Qian Sun

Yong Liang

NERCITA The State Key Laboratory Program(NERCITA-KFKT2013

Keywords: Proté gé, SWRL, Proté gé -Owl API, pear, ontology, query, JESS

This paper studied the construction of Pear Diseases Domain Ontology (PDDO), and the realization of query system based on PDDO and SWRL. First, an approach to build PDDO based on SWRL was proposed, which consists of confirming core concepts, adding the properties of concepts and the relationships between concepts, adding the instances of concepts, representing domain ontology, adding SWRL rules and reasoning. Then the query system model and implementation algorithm were given. The query system, which integrates SWRL with Jess reasoning engine based on the SWRL rules, realized disease query, instance query, and diagnosis query by Proté gé -OWL API. The query system realized excavating implicit relationships and renewing PDDO relative to previous system. Through the query system based on reasoning pear diseases knowledge can be obtained from PDDO according to user needs, furthermore, new and inferred knowledge are written to the PDDO owl file.

Introduction

With the development of owl ontology language, more and more knowledge systems based on domain ontology are developed. The development of knowledge system includes knowledge representation, storage, reasoning, query and so on, in which query and reasoning are the key technologies, through them knowledge can be obtained from ontology [START_REF] Hua | Ontology Storage and Querying Technology[END_REF] . In order to realize pear diseases knowledge query based on reasoning, the query system based on PDDO and SWRL is studied in this paper.

Technologies and Tools on Ontology

Proté gé

Proté gé is a open source ontology editor, which allows user to model ontology. The Proté gé platform provides two main ways of modeling ontology via the proté géframes and proté gé -owl editors. Proté gé ontology can be exported into a variety of formats including OWL [START_REF] Harmelen | OWL Web Ontology Language Reference[END_REF] ,RDF(S) [3] , and XML Schema. Further more, Proté gé can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools [4] .

Proté gé -OWL API

The Proté gé -OWL API is an open source Java library for the Web Ontology Language and RDF(S). The API provides classes and methods to load and save OWL files, to query and manipulate OWL data models, and to reason. Furthermore, the API is optimized for the implementation of graphical user interfaces [5] .

SWRL

SWRL is a Semantic Web Rule Language based on a combination of the OWL DL and OWL Lite sublanguages of the OWL Web Ontology Language with the Unary/Binary Datalog RuleML sublanguages of the Rule Markup Language. SWRL includes a high-level abstract syntax for Horn-like rules in both the OWL DL and OWL Lite sublanguages of OWL. A model-theoretic semantics is given to provide the formal meaning for OWL ontology including rules written in this abstract syntax [6] .

JESS

Jess is a Java-based rule engine. Jess system consists of a rule base, fact base, and an execution engine. It has been used in Proté gé -based tools, e.g. SWRLJessTab [7] , SweetJess, JessTab.

Construction of Pear Diseases Domain Ontology Based on SWRL

At present, ontology construction methodologies have not been standardized, there are numerous frequently quoted approaches. In this paper, an approach for building domain ontology based on SWRL is proposed. The process of it consists of the following phases:

Confirming Core Concepts

In this case, core concepts of pear diseases domain are collected according to features needed by diseases diagnosis and pear diseases. Firstly, "Pear-tree" is selected as the first concept, and then the concepts relating to "Pear-tree" are selected. Table1 gives the names and explanations of these concepts.

Dynamicsymptom

Reflects the symptoms of the diseased pears , instance: rotting.

Adding the Properties of Concepts and the Relationships between Concepts

In this case, every core concept has different properties to be added, for example: "describe" is added as the property of "disease" to describe the features of diseases. In addition, the relationships between concepts are added. Table2 gives the relationships between "Pear-tree" and other concepts.

Table 2. The relationships between "Pear-tree" and other concepts

Name Explanation

At-part Reflects the relationship between "Pear-tree" and "part" Has-ds Reflects the relationship between "Pear-tree" and "Dynamicsymptom" Has-c

Reflects the relationship between "Pear-tree" and "Color" Has-disease Reflects the relationship between "Pear-tree" and "Disease" Has-shape Reflects the relationship between "Pear-tree" and "Shape" Has-feature Reflects the relationship between "Pear-tree" and "Feature" Has-pathogen Reflects the relationship between "Pear-tree" and "P-kind" At-period Reflects the relationship between "Pear-tree" and "Growing-period"

Adding the Instances of Concepts

It is necessary for building domain ontology to supply the instances of concepts. For example: in this case, "change color" , "die", "dry-rot", " falling-off", " putrescence" , "rotting", "spotting", "wilting " are added as instances of "Dynamic-symptom". "branch", " fruit", "fruit-stem", "leaf", and "root" are added as instances of "Part". Names of pear diseases are added as instances of "Disease", for example:. "Steptomyces-scabis", "Pear-Brown-blight", "Pear-Rust", "Pear-Valsa-Canke", "Pear-anthracnose", "Pear-blackPedicle-disease", Pear-black-shank", " Pear-blackspot", "Pear-blight" and so on.

Representing Pear Diseases Domain Ontology

In this case, proté gé 3.4.8 is selected as the developing tool, so the PDDO can be represented by OWL. Further illustrate below: Firstly, according to the confirmed core concepts, corresponding classes of PDDO are created by using proté gé . Classes structure of PDDO can be shown by OWLVizTab in proté gé (see Fig. 1). Secondly, data properties and object properties of classes are added, relationships between concepts can be represented by way of adding object properties. For example: Fig. 2 represents the relationships between "Pear-tree" and other classes by JambalayaTab. Finally, instances of classes are added.

After that, an owl file ( tree.owl ) is created by proté gé .

SWRL Editor

The SWRL Editor is an extension to Proté gé -OWL, which supports the interactive editing of SWRL rules. The editor can be used to create, edit, read and write SWRL rules .It is accessible through the SWRLTab within Proté gé -OWL [8] (see Fig. 3).

Building SWRL Rules Library

A rule axiom consists of an antecedent (body) and a consequent (head), each of which consists of a set of atoms. Atoms can be of the form C(x), P(x,y), sameAs(x,y) or differentFrom(x,y), where C is an OWL description, P is an OWL property, and x,y are either variables, OWL individuals or OWL data values. where both antecedent and consequent are conjunctions of atoms written a1 ∧ ... ∧ an. Variables are indicated using the standard convention of prefixing them with a question mark (e.g., ?x) [ 9] . For example: the symptoms of "rust of pear" are as follow: it mainly destroys leaves, makes leaves show circle yellow scabs, further more some yellow acicular points on scabs. The description of this symptom by SWRL rule is as follow: Pear-tree(?x) ∧ At-part(?x, leaf) ∧ Has-ds(?x, spotting) ∧ Has-c(?x, yellow) ∧ Has-shape(?x, circle) ∧ Has-feature(?x, yellow-acicular-small-point) → Hasdisease(?x, Pear-Rust) Using this syntax, some rules relating to pear diseases diagnosis are created by the SWRL Editor, so that SWRL rules library based on PDDO is built. (see Fig. 3).

Reasoning Based on SWRL Rules

The SWRL Editor itself does not perform any inference. However, a bridge mechanism is provided to allow interoperation with rule engines [START_REF]Knowledge Representation and Semantic Reasoning of Mandarin Fish Disease Diagnosis Based on Ontology and SWRL[END_REF] . At present, the Jess rule engine is supported, and is accessible through the SWRLJessTab that is a plug-in to the SWRLTab in Protege-OWL. It supports the execution of SWRL rules and provides a graphical interface to interact with the SWRLJessBridge. After editing rules, "OWL+SWRL->JESS" button can be pressed, which can transfer all SWRL rules and Pear diseases OWL knowledge to the Jess rule engine. When "Run Jess" button is pressed, Jess will run its inference engine and possibly generate new knowledge [START_REF]Excavating ImplicitRelation Based on SWRL[END_REF] . At that point, this inferred knowledge can be passed back to the owl file (tree.owl) by pressing the "Jess->OWL" button (see Fig. 3). In this case, reasoning based on rules realizes building new relationships between instances of "Pear-tree" and "Disease". For example: a new relationship between "pear3" the instance of "Pear-tree" and "Pear-Rust" the instance of "Disease" is inferred by the above SWRL rule ,that is "pear3" Has-disease " Pear-Rust" (see Fig. 4). In order to design query system, the model of it is built at first .The model consists of six modules, which are shown in the Fig. 5. Further illustrate below: By using interactive query interface, users can customize query conditions, which are sent into query processor. The functions of the query processor are executing corresponding algorithms and calling Proté gé -OWL API methods according to the given query conditions. Parsing ontology file, it is a way to access and draw information from ontology file. Jess rule engine can transfer all SWRL rules and Pear diseases OWL knowledge to the engine, run its inference engine, and pass inferred knowledge back to the owl file .Finally, the query results can be outputted in the visual interface.

Parsing the Pomology Domain Ontology

In this study Proté gé -OWL API is used to parse the PDDO. An OWLModel is created through the ProtegeOWL.createJenaOWLModel(), which can load an OWL files, then the resources can be created, queried, deleted through the methods of OWLModel .In this case , owlModel1 is created to load "tree.owl" file. The methods of Proté gé -OWL API that are used in this study are listed below: the certain OWLClass can be obtained by calling of getOWLNamedClass(), the certain RDFIndividual can be obtained by calling of getRDFIndividual(), the certain RDFProperty can be obtained by calling of getRDFProperty(), calling getUserDefinedOWLObjectProperties() can get all object properties collection.

User Query Interface

Query professor

Parse file by Proté gé Owl API Ontology File

Individuals Rules

Jess rule engine

Visual output

Facts Base

Rules Base

Classes properties

Fig.5. Flow-process diagram of query model

Realization of Query System based on PDDO and SWRL

This query system realizes three kinds of query, which are disease query, instance query, and diagnosis query. Further illustrate below:

1) Disease query

The instances of "Disease" class are listed on the left side of visual query interface, and the TextArea is on the right side. While user selects one from the list, the introduction of the certain pear disease is displayed in the TextArea (see Fig. 6). Details of the method are as follows: Firstly, the instances list of "Disease" class are realized by using JList. GetOWLNamedClass() is called by the defined owlModel1 to obtain the OWLClass "Disease", and then getInstances() is called by it to obtain all instances of OWLClass finally, the instances are saved into a collection, iterated, and added into the listModel of JList. The algorithm is as follows: Collection ins=Disease.getInstances(); DefaultListModel listModel = new DefaultListModel(); for(Iterator i4=ins.iterator();i4.hasNext();) { OWLIndividual in=(OWLIndividual ) i4.next(); String t=in.getLocalName(); listModel.addElement(t); } Secondly, addMouseListener () is added to answer that the user clicks one from the list. Calling GetRDFIndividual() that OWLModel provides to change the instance selected by user into the RDFIndividual, and then getPropertyValue()is called by the certain RDFIndividual to obtain the value of the "describe" data property.

2) Instance query

Interactive instance query supports query on the instances of classes based on SWRL rule. User can select a property from properties of classes through the "property" JComboBox, and then select a value of the certain property through "value" JComboBox. If the "∧" button is pressed , another property and it's value can be selected. While user presses the "query" button, the query results are displayed in the TextArea. For example : if the instances of "Pear-tree" class that show yellow circle spots on the leaves would be queried , user can select "At-part", "leaf" , "Has-ds", "spotting" , "Has-c", "yellow", "Has-s", "circle" and press "query" button at last (see Fig. 7).Details of the method are as follows: Firstly, obtaining properties and corresponding values to generate a string used by SQWRL queries. For example: with regard to the above query, the string generated is as follows : Pear-tree(?x) ∧At-part(?x, leaf) ∧ Has-ds(?x, spotting) ∧ Hasc(?x, yellow) ∧ Has-shape(?x, circle). Secondly, creating an instance of SQWRL query engine for owlModel1. Finally, running SQWRL queries. Calling the runSQWRLQuery()that SQWRL query engine provides to run SQWRL queries.

3) Diagnosis query

Interactive diagnosis query supports adding instances of "Pear-tree" class into the PDDO and diagnosis based on reasoning. User can input the name of a instance in the "name" JTextField, select properties from properties of "Pear-tree" class through the "property" JComboBox, and then select values of properties through "value" JComboBox, after that, pressing "add" button can realize adding a new instance, it's properties, and values, furthermore, pressing "diagnosis" button can realize diagnosing the disease that the certain instance gets by reasoning based on SWRL(see Fig. 8). Details of the method are as follows: Firstly, adding a new instance, properties and values. GetOWLNamedClass() is called by the defined owlModel1 to obtain the OWLClass "Pear-tree", and then createOWLIndividual() is called by this OWLClass to create a new instance in owlModel1, in the end setPropertyValue (OWLProperty, OWLIndividual) is called by the certain instance to add it's properties and values. Secondly, reasoning based on SWRL. Getting all SWRL rules ( SWRL rules library) using the SWRLFactory, and then creating an instance of SWRLRuleEngine for owlModel1 by SWRLRuleEngineFactory.create(), finally using the infer( ) that SWRLRuleEngine interface provides to load rules and knowledge from owlModel1 into a rule engine, run the rule engine, and write any inferred knowledge back to owlModel1.

Thirdly, inferred knowledge are written to the owl file. Fig. 9 shows the modified file, the blue block represents the instance, it's properties and values that are added in Fig. 8, "<Has-disease rdf:resource="#Pear_Rust"/>" of blue block is a new relationship obtained by reason. The algorithm is as follows: FileOutputStream outFile1=new FileOutputStream(uri);// uri file path Writer out=new OutputStreamWriter(outFile1,"UTF-8"); OWLModelWriter omw=new OWLModelWriter(owlModel1,owlModel1.getTripleStoreModel(). getActiveTripleStore(),out);omw.write();out.close(); FileInputStream file1 = null;file1 = new FileInputStream(uri1);

Conclusion

In this paper, the modeling of PDDO based on SWRL is studied, design and realization techniques of the query system based on PDDO and SWRL are proposed. This query system based on reasoning realizes disease query, instance query, and diagnosis query by using Proté gé -Owl API, furthermore, it supports interactive query. Through it pear diseases knowledge can be obtained from PDDO according to user needs, new and inferred knowledge are written to the PDDO owl file. Query results show that the query system based on PDDO and SWRL is practical for users to query information from PDDO.

Fig. 1 . 2 .

 12 Fig.1. Classes structure of PDDO Fig.2. Relationships between "Pear-tree" and other classes

Fig

  Fig.3. SWRL Editor Fig.4. New relationship inferred

Fig. 6 .

 6 Fig.6. Disease query interface Fig.7. Instance query interface

Fig. 8 .

 8 Fig.8. Disease query interface Fig.9. modified owl file

Table 1 .

 1 The general concepts of PDDO

	Name	Explanation
	Disease		Refers to the categories of the pear diseases
	Growing-period		Reflects the time that pear elapses from
			seeding to mature
	Part		Refers to the diseased parts of pear,
			instances: root, fruit
	Pathogen	Feature	Reflects the features of pathogens.
			Instance: black small point
		P-kind	Refers to the kinds of the pathogens that
			cause pears fall ill
	Pear-tree		Refers to pears
	Symptom	Color	Reflects the variety of colors that the
			diseased parts of pears change to
		Shape	Reflects the variety of shapes of the spots.

Acknowledgment

I would like to express my gratitude to all those who have helped me during the writing of this thesis. I acknowledge the help of Professor Liang Yong. I do appreciate his professional instructions. Last but not the least, my gratitude also extends to my family who have been assisting, supporting and caring for me all of my life.


Fu Junqian

Xiao Deqin
email: deqinx@scau.edu.cn

Deng Xiaohui

Agricultural Field Environment High-quality Image Remote Acquisition

Keywords: agricultural field environment, image acquisition, wireless transmission

In order to realize the acquisition of high-resolution and highprecision image in agricultural field environment, the agricultural field environment high-quality image remote acquisition system was designed, based on Canon DSLR camera. This paper made detailed design about core algorithm, including remote acquisition system architecture, camera access, image transmission and command response. The scheme used Fit-PC to control Canon 550D DSLR camera for image acquisition and WIFI wireless transmission module to transmit collected images to the remote server. Meanwhile, the server could also remotely operate camera to collect images in different ways through wireless transmission command, according to different needs. The experimental results showed that the system had three main advantages as high resolution, precision and image transmission rate in collecting images. The resolution of images collected in each node was up to 5184×3456, and the effective breadth of Canon camera image was 17 times as the Logitech camera.

In addition, at a distance of 16 meters, image captured by Logitech camera could not see the target while the same one captured by Canon camera could still clearly display. Moreover, the average speed of wireless images transmission to the server was 0.97MB/S. Therefore, this system could meet the requirements of high-quality image acquisition and transmission in agricultural field environment.

Introduction

In recent years, with the rapid development of computer technology, computer image processing technology has matured and has been widely used in agricultural field, its main application focused on crop seed resources detection, grading of agricultural products, agricultural machine vision, growth monitoring of agricultural products, precision irrigation, and assessment of agricultural machinery. Image information of farmland crops was important agricultural basic information, playing a key role in the early warning and monitoring field of crop growth status, environment and field insect pests in large area [START_REF] Jie | Farmland image acquisition system based on GPRS and ARM[END_REF].

During these years, many scholars have studied on agriculture image acquisition. Fan Fengyi, etc, designed the agricultural remote monitoring system based on ARM and CMOS camera [START_REF] Xiong Yingjun | Design on system of acquisition and wireless transmission for farm land image[END_REF], using the wired way to transmit images. Xiong Yingjun, etc, combined ZigBee and GPRS, designed the system of acquisition and wireless transmission for farmland image [START_REF] Fengyi | Agricultural remote monitoring system based on embedded linux[END_REF]. Meanwhile the highest theoretical speed of GPRS network built by China Mobile was 171.2kb/s, but in fact current users' access speed was about 20kbps-40kbps, due to the transmission speed of actual data affected by network encoding and terminal support [START_REF] Song | The rate of transmission of the GPRS data system[END_REF]. Therefore, it needed about 30 seconds to transmit a resolution of 640 × 480 BMP formatted image, and several minutes were needed for high-quality images. Zheng Yehan, etc, designed the wireless image acquisition system based on ARM and wireless LAN [START_REF] Yehan | Design and implementation of wireless image capturing system[END_REF]. However, it would never meet the requirements of agricultural image analysis as 640 × 480 resolution collected by all these image acquisition systems. For example, the characterization of crop diseases and insect pests is usually not obvious in the low-resolution images, so it needs to magnify them by large multiples to identify. In a word, high-quality images are urgent needed in agricultural field.

This paper designed and realized an agricultural field environment high-quality image acquisition scheme based on Canon SLR camera. The scheme based on the Fit-PC-Windows-XP platform, using Canon EOS 550D DSLR(Digital Single Lens Reflex) cameras to get the high-quality images. Moreover,it used Fit-PC by controlling Canon camera to collect images and WIFI wireless transmission module to realize transmitting high-quality images transmission to the remote server.

2

The Architecture of Agriculture Field Environment Highquality Image Remote Acquisition System Fig. 1 showed the sketch map of the system architecture of the scheme. Each node consisted of a Canon EOS550D DSLR camera, a Fit-PC and a WIFI module. Canon DSLR camera and Fit-PC physically connected through USB connection line. The image acquisition system running on the node host established an application-level link with Canon DSLR camera through Canon EDSDK API [START_REF] Canon | Canon EOS Digital SDK EDSDK2.10 API Programming Reference[END_REF], and it used WIFI module to connect to the Internet network in order to transmit the collected images and accepted remote control commands. The server consisted of a PC server and a WIFI module. Through WIFI module, software running on PC connected to the Internet network, aiming to receive images from nodes and sent remote control commands to control nodes. In order to implement functions of timing automatic shooting, remote control manual shooting and automatic transmission in agricultural field environmental highquality image acquisition this paper designed camera access module, shooting control module, command response module and image transmission module. The camera access module implemented application-level link to the camera, so that users could remotely control the camera through the system. The shooting control module included remote control manual shooting sub-module and timing shooting submodule, respectively implementing remote control manual shooting function and timing automatic shooting function. The command response module implemented that sending remote control commands from server to the nodes, receiving commands from sever and executing them. Remote control commands were consist of the shooting command, the timing command and the canceled shooting command. The image transmission module included camera to Fit-PC images transmission submodule and nodes to server images transmission sub-module, respectively implementing images local backup and remote image transmission. Fig. 2 showed the cooperation relationship between the system modules. The Core Algorithm Design of Agriculture Field High-quality Image Acquisition System This section described the functions of camera access module, capture module, image transmission module and command response module and the concrete realization of these modules.

Camera Access Module

This module implemented the access to a camera. It must first realize accessing to a camera to implement controlling the camera. The system first get the camera list that listed all the cameras physically connected to the Fit-PC through the USB interface. Then the system needs to get a camera object corresponding to the index number in the camera list. After successfully getting a camera object, the system should attempt to establish a session with the camera. If succeed, then it had realized an applicationlevel link with Canon DSLR camera. When the system exited, it must close the session with the camera and release the camera object [START_REF] Mitchell | Live View Sample in CSharp[END_REF]. Fig. 3 showed the process of accessing the camera.

Fig. 3. The process of accessing the camera

Shooting Module

This module controlled cameras to capture images. There were two ways to realize the shooting function, according to the needs in agricultural field environment highquality image acquisition system. One was remote control manual shooting, another was timing automatic shooting.

(1) Remote control manual shooting When the user pressed the "shooting" button in the interface, the server would send a message including shooting command to nodes. The node specified by the user controlled its camera to capture the high-quality image after receiving the message. Then the image would be transmitted to Fit-PC. Then the Fit-PC would send the image to the remote server.

(2) Timing automatic shooting "Start timing" button and "end timing" button were in the interface, when user inputted interval and pressed "start timing" button, the server would send a message including timing command and time interval to nodes. After receiving the command, the node specified by the user would control the camera capture high-quality images automatically according to the time interval. When the user pressed the "end timing" button, the server would send a message including canceled timing command to nodes. The node specified by the user no longer periodically controlled its camera to capture images. The server first checked whether the time interval set by the user was greater than the system's maximum allowed value, or was less than the response time of the system. If there were the above-described two situations, the system would prompt the user that time interval was unreasonable and required the user to reset the time interval. The system would not start the timing automatic shooting function before the time interval was set correctly. If the time interval were suitable, the system would enter timing automatic shooting mode. The user could change the time interval all the time. Fig. 4 showed the process of the shooting module.

Image Transmission Module

This module implemented the transmission of images. In this paper, we divided image transmission into image transmission between camera and Fit-PC, image transmission between nodes and server. Both methods are explained below.

(1) Image transmission between the camera and Fit-PC When the camera finished capturing high-quality images, the system needed to transmit the images to the Fit-PC. Image transmission between camera and Fit-PC was mainly relied on the callback function. The system registered the callback function after its initialization. Every time the camera captured an image, the callback function would be involved and send a transmit request to the system. When the system received the transmit request, it would check the local directory whether there was a subfolder named by the date when the system captured the image, this subfolder would be fill with images whose captured date was the same to the subfolder's name. Then the system would add an index after the date when the image was capture to identify the image's index in the subfolder. When everything was ready, the system downloaded images from the camera and saved them into Fit-PC's local hard disk. When the transmission was completed, system would inform the camera that the image transmission success so that the camera could continue to carry out other operations. If failed it would return other error codes prompting the corresponding error [START_REF]Still Image Connectivity for Windows[END_REF].

(2)Image transmission between the nodes and the server Every time the image transmission between the camera and Fit-PC finished, Fit-PC need to transmit the image to remote server by using WIFI module. The remote server collected images from all nodes and recorded their information into database for the subsequent data analysis. The system would first get information of the image, such as file name, file size. At the same time, the system would create a listening port waiting for the server's connection. The server connected to this port and then the system sent image data to server through this port. The system would send a transmitmessage to the remote server about the node ID, the information of the image and the information of listening port through the connection which had already established when the system start. The remote server created a new file prepared for receiving the data of the image according to the information received from the node and tried to connect to the listening port. The format of this message was as table1.The system started reading data from the image and sent it if succeed. Remote server received these data and wrote them to the file. When data transmission finish, the system closed the connection and closed the listening port, the server closed the connection port and inserted the information of the image in the database. Fig. 5 showed the process of image transmission module.

Command Response Module

This module implemented that sending remote control commands from server to the nodes, receiving commands from sever and executing them. After nodes' startup, they would try to establish connections with the server and the remote server would save all these connections. When users pressed buttons in the interface which corresponding to the functions they want the specified node achieved, it would trigger the corresponding button events. Then the server would broadcast a commandmessage to nodes included the node ID declared which node should response this message, controlling commands which included shooting command, timing command and canceled timing command, and additional information (example: time interval). The format of the message was as table2. When nodes received the commandmessage, they firstly checked whether their node IDs were match to the message's. If matched, the node would response differently based on the contents of the command.

If not matched, other nodes would do nothing. Fig. 6 showed the process of the command response module.

System Testing and Running

Users could be smoothly to capture images by remotely controlling nodes and transmit them to the server on display by using the above agricultural field environment high-quality image acquisition system. Fig. 7 showed its timing automatic shooting interface. This paper also carried out comparative analysis for shooting accuracy, shooting distance and transmission performance of this scheme.

Shooting Accuracy Analysis

This section used Logitech pro9000 camera and Canon EOS 550D camera to capture images and then compared them. Firstly, we captured images for different targets at the same distance by using these two cameras and compared them, and then we captured images for the same target at different distance and compared them.

Different Targets at the Same Distance

This test used the above cameras to capture images for vegetable leaves, corn leaves, and tomato fruit and magnified them to compare. All images were captured from the target at a distance of 0.5m.Each image was five times magnified. These images were showed as Fig. 8.The left side images were captured by Logitech pro9000 camera and the right side images were captured by Canon EOS 550D camera. In the first group of images contrast, the image captured by Logitech camera showed the vegetable leaves' macula lutea hazily while the image captured by Canon camera showed the leaves' macula lutea clearly. In the second group of images contrast, the image captured by Logitech camera could only showed lesions on the corn leaves hazily while the image captured by Canon camera could show a clear reduction of lesions on the corn leaves.

In the third group of images contrast, people could not surely determine whether the tomato exist insect pests through the image captured by Logitech camera but they could surely confirm the tomato existed insect pests through the image captured by Canon camera. From the effect of contrast, the scheme could better satisfy the demand of agricultural the image analysis. This test used Logitech pro9000 camera and Canon EOS 550D camera to capture images for the same target tomato at deferent distance respectively. Firstly, this test used the cameras to capture images at a distance of 0.5m to the target, then 1m, 2m, 4m, 8m, 16m. Then we used red rectangle to identify the target. Figure9 (a) showed images captured by Logitech pro9000 camera zooming to the original images of 17% and Figure9 (b) showed images captured by Canon camera zooming to the original images of 1%. However, they had the same display size in Fig. 9. Therefore, the effective range of Canon camera image was 17 times as the Logitech camera.

Transmission Performance Analysis

Agricultural field environment high-quality image acquisition system transmitted images by using WIFI module on nodes. Data send by WIFI module passed through the router, eventually received by the server. The effective radius of a single highpower router was about 1.5km. With cascades of routers, it could improve the transmission range so that we could achieve long distance agricultural image acquisition. This test captured images periodically through remote controlling camera and transmitted them to the remote server. The time interval was 10 seconds and this test totally captured 150 images. All images were successfully transmitted and the image transmission speed was from 0.72MB/S to 1.18MB / S, and the average speed of the transmission was 0.97MB / S. Fig. 11 showed the result of this test.

Conclusions

In this paper, aiming at the demand of current agricultural field environments on highquality image acquisition, we designed and implemented an agricultural field environment high-quality image remote acquisition system based on Canon DSLR camera. And it used the system to conduct a series of experiments to test the feasibility of the system, and carried out comparative analysis for shooting accuracy, shooting distance and transmission performance. The scheme could achieve real-time high-quality image acquisition of agricultural field environment, to a certain extent, meeting the demand of agricultural high-definition image analysis. It was worth to mention that this scheme based on Fit-PC was a relatively high cost at the present stage. Therefore, the next step we should take was to migrate this system to a low-cost embedded platform in order to carry out large-scale applications

Fig. 1 .

 1 Fig. 1. System Architecture

Fig. 2 .

 2 Fig. 2. System overall collaboration diagram

Fig. 4 .

 4 Fig. 4. The process of the shooting module

Fig. 5 .

 5 Fig. 5. The process of image transmission module

Fig. 6 .

 6 Fig. 6. The process of the command response module

Fig. 7 .

 7 Fig. 7. System interface diagram

Fig. 8 .

 8 Fig. 8. The comparison of images for different targets at the same distance

Fig. 9 .

 9 Fig. 9. (a) Images captured by Logitech pro9000 camera (b) Images captured by Canon EOS 550D cameraFurthermore, Fig.10showed the situation that each image in Figure9 amplified five times in target area. As we can see, at a distance of 0.5m, image captured by each camera could clearly show the target. However, at a distance of 1m, image captured by Logitech camera began to appear some noise to influence us to observe the target clearly. The farther the distance, the more noise generated, and we would be more and more difficult to observe the target. On the other hand, images captured by Canon

Fig. 10 .

 10 Fig. 10. (a) Magnified images captured by Logitech pro9000 camera (b) Magnified images captured by Canon EOS 550D cameras

Fig. 11 .

 11 Fig. 11. Transmission performance analysis test result diagram

Table 1 .

 1 The format of transmit-message messages

	Length(byte)

Table 2 .

 2 The format of command-message messages

	Length(byte)

Acknowledgment

The authors would thank the project of the National Key Technology R&D Program (Project Name: Key Technologies for Agricultural Field Information Comprehensive Sensing and Rural Extension, Contact Number: 2011BAD21B01); the Natural Science Foundation of China (Contact Number: U0931001).


Nana Han

Yangren Wang

Stochastic Simulation and Application of Monthly Rainfall and Evaporation

Keywords: precipitation, evaporation, seasonal autoregressive model, irrigation water

Statistic was done for the precipitation and evaporation monitoring data of Yuncheng from 1971 to 2007. The first-order seasonal autoregressive models were set up , considering separately for the normal and skewed distribution of rainfall and evaporation. Long series of monthly precipitation and evaporation sequences were generated. Compared the average, standard deviation and other parameters of simulation sequences with measured sequence, the results of skewed simulation had better agreement with the measured one. Then it proved that skewness model maintains the main statistical characteristics of the measured sequence. 20 groups of equal length with the measured sequence of precipitation and evaporation sequence could be generated through the skewness model. The sequence of irrigation water of the winter wheat multiple corns planting type had been calculated, using the "  " value method. And comparative analysis with the measured result was performed. The simulation computed result was found in good coincidence with the measure computed result.

Introduction

Crop irrigation water can be determined by the field water balance method, using the precipitation and evaporation data. Based on the long series of data, the scale of irrigation projects can be determined. Thus many of the data needed, actually the measured data were limited. Aiming at the randomicity of precipitation and evaporation, the precipitation and evaporation were randomly treated, and simulated using random hydrology methods. Then Long series of irrigation water sequence can be calculated in order to develop irrigation schemes more in line with actual crop water demand. Azhar [START_REF] Azharah | Modeling irrigation schedules for lowland rice with stochastic rainfall[END_REF] considered evapotranspiration, precipitation randomness, introduced a minimum of irrigation water requirement of rice cumulative probability distribution, and used to guide irrigated rice irrigation. Huan-Jie Cai [START_REF] Huanjie | Stochastic simulation of Precipitation and its application in irrigation[END_REF] simulated the daily precipitation and studied the irrigation problems of wheat and maize. Zuo Xiaoxia [START_REF] Xiaoxia | Irrigation process of paddy based on random simulation of precipitation and evaporation[END_REF] generated long series of precipitation and evaporation by the stochastic hydrology method. Then a long series of rice irrigation process were calculated using the cross-correlation of rainfall evaporation. Wen Ji [START_REF] Ji | Stochastic simulation model for winter wheat irrigation[END_REF] simulated the evapotranspiration, and the results derived stochastic simulation model of winter wheat irrigation. Zai Song-mei [START_REF] Song-Mei | Stochastic Simulation for Crop Water Requirement [J][END_REF] simulated the precipitation and evapotranspiration using random hydrology methods based on time-series, and then the simulated values were used into the water balance equation to determine the irrigation time and amount. Wen Ji [START_REF] Ji | Research of Stochastic Simulation Technology of Crop Irrigation[END_REF] analyzed the stochastic simulation techniques of crop irrigation and pointed out that it has strong universality. This paper simulated the sequences of precipitation and evaporation by the seasonal autoregressive model, and calculated the sequence of crop irrigation water in Yuncheng.

2

Materials and Methods

Model

Seasonal autoregressive model is used to simulate monthly precipitation and evaporation. The general form of seasonal autoregressive model is as follows [START_REF] Jing | Stochastic hydrology [M[END_REF] :

, 1, , 1 2, , 2 , , ,
...

t t t p t p t z z z z                     (1)
Where

 , t Z is standardized sequence, 1,  、 2,  、 … , p 
 are respectively the auto regression coefficients of the season from 1 to p orders, and , t   is the independent random sequences of the t year of the season. Standardized sequence which is to eliminate the seasonal effects of average、 variance can be obtained by standardizing the hydrological series. Formula is as follows.

, , t t xu z       (2)
Where u  is average,   is standard deviation, and ,

t x  is seasonal hydrological sequence(t=1,2,…,n,n is the number of years; =1,2,…, w ,
w is the number of seasons), which can be represented through matrix.

  1,1 1, 2 1,3 1, ,1 ,2 ,3 , , ,1 ,2 ,3 , ...
x x x x x x x x x x x x x                   
Studies show that a first or second order model will be able to meet the requirements. Thus this article uses a first order seasonal autoregressive model. When p = 1, the model is as follows.

, 1, , 1 , t t t z z          (3)

Estimation of Model's Parameters

Parameters of a first order seasonal autoregressive model are

u  、 2   、   , 1 、 and 2 ,   ( w  , 2 , 1   ). Estimate of u  and 2 
 can be separately reached with the average ( x ) and variance ( 2 s  ) of the sample.

, 1

1 ˆn t t u x x n        (4) 2 2 2 , 1 1 ˆ( ) 1 n t t s x x n            (5)   , 1 = 1 1 1 1 , , , 1 , 1 ) 1 ( )
)( ( ˆ                   s s n x x x x r n t t t (6) Where  , 1
r is the  season's first-order sample autocorrelation coefficient of the , t x  sequence, which indicates the linear correlation between the and

1   season.
If the sequences of precipitation and evaporation are considered normal one, then the independent random sequence , t   is pure normal distribution. The formula is as follows.

       , , , t t  (7) 2 , 1 , 1     r   (8)      , 2 , 1 2 cos ln 2 u u   (9) Where  , 1 u and  , 2
u are uniformly distributed random numbers, and   is the independent standard normal pure random sequence, namely

) 1 , 0 ( ~N   .   , t can be obtained by a lot of  , t u .
If the sequences of precipitation and evaporation are considered skewed one, then the independent random sequence , t   is skewed distribution. In hydrology and water resources, the distribution of , t   more considers is a P-Ⅲ type one. The formula is as follows.

      , , , t t   (10)        , , , , 2 ) 36 6 1 ( 2 3 2 , ,          S S t S S t C C C C (11) 2 3 , 1 , 1 3 , 1 ) 1 ( 1 , , ,         r C C C x x S S S      (12) 3 1 3 , ) ( 3 1 ,     s x x n C n t t S x      (13) Where  , t  is standardized P-Ⅲ distributed random variable ,  ,  S C is coefficient of skewness of  , t  ,and  , x S C is coefficient of skewness of  , t
x .

Calculation of Irrigation Water

Irrigation water is the required amount of water of irrigated land, which is drawn from the source. It depends on the factors such as the irrigated area, crop cultivation, soils, hydrogeological and meteorological conditions. Farm irrigation water is calculated as follows:

P E M     0 (14)
Where M is crop irrigation water of the crops' whole growth period (mm),  is water demand coefficient, which determined by the measured data (see table 1), 0 E is water surface evaporation of the crops' whole growth period (mm), P is precipitation of the crops' whole growth period (mm), and  is effective utilization coefficient of precipitation, which determined by relevant information, the value of  is 0.9 in the months of 11,12,1-4,and in other months is 0.8. Table 2 and Table 3 showed that the average,standard deviation, coefficient of skewness and first autocorrelation coefficient of the skewness sequence all well maintained for the measured one. However the standard deviation and the first order autocorrelation coefficient could be maintained and the other parameters varied greatly for the measured sequences, such as the average of simulated precipitation in September deviated larger from the measured one (see Figure 1), and the coefficient of normal sequence deviated largest from the measured one (see Figure 2). The results showed that the normal and the measured precipitation sequences didn't come from the same total. But the parameters of normal evaporation sequence remain good, except the coefficient of skewness.

In addition, negative values were found in the simulated sequence of precipitation regardless of the normal or skewed distribution. It did not match the actual situation. Thus statistic was done for the sum of the negative number and negative value in three groups of 100 length simulated sequences. The results were shown in table 4. It also could be seen from table 3 that not only the number of negative in the skewness sequence was less than in the normal one, decreased by nearly 50%, but also the negative sum in the skewness sequence was much smaller than the normal one, decreased by nearly 90%. The result showed that precipitation sequence should be simulated by the skewness model.

Analysis of Irrigation Water's Frequency Curve

Through the skewness model, 20 groups of equal length with the measured sequence of precipitation and evaporation sequence can be generated. The sequence of irrigation water of the winter wheat multiple corns planting type had been calculated using the formula (12). Then based on the measured sequence of precipitation and evaporation, this paper calculated the measured irrigation water sequence using the "  " value method. Statistic was done for the data of irrigation water by frequency calculation method. Finally, five simulation groups of irrigation water's frequency curves were compared with the measured one. The results were shown in figure 3. Figure 3 showed that simulated frequency curves fit the measured one good. 20 groups of equal length with the measured sequence of simulated irrigation water were compared with the measured one. The results were shown in table 5 and 6. As can be seen from table 5, the average、standard deviation and coefficient of skewness of simulated sequences well maintained for the measured one. Table 6 showed that the differences between simulated irrigation water sequences and measured one were little, and the difference between the maximum and minimum values was from 100 to 200 mm. If approximatively taking the average of 20 simulated groups as the average of total, the measured values of irrigation water were lager than the total's average between the 50% and 90% frequencies. Thus if the scale of irrigation projects was determined using the measured sequence, the result could be larger than the actual.

Conclusions

Precipitation and evaporation of Yuncheng were simulated by the seasonal autoregressive model, which considered separately for the normal and skewed distribution of precipitation and evaporation. Long series of monthly precipitation and evaporation sequences were generated. The simulation results showed that the skewed simulation had better agreement with the measured. Then based on the data of skewed distribution, this paper established the relations formula of water evaporation and crop water requirement, and calculated the crop water requirement. The long series of crop irrigation water were obtained through the water balance calculation. Comparing with the measured irrigation water, it verified the practicability of the model. The results showed that the model was feasible in allowable error.

Figure 1 .Figure 2 .

 12 Figure 1. Comparison of the average of precipitation simulated and measured sequences

Figure 3 .

 3 Figure 3. The frequency curves of irrigation water

Table 1 .

 1 The value of  Statistic was done for the precipitation and evaporation monitoring data of Yuncheng from 1971 to 2007. Considering separately for the normal and skewed distribution of rainfall and evaporation, this paper set up the first-order seasonal autoregressive models. Then three groups of one hundred length sequences were generated. And thus this paper analyzed the parameters of model and application of model. The results were shown in Table2 and Table 3.

	month	1	2	3	4	5	6	7	8	9	10	11	12
		0.4	0.552 0.665 0.722	0.74	0.743	0.75	0.83	0.73	0.397 0.593 0.462

Table 2 .

 2 Results of model suitability testing of Yuncheng district's monthly precipitation

			Parameter					monthly precipitation(mm)
				1	2	3	4	5	6	7	8	9	10	11	12
			measured	6.2	7.4	17. 2	35. 4	46. 0	64. 6	106 .3	83. 6	99. 8	50. 3	23. 1	6.4
	x		Simulation (normal)	6.5	7.3	17. 7	34. 3	45. 9	66. 6	104 .3	84. 7	88. 5	50. 1	22. 7	6.5
			Simulation (skewness)	6.1	7.6	19. 2	33. 4	47. 0	62. 9	106 .2	82. 3	98. 1	50. 1	23. 1	6.9
			measured Simulation (normal)	7.9 8.2	7.0 6.9	15. 7 17. 0	26. 1 26. 2	32. 7 31. 3	50. 6 50. 4	61. 1 51. 7	55. 7 58. 0	97. 5 107 .3	35. 3 33. 8	30. 6 31. 2	7.5 7.2
			Simulation (skewness)	7.3	6.7	15. 9	24. 6	34. 4	49. 2	54. 2	50. 6	91. 8	34. 6	29. 1	7.1
			measured	2.0 44	1.1 48	1.2 15	1.4 51	0.8 68	2.6 00	0.8 35	1.2 86	3.1 76	0.7 89	2.5 78	0.9 98
	Cs	Simulation (normal)

Table 3 .

 3 Results of model suitability testing of Yuncheng district's monthly Evaporation

	Parameter	monthly evaporation (mm)
		1	2	3	4	5	6	7	8	9	10	11	12
	measured	50.	87.	155	195	256	316	285	258	171	127	74.	50.
	x	5		.8	.6	.6	.0	.6	.8	.3	.5	1	9
	Simulation	49.	87.	157	194	258	319	290	264	172	129	74.	51.
	(normal)	8	9	.4	.1	.0	.8	.8	.3	.9	.7	7	6
	Simulation	50.	86.	156	197	256	319	285	253	169	124	74.	51.
	(skewness)	2	2	.3	.0	.6	.5	.6	.1	.8	.7	8	7
	  measured	13. 2	21. 0	38. 5	29. 0	63. 6	73. 8	68. 9	70. 5	44. 4	36. 2	18. 0	11. 4

Table 4 .

 4 Statistics of the number and the negative sum of the negative in the simulated precipitation sequence

	item	Simulation(normal) sample1 sample2 sample3 average sample1 sample2 sample3 average Simulation(skewness)
	Number of the negative	150	138	168	152	67	66	93	75
	Sum of the negative	-2676.5	-2800.7	-2776.4	-2751.2	-274.9	-274.6	-439.1	-329.5

Table 5 .

 5 Statistical parameters of irrigation water simulation sequences and measured values sequences

		x			C	s
	measured	863.6	242.1	-0.333
	simulation average	865.9	217.8	-0.306

Table 6 .

 6 Comparison of measured and simulated values at different frequencies (mm)

	frequency	measured	simulation	max	min	max-min
	25%	670	717	810	636	174
	50%	932	883	942	824	118
	75%	1069	1027	1086	944	142
	90%	1141	1134	1205	1036	169
	95%	1224	1256	1315	1122	193

Acknowledgment

Funds for this research was provided by the National Science and Technology Support Program (2012BAD08B01).


Yongshan Wan
email: yswan@sdau.edu.cn

Kun Zhang

Benjing Zhu

Fengzhen Liu

Research and Design of Peanut Diseases Diagnosis and Prevention Expert System

Keywords: Peanut diseases, Diagnose, Expert system

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

There are many types of peanut diseases, happen frequently, which seriously affected the yield and quality of peanut. The occurrence of disease is affected by many factors. It is complex, boundary fuzzy, uncertainty, poor controllability (Wan, 2003; [START_REF] Wang | Shandong Peanut[END_REF][START_REF] Himin Sun | Expert system (ES) and its application in agriculture[END_REF]. Due to the shortage of basic technical personnel and lack of knowledge, when peanut diseases occurs, the correct diagnosis and proper treatment can not be given at the first time, which bring about a large area of peanut diseases and seriously impact on the yield and quality of peanut. Therefore, the scientific and timely for the diagnosis and treatment of the diseases of peanut would be improved through studying the method of diagnosis and treatment of diseases of peanut and setting up peanut diseases diagnosis and prevention expert system. It"s especially good for the diagnosis and prevention of major peanut diseases, which would greatly enhance the level and efficiency of peanut diseases prevention and control. There are lots of research and application for plant protection expert system abroad, and there are many successful cases. Such as PLANT/ds soybean disease diagnosis expert system developed by the University of Illinois is the earliest development for agricultural expert system in the world. The tea (Camellia Sinensis) pest expert system (TEAPEST) developed by Ghosh ( 2003) is object-oriented expert system based on rules. It can identify the most diseases of tea plant, and achieved very good results in practical application. Apple disease management decision-making expert system developed by S. Haley can help farmers by providing advice, how to improve the apple production, and can give proper means to diagnosis after identify the species, and this system is suitable for the less experienced personnel. In recent years, research and application of agricultural expert system in China developed quickly. Many agricultural expert system has been applied in practice and played an important role, and also has made great economic benefits and social benefits. Such as "corn diseases and insect pests prevention expert system" implemented by Jilin University and Jilin Academy of Agricultural Sciences, is one for the farmers based on uncertainty reasoning, multimedia and friendly interface technology (1999). Important vegetable pest diagnosis and consultation system developed by Zheng Yongli (2004) in Zhejiang Province is programmed by the object-oriented relational database Visual FoxPro 8.0, and it adopted the event-driven model such as properties, methods and events programming. While as for peanut, there is no expert system of peanut pest identification, which is systematic and helpful for users. Therefore, we would develop a peanut diseases diagnosis and prevention expert system.

The system development tools

The software is designed based on the operating system Windows XP, using VB6.0 as the expert system programming tool, Access 2003 as the background database, Dream-weaver as HTML browsing software, Photoshop6.0 to process the images.

Structure and Function Design of the System

Structure of the System

The system is developed using the modular design thought. It"s composed of inference engine, database,knowledge base, system maintenance and user interface, etc. The core of the system is the knowledge base and inference engine. System has a modular structure, each module is independent, and the knowledge database and inference engine is separated, so it is easy to maintain and update.

Function of the System

In the principle of usability, the system realized the function of peanut disease diagnosis, disease information querying, scientific application of pesticides consultation and knowledge base maintenance, etc. Each functional module is described as follows: 3.2.1 Disease Diagnosis Diagnose the diseases which have occurred in the current field, according to the characteristics of symptoms of disease the user supplied; then all the information of the diseases diagnosis will be given, including symptoms, etiology, pathogenesis, prevention measures and the picture for the characteristics.

Disease Information Querying

The module provides advisory function for the user. It mainly provides disease symptoms, occurrence, control methods and other aspects of the information queried. There are five types of disease included, namely, fungal, bacterial diseases, viral diseases, nematodes disease, and noninfectious diseases. Users can query the information, learnt identify, improve the correct rate of the recognition only a simply click of the mouse.

Advisory scientific medication

Mainly to provide service of retrieval, query, consulting and learning about relevant information about commonly agricultural chemicals used in peanut. For example, characteristics of pesticide, the commodity name, English name, Chinese name, and control object toxicity, control method and characteristics of the function and so on.

System maintenance

This part mainly used for maintenance of system database, knowledge base, picture library editing, updating, deleting, expansion and system program, etc.. Also it can detect the consistency and integrity of knowledge, to avoid redundant or the contradictions of old and new knowledge in the knowledge base, and make certain limits of user authority to ensure the normal operation of the system.

Realization of system functions 4.1 Establishment of Database

The system contains disease database, pesticide database and disease diagnosis database. The structure and examples of the three databases are as follows (table 1

Realization of Disease Diagnosis

Organization Structure of Knowledge

Built diagnostic tree about disease (occurred in root, stem, leaf and plant) and characteristics of the damage, by collecting the characteristics of peanut diseases, summarizing these data and sorting out the peanut disease retrieval table, finally the making the retrieval table into diagnostic tree for computer program reasoning, as shown in figure 1. Each node in Fig. 1 corresponds to a group of knowledge. The method of diagnosis tree analysis will promote an unknown problem layer by layer, finally get the disease name.

Fig. 1 The knowledge structure of peanut disease diagnose Note: Group 2:Lesion round or irregular in shape. This system is distinct uniformly by the symptoms of 1, 2, 3. If give the conclusion directly by the symptoms of nodes, can get the final point, otherwise continue to establish the node, and so on, until all nodes come to the final node.

Knowledge Representation

The system adopts two levels including the rule frame and the body of the rule to describe the knowledge of disease diagnosis through the way of thinking and the hierarchical structure model that analysis of disease diagnosis. Rule frame reflects the logic relationship between the premise and conclusion factor, which is a reasoning framework; the rule body reflects the value knowledge between premise factors and conclusion factors, it can contain formulas, may also have a set of rules. Its general form is: IF Ei(I) then A The Ei (1, 2, 3,...... , n), as the logic relationship of "and", Ei and A are collectively referred to "fact", "concept" or "factors" in "rule frame + rule body". Take the peanut leaf disease as an example: RSn:IF lesion mildew layer THEN Name of peanut diseases RB: IF gray mold on lesion THEN peanut brown spot IF lesions with white mycelium and sclerotium THEN peanut leaf blight IF lesion dark brown, in concentric rings of spots, with dark brown mildew THEN peanut brown spot IF lesion brown by yellow, dark brown edge with a yellow halo, rupture, like anxiety, with small black spots THEN peanut focal spot Among them, RS represents the rule frame identifier, n is rule frame number, and is also the rule group number. RB is the rule body identifier, and the downward to the next rule frame identifier are all the rule body content in the rule group. In the system, the rule groups are mutual independence, the group number can be identified arbitrary, as long as not to repeat, that is place of the rule group is arbitrary. System will put similar knowledge in the same group rules, such as the diagnosis of diseases based on leaf symptoms, rule body expresses the solution process to the conclusion set organically and naturally, avoiding redundant and dispersion in writing rules, knowledge base is compressed greatly, and the speed of reasoning is accelerate greatly.

Realization of inference engine

The system adopts depth-first method to perform comprehensive reasoning, by analyzing the process of peanut disease diagnosis, summarizing the experts" advice and experience. The search tree for disease diagnosis established, and the search is from the top to down along the hierarchical structure of knowledge. The reasoning process of the system can be seen as a search process for the results along the search tree. The basic idea of depth-first search is: start from the initial node S, select a node to inspect among the child nodes, if not the target node, then select another node to inspect, search down so on and so on. When reach some child node, if the node is not the destination node also cannot continue to expand, and then select its sibling nodes to inspect. Take peanut leaf disease as an example: firstly, using reverse reasoning, to determine the position of peanut diseases is in peanut leaf, and then using the depth-first method forward reasoning, confirm the diagnosis group (i.e. initial node) for lesion plaque surface mildew layer, select a node in its child node to inspect, namely specific symptoms contained, if not the target node, then inspect in the next level of symptoms, search down until the diagnosis results are obtained. If the diagnostic results are inconsistent with the actual, this disease is not the target node and also cannot continue to expand, then reselect the symptoms to inspect, repeat the process, until the results are obtained.

Main program

Programming method to realize the function of disease diagnosis is as follows.

Conclusion

The system adopts the modular design, which only needs to improve and expand the specific modules. It can effectively provide the details of peanut diseases and the comprehensive control technical point of diseases, through collecting the massive peanut disease relevant knowledge, and analysising comprehensively the knowledge base. The system can provide perfect consult service for agricultural technicians and farmers against peanut diseases, change the present situation of blind use of pesticides, and protect the ecological environment. So it can play an important role, and also can alleviate the situation of seriously lack of the advanced agricultural technology personnel. So it has practical significance to promote peanut diseases diagnosis and prevention expert system. The accuracy of the model and fitting in with the actual production are the critical factors to decide the success of the system, therefore it also need continuous improvement to verify the system in future.

Fig. 4 .Fig. 5 .

 45 Fig.4. Disease query interface

Table 2 .

 2 ., table2., table 3.). Structure and examples of pesticide database

		Table1. Structure and examples of diseases database
	Fields	Field type	Field length	Example
	Disease name	text	14	leaf spot of peanut
	alias	text	30	early leaf spot of peanut
	Symptoms	remarks	automatically	omit
	Pathogens	remarks	automatically	Groundnut Cercospora, belonging to ascomycotina
				fungi
	Disseminated	remarks	automatically	omit
	cycle
	Pathogenic factors	remarks	automatically	The optimum temperature 25-28 ℃, the onset heavy
				in rainy and humid climate,
	Prevention method remarks	automatically	Selection of resistant varieties, spraying pesticides
	Disease picture	OLE object		omit
	Fields	Field type Field length	Example
	Chinese name	text	10	多菌灵
	English name	text	20	carbendazim
	Trade name	text	30	MBC
	Categorical attribute	text	20	bactericide
	Dosage	text	100	25%,50%carbendazol wettable powder

Table 3 .

 3 Structure and examples of diagnose of diseases database

	Fields	Field type	Field length	Example
	Diseased location	text	10	leaf
	Symptom 1	text	50	spot round or irregular in shape
	Symptom 2	text	50	spot with halo around
	Symptom 3	text	50	Spots of dark brown, large, yellow halo, Grey
				Mildew account raw when wet
	Disease name	text	20	cercospora brown spot of peanut

  brown irregular moire spot appears first in the tip and edge of the leaf, leaf rot off, when humidity is big, with white mycelium and sclerotium. The first layer: Onset location, there are four parts, the whole plant, leaf, root and stem. Second layer: Symptom group, which is the common features reorganized and extracted by a part of a disease experts and knowledge engineers. Third layer: Characteristics of disease symptoms, is the further detailed description.

	Symptom 21:Lesion tea brown or dark brown, with yellow halo, gray powder
	mildew in the wet spot surface
	Symptom 22:Stellate black spot at the beginning, chlorotic with halo, then reticulate
	brown spots, with small black protrusions.
	Symptom 23:The lesion sag middle, uplift at the edge, adaxially yellow-brown,
	caused perforation.
	Symptom 24:A dark
		Peanut Disease
	Plant	Leaf	Root	Stem
	Group 1 group	Group 2		Group 4
	1
	Symptom 2 1	Symptom 2 2	Symptom 2 3	Symptom 2 4
	Brown spot	Net blotch	Scab	Leaf blight

Acknowledgment

The project received financial support from the China Agriculture Research System (CARS-14).

The operation interface of peanut diseases diagnosis is as follows. Firstly, select disease symptoms according to the local actual situation (Fig. 2). Then we get the diagnosis results after the "start diagnosis" button been clicked (Fig. 3).

Realization of the query function

The system will add the corresponding Chinese name exists in the peanut disease inquiries to the list of user interface box, gain the corresponding diseases of picture and text information, by selecting one of the Chinese name or calling the query We can get more information about a disease when we know the disease name. Click button of "query according to the disease name" in the query interface shown in Fig. 4. Select the name of the disease in the list, and then click the "search" button. It came to the interface of query result (Fig. 5).


Taibo Liang
email: taibol@163.com

Jianwei Wang

Yanling Zhang

Jiaqin Xi

Hanping Zhou

Baolin Wang

Qisheng Yin

Spectral Characteristics of Tobacco Cultivars with Different Nitrogen Efficiency and its Relationship with Nitrogen Use

Keywords: tobacco (Nicotiana tabacum L.), nitrogen use efficiency, spectral characteristics, nitrogen utilization

To investigate the relationship between spectral characteristics of tobacco cultivars and their nitrogen use characters, four tobacco cultivars with different nitrogen use efficiency were used in a 15 N pot experiment. The result showed that, in the visible light range, the spectral reflectance was lower in higher nitrogen level (N2) than N1, while opposite in near infrared range. The spectral reflectance of K326 and HD were lower than ZY100 and NC89 in visible light range, which closely related to their higher chlorophyll content. Both the nitrogen utilization rate of basal fertilizer and topdressing fertilizer in HD and K326 were higher than that in ZY100 and NC89 under N1 and N2 levels. The basal fertilizer use efficiency was negatively correlated with ρ550, and significantly positively correlated with RVI (800, 550), DVI (800, 550) and NDVI (800, 550) both in N1 and N2 level. The top dressed fertilizer use efficiency was significantly positively correlated with RVI (800, 550), DVI (800, 550) and NDVI (800, 550) in N1 level. Therefore, spectral characteristics can be an important method for diagnosing tobacco nitrogen metabolism characteristics.

Introduction

Tobacco is an important economic crop in China, but also an important model plant in science research. In the tobacco production, in order to get higher yield and income, excessive application of nitrogen fertilizer occurred from time to time, which caused potential threat to field ecosystem. Therefore, how to improve the tobacco nitrogen use efficiency has become a hot topic in tobacco nutrition study. Some studies showed that, crop nitrogen use efficiency closely related to cultivars, nitrogen levels and soil conditions and so on [START_REF] Serret | The Effects of Urea Fertilisation and Genotype on Yield, Nitrogen Use Efficiency, 15 N and 13 C in Wheat [J[END_REF][START_REF] Gallais | An Approach to the Genetics of Nitrogen Use Efficiency in Maize [J][END_REF][START_REF] Gill | Root Induced Changes in Potential Nitrification and Nitrate Reductase Activity the Rhizospheric Soil of Wheat (triticum aestivum L) and Chichpea (cicer arietinum L.) [J][END_REF]. In certain ecological conditions, tobacco nitrogen use efficiency have significant differences among different genotypes, which closely related to plant nitrogen metabolism and nitrogen uptake and use efficiency [START_REF] Juan | Grafting to Improve Nitrogen-use Efficiency Traits in Tobacco Plants [J][END_REF][START_REF] Kruse | Interaction of sulfur and nitrogen nutrition in tobacco (Nicotiana tabacum) plants: significance of nitrogen source and root nitrate reductase [J][END_REF][START_REF] Tang | Transformation Law of Different Formal Nitrogen Fertilizer in Paddy Field and its Effect on Growth and Nicotine Content of Flue-cured Tobacco[END_REF]. In recent years, the use of remote sensing technique for real-time monitoring and plant nutrition diagnosis has become a hot topic in the application of remote sensing in agriculture. Hyperspectral technology, has some advantages such as wide spectrum range, high resolution, and large amount of data [START_REF] Wang | Correlation Between Corn Leaf Spectral Reflectance and Leaf Total Nitrogen and Chlorophyll Content under Different Nitrogen Level [J][END_REF]. Through spectral changes monitoring, the relationship between spectra reflectance of crops and the leaf area index, aboveground biomass, chlorophyll content can be studied, which can provide basis for crop growth monitoring and yield estimation. [START_REF] Feng | Monitoring Canopy Leaf Pigment Density in Wheat with Hyperspectral Remote Sensing [J][END_REF][START_REF] Huang | Monitoring of Cotton Canopy Chlorophyll Density and Leaf Nitrogen Accumulation Status by Using Hyperspectral Data [J][END_REF][START_REF] Royo | Usefulness of Spectral Reflectance Indices as Durum Wheat Yield Predictors Under Contrasting Mediterranean Conditions [J][END_REF]. Study showed that, crop nutrition condition closely related to spectral characteristics, and the crop spectra reflectance had significant differences under different nitrogen levels [START_REF] Dai | Correlation Between Agronomic Parameters and Spectral Vegetation Index in Winter Wheat Under Different Nitrogen Levels [J][END_REF][START_REF] Sun | Spectral Characteristics of Corn Under Different Nitrogen Treatments[END_REF]. The relationship between leaf nitrogen accumulation and ratio of near infrared and green band R 810 / R 560 was established, to monitor nitrogen nutrition of rice [START_REF] Xue | Diagnosis of Nitrogen Statues in Rice Leaves with the Canopy Spectral Reflectance[END_REF]. Tobacco nitrogen use efficiency have significant difference among different cultivars, however, the relationship between spectral characteristics and nitrogen utilization of different varieties was rarely reported. In this study, pot experiment was carried out to study the spectral characteristics of different nitrogen efficiency tobacco cultivars under different nitrogen levels and their relationship with nitrogen utilization, so as to provide theoretical basis for breeding and nutrient management.

Materials and Methods

Experimental Design

A 15 N tracer pot experiment was performed in the greenhouse of Zhengzhou Tobacco Research Institute of China National Tobacco Corporation, Zhenzhou, China, from April to August (growing season) in 2012. Four cultivars, K326, Zhongyan 100 (ZY100), Hongda (HD) and NC89 were employed in the experiment under two nitrogen levels, N1 (1.0 g pot -1 ) and N2 (3.0 g pot -1 ), respectively. Isotope fertilizer were ammonium nitrate 15 N double labeled (abundance of 10%) and 15 N potassium nitrate (abundance of 10%), which provided by Shanghai Research Institute of Chemical Industry. In ten pots, 15 N ammonium nitrate fertilizer were applied before transplanting, and common potassium nitrate fertilizer were top dressed. In other ten pots, the order of fertilizer was opposite. Seventy percent N, all P 2 O 5 and part of the K 2 O fertilizers were applied before transplanting. Other parts of the N and K 2 O was top dressed as potassium nitrate at the resettling growth stage. Each treatment had 20 pots, wherein each pot contained 15 kg soil.

Measure Items and Methods

At the vigorous growth stage (60 days after transplanting) and maturity stage (85 days after transplanting), the spectral characteristics was determined with ASD FieldSpec Hand-Held (AnalyticalSpectral Device,USA). Meanwhile, the tobacco plants were sampled for determination of dry weight and nitrogen content.

The chlorophyll content was measured by the method as described by Porra et al [START_REF] Porra | Determination of Accurate Extinction Coefficients and Simultaneous Equations for Assaying Chlorophylls a and b Extracted with Four Different Solvents: Verification of the Concentration of Chlorophyll Standards by Atomic Absorption Spectroscopy[END_REF]. The 15 N samples were analyzed by ZHT 2 O 2 mass spectrometer in Academy of Agriculture and Forestry of Hebei Province.

Data Analysis

Using Microsoft Excel 2010 and DPS (Data Processing System) for data processing and statistical analysis. RVI(λ1, λ2)=ρλ1/ρλ2; DVI(λ1, λ2)=|ρλ1-ρλ2|; NDVI(λ1, λ2)=|ρλ1-ρλ2|/(ρλ1+ρλ2); among them, ρ was reflectance, λ was wave length.

Results and Analysis

Spectral Characteristics of Tobacco Cultivars with Different Nitrogen Efficiency

Figure 1 showed the spectral reflectance of different tobacco cultivars in two nitrogen levels. In the visible light range, the chlorophyll absorption of visible light formed obvious "green peak" at 500-600nm. From 670-760nm, the spectral reflectance increased rapidly with the increasing wavelength, and formed high reflection platform in the near infrared range 780-1050 nm. Tobacco leaf spectral reflectance showed significant differences under different nitrogen levels. In the visible light range, the spectral reflectance was lower in higher nitrogen level (N2) than N1, while opposite in near infrared range. Compared among different cultivars, the spectral reflectance of K326 and HD were lower than ZY100 and NC89 in visible light range, which closely related to their higher chlorophyll content. In the near infrared range, the spectral reflectance of four cultivars showed the different order in N1 and N2, which showed the difference of response to nitrogen level among different cultivars. N2 level, they were 45.04%~48.29% and 51.71%~54.96%, respectively. Therefore, tobacco plant absorbed more nitrogen from soil under lower nitrogen level, while more nitrogen from fertilizer under higher nitrogen level. Compared among different cultivars, the proportion of nitrogen accumulation from fertilizer of ZY100 was higher than other cultivars under both N1 and N2 levels.

According to nitrogen accumulation amount, all the nitrogen from fertilizer, soil and total amount were higher in HD and K326 than that in ZY100 and NC89 under both N1and N2 levels.

Table 1. Nitrogen use efficiency of different tobacco cultivars

TNUA: total nitrogen uptake amount; NUA: nitrogen uptake amount; NUR: nitrogen uptake ratio; NDFF: nitrogen derived from fertilizer; NDFBF: nitrogen derived from basal fertilizer; NDFTF: nitrogen derived from topdressing fertilizer; NDFS: nitrogen derived from soil.

The nitrogen use efficiency was calculated by 15 N technique in different treatments (Table 2). Under N1 level, the utilization rate of basal fertilizer were 30.30%~43.90%, and topdressing fertilizer were 49.20%~66.30%; while under N2 level, that were 31.21%~39.99% and 46.55%~57.01%, respectively. The nitrogen utilization rate of topdressing fertilizer was significantly higher than that of basal fertilizer. Compared among different cultivars, both the nitrogen utilization rate of basal fertilizer and topdressing fertilizer aligned as HD and K326> ZY100 and NC89 under N1 and N2 Also it showed that, the nitrogen residual rate of topdressing fertilizer were lower than that in basal fertilizer. Compared among different cultivars, both the soil residual rate and loss rate were lower in HD, which may be related to the well root growth and higher nitrogen use efficiency in this cultivar.

Table 2. Nitrogen use efficiency of different tobacco cultivars

The Correlation Analysis Between Vegetation Index and Nitrogen Metabolism Index

It showed that (Table 3), the chlorophyll content was significantly negatively correlated with ρ550, and significantly positively correlated with RVI(800,550), DVI(800,550) and NDVI(800,550) in N1 level. The total nitrogen uptake amount was significantly negatively correlated with ρ550, and significantly positively correlated with RVI (800,550), DVI (800,550) and NDVI (800,550) in N2 level. The BFUE was negatively correlated with ρ550, and significantly positively correlated with RVI Table 3. The correlation index between some vegetation index and Chlorophyll, TNUA, BFUE, and TFUE.

TNUA: total nitrogen uptake amount; BFUE: basal fertilizer use efficiency; TFUE: topdressing fertilizer use efficiency.

Conclusion and Discussion

Tobacco leaf spectral reflectance showed significant differences under different nitrogen levels, which showed the difference of response to nitrogen level among different cultivars. Studies showed that the spectral reflectance closely related to chlorophyll content [START_REF] Li | Relationship Between Hyperspectra Parameters and Physiological and Biochemical Indexes of Flue-cured Tobacco Leaves [J][END_REF][START_REF] Meng | Study on Correlation Between Chlorophyll Density of Winter Wheat and Hyperspectral Data[END_REF]. In the visible light range, the tobacco cultivar with higher nitrogen use efficiency showed lower spectral reflectance, which closely related to their higher chlorophyll content. The canopy spectral characteristics under different nitrogen conditions were analyzed in previous studies [START_REF] Wang | Correlation Between Corn Leaf Spectral Reflectance and Leaf Total Nitrogen and Chlorophyll Content under Different Nitrogen Level [J][END_REF][START_REF] Dai | Correlation Between Agronomic Parameters and Spectral Vegetation Index in Winter Wheat Under Different Nitrogen Levels [J][END_REF]. Tang et al [START_REF] Tang | Hyperspectral Data and Their Relationships Correlative to the Pigment Contents for Rice Under Different Nitrogen Support Level[J][END_REF] had illustrated that the spectral difference were clear for the canopy and leaves of rice under different nitrogen levels. In this study, the spectral reflectance in the visible light range was lower in higher nitrogen level, which consistent with previous studies. Some studies showed that crop nitrogen nutrition condition can determined using canopy spectral, however, the relationship between canopy spectral characteristics and plant nitrogen use was rarely reported. In this study, the basal fertilizer use efficiency was negatively correlated with ρ550, and significantly positively correlated with RVI (800, 550), DVI (800, 550) and NDVI (800, 550) both in N1 and N2 level. The topdressing fertilizer use efficiency was significantly positively correlated with RVI (800, 550), DVI (800, 550) and NDVI (800, 550) in N1 level. Therefore, spectral characteristics can be an important method for diagnosing tobacco nitrogen metabolism characteristics.

Fig. 1 .

 1 Fig. 1. Changes of spectral characteristics of four tobacco cultivars with different nitrogen efficiency

(

  800,550), DVI (800,550) and NDVI (800,550) both in N1 and N2 level. The TFUE was significantly positively correlated with RVI (800,550), DVI (800,550) and NDVI (800,550) in N1 level.

Acknowledgement

This study was supported by the National Natural Science Foundation of China (31101120) and the key Science and Technology Program of Henan Province, China (102101110600).


Jianhua Jin

Yangren Wang

Effects of Water and Nutrition on Photoassimilates Partitioning Coefficient Variation 1

Keywords: winter wheat, photoassimilates, partitioning coefficient

Photoassimilates partitioning was studied using the experimental data of winter wheat in 2008-2009 at irrigation experimental station of Tianjin Agriculture University.The Research showed that when relative development stage (RDS) was greater than 0.35 fr(root) ,fr(stem and sheath)and fr(leaf) decreased ,fr(Ear) increased with the increase of RDS,and turned into negative When RDS were 0.59,0.67,0.7 respectively.It was due to the existence of repartitioning of photoassimilates, and photoassimilates storaged in root , stem and sheath and leaf began to transport to ear.Water and nutrition had a certain influence on the partitioning of photoassimilates among organs.Excessive water and nutrition were unfavourable for the transportation of photoassimilates to the growth center.

Introduction

Photoassimilates partitioning, known as dry matter partitioning, mainly refers to the partitioning of organic matter producted through photosynthesis among organs [START_REF] Marcelis | Sink strength as a determinant of dry matter partitioning in the whole plant[END_REF].

The status of water and nutrient in soil have great influence on photoassimilates accumulation and partitioning [START_REF] Marcelis | Modelling biomass production and yield of horticultural crops: a review[END_REF].

Variation of photoassimilate partitioning coefficient and the effects of diffrent water and nutrition treatment on winter wheat photoassimilate partitioning were discussed ,which will provide a theoretical basis for determining the amount of water and nutrition of the high yield for winter wheat.

1 Tianjin Nature Science Foundation(10JCBJC09400)

Experimental design

The experiment were conducted at irrigation experimental station of Tianjin treatments.Each treatment contained 3 replicates, the area of a plot was 40m 2 .the total area of two treatment was 240m 2 .There was no isolation zone between replicates, .Three plots of one treatment were adjacent. There was protection zone in test area,with width of more than 3m.

Test items and methods

The test index included dry matter, soil moisture,nutrition, plant height, leaf area, temperature, wind speed, radiation,etc. The measuring of dry matter: 10 representative plant samples were chosed and cut against ground.The root were got through a stratified sampling method with root drill, 10cm a layers,washed with tap water, dived into several groups according to plant organ subsequently.Each groups dryed to constant weight under the conditions of 80℃ after fixxing 30 minute under the conditions of 105℃, then weighted.

Soil moisture was determined by neutron instrument, with measuring depth was 160cm and testing one time each 20cm .

Temperature, wind speed and radiation, etc meteorological data were from meteorological station of Xiqing , Tianjin city.

Data analysis

Dry matter of Every organs were measured for 8 times from March 27,2009 to June 15,2009.The data were analyzed by Excel2003 .The variation of dry matter of every organs with time were simulated to determine every organs daily dry matter . The difference between daily dry matter weight and the previous day's was the dry matter increment.the ratio of dry matter increment to the total of each organ was definded as partitioning coefficient of an organ.

results and analysis

The photoassimilates partitioning coefficient of organ of medium water and nutrition

Photoassimilates from the plant photosynthetic organ is distributed to each organ.Plant organs(root,stem,leaves and storage organs) receive photoassimilates for the maintenance of their physiological activity and growth.The rate of photoassimilates partitioning to each organ is in connection with RDS, the amount of which depends on photoassimilates partitioning coefficient. The photoassimilates partitioning coefficient is as follows:

Fgass org GAA org fr ) ( ) ( 
Where org is the plant organ, ) (org fr or potential capacity to photoassimilates of sink [START_REF] Marcelis | Modelling biomass production and yield of horticultural crops: a review[END_REF]. Priority refers to the order of meet the need to photoassimilates of sinks when photoassimilates supply is not sufficient.According to the previous studies, the priority order of plant organs from strong to weak were seed, fruit (stem and leaves), cambium layer, root and storage structure [START_REF] Minchin | What determines carbon partitioning between competing sinks?[END_REF][START_REF] Wardlaw | The control of carbon partitioning in plants[END_REF].Fr(ear) was the bigest because the ear competitiveness to photoassimilate was greater than root, stem and sheath, leaf when RDS was greater than 0.59, and increased with increasing RDS.

Fig. 1. Photoassimilates partitioning coefficient of winter wheat of medium water and nutrition

It can be seen from Figure 1 that fr(root) ,fr(stem and sheath) and fr(leaf) turned into negative When RDS were 0.59,0.67,0.7 respectively . It was due to the existence of repartitioning of photoassimilates.Component of plant body with the exception of cell walls ,even the inorganic substance, can be redistributed to other organs.For example, when the leaf senescence, most of the sugar and N, P, K etc evacuated and redistribute to new organs nearby.When RDS was greater than 0.59 ear become the growth center of winter wheat,so photoassimilates storaged in root , stem and sheath and leaf began to transport to ear. The amount of photoassimilates of root , stem and sheath and leaf decreased with time,so the corresponding photoassimilates partitioning coefficient were negative.Photoassimilates supply to ear mainly,so fr(ear) was the maximum.when RDS=0.95, partitioning coefficient of ear reached 6.35.The ratio of photoassimilates transported from stemandsheath was the largest,followed by leaves and roots.

Effects of water and nutrient on photoassimilates partitioning coefficient

The data of high water and nutrition ,medium water and nutrition treatments were analysized to detect whether the nutrition and water had influence on the partitioning coefficient.It can be seen from Figure 2 that photoassimilates partitioning coefficient of root,stem and sheath, leaf of high water and nutrition treatment were greater than that of medium water and nutrition. That was, vegetative growth of high water and nutrition was luxuriantly than that of medium water and nutrition.When RDS was 0.90 fr(root) ,fr (stem and sheath) , fr(leaf), fr(ear) of medium water and nutrition were -0.18,-0.47,-0.89,2.54 respectively . Fr(stem and sheath), fr(leaf), fr(ear) of medium water and nutrition were -0.13,-0.25,-0.45,1.83 respectively.It showed that excessive water and nutrition made against the transportation of photoassimilates to the growth center.The ratio of photoassimilates transported to ear from root , stem and sheath and leaf of high water and nutrition treatment was lower than that of medium water and nutrition treatment.So water and nutrition should be controled appropriately in later growth period in order to realize high yield of winter wheat.

Data comparison

Photoassimilates partitioning coefficient of all organs measured by P.M.Driessen [START_REF] Yu | Land-use systems analysis[END_REF] were shown in Table 1.It can be seen that fr(root) fr(stem ) and fr(leaf) were 0.00, and fr(ear) was 1.00 when RDS was greater than 0.56. In his study, the minimum and maximum of Photoassimilates partitioning coefficient of all organs were 0.00 and

Conclution

When RDS was greater than 0.35 fr(root) ,fr(stem and sheath)and fr(leaf) decreased ,fr(Ear) increased with the increase of RDS.When RDS were 0.59,0.67,0.7 fr(root) ,fr(stem and sheath)and fr(leaf) turned into negative respectively .It was due to the existence of repartitioning of photoassimilates,and photoassimilates storaged in root , stem and sheath and leaf began to transport to ear. It showed that water and nutrition had a certain influence on the photoassimilates partitioning coefficient among organs, and excessive water and nutrition were unfavourable for the transportation of photoassimilates to the growth center.The ratio of photoassimilates transported to ear from root , stem and sheath and leaf of high water and nutrition treatment was lower than that of medium water and nutrition treatment .

In future research photoassimilates partitioning in the conditions of limited water and nutrition should be studied , in order to provide theoretical basis for the determination of high yield and high efficiency water and ferlilizer utilization of winter wheat.

,。

  photoassimilates partitioning coefficient of medium water and nutrition was shown in figure1.It can be seen from Figure1that fr(root) ,fr (stem and sheath) and fr(leaf) decreased ,fr(Ear) increased with the increase of RDS when RDS was greater than 0.35. It was the reason that the absorption and using process of sinks had significant effects on the partitioning of assimilates. The photoassimilates partitioning of each sink dependde on the competition ability of each sink mainly.The stronger the competition ability was,the more photoassimilates.Competition ability have two meanings, strength and priority.Strength is defined as the potential demand

-

Fig. 2

 2 Fig.2 photoassimilates partitioning coefficient among organs

  Agriculture University in winter wheat growing season, 2008-2009, located in Xiqing district, Tianjin,longitude 116 0 57 ', latitude 39 o 08 ', height of 5.49 m above sea level.

	Xiqing district belongs to the warm temperate semi-humid continental monsoon
	climate zone. The soil of 0-2.7m was medium loam;The level of NO 3 -N, NH 4 -N of
	0-0.6m were 3.87g/kg ,3.05g/kg respectively. Winter wheat varieties was 6001.
	water(May 7, 2009) , filling water (May 18, 2009) respectively; Irrigation times of
	medium water treatment was 3, winter water(December 2, 2008), jointing water(April
	9, 2009), filling water (May 18, 2009) respectively;Irrigation quota was 60mm for all

Winter wheat were sowed in October 7, 2008, harvested in June 15, 2009.

The test consisted of 5 treatments,2 of which were choosen for analysis in this paper, high water and nutrition ,medium water and nutrition(on behalf of the local normal water and nutrition level).Base manure of high nutrition treatment was compound fertilizer 750kg/hm 2 .After manuring was urea 225 kg/hm 2 . Base manure of medium water and nutrition was compound fertilizer 450kg/hm 2 ,After manuring was urea 150 kg/hm 2 . Irrigation times of high water treatment ( the whole growth period) were 4, winter water(December 2, 2008), jointing water(April 9, 2009), esring

Table 1 .

 1 1.0 without the consideration of the transportation of photoassimilates among organs.However fr(root) ,fr (stem and sheath) and fr(leaf) turned into negative When RDS were 0.59,0.67,0.7 respectively, fr(ear) was 6.35 when RDS=0.95 in the paper.It was due to the existence of repartitioning of photoassimilates, and photoassimilates storaged in root , stem and sheath and leaf began to transport to ear. Photoassimilates partitioning coefficient of all organs of winter wheat

	RDS	0	0.11	0.20	0.35	0.47	>0.56
	fr(leaf)	0.50	0.66	0.56	0.34	0.10	0.00
	fr(root)	0.50	0.34	0.23	0.09	0.04	0.00
	fr(stem)	0.00	0.00	0.21	0.57	0.86	0.00
	fr(s.o.)	0.00	0.00	0.00	0.10	0.00	1.00