Yv-Mei Dang

Xin Lv

A And nd nd nd I I I Information nformation nformation nformation M M M Management anagement anagement anagement D D D Decision ecision ecision ecision S S S Support upport upport upport S S S System ystem ystem ystem

Keywords: Key-words. . . .Knowledge model, Fertilization recommendation, Decision support system, SQL+JSP+Win2000

Based on the regional difference in soil fertility,the nutrient uptake of crop,the nutrient supplying capacity of soil and the response of fertilizers,etc.,this study took the 81th regiment of the 5th agricultural division in Xinjiang Bingtuan as the training and cooperative region,and established the information system of soil nutrients for cotton fields and recommendation model of fertilization.Constructed comprehensive,digital and intellectualized WEB-based cotton fertilization recommendation and information management decision support system by using SQL+JSP+Win2000.The system had been applied successfully in 2005 and 2006 there,and in 2006 the mean yield per hm 2 increased by 7.3%than that of former 3 years,and the fertilizer cost per hm 2 reduced 114 yuan(RMB).

Introduction Introduction Introduction Introduction

In this research ,it made collection and reorganization of field soil information from all regiments through network, provides the accurate and reliable soil material, take quantitative dynamic relationship as the master line that between the crops goal output 、 the soil nutrient conditions and fertilization measures, construct Dynamic Knowledge Model for cotton fertilization [START_REF] Yan | A Knowledge Model-and Growth Model-Based Decision Support System for Wheat Management[END_REF] [START_REF] Yu-Long | Programming Technology of J2EE,Beijing:the publishing house of qinghua university and[END_REF]; on this foundation, further unifies the soil fertility management system, constructs cotton information management system and Fertilization Decision Support System which are comprehensive 、 digitization and the intellectualization based on the platform of SQL+JSP+Win2000 with Web-based.the system has no high demand that only browser in clilnet,and provides a uniform interface,easy operation and Installation Maintenance [START_REF] Xiong Fan Lun | Agricultural Expert System and Development Tools[END_REF].so as to provide the decision support for science fertilization more intuitively.

Material Material Material Material and and and and methods methods methods methods

The system is composed of the fertilizer database,cotton fertilizer and management system(Knowledge model and Fertilizer model), the method storehouse,cotton fertilizer knowledge base, Inference Engine and technical personnel and man-machine connection. [START_REF] Xiong Fan Lun | Agricultural Expert System and Development Tools[END_REF] (Fig. 1)

2.1Establishment Establishment Establishment Establishment of of of of Foundation Foundation Foundation Foundation database database database database

Establish cotton foundation databases by the information of 81th Regiment from 1998-2005 years about fields soil fertility, soil fertilizing kinds and quantity previous years, the variety and the output of crop planting, disease and insect pests and irrigation information and so on. The field soil fertilizer information be given by technician to take soil samples to make chemical test, the data be provided by Testing and Analysis Center of farm, to ensure the reliability and authenticity of information data . datas and the parameters can be called by each model, the result be returned to dynamic database,and realize resource sharing of database and model base.

2.3 .3 .3 .3 E E E Establishment stablishment stablishment stablishment of of of of s s s system ystem ystem ystem knowledge knowledge knowledge knowledge library library library library

There is main technical knowledge which are qualitative and cannot be expressed by model at present in cotton Cultivation Management knowledge system,involves prepares before sowing (seed treatment, ploughing, application method of base fertilizer, moisture management, forecasting and prevention of plants on pests and weed control), sowing date, density knowledge, the knowledge of crop nutrition and fertilizer, the knowledge of cotton moisture management , the knowledge of cotton growth and development, the knowledge of plant protection and so on.It including:variety selection ,density knowledge,sowing date,the relation between sowing date and accumulated temperature,suitable sowing date,the relation between cotton sowing date and yield formation,the knowledge base of fertilizer and crop nutrition,water management,chemistry control,pest and weed and prevention knowledge base,plant protection.

Inference Inference Inference Inference Engine Engine Engine Engine

It is the logical core of the system make inference using the knowledge to data.It make inference to the data in database by controlling the knowledge base, and draws the new conclusion.In other words, the inference Engine is one kind of strategy procedure. Production inference strategy used in this system,the inference process is:

< Known > rule: If A then B
< Known > precondition: A< new > conclusion: B 2 2 2 2.6 .6 .

.C C C Connection onnection onnection onnection of of of of m m m man-machine an-machine an-machine an-machine

IE6 is basic interface in this system,can make interaction with user by draw menus,toolbars,icons,graphics and tables, selective prompting be given,you can make selection by menus, the whole operation can be completed only by computer mouse and keyboard, then user can get the model and output the results.meanwhile,the system provides the help files to make explanation for using the system. In addition, the contact surface of system has the good fault tolerance too, gives the error message and the processing prompt through examination common mistake by setting error trap, to ensure the correctness by user input.

Results Results Results Results and and and and discussion discussion discussion discussion

The system of cotton information management and cotton fertilization recommendation decision support system based on WEB realizes the main function such as :data management,system management, information query, fertilizer recommendation, soil evaluation, the expert knowledge and consultation, the result output and the system maintenance management and so on (Fig. 2345).

3.1 .1 .1 .1 Data Data Data Data management management management management module module module module

There are 5 attribute database management module in the module [START_REF] Yan | Information Management and Fertilizing Decision System Based on Soil Nutrient of SuperMap IS Cotton Field[END_REF] (soil basic information database, fertilizer information database, fertilizer amount of previous years, fertilizer parameter database, user information database), each administration module has the operation functions such as: increase, deletion, saves, printing, search, sorting, screening and so on.It give the right of remote input and revise to user,advanced user may renew the attribute data whenever, so make the database can always reflect the newest tendency of farmland nutrients and other management information.Also user may carry on maintenance to database and module base(mainly to data edition, update and so on).At first ,data be inputed in the database, so as to be called for inquiry and recommendation.

3.2 .2 .2 .2 Information Information Information Information inquiry inquiry inquiry inquiry module module module module

Provides two ways to inquire for fertilizing scheme and field information.The user can copy the data from database according to the need, simultaneously the data in database can be batch introduced into through this contact surface. Information query and screening realizes information acquisition function according to the query condition and screening condition the user combined to the data in database [START_REF] Yan | Information Management and Fertilizing Decision System Based on Soil Nutrient of SuperMap IS Cotton Field[END_REF].The table for Inquiring or screening may be the table of expert system standard, also may be the result which the system recommendation decision-making leaves, the data message inquire may be printed directly and derive.(Fig. 2)

.M M M Module odule odule odule of of of of s s s soil oil oil oil appraisal appraisal appraisal appraisal

Primary to compute membership degree of Organic Matter, available nitrogen, available P, available K, and obtains the index value of comprehensive evaluation.(Fig. 3)

T T T The he he he design design design design of of of of soil soil soil soil fertilizer fertilizer fertilizer fertilizer recommendation recommendation recommendation recommendation mod mod mod modu u u ul l l le e e e

The module including conventional fertilization recommendation by soil test, drip irrigation fertilizer recommendation, Effect Function recommendation, organic fertilizer recommendation and microelements fertilizer recommendation. Under the suggestion of guide(Fig. 4),the user can input the data of soil nutrient, fill in fertilizer amount of strip field,variety selection of fertilization and establish goal output according to local actual situation.The system judges whether the data user input is reasonable based on the ordinary years' data of meteorology,soil, variety and so through operating the knowledge model, if reasonable, then make fertilizer recommended by calling the knowledge module of fertilizer module, according to the information user filled in, the result of fertilizer recommendation can be printed on the formula to apply fertilizer card or data export by Excel,the user can modify,edits and prints the recommendation result by Excel datas exported. If unreasonable, the system modify the plan which have be made, and sends it to the fertilizer model to make forecast, so circulates, until generating a set of fertilizer recommendation plan which meet requirement [START_REF] Kun-Wu | The Design and Implementation of the Expert System Based on Database[END_REF].

. Conclusion Conclusion Conclusion Conclusion

Precise fertilization is important part of Precision Agriculture, It is the best fertilizer plan established above the scientific method to fertilizer.This system construct comprehensive digital and intelligent decision support system based on WEB using the SQL+JSP+Win2000, and collected and sorted soil information of all regiments by Internet, and provides the accurate and reliable soil material, then obtained the comprehensive index of soil fertility using fuzzy mathematics principle, make fertilizer recommendation, establish fertilization model of soil nutrient, the fertility district and the formula district of crop specific fertilizer; establish the balance fertilization system of nitrogen, the phosphorus, the potassium and the trace element according to the consideration about soil supplying nutrient capability and fertilizer needs of crops. It will be important theory value and the practice significance to implementing balance fertilization technique and generalization of other Scientific Research Achievements.

2 2 2 2

 2 .2 .2 .2 .2 establishment establishment establishment establishment of of of of Model Model Model Model and and and and model model model model base base base base Based on test data, observation data and the agronomic data, we established the model of fertilizer recommendation,production prediction and Soil Evaluation,formed a model database of basic knowledge. It established the standardized fertilizer model for different soil fertility and yield level through experiment, accordance with all kinds of factors which established, combined with soil science, fertilizer science, as well as the local soil texture and expert experiences [4].Seeks the best economical fertilizer rate, the biggest profit fertilizer rate, the maximum yield fertilizer rate, best proportion and time distribution of fertilizer. Simultaneously, summarize the experience gradually and modify the original model, causes it to meet actual demand.

2 2 2 2 . 4 . 4 . 4 . 4

 24444 Method Method Method Method base base base baseIt is the tool to save the module of method,composed of various algorithm program which are more generality and flexibility.

Fig

  Fig.2 Information Query

Fig. 3

 3 Fig.3 Management of Soil evaluation

Fig. 4 Fig. 5

 45 Fig.4 Frame work of fertilize recommendation


Yanxia Zheng

Shaoyuan Feng

Na Jiang

Qingyi Meng

{ Yanxia

Shaoyuan Zheng

Na Feng

Qingyi Jiang

Meng

Risk Assessment of Reclaimed Water Utilization in Basin based on GIS

Keywords: Reclaimed water, Risk assessment, GIS, Beiyun river

The reclaimed utilization of urban wastewater is an indispensable way for solving the crisis of urban water resource. In order to meet the needs of reclaimed water pollution control,the risk assessment model for reclaimed water utilization in basin are established. GIS was employed to derive all the data and evaluate model in order to get spatial variance and distribution of the risk levels on different scales within study area. The evaluation results can supply the pollution control with target areas. Based on the analysis of reclaimed water data of Beiyun river basin, the paper sets up an index system of risk assessment of reclaimed water. In addition, it applies the mathematics theory to making a comprehensive assessment on the utilizing risk of reclaimed water in the research object, which provides a scientific foundation for risk management of reclaimed water in theory and practice.

Introduction

There is an increasing trend to require more efficient use of water resources, both in urban and rural environments. Wastewater reclamation has been the subject of a number of studies. Types of wastewaters used for recycling include treated and untreated sewage effluent [START_REF] Shereif | A demonstration of wastewater treatment forreuse applications in fish production and irrigation in Suez. Egypt[END_REF][START_REF] Asano | Wastewater reclamation and reuse in Japan: overview and implementation examples[END_REF][START_REF] Haarhoff | Twenty-five years of wastewater reclamation in Windhoek, Nambia[END_REF] , storm water runoff [START_REF] Asano | Wastewater reclamation and reuse in Japan: overview and implementation examples[END_REF][START_REF] Dillon | Artificial recharge of groundwater[END_REF] , domestic greywater [START_REF] Anderson | Current water recycling initiatives in Australia: scenarios for the 21st century[END_REF] , and industrial wastewater [START_REF] Asano | Wastewater reclamation and reuse in Japan: overview and implementation examples[END_REF][START_REF] Guillaume | Irrigation of vegetable crops as a means of recycling wastewater: applied to Hesbaye Frost[END_REF] . During the last decade, municipal wastewater reuse has emerged as an important means of supplementing water supplies in a large number of regions throughout the world. In many instances, reuse is also promoted as a means of limiting wastewater discharges to water environments. Wastewater reclamation is used in many ways. Effluents are reused for irrigation purposes in many countries around the world on all of the populated continents. Wastewaters can often contain significant concentrations of organic and inorganic nutrients for example nitrogen and phosphate. There is potential for these nutrients present in recycled water to be used as a fertilizer source when the water is recycled as an irrigation source for agriculture [START_REF] Meli | Influence of irrigation with lagooned urban wastewater on chemical and microbial soil parameters in a citrus orchard under Mediterranean condition[END_REF][START_REF] Ramirez-Fuentes | Characteristics, and carbon and nitrogen dynamics in soil irrigated with wastewater for different lengths of time[END_REF] . There have been a number of risk factors identified for using reused waters for purposes such as agricultural irrigation. Some risk factors are short term and vary in severity depending on the potential for human, animal or environmental contact, while others have longer term impacts which increase with continued use of recycled water. So health and environmental risks associated with water reuse have been addressed.

The application of risk assessment to protect human health has grown over the last 60 years, but it is only during the last years that reclaimed water risk assessment has become more researched. The aim of reclaimed water risk assessment is the estimation of risk of adverse effects to communities of species in locations that are potentially exposed to pollutants.

Health and environmental risks associated with water reuse are now dealt with in a thorough manner by guidelines used in many countries. Guidelines pertaining to chemical contaminants are typically limited to bulk parameters such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH and total suspended solids (TSS). In many situations these simple parameters provide suitable surrogate indications of the likely presence of chemical species of concern. However, for more highly treated reuse waters, they are of limited sensitivity. Furthermore, for some applications, an accurate assurance of specific chemical concentrations is important.

Today, the assessment of whether a substance can pose a potential risk to the organisms present in the environment is based on hazard quotients. Many uncertainties come into the question when trying to calculate the hazard quotients. Theoretically, the exposure is calculated from measured values in the different environmental compartments. However, in most of the cases, this data is unavailable or insufficient due to the complexities inherent to environments. Due to this fact, models have become very useful tools in predicting concentrations of certain contaminants in specific compartments. In many risk assessments, the actual concentrations in the environment are not measurable so risk assessors must use models to predict what these concentrations will likely be.

In China, there is very little analytical information available concerning the occurrence and fate of risk assessment during reuse applications. The paper is cooperation in Beijing in Beiyun river. For the recycled water for landscape water is evaluated. Establish the recycled water utilization assessment parameter. The purpose of this paper is to provide quantitative basis of risk assessments.

Study area description

The study was carried out along the Beiyun River, located in the Haihe Basin, one of main basins present in China. With an area of about 4348 km 2 , the basin is situated in Beijing, with mean precipitation of 532.5mm (1980-2000) a year. The precipitation is concentrated in certain months of the year causing large flow fluctuations of the watercourses and therefore, leading to important differences in the dilution of the compounds present in waste water discharges. Fig. 1 shows the location of the Beiyun River Basin within Beijing. Beiyun river has completed some city sewage treatment plant, with design processing ability of 254 million ton per day, actual treating capacity is 214.4 million ton per day.

Assessment of reclaimed water

In China, water quality standard in the reclaimed water use are different due to reclaimed ways. For example: reclaimed in agriculture perform "Standards for irrigation water puality" ( GB 5084-2005 );using water in fisheries perform " Water quality standard for fisheries "(GB 11607-89 );using in scenic environment, and include water function range, perform "Environmental quality standards for surface water"(GB 3838-2002),and over water function range, perform reuse of " urban recycling water-Water quality standard for scenic environment use"(GB/T 18921-2002);using in industry cooling perform "Code for desing of wastewater reclamation and reuse"(GB/T 50335-2002); using in city cooling perform reuse of recycling water for urban water quality standard for urban miscellaneous water consumption 》(GB/T 18920-2002).

This paper process water quality assessment depend on primary(A) of discharge in sewage plant. Because Beiyun river includes in water function range , perform "Environmental quality standards for surface water"(GB 3838-2002). Comparison between water quality with the primary discharge standard and "Environmental quality standards for surface water" ( GB 3838-2002 ):poor V species,main overpollutant are COD Cr 、NH 4 + -N and TP.

Conclusions

Discharge of treatment facilities is bigger in Beiyun river. The area of biggest emissions is Tongzhou district, Percentage of 64.1%. The second discharge area is Changping district, Percentage of 21.2%. So it is necessary to improve sewage plant effluent standards to improve the quality of water environment of Beijing rivers.

Overall, the irrigation water can be directly used in the secondary water level of treatment facilities. The use of recycled water for the irrigation of crops has benefits in using a resource that would otherwise be discarded and wasted. Using recycled water also reduces the pressures on the environment by reducing the use of environmental waters. Reclaimed water must pass through different degree of deep processing and the corresponding standards for direct using, and the depth of water storage with reclaimed water to avoid entering drinking water range. Ongoing research will improve the use of reclaimed water as well as increasing public confidence.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Beiyun River inside Beijing

Table 1 .

 1 Comparative analysis between water quality of reclaimed water and surface water environment quality standards (mg/l).

	NO	Analyze item	Ⅲ	Ⅳ	Ⅴ	primary (A)	Remarks
	1	Temperature(℃)	_	_	_	_	_
	2	PH	6-9	6-9	6-9	6-9	_
	3	DO≥	5	3	2	_	_
	4	COD Mn ≤	6	10	15	_	_
	5	COD Cr ≤	20	30	40	50	over
	6	BOD 5 ≤	4	6	10	10	over
	7	NH 4 -N≤	1.0	1.5	2.0	5 ( 8 )	over
	8	TP≤	0.2 (0.05)	0.3 (0.1)	0.4 (0.2)	0.5-1	over
	9	TN≤	1.0	1.5	2.0	15	over
	10	Cu≤	1.0	1.0	1.0	0.5	ok
	11	Zn≤	1.0	2.0	2.0	1.0	ok
	12	fluoride≤	1.0	1.5	1.5		_
	13	Se≤	0.01	0.02	0.02	0.1	over
	14	As≤	0.05	0.1	0.1	0.1	ok
	15	Hg≤	0.0001	0.001	0.001	0.001	ok
	16	Cd≤	0.005	0.005	0.01	0.01	ok
	17	Cr(six valence)≤	0.05	0.05	0.1	0.05	ok
	18	Pb≤	0.05	0.05	0.1	0.1	ok
	19	cyanide≤	0.2	0.2	0.2	0.5	ok
	20	Volatilization phenol≤	0.005	0.01	0.1	0.5	over
	21	Oil≤	0.05	0.5	1.0	1	over
	22	LAS≤	0.2	0.3	0.3	0.5	ok
	23	sulphide≤	0.2	0.5	1.0	1.0	ok
	24	coliform(a/L)≤	10000	20000	40000	1000	ok

Acknowledgments

The authors are grateful for support from the Restorative Project of Urban Water Environment ( 2008ZX07209-002 and 2008ZX07209-004 ) and Community Foundation of Ministry of Water Resources(200901083).


Jingbo Liu
email: liujingbohn@163.com

Jian Wang

Bingbo Gao

Research on the Method of Geospatial Information Intelligent Search Based on Search Intention Model

Keywords: geospatial information search, knowledge engineering, search intention model

The paper, focusing on the problem that the overall correlation level of the research result is low in the current geospatial data search system, establishes users' search intention models, researches the intelligent and professional geospatial information search method, and makes use of spatial cognition theory and knowledge engineering methods to make up for the shortcomings of the lack of semantic information in the traditional keyword-based information retrieval. The geospatial information of towns and villages spatial planning field involves multitudinous theme and has complex structure and large scale. There has always been lack of appropriate method to complete the information search and processing activities. This paper takes geospatial data search in towns and villages spatial planning field for example, constructs the professional knowledge base by knowledge engineering, implements semantic reasoning and establishes users' search intentions model by combining the characteristics of geospatial information. The ultimate goal is to understand users' search intentions as far as possible and improve the accuracy of geospatial data search.

Introduction

With the advent of Internet era, the amount of available geospatial information on the Internet has dramatically grown. Compared with non-spatial information, geospatial information has more complex composition. How to improve the overall correlation level of geospatial information between search results and users' search intentions is one of the key problems to be solved.

Metadata is defined as data about data. For geospatial data, metadata can describe the content, quality, condition and other relevant background information about characteristics. The search models of existing geospatial information search systems are usually designed based on geospatial information metadata contents. These search systems match the related keywords users enter with spatial metadata database and return the results to users. To a certain degree, keywords can express users' search intentions, but this method has some shortcomings. The keywords submitted to computers are just physical sign in this method. It seriously dissevers the semantic relationship among the words users type and doesn't contain any semantic information in search process, leading to satisfactory search results [START_REF] Alon | Theory of answering queries using views[END_REF]. Since this query mechanism is lack of intelligence and knowledge inference, it is more and more difficult to meet the growing needs.

In the field of the towns and villages spatial planning field, planners need to collect and analyze a variety of geospatial information. The geospatial information involves multitudinous theme and has complex structure and large scale. However, there has always been lack of appropriate method to complete the information search and processing activities. After analyzing the shortcomings of the traditional geospatial information search systems, the paper puts forward a semantic-based intelligent search method.

Intention Model

The Composition of Intention Model

In the theoretical model of intelligent agents, belief, desire and intention are regarded as the basic psychological elements of intelligent agents. It is generally believed that faith describes subject's basic view on the environment and the subject uses it to express any future possible situation. Desire is obtained directly from belief, including the subject's judgement on future situation. Intention is the goal or sub-goal chosen to achieve. In other words, the current goal or sub-goal is the subject's intention. Intention model is the formal expression of intention, that is to say how to reflect the target models users choose [START_REF] Rao | BDI agents: From theory to practice[END_REF]. Each sub-model has an input interface used to express users' different search intentions. Especially, in the topic intention sub-model and measure intention sub-model, the search intention users type will be submitted to inference engine. Then inference engine will reason with the knowledge base built in a knowledge engineering method. The knowledge base consists of measure ontology, planning domain ontology and inference rules.

Ontology Overview

An ontology is a formal, explicit specification of a shared conceptualization [START_REF] Stuber | Knowledge Engineering, Principles and Methods[END_REF]. 'Formal' refers to the fact that the ontology should be machine readable, which excludes natural language. 'Explicit' means that the type of concepts used, and the constraints on their use are explicitly defined. 'Shared' reflects the notion that an ontology captures consensual knowledge, that is, it is not private to some individual, but accepted by a group. Ontology is suitable for knowledge representation, knowledge sharing and interoperability [START_REF] Saias | A methodology to create ontology-based information retrieval systems[END_REF].

Introducing the Measure Ontology into the Measure Sub-module

This paper introduces measure ontology and inference rules to express users' measure search intentions.

There is no concept of measure inference in traditional geospatial information search system. Users need to use measure knowledge, such as scale and precision, to determine whether the current data could meet their needs. Because of the multiple form of measure, especially the complicated dependent and interactive relationship among the different measure variables, it is difficult for the majority of non-geographic experts to judge the appropriateness of the measure.

Adding measure ontology to geospatial information search system not only simplifies users' maneuverability, but also improves the accuracy and efficiency. As a new constraint variable, measure is effective in narrowing search space and enhancing the search efficiency. The core issue to be solved during introducing measure inference into search process is the inconsistency between the measure variables users type and the measure variables to be searched. Consequently, measure sub-module introduces the measure ontology and inference rules that inference engine can 'understand' and realizes intelligent search.

Introducing the Planning Domain Ontology into the Topic Sub-module

The shortage of study on relevant information at the conceptual level, including the lack of information standards and domain ontology, leads to the unsatisfactory results to some degree. Using domain ontology to create semantic tagging for metadata and support semantic-based information search is a method to be mature. The development and application of the planning domain ontology will be used as the basis of the information intelligent process. This study seeks to establish like intelligent information search and process system for space planners. We firstly introduce domain ontology to assist users to express the topic intentions, and then create semantic tagging for metadata by the ontology.

The System Design

Overall Design

The system consists of system interface, knowledge base, inference module, search module, metadata database and spatial information database. Figure1 shows the system framework. Secondly, we create semantic tagging for the metadata of the metadata database with the two ontologies. When users type search intention through the search interface, the system will reason out relevant data with the help of the ontology and inference rules and return the results to users.

System Interface

User interface module is composed of space module interface, time module interface, topic module interface, measure module interface and results feedback interface. The topic interface and measure interface are the core interfaces of the search system. With the two interfaces, the system can fully understand the users' search intention to improve its accuracy in an intelligent and professional geospatial information search method. In the space interface, users can specify the spatial location of space objects and the spatial relationship among adjacent space objects. In the time interface, users can specify the time span of geospatial information.

Information search is the process of interacting with search system dynamically. Users often could not achieve their goals at a time while searching information. They need to option result feedback and interact with search system several times. Consequently, in this paper, the search system gets the related information by the inference engine and search engine, returns the results to users and allows users to select a second time or more.

Knowledge Base

Knowledge base is the collection of facts, rules and concepts. In terms of knowledge storage, the institution to store and manage the knowledge is called knowledge base [START_REF] Yu | Knowledge Base System Study Based on Ontology[END_REF]. Knowledge base system involves a key issue: knowledge representation. Knowledge representation is the key problem in knowledge base system. It should represent knowledge in the way that computers can "understand". In this paper, we realize knowledge representation with ontologies described by OWL [6]. OWL is a language proposed by the W3C consortium to be used in the "semantic-web" environment for the representation of ontologies. This language is based in the previous DAML+OIL language and it is defined using RDF. The inference rules in the knowledge base are written by SWRL [START_REF] Horrocks | SWRL: A Semantic Web Rule Language Combining OWL and RuleML[END_REF]. The characteristic of SWRL is that it can be regarded as a part of ontology, and be supported by a lot of reasoning tools.

Inference Module

Inference engine is the core part of the inference module. It is used to drive answer from the knowledge base for the ultimate purpose of formulating new conclusions. Knowledge reasoning is the process that inference engine reasons by the existing knowledge and returns the results to users in a friendly way.

Geospatial Information Database and Metadata Database

Geospatial information database is used to store geospatial information. Geospatial metadata is a summary document providing content, quality, type, creation, and spatial information about a data set.

From a data management perspective, metadata is important to maintain an organization's investment in spatial data. Metadata database is regarded as the repository into which all geospatial information metadata documents are stored. In this paper, we create semantic tagging for geospatial information metadata with the above ontologies and inference rules.

The Realization of the System

The Realization of the Knowledge Base

The knowledge base includes the planning domain ontology, the measure ontology and the inference rules. Establishing domain ontology is the process of abstracting and refining the objects in the real world and mapping them to the computer world [START_REF] Noy | Ontology Development 101: A Guide to Creating Your First Ontology[END_REF]. When establishing planning domain ontology, we should consider planning requirements, go by background knowledge, research the information space planners need and reference the planning field standards and related metadata specifications. In order to realize sharing, the concept must comply with certain professional standards, or it will lose the significance of establishing ontology. The planning domain ontology is used to help users to express their topic intentions. Intelligent search is mainly reflected in the reasoning process with the help of the inference rules which is written by SWRL. We lay down two kinds of inference rules so that the system can achieve two kinds of reasoning. Firstly, when planners search information in any planning step, they can get various information suggestions of the step. Secondly, planners can get the upper and lower categories of the domain ontology when they select each information category.

The measure ontology in this system is composed of the following five categories: scale, spatial resolution, dimension, data accuracy and geometric dimensionality. The scale instances of the measure ontology are composed of common scales, ranging from 1:500 to 1:500 million. The spatial resolution instances consist of common resolutions. Considering the realization of reasoning, the dimension instances only include the length dimensions and the accuracy instances only include the length precisions. The various kinds of measures are not isolated, with some transformational relations. In the search system, we lays down a series of inference rules according to practical application so that users can utilize one or more instances to deduce other corresponding examples by means of inference engine. When users type their search intentions, the system will form a measure variable constraint set according to the users' queries and return relevant data from database with the constraint set.

The Realization of the Inference Module

Jena inference engine is used as the inference engine in the search system. Jena is a Java framework for building Semantic Web applications. It provides a programmatic environment for RDF, RDFS and OWL, SPARQL and includes a rule-based inference engine. Jena is open source and grown out of work with the HP Labs Semantic Web Programme.

In the system, inference engine queries ontologies by SPRAQL (simple Protocol and RDF Query Language). SPRAQL is a query language defined for RDF data model [9]. SPRAQL model contains a collection of triples, called the basic graph pattern. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware.

SPARQL contains capabilities for querying required and optional graph patterns along with their conjunctions and disjunctions. SPARQL also supports extensible value testing and constraining queries by source RDF graph. The results of SPARQL queries can be results sets or RDF graphs.

The Realization of the Metadata Database

Metadata database is a database of data about data. In this study, the spatial information metadata standard is ISO19139, which is proposed by ISOTC211 group .It defines more than 300 metadata elements, being mainly used for geographic information and spatial data. The elements are divided into 14 packages, including identification information, data quality information, description information, space and time reference system information, data content information, and so on.

Identification information of the ISO19139 contains the topic element used to describe the phenomenon of space or space target attributes. The planning domain ontology is used to create semantic tagging for the topic element of the metadata so that the search system could support semantic reasoning [START_REF] Cho | Designing Metadata with Existing Ontologies[END_REF]. ISO19139 metadata standard only contains scale and spatial resolution. It is insufficient to support the above five measure types of measure ontology. Consequently, this study expands the measure of the standard, including scale, spatial resolution, dimension, data accuracy and geometric dimensionality. Finally, we create semantic tagging for geospatial information metadata with the above mentioned measure ontology.

The System Evaluation Result

In this test, we choose common measure and topic instances whose corresponding data exist in the database, and then judge the relativity of search results manually. Taking the maneuverability into account, this experiment only has two kinds of evaluation results: correlated and uncorrelated. This paper compares the keyword-based search with the ontology-based search (Table 1) and takes recall ratio and precision ratio as the level of the efficiency. Recall ratio is the number of records retrieved in a database search divided by the total number of relevant records in the database. Precision ratio is the number of relevant retrieved records divided by the total number retrieved in a database search.

Figure. 2. Recall ratio result

Figure.3. Precision ratio result

As it can be seen from Figure 2, the ontology-based semantic search is higher than the search based on keywords at the recall ratio. There is no concept of measure inference in traditional geospatial information search system, leading to a low recall ratio. But, in this search system, when users input measure needs, the search system will introduces the measure ontology and inference rules to infer user's measure search intentions and mine more implicit concept. For example, in this test, when users input 1:100000 in scale, the system will return the other four corresponding measure items. Besides, after having introduced the planning domain ontology to assist users to express the topic intentions, the search system can infer the relevant topics based on the topic users input. Therefore, the recall ration is higher than traditional method.

Figure 3 shows that the search system based on the measure ontology is higher than the search system based on keywords at the precision ratio. Adding measure ontology to geospatial information search system not only simplifies user's maneuverability, but also improves the accuracy. As a new constraint variable, measure is effective in narrowing search space. We can also conclude that the search based on the planning domain ontology is higher than the search based on keywords at the precision ratio in many instances. But, in some cases, the system appears lower precision ratio for the reason that it reasons out too much related concept. Therefore, the search system provides users a second choice to improve the precision ratio. In summary, the method is superior to the traditional method at the precision ratio.

Conclusions

In this paper, we firstly analyzed the deficiencies of current geospatial information search systems.

Then, combining with the spatial information search of the villages and towns spatial planning field, we designed and developed a spatial information search system based on search intention model.

Considering the characteristics of geospatial information, the search intention model was composed of topic sub-module, measure sub-module, space sub-module and time sub-module. In topic sub-module and measure sub-module, we established a knowledge base which includes ontologies and inference rules, and then realized the knowledge reasoning. Compared with traditional keyword-based search, this search mechanism can find implicit information and raise the recall ratio and precision ratio to some degree. There are still some shortcomings in the search system. In future work, we will dig users' search requirements deeply and improve the inference rules.

  Geospatial data describes three basic characteristics of various kinds of phenomenon in the real world: spatial characteristic, time characteristic and thematic attribute characteristic. In this study, considering the characteristics of geospatial information, the search intention model consists of four sub-models: topic intention sub-model, measure intention sub-model, space intention sub-model and time intention sub-model.

Figure. 1 .

 1 Figure. 1. system frameworkThe basic process of geospatial information search based on the intention model is as follows. Firstly, with the help of domain experts, we construct the planning domain ontology and the measure ontology.

Table .

 .

	1. Test Question Set.
	Number		Module							Query Input
	1		Measure Module					scale 1:10000
	2		Measure Module					scale 1:1000000
	3		Measure Module					precision 10.0m
	4		Measure Module					dimension m
	5		Measure Module					resolution 50m
	6		Topic Module					population
	7		Topic Module					industry
	8		Topic Module					climate
	9		Topic Module					highway
	10		Topic Module					water resources
		Recall Ratio
	100%
	90%
	80%
	70%
	50% 60%											ontology
	40%
	30%											keyword
	20%
	10%
	0	1	2	3	4	5	6	7	8	9	10	Query

Acknowledgements. This research was supported by National key technology R&D program (2006BAJ05A09) and State science and technology support projects (2008BAB38B04). The first author is grateful to Beijing Research Center for Information Technology in Agriculture for providing him a good research condition.


Wanying Qiu

Simulation Study of Single Line-to-Ground Faults on Rural Teed Distribution Lines

Keywords: simulation, fault location, teed line, single line-to-ground fault

Teed lines are widely used in rural power networks in China. The paper theoretically analyzes the voltages and currents when a single line-to-ground fault occurs, constructs a new mathematical model for fault location on the teed lines, performs corresponding MATLAB simulations. Simulation results show that currents near the source (bus M) are almost independent of fault positions and fault resistances. However, voltages at bus M are sensitive to fault positions while fault voltages at fault position are only sensitive to transient resistances. A novel approach to fault location on teed lines is proposed. First, the faulted line is identified with remote control equipment. Then, the transient resistances are calculated according to the fault voltages. And at last, the fault distances are computed according to the voltages at bus M and the calculated transient resistances.

Introduction

Power lines are typically teed in rural distribution systems in China. Faults occur when equipment insulation fails, due to system overvoltages, lightning, contamination or other mechanical causes. Single line-to-ground faults account for about 90% of the total faults according to the statistics [START_REF] Zoran | New One Terminal Digital Algorithm for Adaptive Reclosing and Fault Distance Calculation on Transmission Lines[END_REF][START_REF] Venkatesan | A Real-Time Hardware Fault Detector Using Artificial Neural Network for Distance Protection[END_REF][START_REF] Sukumar | New Fault-Location Method for a Single Multiterminal Transmission Line Using Synchronized Phasor Measurements[END_REF][START_REF] Salat | Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach[END_REF]. Fault location equipment is developed to find and clear faults as soon as possible. Unfortunately, the accuracy and reliability of present location equipment for rural power networks are not satisfactory. Therefore, it is necessary to study the characteristics of faults in rural power networks and lay a solid foundation for developing corresponding fault location equipment.

Theoretical Analysis

Consider a single line-to-ground fault from phase a to ground at the general three-phase bus shown in Figure 1. For generality, a fault impedance Zf is included. In the case of a bolted fault, Zf=0, whereas for an arcing fault, Zf is the arc impedance. In the case of a insulator flashover, Zf equals the total fault impedance between the line and ground, including the impedance of the arc and the post or tower. Since any of the three phases can be arbitrarily labeled phase a, analysis results can be easily applied to phase b or phase c.

From Figure 1, fault conditions in phase domain are: .

ag f a U Z I = (2)
Transforming fault conditions to sequence domain, the sequence components of the fault currents are obtained:

0 1 2 . 3 f f U I I I Z Z Σ = = = + (3)
where U f is prefault voltage and ZΣ=Z 0 +Z 1 +Z 2 .

The fault current on phase a can be derived by transforming the sequence components to the phase domain:

3

.

3 f a f U I Z Z Σ = + (4)
The line-to-ground voltages can then be determined from equation (2).

In the case of a bolted fault, Z f =0, it can be easily obtained that the line-to-ground voltage U ag =0, while U bg =U cg =1.732Uph, where U ph is the phase voltage.

MATLAB Simulations

Typical teed lines are shown in Figure 2. The MATLAB/Simulink simulation circuit is illustrated in Figure 3. 1. 1 implies that currents through bus M are almost independent of fault positions and fault resistances. In other words, fault currents are of little significance for fault location. However, voltages at bus M are sensitive to fault positions while fault voltages at fault position are only sensitive to transient resistances. Therefore, a new approach to fault location on teed lines is presented as follows. First, the faulted line, MR, RN, or RS, is identified with remote control equipment. Then, the transient resistances are calculated according to the fault voltages, which are approximately equal to voltages at bus N or bus S. And at last, the fault distances are computed according to the voltages at bus M and the calculated transient resistances.

Table 1. Parts of the Simulation Results

R

f =20Ω R f =500Ω D/km I M /A U M /V U F /V I M /A U M /V U F /V 3 62

Conclusions

Fault currents through bus M are almost independent of fault distances and fault resistances. While voltages at bus M are sensitive to fault distances, and fault voltages at fault position are only sensitive to transient resistances. Therefore, a new idea to locate the fault on teed lines is propose. Firstly, identifying the faulted line; secondly, calculating the transient resistances; and at last, computing the fault distances.

Fig. 1 .

 1 Fig. 1. Single line-to-ground fault

Fig. 2 .

 2 Fig. 2. Typical teed lines

Fig. 3 .

 3 Fig. 3. Simulation circuit In Figure 2, l MR =9 km, l RN =6 km, l RS =3 km. Each line is equivalent to a pi-block as shown in Figure 3. Parameters are from real statistics (written in MATLAB grammar). Source parameters are: Phase-to-phase rms voltage: 10500 V; Phase angle of phase A: 0 degree; Frequency: 50 Hz; Internal connection: Y; Source resistance: 0.1638 Ohms; Source inductance: 0.000236 H. Loads are: PAN=PBN=PCN=100000W; QAN=QBN=QCN=40000Var; PAS=PBS=PCS=300000W; QAS=QBS=QCS=40000Var. Line parameters are: r1=0.330 Ω/km, L1=0.001126 H/km, C1= 9.14e-9 F/km. Set Rf=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 500, 1000] Ω, change the fault point one by one, run MATLAB /Simulink/ SimPowerSystems, and many pages of simulation results are acquired. Parts of the results are shown in Table1.


Yingsa Huang
email: huangyingsa_148@126.com

Jianping Hu

Deyong Yang

Xiuping Shao

Fa Liu

Simulation Analyze the Dice and Shape of the Dicer based on ADAMS

Keywords: Dice, Diced section, Shape, ADAMS, Simulation

The Fruits and Vegetables diversacut dicer is composed of impeller roller, circular knives and crosscut knives. The mismatch of the rotational speed ratio and the unsuitable central position arrangement between the impeller and the crosscut will directly affect the quality of the diced section which is apt to appear marked arc, incline and so forth. Therefore, In this paper, using the virtual prototype technology to build the 3D model of fruits and vegetables diversacut dicer, and import it into ADAMS (dynamic analysis software of mechanical system) to simulation analyze the different shape of diced section when changing the rotational speed ratio from 0.11 to 0.22 and the central horizontal spacing between the impeller and the crosscut vary from 260mm to 310mm. The results indicated that the mismatch between the impeller speed and the crosscut speed is the main factor which causes the incline surface during dicing process. If the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, and the rotational speed ratio set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21, the optimal diced section can be acquired so as to provide a basis for improving the diced quality.

Introduction

In recent years, to satisfy the requirement for large number of fresh-cut fruits and vegetables in the food processing industry in our country, a new Fruits and Vegetables diversacut dicer was designed absorbed the traditional handiwork technologies and the advanced technology from the foreign countries. This dicer can solve the problem of unable for automatically feed, automatic cut and smooth discharge which existed in the traditional dicer very well. Additionally, it enhanced work efficiency, reduced working strength and satisfied the scale-production request in fruit and vegetable processing industry [START_REF] Bi | Studies on Slicing Techniques and Material Characteristic of Lotus Root[END_REF][START_REF] Wei | the Simulation and Optimization on the New Slicing Machine of Lotus Root Based on Virtual Prototype Technology[END_REF]. But there still existing many shortages, for example the mismatch of the rotational speed ratio and the unsuitable central position arrangement between the impeller and the crosscut directly affect the quality of the diced section which is apt to appear marked arc, incline and so forth.

This paper applied virtual prototyping technology, carried out kinematics simulation and analysis on the dicer to analyze the main factors which influenced the quality of the diced section, so as to provide evidence for improving the quality of diced section.

Structures and Work Principles of the Diversacut Dicer

The diversacut dicer is composed of impeller roller, circular knives and crosscut knives, which is shown in Figure 1. The dicer adopted diversacut technology, including feeding by rotary propeller, slicing by centrifugal cutting method, cutting strips by circular knives and finally cut by crosscut knives. The main parameters of the dicer was set that the inner diameter of impeller roller is R 1 =200mm and 6 paddles in the impeller,the radius of the circular knives is R 2 =45mm and the radius of the track of the crosscut knifepoint is R 3 =110mm. The amount of the circular and the crosscut knives can be adjusted according to the cutting specifications.

Virtual Prototype Modeling

According to the designed parameters of the diversacut dicer, the 3D solid model of the machine part was established in UG4.0 and the assembly was completed, thereby established the virtual prototyping parameterized model [START_REF] Kong | Design of Cotton Transplanter Based on Virtual Prototype[END_REF][START_REF] Li | Optimization Design and Simulations of the Apple-picking-robot arm[END_REF]. Then imported the model into ADAMS using of the Model Data exchange interface which provided by UG4.0 and the dynamic analysis software of mechanical system ADAMS. After the successfully transformation of the model, the constraints was added in ADAMS, including Fixed pair joint between the slicer and the impeller roller, Fixed pair joint between the slicer and the crosscut knives holder, Fixed pair joint between the crosscut knives and the crosscut knives spindle, Revolute pair joint between the crosscut knives holder and the crosscut knives spindle, Translation pair joint between the slicer and the ground. Then added motions on the model included a rotational motion and a translational motion [START_REF] Kong | Design of Cotton Transplanter Based on Virtual Prototype[END_REF][START_REF] Li | Optimization Design and Simulations of the Apple-picking-robot arm[END_REF][START_REF] Wang | Study on a Virtual Prototype Based Hay Highly Compressing Process[END_REF]. Then moved and rotated the model in order that the bottom of the slicer is at the origin and the slicer is along the Y-axis.

Simulation Analyzing the Shape of Diced Section

simulating Condition Setting

The rotational velocity of the impeller roller was set at 120r/min while the rotational speed ratio between the impeller roller and the crosscut knives which is indicated by was set from 0.11 to 0.22 and the specification of the diced product was 20mm×20mm×20mm.

In order that the deformation of the fruits and vegetables material during the cutting process is lesser and the simulating result conforms with the actual result, the structural arrangement of the machine should be compact, what is to say is that the triangular region between the impeller roller, the circular knives and crosscut knives is small, as shown in figure 2. The arrangement of the impeller roller and the crosscut knives is shown in Table 1.

Analysis of the Simulation Results

The cutting experiment indicated that the irregularity of the diced section was caused during the process of dicing by the crosscut knives. Especially, the rotationl speed ratio and the central position arrangement between the impeller roller and the crosscut knives greatly affect the quality of the diced section. In the simulation analysis, the reverse method that the crosscut knives moves upward along the slicer blade while rotating was adopted. The coordinate curve of the crosscut knifepoint can be got, which is the shape curve of the diced section, then provided the basis for optimizing the arrangement and determine the transmission of the dicer [START_REF] Li | Optimization Design and Simulations of the Apple-picking-robot arm[END_REF][START_REF] Wang | Study on a Virtual Prototype Based Hay Highly Compressing Process[END_REF][START_REF] Wang | Parameter Analysis of the Planting Process of 2ZT-2 Beet Transplanter[END_REF][START_REF] Zhang | Virtual Test on the Finger-chain Type Sugarcane-Lifter Based on ADAMS[END_REF].

When the arrangement of the impeller roller and the crosscut knives was set that the horizontal spacing =260mm~310mm, the rotationl speed ratio between the impeller roller and the crosscut knives was set that

x Δ As can be seen from the Figure 3,4,5,6, when the horizontal spacing between the impeller roller and the crosscut knives is 260mm~275mm, choosing the rotational speed ratio from 0.11 to 0.22, the diced section appears incline surface, and the larger of the ratio, the more obviously of the incline. As can be seen from the Figure 7 to Figure 11, when the horizontal spacing between the impeller roller and the crosscut knives is 280mm~300mm, choosing the rotational speed ratio from 0.11 to 0.22, the ideal diced section can be acquired, and it appears marked arc surface. As can be seen from the Figure 12,13, when the horizontal spacing between the impeller roller and the crosscut knives is 305mm~310mm, choosing the rotational speed ratio from 0.11 to 0.22, the diced section appears incline surface, and the larger of the ratio, the less obviously of the incline.

Irregularity Degree Analyzation of the Diced Section

In this paper, the quality of the diced section was measured by the Irregularity degree that the vertical distance between the section peak and the bottom 'h', as shown in Figure 14.

Through analyzing the data of the simulation results, the Irregularity degree of the diced section under different rotational speed ratio and central position arrangement between the impeller and the crosscut was got, which is shown in Figure 15. As can be seen from the Figure 15, if the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, when the rotational speed ratio set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21, the irregularity degree is the least, and the quality of the diced section is the best.

Conclusion

1. Through kinematics simulation of the cutting mechanism, come to a conclusion that when the horizontal spacing between the impeller roller and the crosscut knives is 260mm~275mm and 305mm~310mm, the rotational speed ratio adjust from 0.11 to 0.22, the ideal diced section can't be acquired. What's more, the diced section appears obviously incline surface.

2. When the horizontal spacing between the impeller roller and the crosscut knives is 280mm~300mm, the rotational speed ratio adjust from 0.11 to 0.22, the ideal diced section can be acquired, the diced section appears marked arc surface. The simulating result provided a basis for the central position arrangement between the impeller roller and the crosscut knives.

3. Through the Irregularity degree analyzation of the diced section, the ideal parameter combination that if the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, the rotational speed ratio should be set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21 was acquired. In these parameters, the quality of the diced section is best, as provided a basis for optimized the working parameter of the dicer next.

1 .Fig. 1 .

 11 Fig. 1. The 3D model of the dicer machinery

Fig. 2 .

 2 Fig. 2. The structural arrangement of the machine

  Figure 13 respectively shown the simulation result of the shape curve of the diced section.

Fig. 3 .Fig. 4 .

 34 Fig. 3. The shape curve of the diced section when =260mm

Fig. 5 .Fig. 6 .

 56 Fig. 5. The shape curve of the diced section when x Δ =270mm

Fig. 7 .Fig. 8 .Fig. 9 .Fig. 10 .Fig. 11 .

 7891011 Fig.7. The shape curve of the diced section when x Δ =280mm

Fig. 12 .Fig. 13 .

 1213 Fig.12. The shape curve of the diced section when x Δ =305mm

Fig. 14 .Fig. 15 .

 1415 Fig.14. The Irregularity degree of the diced section

Table 1 .

 1 The arrangement of the impeller roller and the crosscut knives horizontal spacing x

		vertically spacing y Δ /(mm)
	260.00	188.75
	265.00	183.19
	270.00	174.03
	275.00	167.67
	280.00	162.26
	285.00	161.36
	290.00	161.98
	295.00	159.86
	300.00	159.86
	305.00	159.86
	310.00	154.68

Δ /(mm)

Acknowledgements

This work was financially supported by Tackling key Problems of Science and Technology of Jiangsu Province (BE2008385).


Liang Tang

Feng Tan

Haiyan Jiang

Xiaojun Lei

Weixing Cao

Yan Zhu
email: yanzhu@njau.edu.cn

Root Architecture Modeling and Visualization in Wheat

Keywords: Wheat, Root, Architecture, Model, Simulation, Visualization

This paper aims to develop a root morphological model in wheat (Triticum aestivum L.), and the realistic visualization of root growth under different soil conditions in wheat. Based on the topology of the wheat root system, historical literature and experimental data, a root morphological model in wheat was developed preliminarily using thermal time as a driving factor, including sub-models of root emergence, root growth rate, and root axis curvature. Based on 3D visualization technology, a three-dimensional visualization model of the root axis in wheat was developed by using the platform of VC++.net and OpenGL library, including sub-models of geometry, texture mapping, and light rendering. Integrated the established root morphological model and the visualization model, the three-dimensional visualization of a root system in wheat under different soil conditions was realized. This study lays a foundation for further development of a visualization system for the whole wheat plant.

Introduction

Plant root systems play an important role in the development of new wheat germplasm with improved drought tolerance, nutrient and water uptake efficiencies and lodging resistance [START_REF] Manske | Root architecture-wheat as a model plant[END_REF]. Root system development and structure are particularly difficult to study because of the difficulty of observing and quantifying the architecture of actual roots and the complexity and plasticity of roots as geometric objects [START_REF] Lynch | Simulation of root system architecture[END_REF].

Simulation models and computer visualization are useful tools to study root architecture. Several studies dedicated to the visualization of root system architecture have emerged during these last years, using different approaches. One approach is of fractal description [START_REF] Fitter | Fractal characterization of root system architecture[END_REF][START_REF] Ozier | Fractal analysis of the root architecture of Gliricidia sepium for the spatial prediction of root branching, size, and mass: model development and evaluation in agroforestry[END_REF][START_REF] Van Noordwijk | Proximal root diameters as predictors of total root system size for fractal branching models[END_REF], which uses statistical relationships between typical dimensions (e.g., length between branches, branching angles and diameters) observed locally throughout the root system. This approach, however, is essentially static because it does not rely on morphogenetic processes, thus rendering the models nearly unable to simulate architectures bearing developmental information. The approach based on development [START_REF] Lynch | Simulation of root system architecture[END_REF][START_REF] Diggle | ROOTMAP -a model in three-dimensional coordinates of the growth and structure of fibrous root systems[END_REF][START_REF] Pages | Root Typ: a generic model to depict and analyse the root system architecture[END_REF][START_REF] Clausnitzer | Simultaneous modeling of transient three-dimensional root growth and soil water flow[END_REF] has been used more often. It formalizes and combines in a mathematical framework the main developmental rules involved in the dynamics of root system architecture. L systems [START_REF] Lindenmayer | Mathematical models of cellular interaction in development, Parts I and II[END_REF][START_REF] Prusinkiewicz | The Algorithmic Beauty of Plants[END_REF] is another approach which has been used for root architecture modeling and visualization [START_REF] Shibusawa | Hierarchical modeling of a branching growth root system based on L-system[END_REF][START_REF] Daniel | Root growth simulation using L-system[END_REF]. This approach links with a physiological model to simulate both plant function and structure, but it has not often been applied to the plant root. Greenlab is a functional-structural model of plant growth for environmental applications, examples have been applied to root system modeling [START_REF] Zhang | Development and application of three-dimensional growth model of root system in wheat seedling[END_REF][START_REF] Zhang | Three-dimensional model simulating development and growth of cotton root system[END_REF] and performing well, however visualization technologies like random processing and collision detection were not studied in detail.

Wheat (Triticum aestivum L.) is the most important grain crop in the world, more than half of world's population relies on wheat as its primary food staple. Existing studies on wheat root growth modeling and visualization emphasize either developmental processes or visualization technology. Thus, the objectives of this paper are to develop a 3D root morphological model in wheat based on historical literature and experimental data and realistic root growth visualization in wheat under different conditions by using 3D geometrical modeling techniques.

2

Structure of wheat root system

Two root types are distinguished in wheat: the seminal roots (also called primary roots), which develop at the scutellar and epiblast nodes of the embryonic hypocotyl of the germinating caryopsis; adventitious roots, which subsequently emerge from the coleoptilar nodes at the base of the apical culm and tillers [START_REF] Manske | Root architecture-wheat as a model plant[END_REF]. These two categories of roots function in a complementary manner, and thus the root system must be considered as a whole. The number of primary roots in a cereal is usually five to seven, but may reach ten. Lateral roots (also called branch root) emerge on the primary roots and adventitious roots when roots grow until a certain period. First order and second order lateral roots also can grow on the primary roots and adventitious roots at a certain interval. Thus, the wheat root system is comprised of primary roots, adventitious roots and different orders of lateral roots in the soil (Fig. 1). A binary tree structure was used to analyze the structure of wheat root system. The root system can be divided into different types of root axis, each root axis has several nodes, from each node can emerge two sub-trees. The root tip has no children nodes. These nodes include root growth information including root hierarchy and branch information (Fig. 2).

Morphological model descriptions

The root morphological model simulates the dynamic growth processes of the three dimensional structure of a root. The basic information needed to model the morphology of a single root is the number of roots, the times of root (primary roots, adventitious roots, and lateral roots) emergence, the growth rate of the root axis, and the growth direction of the root axis. Each root was thought of a collection of axial segments which change direction according to the morphological model. emerging on each node display radial symmetry [START_REF] Ma | Wheat Roots[END_REF]. The number of primary roots (N) is significantly related to thermal time (TT, o Cd), as following:

N=2.64• ln(TT)-6.28 .

Adventitious roots emergence. The growth of leaves, tillers and adventitious roots has a synchronous relationship: when leaf n+3 has emerged, a root emerges on leaf node n [START_REF] Klepper | Root and shoot development in winter wheat[END_REF]. Integrating a previous study on the leaf phyllochron [START_REF] Yan | A simulation model of above-ground organ formation in wheat[END_REF], the relationship between emergence of the adventitious root and thermal time was described by equation [START_REF] Lynch | Simulation of root system architecture[END_REF].

               SN a b a PHYLL a b PHYLL TT b a 1 ) 3 ( 102 0 ) 1 ( 102 ) 2 ( ( 2
)
where a is the tiller position where the adventitious root grows, and b is the node position on the tiller where the root grows. TT a(b+2) is the thermal timewhen a root emerges on node b of tiller a ( o Cd), PHYLL is phyllochron, 102 is the thermal time from sowing to emergence ( o Cd), SN is the number of effective tillers.

Lateral roots emergence.

According to historical experimental data and the literature [START_REF] Zhang | Analysis and modeling of the root system architecture of winter wheat seedlings[END_REF], the branching interval of main and first order lateral roots were 0.2 mm o Cd -1 , and secondary order lateral roots were 0.05mm o Cd -1 . Branching can happen in the intervals. Branching probabilities were calculated by the interval of the adjacent two lateral roots.

The lateral root radius after branching was determined by a law which adopted by SimRoot [START_REF] Lynch | SimRoot: Modeling and visualization of root systems[END_REF], the section area before root branching was calculated by the section areas after root branching.

d=α×(d 1 2 +d 2 2 ) ( 3
)
where d is root diameter before branching (mm); d 1 and d 2 are the lateral root diameters after branching (mm); α is weighting coefficient, based on experimental data, taking an α value of 0.6 in this study.

Root growth rate

Different types of roots have different growth rates, the growth rate of a primary root can be several centimeters per day, while the growth rate of secondary order lateral root can be several millimeters per day. Roots grow rapidly when thermal time is less than 120 o Cd. Above 120 o Cd, root growth rate decreases. The growth rate of a single root was calculated as following:

When thermal time was less than 120 o Cd, the growth rate (V(TT)) of primary, adventitious and first order lateral root were:

V(TT)=-7×10 -6 TT 2 +0.0375 TT -17 . ( 4
)
The growth rate of secondary branch of root:

V(TT)=-1×10 -6 TT 2 +0.0564 TT -47 . ( 5
)
When thermal time was greater than 120 o Cd, the growth rate of primary and adventitious root was:

V(TT)=2.1ln(TT)+5.2 . ( 6
)
The growth rate of first order lateral root was:

V(TT)=5.4ln(TT)-20 . (7)
The growth rate of secondary order lateral root was:

V(TT)=5.2ln(TT)-21 . (8)

Growth direction of root

Growth direction of primary and adventitious root axis. The method developed by Clausnitzer and Hopmans [START_REF] Chen | A two-dimentional, dynamic model for root growth distribution of potted plants[END_REF] was adopted in this study for the calculation of the growth direction of the root axis. The growth direction (D) is computed from three directional components: the initial direction (D -1 ) of the root at the previous time step, geotropism (D G ), mechanical constraint (D M ) which may be considered as isotropic (random vector). The three vectors determine the final growth direction:

D = D -1 + D G + D M . (9)
The final position of root axis is rotated to an orthonormal coordinates, and can be denoted as following:

X 2 =X 1 +L• cosα (10a) Y 2 =Y 1 +L• cosβ (10b) Z 2 =Z 1 +L• cosγ (10c) (X 1 , Y 1 , Z 1
) is initial growth position of the root axis in a growth cycle, (X 2 , Y 2 , Z 2 ) is the final growth position of the root axis in a growth cycle, L is the lenth of the root axis, α, β, γ are decided by equation [START_REF] Lindenmayer | Mathematical models of cellular interaction in development, Parts I and II[END_REF].

Growth direction of lateral root axis. The 3D structure of a primary root is rotated to an orthonormal coordinates (Fig. 3a,b). This amount of rotation is calculated to find the position of the root in 3D space. Then the growth direction of a new lateral root on the primary root was calculated. Finally, the orthonormal coordinates was rotated back to its original orthonormal coordinates. Fig. 3a shows that the final growth direction of a new lateral root P 2 P 3 was determined by the vectors P 0 P 1 and P 1 P 2 . The three points P 0 , P 1 and P 2 were rotated to XOY of an orthonormal coordinates, the corresponding points were P 0 ', P 1 ' and P 2 ', the growth direction of a new lateral root P 2 'P 3 ' was denoted by a vector v, which can be divided to each coordinate, as ( v 1 , v 2 , v 3 ) (Fig. 3b), thus,

  cos sin 1    v v (11a)  cos 2   v v (11b)   sin sin 3    v v (11c) 2 3 2 2 2 1 v v v v    . ( 11d
) v =length of vector v, v / v =unit vector.
The required rotation is calculated by using the definition of an orthonormal axial system x, y, z:

            v c c c v y y b v x x a / ) ( / ) ( / ) ( 0 1 1 0 1 1 0 1 1 Unit vector x=(a 1 , b 1 , c 1 ) (12)
Similarly, Y=(a 2 , b 2 , c Reverse rotation and translation will bring the structure back to its original position. Vector v, representing the new link P 2 P 3 , will thereby be multiplied by the transposed matrix R.

Root axis curve formation. The root axis was comprised of numerous root segments. The root axis grows 1 o Cd as a step. When the root axis grows a certain length, from the root axis emerges a node which produces a lateral root according the rule explained in 3.1.1. The curve of the root axis was formed in this manner of root growth, as showed in Fig. 3c Water stress. Wheat root architecture is influenced by soil water content. The relative growth rate was used simulate the root growth process under water stress [START_REF] Mech | Visual models of plants interacting with their environment[END_REF].

) ( ) ( ) ( ) ) ( )( ( ) ( 0 0 t R t w w w w t w t w t Y Rdt dR i i R            0 ) ( w t w w i   . ( 14
)
where R(t) is the root weight, w(t) is the actual soil water content, W i is the minimum soil water content, w 0 is the critical soil water content, YR(t) is a genetic factor of the root in different developmental stages, and α(t) is a function coefficient of the root.

Nitrogen deficit. Low soil nitrogen content can stimulate root growth, and high soil nitrogen content would restrict root growth. According to the CERES model [START_REF] Godwin | Nitrogen balance and crop response to nitrogen in upland and lowland cropping system[END_REF], Nitrogen deficit factor (FN) which is a value within 0-1 can be calculated as following: FN= 1-(TCNP -TANC)/(TCNP-TMNC) .

(

) 15
where TCNP, TANC and TMNC are critical, actual and minimum soil nitrogen content, respectively.

Random processing

To avoid rigid root curves while using the visualization model, the root morphological parameters were randomized to enhance the effect of -shake‖. Based on the morphological feature parameters outputted from the wheat root morphological model, it was necessary to add a random variable between the high-low limit to realize realistic effect. The random variable was limited to a certain range, and the accuracy rating was evaluated by average value of the parameters. According to central limit theorem and large number theorem, this model constructs random variable as described in equation [START_REF] Ma | Wheat Roots[END_REF].

The node of lateral root emergence

Initial node Length of nodes interval 58 . 2 / ) ( 58 . 2 / ) ( 2 min 1 max r N N r N N N a a r     (16)
where N r is random variable; N a is an average of a morphological feature parameter; N max is the maximum morphological feature parameter; N min is the minimum morphological feature parameter; r 1 and r 2 are the random variables subject to the normal distribution based on N[0,1].

Visualization model

In order to make the visualization of roots looks more realistic, a visualization model was developed by using 3D dimensional graphic technology on the platform of VC++.net and OpenGL library, including a root axis geometric sub-model, and a texture and illumination sub-model.

Root axis geometric sub-model

A root system is comprised of a number of root axes, the root axes can be thought of as numerous connections of truncated cones. A truncated cone has four parameters: radius of the upper (R 1 ) and lower (R 2 ) circles, the height (L), and the direction of root axis [START_REF] Yan | A simulation model of above-ground organ formation in wheat[END_REF] (Fig. 4a). The connection of the truncated cones should be followed by two conditions: the forepart of the truncated cone is larger than the posterior; the primary and adventitious root is thicker than lateral roots. When two truncated cones are connected, a gap appears, as showed in Fig. 5a, which affects the visualization effects. A polygon grid algorithm [START_REF] Lluch | Modelling tree structures using a single polygonal mesh[END_REF] was adopted to eliminate these gaps (Fig. 5b). Roots are embedded in soil and grow in a dark environment, however, root structure can be looked clearly under illumination. The illumination model in the OpenGL library was adopted for simulating the illumination effect. The root surface was drawn by a texture model, which was constructed by using photos of a root at different stages with textures and integrated with the geometric model. The format of texture picture was 32*32 bmp.

Visualization of wheat root --Example applications

Based on the root topological structure, linking root morphological model and visualization model, visualization of wheat root was realized by using 3D geometrical modeling techniques. Fig. 6 shows that the time-course simulation of root growth under different growth stages in wheat. The root system was from simple to complex, and the number of roots varied greatly. An experiment was conducted to compare the visualization output in this study with field observations. The experiment was conducted at the Nanjing Agricultural University Experimental Station in Nanjing (32°02′ N, 118°50′ E), China in 2009. The sowing date was 11 Nov. The cultivar Huaimai 25 was evaluated. Fig. 7 shows the comparison between observed and simulated single root architecture. The observed root structure scanned by the WinRhizo system (Regent Instrument Inc., Canada) indicated that the observed secondary order lateral roots were less than the simulation because of mechanical damaged from manual sampling. Fig. 8 illustrates a reasonably good comparison between the visualization of root architecture and the observed root architecture at the seedling stage.

Discussion and Conclusions

This paper described a root morphological model for simulating root growth and development under different conditions and a visualization model for the realistic visualization of root architecture in wheat. We aimed at the actual characteristics of root architecture in wheat. It is different from previous studies [START_REF] Diggle | ROOTMAP -a model in three-dimensional coordinates of the growth and structure of fibrous root systems[END_REF][START_REF] Lynch | SimRoot: Modeling and visualization of root systems[END_REF], which aimed at a synthesis, generic root architectural model and its ability to simulate a diversity of root system architectures. Some of methods applied in those models were adopted in this study.

Other studies on visualization of root architecture used different approaches, such as fractal description [START_REF] Lynch | Simulation of root system architecture[END_REF][START_REF] Fitter | Fractal characterization of root system architecture[END_REF][START_REF] Ozier | Fractal analysis of the root architecture of Gliricidia sepium for the spatial prediction of root branching, size, and mass: model development and evaluation in agroforestry[END_REF], development rule [START_REF] Van Noordwijk | Proximal root diameters as predictors of total root system size for fractal branching models[END_REF][START_REF] Diggle | ROOTMAP -a model in three-dimensional coordinates of the growth and structure of fibrous root systems[END_REF][START_REF] Pages | Root Typ: a generic model to depict and analyse the root system architecture[END_REF], L-systems [START_REF] Prusinkiewicz | The Algorithmic Beauty of Plants[END_REF][START_REF] Shibusawa | Hierarchical modeling of a branching growth root system based on L-system[END_REF], and Greenlab [START_REF] Daniel | Root growth simulation using L-system[END_REF][START_REF] Zhang | Development and application of three-dimensional growth model of root system in wheat seedling[END_REF]. These studies on wheat root either paid attention to the development or briefly described the visualization technology [START_REF] Van Noordwijk | Proximal root diameters as predictors of total root system size for fractal branching models[END_REF], or only observed and simulated the root morphology at the seedling stage [START_REF] Daniel | Root growth simulation using L-system[END_REF].

Based on the topology of the root system in wheat, historical literature and experimental data, a root morphological model of wheat was established preliminarily using thermal time as a driving factor, including a root emergence sub-model, a root growth rate, and a root axis curve sub-model. Based on 3D visualization technology, a three-dimensional visualization model of a root axis in wheat was developed by using the platform of VC++.net and OpenGL library, including the sub-models of geometry, texture mapping, and light rendering. Integrating the established root morphological model and the visualization model, the three-dimensional visualization of root system growth in wheat under different conditions was realized.

Roots growth and development is very complicated with a high degree of plasticity as influenced by many different factors, such as soil chemical and physical properties, climate elements, and crop genetics. Further study is needed to focus on soil, crop genotypes, and environmental conditions.

Fig. 2 .

 2 Fig. 2. Hierarchical structure of root nodes in wheat

  Now we have the unit vectors X, Y and Z with their components, which form a matrix R:

Fig. 3 .

 3 Fig. 3. Growth direction of lateral root in wheat

Fig. 4 .Fig. 5 .

 45 Fig. 4. The basic model of root segment

Fig. 6 .Fig. 7 .Fig. 8 .

 678 Fig. 6. Visualization of root system growth under different growth stages in wheat

Acknowledgments. We are thankful that the study was supported by the National Natural Science Foundation of China (30800136) and the National Basic Research Programme of China (2009CB118608). We also would like to thank Professor Albert Weiss at School of Natural Resources, University of Nebraska-Lincoln, for revising the English of this manuscript.


Weidong Zhuang

Chun Wang

Research on on on on F F F Farmland armland armland armland

Keywords: DGPS, information collecting and processing, area survey, precision agriculture

In order to realize the farmland information pinpointing acquisition, this paper has conducted the technology research for farmland information collecting and processing based on the DGPS. According to the precision agriculture's demand which agricultural condition information should be made positional, fast, precise, continuous acquisition, system uses the DGPS receiver and the touch-screen computer, uses Microsoft Access database and uses Visual Basic 6.0 to establish the application program, which used in the farmland information acquisition and processing. According to the farm land contract's demand, the method is established by using DGPS receiver to survey and drawing for field length of side and the area, which was applied in the farm and the effect was good. The result shows that this technology had very good usability.

Introduction Introduction Introduction Introduction

The application of Precision Agriculture can change the traditional agriculture of extensive and managing mode, essences the farmland homework, reduces the production cost, protect the ecological environment, increase economic benefits. GPS (global positioning system) and GIS (geographic information system) is the key technology of precision agriculture, and it is in large quantities of application in agricultural production [START_REF] Zhuang | Research on GPS and GIS Applied in Precision Agriculture[END_REF][START_REF] Qing | Variable rate irrigation control system integrated with GPS and GIS[END_REF][START_REF] Devlin | Performance accuracy of low-cost dynamic nondifferential GPS on articulated trucks[END_REF][START_REF] Qing | Field vehicle position monitoring system based on GPS and GIS[END_REF][START_REF] Stoll | Guidance of a Forage Harvester with GPS[END_REF][START_REF] Smith | GPS Position Latency Determination and ground speed calibration for the SATLOC M3[J][END_REF][START_REF] Yong | Information Processing System for Precision Agriculture Based on GPS and GIS[END_REF][START_REF] Xinzhong | Application test of soybean sowing by variable fertilization seeder in Heilongjiang reclamation areas[END_REF] . This article is aimed at the need that is the measure of fast farmland. The system uses differential signals of the DGPS receiver, develop application software for receiving and dealing with GPS data, farmland circumference, measuring and calculating the area of land and drawing, in order to improve the efficiency of information acquisition and farmland quality, reduce cost, and easy work.

2 2 2 2 R R R Receiving eceiving eceiving eceiving and and and and processing processing processing processing the the the the GPS GPS GPS GPS data data data data 2.1 2.1 2.1 2.1 R R R Receiving eceiving eceiving eceiving the the the the GPS GPS GPS GPS data data data data GPS receiver usually adopts the provisions of NMEA 0183 data output of information, It is should be at least two GPS positioning data that contains GGA and RMC. The computer can receive data form GPS receiver by port RS -232, and will locate data income with MSComm control. The GPS data is analyzed, and some information is got from GPS receiver such as latitude, longitude of the receiving antenna and differential information, satellite number, speed, etc.

2.2 2 2 2 Limiting Limiting Limiting Limiting average average average average filtering filtering filtering filtering

Limiting average filtering are refer to the recent data which each time the sampling arrives to carry on Limiting processing first, then carries on average filter processing. It is advantage that it can eliminate the accidentally appears pulse interference which can cause sampling value deviation. Because the data obtained from DGPS receivers in the positioning information which includes velocity information, and the speed information can be used for limiting average filtering in the field survey, so as to improve the accuracy of the measurement. A position's information for the field of sampling point, can use speed v is smaller than setting threshold VT, in n times average point positioning information as the measured value, Formula is as follows:

∑ < = n T i i V v if X n Y 1 ) ( , 1 (1) 2 2 2 2.

. . .3 3 3 3 Projection Projection Projection Projection transformation transformation transformation transformation

Because the GPS receiving data is three-dimensional coordinates, but,it usually uses the plane rectangular coordinates in the actual survey. Therefore it must be carried on the coordinate transformation. For actual surveys the application, WGS-84 ellipsoidal coordinates, which use latitude and longitude, should be transformed to the plane rectangular coordinate system according to 6 degree or 3 degree belt projections. Now, Gauss-Kruger or UTM projection is often be used, they belong to the horizontal axis of the Mercator projection. When the central meridian coefficient of length m0=1, it called the Gauss projection. When m0=0.9996, it called the UTM projection. It should be pointed out that when farmland cross standard project belt, the unique central longitude should be established for the all fields in same projection zone.

)、( 22 , y x

), The distance of two points dAB can be computed with the plane distance formula, Formula is as follows:

( ) ( ) 2 1 2 2 1 2 y y x x d AB - + - = (2)
Under the situation of every of farmland vertex coordinates ( k k y x , ) , farmland perimeter P formulas is as follows:

( ) ( ) ∑ = + + - + - = n k k k k k y y x x P 1 2 1 2 1
Note: ( )

1 1 x x n = + , 1 1 y y n = + (3)
∑ = + + - = n k k k k k y x y x S 1 1 1 2 1 Note: 1 1 x x n = + , 1 1 y y n = + (4)
If the farmland vertex is clockwise arranged, its area S is negative, When it is arranged for counterclockwise, its area s is positive [START_REF] Weidong | The Arithmetic of DGPS Guidance for Agriculture Machinery Straight Line Operation[END_REF] . Therefore, the area can be calculated from absolute S, then all the farmland area is positive number.

According to the precision agriculture's demand which agricultural condition information should be made positional, fast, precise, continuous acquisition, we develop the application software .system uses Visual Basic 6.0 to establish the application program and uses MSComm controls and the DGPS receiver, uses Microsoft Access 2000 database to store location data. uses ADO database connection [START_REF] Weidong | Agriculture Machinery Operation Guidance System Based on GPS and GIS[END_REF] . Software should finish the function that the GPS receives processing information, farmland area, perimeter of measurement, storage data and calculated data and drawing, the block diagram storage etc. The function of software diagram is to see figure 1. The development of software is operated as figure 2.The database for the measurement data of land is in table 1. Table Table 1 1 1 It can be shown from the table 2, area measurement using DGPS is repeated well and measurement error is very small. Furthermore, with the increase of measuring plot area, the measure of the relative errors is decreased. It can completely meet the requirement of agricultural land area. Table Table 2 2 2 2 Using software and the DGPS receiver compared with traditional method of using altazimuth and meter rule that the first is measuring speed, high accuracy and the application effect is very good.

The main of the program

Receiving and processing GPS information

Length area measurement

Measurement and storage data

Reading measurement data

Calculation and drawing

Storage of diagram

Table Table

C C C Conclusion onclusion onclusion onclusion

The Satellite different signal high-precision DGPS receiver in agricultural production is widely used in the field, Such as farmland surveying and mapping, soil sampling, fertilization, crop growth, production monitoring etc. According to the demand of agricultural production units, using DGPS receiver, touch screen, the development of the farmland information collection and processing software can improve the efficiency and quality of the farmland information acquisition, reduce costs and the difficult work..

2

 2 Farmland Farmland Farmland Farmland area area area areaIn consideration of undulating plot, plot is flat polygon farmland, In the case of measuring every farmland vertex coordinates ( k k y x , ) , Farmland area can be available of plane deformation formula to calculate , formulas is as follows:

Fig.

  Fig. Fig. Fig. Fig.1 1 1 1. . . . Structure Structure Structure Structure of of of of software software software software system system system system

5 5 5 Area Area Area Area measurement measurement measurement measurement test test test test and and and and application application application application 5 5 5 5.1. .1. .1. .1. Area Area Area Area measurement measurement measurement measurement test test test test

  In order to test the performance of the system in June 2009, using AgGPS 332 receiver, the touch-screen vehicle carries the computer and the MSA2 satellite signal is to test the DGPS area measurement that chooses 3 quadrilateral plot in Heilongjiang Bayi Agriculture University campus, it is tested by 3 replications respectively. Measuring results in table 2.

	ID	Date	Time	Longitude	Latitude
	1	09-06-27	11:02:41	125.1583	46.58145
	2	09-06-27	11:05:52	125.1581	46.58743
	3	09-06-27	11:08:38	125.1659	46.58799
	4	09-06-27	11:11:39	125.1660	46.58150
	5

1. . . . Example of field survey data.

Area Area Area Area measurement measurement measurement measurement applications applications applications applications

  . . . . Measurement and analysis of DGPS areaFrom June 2009 to October 2009, according to the land contracting requirements of one of the agriculture company in Qiqihar, heilongjiang province, China. AgGPS332 and the software is used for the measure the farmland area which was 22817 hm 2 .

		Location 1		Location 2		Location 3
		Circumference	Area	Circumference	Area /	Circumference/	Area
		/m	/m 2	/m	m 2	m	/ m 2
	1	137.3	1096.3 185.3	1988.6 2570.9	409723.4
	2	137.1	1093.2 185.4	1985.4 2571.8	410017.6
	3	137.7	1102.9 185.6	1991.0 2572.4	410221.4
	Average	137.4	1097.5 185.4	1988.3 2571.7	409987.5
	Maximal	0.24	0.50	0.09	0.15	0.03	0.06
	relative
	error/%

5 5 5 5. . . .2 2 2 2. . . .

3 3 3.1 .1 .1 .1 Farmland Farmland Farmland Farmland perimeter perimeter perimeter perimeter IF we know two points coordinates( 1 1 , y x