-2017 stage, the precipitates appear to move with the SiO 2 /Si interface (no precipitate could be detected in SiO 2 and in Si by TEM), probably thanks to an oxidation/dissolution process of the precipitate in SiO 2 and a growth process in Si supported by interfacial atomic diffusion. Later, due to the oxidation rate difference between Si and Fe silicide (Si oxidation faster than Fe silicide oxidation), the SiO 2 /Si interface get deeper than the clusters, forming a Si neck underneath the precipitates (step 4 in fig. 5). Furthermore, the push-out mechanism of Fe atoms from SiO 2 to Si during the oxidation of Fe-Si precipitates leads to a simultaneous increase of Fe atoms and decrease of Si atoms at the edge of the precipitates, promoting the formation of core-shell Fe silicide precipitates, with a Si-rich core corresponding to the initial β-FeSi 2 phase (red color in step 5 in fig. 5) and a Fe-rich shell corresponding to the α'-Fe 3 Si phase (green color in step 5 in fig. 5). Fig. 6-3 presents a TEM image of a core-shell precipitate fully incorporated in the SiO 2 layer, with the Si neck formed underneath, due to a masking-like effect of the cluster during oxidation. In a last step, the Fe-Si clusters get finally totally oxidized (step 6 in fig. 5), leaving a pure Si pyramid-like defect at the SiO 2 /Si interface, as well as small Fe-rich clusters incorporated in SiO 2 , surrounding the pyramids, as shown in the STEM-HAADF image 4 in fig. 6.

Summary

W diffusion in Si was studied using SIMS profiles and TEM observations. W diffusion profiles cannot be simulated using standard diffusion mechanisms used for known Si impurities. However, experimental observations could be simulated using a model including two simultaneous diffusion mechanisms, based on three W-point defect reactions: the dissociative and the kick-out reactions, as well as an original reaction leading to the formation of W-Si self-interstitial pair located on the Si interstitial lattice. Ab initio calculations should be performed in order to confirm the validity of this model. Fe redistribution during the oxidation of Fe-contaminated Si was also studied using SIMS and TEM measurements. Fe atoms are observed to diffuse in Si for concentrations significantly larger than the Fe solubility limit at the beginning of oxidation. Fe precipitates nucleate and grow at the SiO 2 /Si interface during oxidation, forming Fe silicides. The silicide precipitates lead to the formation of pyramidal Si defects at the SiO 2 /Si interface due to a precipitate masking-like effect linked to the oxidation rate difference between silicide clusters and Si.

This work was supported by the French Ministry of Industry in the framework of the project COMET (Grant No. FUI-AAP9-COMET).

Fig. 1 .

 1 Fig. 1. W SIMS profiles measured after pre-annealing (solid line) and a subsequent diffusion annealing at 776 (solid squares), 837 (open triangles), 886 (solid circles), and 934 °C (open squares) for 48 hours. The cross line corresponds to the simulation of the annealing performed at 934 °C for 48 h.

Fig. 2 .

 2 Fig. 2. Cross-sectional TEM images obtained on the sample implanted with a W dose of 1 × 10 15 ions cm -2 , after annealing at 900 °C for 3 hours: a) with oxidation, and b) without oxidation.

Fig. 3 .

 3 Fig. 3. SIMS profile showing the W distribution in the sample implanted with 1 × 10 15 W at cm -2 , after oxidation at 1100 °C for 44 minutes. The arrow shows a diffusion tail observed for C W ≤ 2 × 10 17 cm -3 .

Fig. 4 .

 4 Fig. 4. Fe SIMS profiles measured in the Si substrate after (solid line) and after oxidation at 900 °C (a) for 5 min (open circles), and b) for 592 min (open triangles). The colored area corresponds to the grown SiO 2 layer. The arrow shows the location of the Fe concentration maximum after oxidation.

Fig. 5 .

 5 Fig. 5. Schematics showing the 6 steps observed during the oxidation of Fe-contaminated Si substrates (black line for the SiO 2 /Si interface, red color for Fe-rich regions and precipitates).

Fig. 6 .

 6 Fig.6. Cross-sectional HR TEM (1-3) and HR STEM-HAADF (4) obtained on the sample implanted with a dose of 1 × 10 15 Fe at cm -2 , after annealing (from left to right) 1) at 900 °C for 5 min, 2) and 3) at 900 °C for 592 min, and 4) at 1100 °C for 12 min. The dash line in image 3 delimitates the interface of a Fe-Si precipitate.

Defect and Diffusion Forum Vol. 383