Guifen Chen

Yuqin Yang

Hongliang Guo

Xionghui Sun

Hang Chen

Lixia Cai

Analysis and Research of K-means algorithm in soil fertility Based on Hadoop platform

Keywords: K-means algorithm, Hadoop platform, MapReduce model, Soil fertility

In order to study the K -means algorithm for evaluation of soil fertility, solve the large amount of calculation and high time complexity of the algorithm,this paper proposes the K-means algorithm based on Hadoop platform.First, K-means algorithm is used to cluster for Nongan town soil nutrient data for nine consecutive years;clustering results show that : the accuracy rate increased year by year, and consistent with the actual situation.Then for the K-means clustering algorithm in processing large amounts of data has the disadvantages of high time complexity, This paper uses the K-means algorithm Based on Hadoop platform to realize the clustering analysis of soil fertility of large amounts of data;the results show that: compared with the traditional serial K-means algorithms, improves the operation speed. The above analysis shows that, K-means algorithm is an effective soil fertility evaluation method;Based on Hadoop platform of parallel K-means algorithm has great realistic meaning to analysis of large amount of data of soil fertility factors.

Introduction

With the wide application of agricultural information technology, 3S technology (GPS, GIS, RS),the Internet of things technology and Expert System (ES) technology are applied extensively in precision agriculture,so that the rapid growth of the agricultural sector data [START_REF] Turner | Land use and land cover in global environmental change: considerations for study[END_REF] .K -means algorithm based on Hadoop platform, can quickly and accurately to the large amount of data of soil fertility carries on the comprehensive evaluation and correct analysis,is of great significance to guide farmers reasonable fertilization. Xiong Chunhong for Jiang Xi tea area soil fertility problems and the status of the heavy metals of fresh tea leaves,she uses mining techniques were analyzed and predicted, creating favorable conditions for soil fertility analysis and comprehensive evaluation [START_REF] Xiong | Assesment and Prediction on Heavy Metals Status for Soils and Fresh Tea Leaves in Jiangxi Major Tea Regions Based on GIS Data Mining Technology[END_REF] .Li Lianghou proposed application of clustering anslysis in classifying site type and evaluating soil fertility [START_REF] Li | Application of Clustering Analysis in Classifying Site Type and Evaluating Soil Fertility[END_REF] .The research on weighted space fuzzy dynamic clustering algorithm by Chen Gui-fen, proved the effectiveness of soil fertility evaluation [START_REF] Chen | Application of Weighted Spatially Fuzzy Dynamic Clustering Algorithm in Evaluation of Soil Fertility[J][END_REF] .The traditional clustering algorithm in processing large-scale data in terms of efficiency of real-time or from the angle of system resources, are not well resolved.In the clustering algorithm, K-means algorithm is the most widely used clustering algorithm based on partition, it has the advantages of rapid and simple; but it has the disadvantage of large amount of calculation and sensitive to the initialization center. In contrast,this paper studies the K-means clustering algorithm based on Hadoop platform of MapReduce parallel programming method, and relevant experiments were carried out for the large-scale soil fertility data .

2.Key technologies and algorithms introduced

MapReduce model

MapReduce is a distributed parallel computing model proposed by Google Labs, its basic idea [START_REF] Qian | Research and implementation of large-scale dataclustering techniques[END_REF][START_REF] Li | Research and implementation of Hadoop +Mahout intelligent terminal based cloud application recommendation engine[END_REF] : (1)The MapReduce database to user program input data set is divided into several small data set,tthen fork copies user processes to other machines in the cluster;(2)A copy of user program has a called master is responsible for scheduling, allocation of jobs to idle worker(Map worker or Reduce worker); (3)After the worker is assigned a Map job, read data from the input data and extract key value pairs, performs Map computation tasks and generate the intermediate key value pairs,and then cached in memory;(4)The middle of the cache key value to be written to a local disk regularly,and is divided into R districts,each corresponding to a Reduce worker, master is responsible for forwarding the middle of the key positions to Reduce worker;(5)Reduce worker read the intermediate key value pairs and to sort them out,so that the same keys of the key to gather together;(6)After sorting the intermediate key value to be Reduce worker traversal, and then for each unique keys, reduce worker will pass key and values associated with the key to the Reduce function, the output generated by the Reduce function will be added to the output file in the partition; (7)When all the Map and Reduce operations are completed, master wakes genuine user program,MapReduce function call returns user program code. [START_REF] Li | Research and implementation of Hadoop +Mahout intelligent terminal based cloud application recommendation engine[END_REF] MapReduce operation process shown in Fig1 [START_REF] Li | Review of MapReduce parallel programming model[END_REF][START_REF] Liu | Open the shortcut leading to the actual Hadoop cloud computing[END_REF] : Fig. 1 MapReduce operation

K-means algorithm

K-means algorithm is a clustering algorithm based on partition method, it is one of the earliest proposed classic clustering algorithm . The main idea of the K-means algorithm is the n object into the k class (cluster) (k ≤ n).In this experiment we refer to the "National Survey of cultivated land fertility and quality evaluation of technical regulations" in the grading standard,Nong'an basic farmland is divided into six grades [START_REF] Chen | Research and Application of Spatial Data Mining Technology for Precision Agriculture[END_REF] .First of all, in the whole soil fertility data set we randomly selected k objects,each object represents an initial cluster center or the initial average.For each remaining object, according to its distance from the center of each cluster, assign it to the nearest cluster.And then calculate the average of each cluster,each object in the database is compared with the average value of each cluster,the object is assigned to the most similar clusters.This process is repeated until the cluster objects are"similar", while objects in different clusters are "dissimilar",namely the criterion function converges and make the minimum square error function.

K -means algorithm steps: Algorithm: K -means. Division of the K-means algorithm based on the average of the objects in the cluster. Input: the number of clusters k and the database contains n objects. Output: k clusters, make the least square error criterion. Methods:

(1) Choose k objects as the initial cluster centers;

(2) Repeat;

(3) According to the average of the objects in the cluster,each object (again) is assigned to the most similar cluster;

(4) Update the average of the cluster, namely calculating the average of objects in each cluster;

(5) To calculate the clustering criterion function;

(6) Until criterion function will not change. [START_REF] Pan | Research and Application of Mining parallel K-means based on meteorological data cloud[END_REF] The disadvantage of K-means algorithm:When using K-means clustering algorithm,k values are given in advance.Normally,We don't know a given data set should be divided into many classes is the most suitable, but in this experiment, we are aiming at soil fertility data, according to the soil fertility grading standards and past experience we can easily set the K value, so in this experiment we do not need to consider this problem;The initial cluster centers are randomly selected, select the initial cluster center has a great influence on the clustering results, once the selection is not good initial values, we may not be able to get the clustering results effectively;K-means algorithm needs to be constantly adjusted sample classification and calculate new cluster centers; therefore, the time complexity of the K-means algorithm is relatively large, and increases with the amount of data [START_REF] Naldi | Evolutionary K-means for distributed data sets[END_REF] ;At the same time, the clustering result of K-means clustering algorithm is easily affected by noise data.;With the application of information technology in agricultural field, the soil fertility data quantity increases,just K-means algorithm of large amount of calculation and high time complexity aspects,this experiment made improvements, and achieved good results.

K-means clustering algorithm to achieve MapReduce

Datasets of MapReduce processing should have such characteristics: it can be broken down into many small data sets, and each of the small data set can be completely processed in paralle [START_REF] Liu | Open the shortcut leading to the actual Hadoop cloud computing[END_REF][START_REF] Wang | G-Hadoop: MapReduce across distributed data centers for data-intensive computing[END_REF] ,soil fertility data is well positioned to meet this demand .The basic idea is that K-means clustering algorithm actualizes MapReduce:each iteration start a MapReduce process, MapReduce to complete data records to the cluster center distance calculation and the new clustering center calculation.Fig2 describes the K-means clustering algorithm MapReduce parallel implementation [START_REF] Zhou | Realization of K-means clustering algorithm based on Hadoop[END_REF] .According to the computing needs of MapReduce, preprocessing data records in the form of rows are stored, so that pre-treatment data can be fragmented by row, and no correlation between the pieces of data, fragmentation process is completed by the MapReduce model, without writing code.

Map function design

The task of the Map function is progressive to read data of soil fertility from the Hadoop Distributed File System (HDFS) file,, calculate the distance from the center to each record and re-mark the new category it belongs to the cluster. The input of Map function is the original data set and the last round of iteration (or initial clustering) clustering center , input data records <key,value>, ie <row number of rows>; output of intermediate results <key, value> that <record category, record> [START_REF] Srirama | Adapting scientific computing problems to clouds using MapReduce[END_REF][START_REF] Xie | Research on Application of Hadoopbased on MapReduce model[END_REF] .

Map To write <Record Category, Record> into the intermediate file ; } [START_REF] Li | Review of MapReduce parallel programming model[END_REF][START_REF] Xie | Research on Application of Hadoopbased on MapReduce model[END_REF]

Reduce function design

The task of Reduce function is based on the output of the Map function, update the clustering center,for the next round of the Map function. Meanwhile,calculating the value of standard measurement function, for the main function whether iteration is over.Before performing the Reduce function,MapReduce will be merged with the result of output in the middle of the Map function,the intermediate results in multiple <key,value> have the same key value pair combined into a pair. the Reduce function's output <key,value> is <Record Category, {record collections} > [START_REF] Srirama | Adapting scientific computing problems to clouds using MapReduce[END_REF][START_REF] Xie | Research on Application of Hadoopbased on MapReduce model[END_REF] . The output <key,value> is<category No., the mean vector + the sum of the squared error of the class>.

Reduce function specifically described as follows: Public void reduce(Text key,Iterable<Text> values, Context context{ for( The same key for all records) { Averaging each attribute; Calculated the record to its centroid distance The above distance sum ; } The <Record Category, the mean vector + sum of square error for each class > write the results file;} [START_REF] Xie | Research on Application of Hadoopbased on MapReduce model[END_REF][START_REF] Zhao | Parallelk-meansclustering algorithm designed Hadoop cloudbase computing platform[END_REF] In the main function called MapReduce process described above, each iteration apply for a new job, until the difference between the sum of the squares of the errors and the last time is less than a given threshold,the iteration ends. Map function last intermediate result is the final results of classification [START_REF] Xie | Research on Application of Hadoopbased on MapReduce model[END_REF][START_REF] Li | PageRank algorithm block matrix under Hadoop-MapReduce[END_REF][START_REF] Jiang | K-means clustering algorithm MapReduce parallel realization[END_REF] .

Experiments and results analysis

Experimental datasources

In recent years, with the application of information technology in agricultural field, we also get a lot of correlated with soil fertility data.The experimental data is mainly from the national "863" plan-"research and application of corn precise operating system" [START_REF] Chen | Research status and development trend of precision fertilization technology[END_REF] project demonstration base-NongAn country of JiLin Province for many years conducted aprecision fertilization after soil fertility data applied research.This paper select the representative soil nutrient data to integrated analysis, such as, Alkaline hydrolysis nitrogen, Available potassium and Available phosphorus. The above clustering results showed that:after continuous precision fertilization, three comprehensive similarity of entire plots of soil nutrient data that are Alkaline hydrolysis nitrogen、Available phosphorus and Available potassium increased year by year,soil fertility tends to bebalanced. Experimental results consistent with the actual situation,indicating that K-means algorithm is an effective soil fertility evaluation method. However, with the passage of time, the data of soil nutrients in Nongan county is increasing year by year, environmental factors associated with the soil fertility is considered, the disadvantages of K-means algorithm which are the large amount of calculation and high time complexity becomes more obviously , So we propose the K-means algorithm based on Hadoop platform.

4.3K-means algorithm based on Hadoop platform to achieve

In the Hadoop environment, the data file stored in Hadoop Distributed File System(HDFS) will be automatically divided into a plurality of data block, each size is 64MB. In Map stage , each data block is processed by a Map task, the whole data files can be assigned to different nodes. Assume that each node can perform m tasks, there are n nodes involved in parallel computing, parallel K-means algorithm's time complexity is n*k*t*o/ (m*n), Compared with the time complexity of the serial algorithm n * k * t * o ,parallel K-means algorithm can significantly improve the operation efficiency .

To analyze the advantages of Hadoop platform in large amounts of data processing, in the case of the same hardware configuration environment, the same size of data,We performed serial K-means algorithm and Hadoop platform K-means algorithm computation time comparison. The experimental results are shown in Tab 2, where T1 is the operation time of serial algorithm, T2 is the operation time of the algorithm under the framework of MapReduce.

Tab.2 Comparison of experimental results

As seen from Tab 2, when the data size is less than 109,794KB, the efficiency of the serial K-means algorithm is higher than K-means algorithm under the framework of single MapReduce, this is because, MapReduce task divides one task into two different stages of Map and Reduce. Data flow implementation process is: (key, value) -->Map stage --> (K1, V1), (K1, V2), (K2, V3) ->sort&shuffle --> (K1, list (V1, V2), (K2, V3) --> Reduce stage --> HDFS, the iterative MapReduce that requires constant read, write and transmit data ;with the increasing of soil fertility data quantity, Running the serial task of the machine,the memory and other resource consumption increases,which will lead to the decline of the performance of the machine; when the data size reach to 114,786KB, it will report that the internal memory is insufficient. While, K-means algorithm under the MapReduce framework is able to cope continues to increase the amount of data, and complete the computing task of large-scale data successfully.

Results and Discussion

Research on the NongAn town 2005 to 2013 soil nutrient data clustering showed that when dealing with large amounts of data, Based on Hadoop platform of parallel K-means algorithm have good operational results and practical significance .

(1)K-means algorithm is used to cluster for Nongan town soil nutrient data for nine consecutive years (2005-2013).Experiments show that after continuous precise fertilization ,comprehensive similarity of entire plots of soil nutrient data increased every year ,the soil fertility tends to equilibrium .Experimental results consistent with the actual situation, indicating that K-means algorithm is an effective soil fertility evaluation method.

(2)K-means algorithm based on Hadoop platform, When dealing with small amount of data, Serial K-means algorithm efficiency is better than under the MapReduce framework parallel K-means algorithm. Because when a small amount of data , in the MapReduce framework of K-means algorithm for each iteration, restarting a new JobTracker, the startup and interactive process will consume some resources.

Serial

File(KB) Records(105) t1(s) t2(s) (3)K-means algorithm based on Hadoop platform, when dealing with large amount of data , Hadoop platform can be well done the calculate of the large amounts of the soil fertility information data , which fully reflects the capabilities and advantages of the Hadoop's processing big data .Running the serial task of the machine,the memory and other resource consumption increases and causes the machine performance degradation,which lead to report insufficient memory and this can not meet the needs of data growth.

(4)With the increase of the amount of data, the Value that the difference of running time under the MapReduce framework divided by the total time tend smaller ,which reflects the stability and reliability of Hadoop platform.

Fig. 2 K

 2 Fig. 2 K -means clustering algorithm MapReduce parallel implementation

  function specifically described as follows: Public void map(Object key,Text value ,Context context){ To calculate the distance the record to each center of mass; Compare the above distance; The record boils down to the class of the nearest centroid;

1 Fig3

 1 Fig3.Clustering results

Acknowledgements

This work was supported by the national " 863 " project (2006AA10A309), National Spark Plan (2008GA661003) and Shi Hang of Jilin province projects (2011-Z20).


Guogang Zhao
email: zhaoguogang2000@qq.com

Haiye Yu

Guowei Wang

Yuanyuan Sui

Lei Zhang

Study On the Detection and Warning System of Rice Disease Based on the GIS and IOT in Jilin Province

Keywords: GIS, Internet Of Things, rice disease, monitor, warning

Rice disease which has intimate connection with climate has a tremendous impact on the output and quality of rice. The present paper intends to construct a web to monitor the climate environment of rice growing by making use of IOT(Internet of things) and to exactly spot the regions where related statistics are collected and warned by taking advantage of GIS and then by means of C# language and SQL server, to realize the visuality of system and database management. Thereafter, under fuzzy clustering algorithm based on the collected environmental data to judge whether the rice disease will happen and need warning in case that unnecessary lose is caused.The finding reveals that by practicing the present system in Rice Disease Department of Academy of Agricultural Science of Chang Chun, the system functions well in detecting the serious disease.

Introduction

Jilin Province is advantageous in grain production and is also one of important commercial commodity grain base. Rice is one of three kinds of corps in jilin province and thus rice disease has influenced the output and quality of rice. Hense, to construct the disease detection system is necessary. Currently, the previous studies on rice disease are as follows: Su Mingyong with his fellows collected the monitoring data and analyzed the data in order to spread the information and put forward the scientific motivation for treatment [START_REF] Mingyong | Construction of rice diseases and insect pests prevention and control system[END_REF][START_REF] Yinyin | Policy research of the rice pest control system in guangdong province[END_REF]. Yuan Tao with his co-workers realized the prior warning by means of case inference technology based on the symptoms of disease [START_REF] Tao | Research on the Comprehensive Prevention and Control System of Rice Diseases and Insect Pests in Yangtze River Farm[END_REF]. However, the above studies obviously just focused on the disease rather than preventing before the disease. Xiao Deqin studied wireless water conductor collecting the data to analyze the water in the rice field, which innovate the previous studies lying in the wireless water conductor that is able to realize to conduct water on a wider range and more remote distance. Besides [START_REF] Deqin | Design and experiment of wireless sensor networks for paddyfield moisture monitoring[END_REF], there are more researches which are related to rice disease based on specialist system and database [START_REF] Xu | Construction of information service platform of integrated pest control of rice based on service-oriented architecture(SOA)[END_REF][START_REF] Min | A Web-based expert system for diagnosis and control management of diseases in vegetable crops cultivated under protected conditions[END_REF][START_REF] Lai | etal.An Image-Based diagnostic expert system for corn diseases[J].Agricultural[END_REF][START_REF] Gonzalez-Andujar | Expert system for pests,diseases and weeds identification in olive crops[END_REF][START_REF] Ss.kashkash | fayyad M.Developing an expert system for plant diseases diagnosis[END_REF]. However, the approaches above are limited on the range of database to infer rather than realize the real-time monitoring based on the latest statistics and then make the policy. The paper will build a wireless conductor net based on IOT mainly being reflected by water temperature, air moisture and daylight illumination of rice field to collect data on which the calculating ways are ensured and then days among monitoring which are mot the same as the set temperature humidity level and illumination are recorded to forecast whether there will be rice disease in the chosen rice field. At this time, GIS will work to present the current number for the above items and their corresponding location. If there needs warning, then the mark will be made in the map. Meanwhile, the message will be sent to the farmer by mobile phone in order to make exact measurement to save rather than lose. So the significance of the present study lies in the above innovation.

System Design

The c/s construction model is adopted to realize the serving way of multiply customer enjoying the data. The framework of the system is as follows: (see fig. 1) Fig. 1. the whole framework of system

Data Communication Agreement

Data communication agreement is a series of agreement which aims to guarantee the communicators to effectively and reliably communicate within the data communication web. In the IOT, communication between server and hardware, between sever and various clients needs to be realized. Therefore, the present communication agreement is the key to ensure the function of system, which adopts statistics agreement as the follows:"data-node number-electricity-air-temperatureair humidity-illumination-water temperature."When the system receives the message from the monitoring equipment, the clipping is made according to the communication format to store in the right database.

Data collection

Data is collected by Socket programmer within C# language. The working procedure is as fig. 2.

Fig. 2. Working procedure of Socket

Function of GIS

GIS not only presents where the data is from but also exactly tells the disease regions when the warning message is got, which facilitates the rice planter to prevent the disease beforehand. The GIS in present system takes advantage of MapObjects composition of ESRI Company in America. It consists of 46 parts, which constitutes 5 categories, individually being items of data operation (10) , targets of map emergence( 17),objects of projection(8),objects of graphs [START_REF] Lai | etal.An Image-Based diagnostic expert system for corn diseases[J].Agricultural[END_REF] and items of address match (4). Among this kinds of C# system, when any item is selected, the corresponding function can be made.

Precautionary Model

The model is realized by fuzzy clustering algorithm based on the data digging. The working procedure is as follows: ①The standardization of data weighing

To construct a primitive database D owing to the issues , among which ij x means the attributive value of j in the i sequence.

             nm n n m m x x x x x x x x x D        2 1 2 22 21 1 12 11
However, in reality, different data will have various weighing standard. So in order to compare numbers between different weighing standardization, data should be standardized between the sections [0,1].

} min{ } max{ } min{ ij j ij ij ij x xi x x x     (1)
② Construction of similarly fuzzy matrix To calculate approach degree of fuzzy collection i and j, the ways are amounts accumulation, coefficient of association, maximum and minimum method, arithmetic minimum mean method and algebra minimum mean method, absolute value pointing method, absolute value minus method, included angle cosine method. The present paper adopts the last one. The format is as follows:

         m k jk m k ik m k jk ik ij x x x x r 1 2 1 2 1 (2)
Based on the formula in the above, the fuzzy matrix

n n ij r R   ) ( is got.
③ Flowing cluster procedure Fuzzy cluster analysis needs a equal fuzzy matrix which should be self-contradictory, symmetry and transitivity. Common fuzzy matrix has got the first two qualities. Thus we can make use of self-square blending to revise matrix R as matrix

) (R t ,that is, k k R R R R R 2 ) 1 ( 2 4 2        (3) )) , (min( max , ) ( 1 2 jk ik m k ij m m ij r r r r R R R         (4) m m ij k t R R t    ) ( ) ( 2 (5)
After the t(R) matrix has been got, the different number elements will be listed from the maximum to the minimum.  chooses the data according to the sequence to form the mobile cluster map which can be used to construct various models when different warning information is spotted.

System Realization

Data Collection in real environment

GIS and Socket functional programming will be made by use of C# language and according to the definition of communication protocols, data capture will be received and obtain data collection sites and corresponding environmental parameter information will be obtained. The collected information is displayed in the system with the result in the fig. 3:

Early warning of rice disease

Through nearly twenty years of history data of Jilin Province are analyzed by fuzzy cluster analysis, according to the number of high temperature and high humidity weather, there are 4 categories: the first category includes no disease with high temperature and high humidity weather number less than 1 days; the second category involves 65% of rice having lighter diseases and 35% medium disease condition; the third kind in high temperature and high humidity weather lasting 4~6 days, 45% rice has mild disease, 36% medium condition and 19% heavy; fourth kinds of high temperature and high humidity weather more than 6 days, the rice disease occurred with 50% lighter, 23% moderate and 27% heavy.

When there is data acquisition, the system will judge according to the early warning model. If there is indeed warning, it will be marked on the map with red, at the same time, the early warning information in the form of text messages will be sent to mobile phone users; if there is no warning, no treatments. Fig. 4 shows the warning at the interface:

Conclusions

In this paper, by using GIS network and Socket programming technology with the use of C# language,it realizes rice environmental data acquisition in Jilin Province, the display of geographic coordinates, the rice disease early warning and the geographical position of early warning and short message sending functions. Meanwhile, it presents a classification of occurrence of rice diseases so that the system in a timely manner to make judgments, suggesting that early warning information.

The system was conducted with a year of operation in Changchun City Rice Research Institute, effectively to give advanced warning against rice blast effect and improve the quality and yield of rice, making project objectives.

Fig. 3 .

 3 Fig.3. The data collection

Fig. 4 .

 4 Fig. 4. Rice disease early warning

Acknowledgment

Funds for this research was provided by National 863 subjects (2012AA10A506-4, 2013AA103005-04), Jilin province science and technology development projects(20110217), China Postdoctoral Science Foundation the 54th surface funded(2013M541308), Jilin University Young Teachers Innovation Project (450060491471).


Guogang Zhao
email: zhaoguogang2000@qq.com

Haiye Yu

Guowei Wang

Yuanyuan Sui

Lei Zhang

Applied Research of IOT and RFID Technology in Agricultural Product Traceability System

Keywords: agricultural products, the traceability system, IOT, RFID

In recent years, more and more attention has been given to the safety of agricultural products, whose demands for the traceability system are increasingly urgent. This paper, in response to the demands, by using the C# programming language, database technology, IOT technology and RFID technology, realized the traceability system of agricultural products, and explained the implementation of network technology and RFID technology in detail. The traceability system can complete the functions of data collection, early warning, control and data automatic input & management during the production process. This system can well solve the problem of data entry in the farming operation, timely and effectively record the information involved in the production of agricultural products, which can ensure the authenticity of the data in the traceability system.

Introduction

With the improvement of people's quality of life, the agricultural products circulation are facing new situations esp.after China entered the WTO, and the security of agricultural products has caused more and more people's attention [START_REF] Shen Guang-Rong | Consideration of Farm Product Safety System in China[J[END_REF]. Because China is a large agricultural country, agricultural products' export plays an important role in the foreign trade of China. In recent years, vegetables, tea, mushrooms, meat, canned food and other agricultural products and processed food, which Chinese exported to America, Japan, the European Union and other countries, have had quality problems, which not only makes China suffer huge economic losses but also makes the exported agricultural products of China lose good reputation [START_REF] Yang Tian-He | Study on Control System of Food Safety from Farm to Table[END_REF]. Traceability system is the important measures and guarantee to promote the production information transparency, improve the health and safety of agricultural products and increase the market competitiveness of agricultural products [START_REF] Jufang | Implementation of pork traceability system based on .NET framwork[END_REF][START_REF] Xinting | Design and application of safe production and quality traceability system for vegetable[END_REF]. In recent years, some domestic experts, from the perspective of information technology, taking the primary products of some vegetables as the research objects, has constructed the traceability system for safety production & management and quality of vegetables, has discussed the key technologies. Some of them has founded the traceability procedure taking the agricultural retrospective codes based on geographical coordinates and multiple encryption as the encoding, and some of them has founded the quality and safty traceability system of agricultural products, which depending on mobile phone 2D barcode recognition. At abroad, there also has some research on the product traceability system, such as the Australian animal identification and traceability system, food traceability system in Japan, the EU beef traceability system, American full traceability system for agricultural products and the Swedish agricultural products traceability management system [START_REF] Minbo | Application of RFID on products tracking and tracing system[END_REF][START_REF] Pettitt | Traceability in the food animal industry and supermarket chains[END_REF][START_REF] Castro | Traceability on the Web: A Prototype for the Portuguese Beef Sector[R[END_REF][START_REF] Mousavi | Tracking and traceability in the meat processing industry: A solution[END_REF][START_REF] Li | Agricultural products quality and safety traceability system based on two-dimension barcode recognition of mobile phones[END_REF][START_REF] Meng | Traceability system of agricultural products quality and safety based on B/S structure[END_REF][START_REF] Xinting | Establishment of intelligent distribution system applying in logistics process traceability for agricultural product[END_REF][START_REF] Jiyang | Application of Dongguan agricultural products quality and safety traceability system[END_REF][START_REF] Jianning | Application of remote video monitoring in agricultural products quality traceability system[END_REF][START_REF] Peng | Traceability system of grain quality safety based on radio frequency identification middleware[END_REF][START_REF] Jianping | RFID-based solution for improving vegetable producing area traceability precision and its application[END_REF]. Based on the above literature, it can be seen that the domestic and foreign experts and scholars have made some achievements in the aspects of vegetable traceability system,but since the complexity of the manufacturing process of agricultural products, the validity and objectivity of data acquisition would not be safeguarded. The IOT technology and RFID technology can solve this problem to a certain extent, which is the research direction of this paper.

The structure of vegetable traceability system

The traceability system for the overall structure is shown in Fig. 1. The system obtains the data from the process of vegetable production by the IOT and RFID system and record them truly and effectively to avoid manual intervention.

Data Acquisition of Vegetables Environment

One of the main process of data acquisition and storage: hardware gathered the data through the wireless network to the specifized gateway,finaly data which is collected will be transmitted the specified IP address (i.e., IP address of the server).The server opens a monitor to create a TCP connection with hardware gateway first and then listens to data sent from the hardware gateway (the process as shown in Fig. 2).Through data packets which is obtained from remote gateway, corresponding recognition and resolution are made to get a variety of distance parameter which we want.Because the node identifier of the corresponding hardware is exclusive, so the data acquisition of hardware packet is done by extracting the hardware identifier, then we can know an exact hardware belongs to which user when the data is compared one to one in the database. After various related parameters are gained, the collected data is stored in the database according to the original construction of the data table in order to inquire and analyze the data later. Meanwhile, the split of the data in chart form is reflected in the user interface to more directly show the changing various parameters in greenhouse condition.

Fig. 2. Flow chart of data acquisition

Message Warning Model

When the server and remote hardware sensor connection is successful, through the analysis of relevant data transmission by remote hardware package, a rough picture of the remote environment can obtain in real-time. Each of the parameters of environment inside will be compared with the critical parameter values in the database.When either higher or lower than the critical value, we will send the corresponding message alerts to the users which the hardware of monitoring node belonged to. Surely, we have to be ready for this critical value given by experts, according to the data, we finally will be got the up-and-low range monitoring parameters by the variety of vegetables planted in a greenhouse with the parameter and the day (day and night), cloud or snow, crop planting stage. According to above, this domain system automatically decide whether to send the messages to the user to achieve intelligent early warning.

Equipment Control

When the early warning information is generated, the remote client through the establishment of communication can submit the instruction of opening or closing to the server and the server will transform the command into control information and then transmitted to the wireless sensor network. After the device receives the control information, the command will be taken into effect. But once a remote hardware changes, the server will send the implementation of results to the client. Meanwhile, for the sake of security, the server will also send the message to the exclusive user.

.

Technical Design of RFID

Bwhat RFID technology means lies in the control reader and writer software and acquire or input the magnetic card and electronic label with the correspoding vegetable traceability information whose function is realized by mi.dll.

Read the information

Conclusions

The system, Using network technology, has realized the automatic acquisition of soil temperature and humidity , air temperature and humidity, illumination and CO2 concentration and so on. The system,depending on whether the collected data can produce early warning, can decide whether need control the corresponding devices to operate, such as rolling machine, irrigation etc. In the process the resulting data is automatically stored in the database, we can understand the process of vegetable production environment and production data for data security, from vegetable traceability system. Through the electronic tags and smart card access to get information and storage, using RFID technology for The farming operation (when or where to use pesticide, packaging, processing and transportation process), packaging, processing and transportation process of vegetable production. RFID technology, reduces artificial complex operation, reduces the information is not timely and not accurate, has solved problem of information reliability in traceability system.

Fig. 1 .

 1 Fig. 1. Vegetable traceability system structure diagram

Fig. 3 .Fig. 4 Fig. 5

 345 Fig. 3. Real time data acquisition

This article, using the C# programming language and the database technology,data bank technology, has realized the data collection, early warning, control in the process of the growth and management of vegetable in the traceability system (e.g. Fig.3, shown in Fig.4), and, using the RFID technology, has realized the effective input of data from vegetables' packing, the processing and transportation to avoided the disturbance from artificial operation, to safeguard the data authenticity and to form the traceable vegetables file information as shown in Fig.5.

Acknowledgment

Funds for this research was provided by National 863 subjects (2012AA10A506-4, 2013AA103005-04), Jilin province science and technology development projects(20110217).

5

The application of the IOT technology and RFID technology


Qi Wang

Wenyong Chen

Research on the principles of user behavior in building information resource sharing system

Keywords: information sharing, information behaviors, information behavior laws

The information resource sharing system as a information movement, will guide users to access and use knowledge or information as the information commons. Many barriers existed in information sharing systems due to the less consideration of the users' behavior in information utilization by designers of the information sharing systems. Therefore, the design of information sharing systems should be fully aware of and respect for user information behaviors, and on this basis, to adhere to the design concept of the user information behavior laws in order to truly complete the mission of the information sharing systems. Otherwise, the principle of user information behaviors will most likely constitute the information sharing "regularity disorder".

Introduction

Information sharing system as a knowledge or information commons, will be a main physical location of users to complete their information behaviors. To build such system, users' needs should be the driving force, users' information behaviors should be respected, users' information use behaviors should be coincided. This is the only way to get users' support and extensive use and complete the constructed mission and achieve its ultimate goal.

Behavioral obstacles of users to use information resources in information commons

At present, domestic and foreign scholars have conducted extensive research on behavioral obstacles of users using information under network environment. It is significant to summarizes those behavioral obstacles in establishment of information commons. Research by Zhang Yiyan et al. shows [START_REF] Zhang Yiyan | The Study of Information Process Gaps[END_REF] that: behavioral obstacles of the users using information are as follow: (1) information identification obstacles, including: literature demand barriers, available information obstacles, information sources integration obstacles, information sources classification barriers, information language barriers; (2) information access barriers, including: information availability barriers, information sources response speed, information sources cost barriers, the carrier type and literature format of information sources obstacles; (3) information use obstacles, including: information quality barriers, information reveal accuracy barriers, information not comparable obstacles. Zhao Yalan et al. have pointed out that [START_REF] Zhao Yalan | Analysis of the Document Information Sharing Barriers in the Conditions of Modern Technology[END_REF], users information obstacles under the conditions of modern technology literature information sharing, including: (1) information identify obstacles; (2) information access obstacles; (3) reading habit obstacles; (4) information screening obstacles, and so on. According to JiLin Agricultural University library's actual experience to participate in the CALIS and CASHL as well as other types of literature information resources sharing systems, users meet the following obstacles in all kinds of literature at various levels of information sharing system, including: (1) Demand obstacles, for which performance are that: resources time span covered in the system are limited, especially in the seventies and eighties and the previous literature of last century only covering relatively small amount, access to the full text of such documents is relatively difficult, update of these data is much slower, with a single document type, document delivery request fill rate is low and sometimes there is deviation in billing system(CASHL). (2) Availability obstacles, e.g. searching platform overmuch and easy to confuse, searching interface not so concise or clear, and search fields limited(CASHL). (3) Tool obstacles, such as system itself not so stable, especially the CALIS.

Users information behaviors are less considered by the designer of information commons which lead the above obstacles [START_REF] Cooper | About Face 3 Interactive Essence of the Design[END_REF]. Therefore, to realize information sharing, users information behaviors should be fully understood and respected through information resources sharing space platform design and the principle should be insisted for meeting the mission of information sharing platform.

3

User information behavior principle in building information resource sharing system

Moores Law

Calvin Mooers-one of the pioneers of the information retrieval research, after a number of studies he found out that users will tend to not use information retrieval system if it brings more trouble to use. The law reveals the rule of users using information retrieval system. For users, the most accessible information is not the most useful information, which means the availability and validity of information is not consistent. The users select a retrieval system putting the information availability as the primary basis.

At present, the more popular understanding of the Moores law focus on the efforts of users obtain the required information in library and information science. Thus, the law can be expressed as: the more difficult using, the more time spending on an information retrieval system, the less using the information retrieval system by users. Roger K Summit, Dialog founder, express it by another way: "Moores law tells us that information using and easiness to access information are proportional." [7] According to the law, information availability is the primary basis for designing of information retrieval and sharing systems. Therefore, the Moores law is not only the rule of designing information retrieval system, but also the guide of designing collection and information system.

Zipf Law

In 1949, linguistics professor George Kingsye Zipf, Harvard University, pointed out "minimum power-saving principle" in his book called "Human behavior and Minimum Power-saving Principle --Human Ecology Introduction" [START_REF] Zipf | Human Behaviour and the Principle of Least Effort: An Introduction to Human Ecology[END_REF]. It put forward that human social activities are affected by this principle, people always hope to use the minimum cost to obtain the maximum benefit. In other words, human behavior is based on the minimum power-saving principle. In fact, this principle is using throughout the whole users information retrieval behavior. Therefore, this law should be followed in the information sharing system, the users will give up obtaining sharing information if his work intensity is too large . It reveals two characteristics of users psychology in information sources selection: (1) The most readily available source of information is the first choice when user selects information source, and then its reliability and quality. [START_REF] Zhao Yalan | Analysis of the Document Information Sharing Barriers in the Conditions of Modern Technology[END_REF]In the process of obtaining information, the user is more likely to minimize the cost of obtaining information at the expense of the information quality. Hence, there are two matters should be considered for designing information sharing system: first, those systems with cumbersome operation, too complicated path in building information commons are not viable; Second, the database of information commons must be context unified, standard terminology, semantics accurate, and expression concise, minimizing the work of understanding the information content. It should build and provide service from user perspective, then research the information commons accessibility how to influence the user requirements from the user psychology, ensure the minimum power-saving principle implementation to meet user needs.

Wang Xiaona put forward seven measures to improve the accessibility of building information commons [START_REF] Xiaona | Accessibility of Minimum Power-saving Principle to Information Retrieval System[END_REF]: (1) physical environment availability: the using interface should be friendly except the geographical position, and can use multimedia to guide users; (2) retrieval function and access points availability: the retrieval ways and types should be varied to fit users' different retrieval habit; (3) database availability: increase the content of the database, such as subject terms, keywords and citation, etc.; (4) system openness and interconnectedness: the system can be connected with others;

(5) remote communication: exchange information to the distance; (6) users assistance: with a complete operating instructions; (7)retrieval system structure availability: promote system efficiency.

In addition, the law is also applicable to the information retrieval list compilement and information document structure designment of information resources sharing space.

3.3

Robin Hood Effect

Developing with the science technology and informatization, the social members information need level has improved. Majority users' need of information tends to average, thus the information resources using also presents a trend of average. The users' information demand will be affected when the information resource is not sufficient as the Robin Hood effect. At present, the trend will be more obvious due to the network equality.

Robin Hood effect reveals two characteristics of the information resources use from the perspective of users behavior [START_REF] Zhou Weibin | Regularity Study of Network Information users' information needs[END_REF]: first, the users' information need must be affected if the information resources is not sufficient; Second, most users' information need level tends to average. Understanding the Robin Hood effect helps to reduce the "block" grade, break mind-set grade, and determine the priority according to the information value maximization standard in setting up information sharing space. The key users' information need should be considered priority, taking into account all users' information resources sharing power to satisfy all users' need.

Information Absorption Limit Regulation

Information absorption process is the process of cognitive activities between users and information. Users' information absorption includes information accepting, processing, understanding and utilizing etc. Users reaction and absorption to the information is faster following the informatization acceleration. But the users' information absorption is limited, in a certain range, when information input and absorption rate exceeds the critical value, the users' information response and absorption rate will be slower, then it will appear the information overload phenomenon, which we called information absorption limitation [START_REF] Yang | Visualization of large category map for Internet browsing[END_REF]. Generally speaking, under the certain period of time, large amount of outside information is provided to users which is far larger than the amount of information the user can absorb, users have to choose and filter those information,and to keep the most important and valuable information.

According to this regulation, designers of information commons, especially in the design of retrieval interfaces, should strictly follow users' information absorption regulation to design multi-types retrieval paths and provide related methods to avoid narrow or expanded searching result. In addition, to avoid reducing the users' distress of information accession, the design of retrieval platform classification system can be neither too professional nor too simplified [START_REF] Peng | Research on the Mental Model for Website Users in the Process of Information Access[END_REF].

Information Foraging Regulation

In the early 1990s, Peter Pirolli and Stuart Card,from California Palo Alto Research Centre (PARC) of United States, found that people's information search pattern under the network environment is similar to animal foraging strategies. They studied users' information behavior and behavior navigation of information landscape (links, description, and other data)under the network environment cooperating with psychologists, then they found that the time, money and energy of users in obtaining information and the needed information obtained needs to reach an optimal balance, like the animal foraging strategies. Thus, they put forward the information foraging theory first time, and pointed out that in the process of information searching and absorption, people should adjust their own information foraging strategy constantly to achieve maximization of information profit according to the information environment [START_REF] Pirolli | Information foraging in information access environments[C/OL[END_REF].

The information foraging theory has three main elements: information patches, information scent and information diet [14 to 15]. Information patches is the abundant information resource in information environment, it can be a website, a paper, a book, a web page, a document through that users search information. Information scent is the extension of information concept clues, it is a subjective assessment for contained information and required information correlation in the information resources during the process of searching information, and it plays a navigated role in information searching through some certain value or utility menu labeling, which decides the path information searching along. Users find the optimal information "patches" with the correlated information clues during searching information on the Internet.Information diet is the kind of information which users choose when they forage information . Due to the regularity of network information resources distribution it consumes scarce resource of time,money and energy when user foraging information, so user needs to select information during searching information. If the searching scope is too narrow, user may need to spend more time to build retrieval model, which makes the searching results partial; If the searching range is too expanded, user may be submerged by information retrieved again. So, in constructing information commons, we should design the information diet to conform user information behavior .

The designers have been paying attention to interface design in the information searching system design. They think the interface must be powerful, too simple interface can not customize questions according to users' need and obtain ideal results. However, actual survey showed that users prefer simple interface, people hope use patterns instead of words to increase the interface usability [START_REF] Xiaona | Accessibility of Minimum Power-saving Principle to Information Retrieval System[END_REF]. Henk J.Voorbij through the network information searching survey pointed: "The advanced retrieval utilization rate is very low" [START_REF] Henk | Searching scientific information on the Internet: A Dutch academic user survey[END_REF]. Bernard j. Jansen also found in the experiment research: "Retrieval results are not obvious affected by complex questions" [START_REF] Jansen | Real life, real users, and real needs: a study and analysis of user queries on the web[END_REF][START_REF] Jansen | The effect of query complexity on Web searching results[END_REF]. These research results show that the retrieval system designers should not have to spend energy on the theory of design advanced retrieval functions, they should focus on how to design better and related online help, how to insert the efficient automatic correction method, how to filter the search results and carry effective sorting, how to provide users with retrieval vocabulary control, etc. Jason Vaughan also pointed out: "Retrieval system designers must consider problems from users perspective.The retrieval system designers are engineers and computer scientists, they attach more importance to comprehensiveness and flexibility of functions, but common users have no experience on retrieval system or database, they just require the design of retrieval tools are easy to use, so there should be a bridge between the thinking ways of these two types people ." [START_REF] Vaughan | Considerations in the choice of an Internet search tool[END_REF] 4 Conclusions Information commons will be a knowledge commons that is a special designed onestop service center and collaborative information sharing environment. The design of it can be based on two ideas: first is open access, second is service. Information commons needs to correspond with hommization design requirements, user information behavior principles and user information behavior regulations.

Acknowledgment

The corresponding author of this paper is Wenyong Chen. This paper is supported by the Youth Foundation of Jilin Agricultural University (Grant NO. 201337), CALIS Programs of the National Agronomy Literature Information Center(Grant NO. 2014026) .