Baojun Li
email: bjli@dlut.edu.cn

Xiuping Liu
email: xpliu@dlut.edu.cn

Yanqi Liu

Ping Hu
email: pinghu@dlut.edu.cn

Mingzeng Liu

Changsheng Wang

Image-driven Panel Design via Feature-preserving Mesh Deformation

Keywords: Image-driven, Mesh deformation, Panel Design, Deformable template

In this paper, we propose an image-driven 3D modeling technique for rapid panel design. Our semi-automatic approach is based on template technique and mesh volume deformation controlled by a special cage. We designed our modeling system to be interactive in 2D, automating the process of shape generation while relying on the user to provide image samples. Once a parametric model template is given, using the contour extracted from images, the new control cage corresponding to mesh models generated. Then the geometry of new panel is automatically recovered from the deformable template model. Our system also allows the user to easily reconstruct other 3D objects in a similar manner, such as realistic-looking plant modeling from images. We show realistic reconstructions of a variety of panels, automobile shapes and demonstrate examples of plant editing.

Introduction

Nowadays polygon meshes are widely used in both geometric modeling and finite element analysis fields. Mesh deformation is useful for providing various shapes of meshes for CAE tools, especially in very early phases of conceptual automotive design. Most of 3D objects are largely dominated by a few typical features which include contours, even and engineered meanings. Thus, to create 3D models with new appearances by processing and reusing the existing models is becoming an extremely important way to ease the efficiency problem of geometric design in computer-aided design and computer graphics. Space deformations (volume deformation) are to this day the method of choice for shape deformation due to its independence of surface representation [START_REF] Gal | iWIRES: An Analyze-and-Edit Approach to Shape Manipulation[END_REF] . A space deformation is defined via a (usually simple) control cage or grid; user-defined deformation of this object is interpolated to the 3D space and evaluated at the input surface points. Space deformations are popular since they can handle various object representations, including parametric surfaces, polygonal meshes with multiple connected components, etc. In addition, space deformations are simple to implement, and they are highly efficient and robust, because the cost of the deformation is mainly dependent on the complexity of the control object and not on the deformed shape. Early space deformations used lattices as control objects, and then had also been explored as FFD [START_REF] Sederberg | Free-form Deformation of Solid Geometric Models[END_REF] , EFFD [START_REF] Coquillart | Extended Free-form Deformation: A Sculpturing Tool for 3D Geometric Modeling[END_REF] , and DFFD [START_REF] Hu | Direct manipulation of FFD: Efficient explicit solutions and decomposable multiple point constraints[END_REF] etc. Later work proposed the use of socalled cages as control objects for shape deformations. Typically, the cage is a very coarse and offsetted version of the input shape. Various coordinate functions have been designed to carry over the deformation of the cage to the entire space, such as mean-value coordinates [START_REF] Ju | Mean Value Coordinates for Closed Triangular Meshes[END_REF] , harmonic coordinates [START_REF] Joshi | Harmonic Coordinates for Character Articulation[END_REF] , Green coordinates [START_REF] Jiang | Symmetric Architecture Modeling with a Single Image[END_REF] . However, cage-based deformation schemes in references [5][6] [7] which preserve differential properties, so far, cannot support direct manipulations very well. Cages generation and its manipulations are more complicate. Thus, in this work, for more adaptive for different kind of FEM meshes and easy to edit in 1D, we adapt modified DFFD [START_REF] Hu | Direct manipulation of FFD: Efficient explicit solutions and decomposable multiple point constraints[END_REF] method as our main deformation technique.

The core techniques of the reuse of existing 3D mesh models using mesh deformation, the key property of mesh editing is interaction techniques and more input examples, such as images, 2D sketches. Masuda et al. proposed a combination method to manually specify varying surface stiffness for panel design [START_REF] Masuda | Application of Interactive Deformation to Assembled Mesh Models for CAE Analysis[END_REF], [START_REF] Masuda | Preserving Form Features in Interactive Mesh Deformation[END_REF] . Gal et al. introduce a so called iWIRES, a novel approach based on the argument that manmade models can be distilled using a few special 1D wires and their mutual relations 0 . In this paper, a simple 1D editing method based on symmetric projection is developed, which deals with the control box of DFFD easily and can support the image examples very well.

In this paper, we introduce an image-driven mesh morphing framework for rapid design of the automobile panels, and also generalize this method to other applications, such as realistic-looking planting modeling from images. Section 2 presents the whole pipeline of the method. Then the detailed algorithm of the mesh deformation and contour extraction from images are introduced in Section 3. Section 4 gives the numerical examples and discussion.

Overview of Image-driven Framework

For production of high quality panels in a short period of time, the more freely and rapid design technique is widely studied in the past few years. Inspired by IWires method [START_REF] Gal | iWIRES: An Analyze-and-Edit Approach to Shape Manipulation[END_REF] and image morphing [START_REF] Chen | Affective Product Shapes through Image Morphing[END_REF] method, our main motivation is to generate new models rapidly from images; meanwhile, users can edit the shapes in 1D.

In this section, we will introduce our framework of the image-driven panel design based on mesh deformation. Fig. 1 shows the whole process of our method proposed in this paper. There are three main parts of this framework, which include contour extraction from images, generation of initial control box CB 0 and new control box CB 1 , deformation and related verification via CAE software and aesthetics respectively. The pipeline of the framework illustrated in Fig. 1 is given briefly as follows.

Pre-processing (Control box generation)

In the pre-processing phase, the deformable mesh template M should be chosen and constructed properly according to needs of the practical panels. The first step of the CB 0 generation algorithm is to compute the bounding box of the initial dense mesh model M by principal component analysis (PCA); the so constructed bounding box captures the major geometric shape of the mesh M. For the simplicity of editioninteraction, we consider the main contour of the model via projection mapping, see Fig. 2. Then the corresponding contour and the control box CB 0 associated with DFFD method should be constructed automatically. Fig. 2(a-d) illustrates the construction process of the control box corresponding to the given model in Fig. 2 (a). More details are described in Section 3 and Section 4.

New control box constructed from images

For the purpose of the image-driven mesh deformation, the key point is to create new control box from input image examples. Moreover, there are two core steps should be done carefully, which are the contour extraction from images with high precision and subsequent reconstruction of the new control box respectively. This stage will be described particularly in one full section due to its complicity with many techniques, see Section 4.

Panel design via mesh deformation

With the deformable template and the control boxes CB 0 and CB 1 are given, the next task is to generate the new panel or model using mesh deformation. The final mesh models are generated to meet requirements of panel CAE analysis, such as crash, NVH, durability and formability. Thus, a general deformation method which is independent of surface representation will be developed. In this paper, FFD method is used. In the next section, the mesh deformation techniques are described in detail.

Design via mesh deformation

The rapid design of panels or carbody shapes is constrained by the conflicting requirements of multiple objectives. For example, designers need a more conventional CAD tool to modify the original models to obtain the shape changes required and time constants necessary for these changes. However, the final design is determined by CAE engineers and designers to verify the artistic and reliability of the shapes. State-of-the-art commercial CAE software, such as LS-Dyna, Nastran, Abaqus, Pam-Crash, Fluent, etc. are all based on meshes. Thus, this paper focuses on the platform which enables the user to rapidly change an existing FE / CFD mesh into a new target shape without having to redraw it in the CAD system. In this section, the mesh deformation method used in this paper will be introduced in detail.

Pre-processing

Firstly, in order to rapidly generate a panel model, it is important that the suitable deformable template should be chosen and constructed. Thus, a series of panels or carbody database is constructed to meet the requirement of the different products.

Once the template model was constructed, the following step is to generate a corresponding control box, which can design new shapes via space deformation method. In this paper, for the sake of edition simplicity, the 2D edition approach will be adopted. Now we introduce our main methods used in this work in brief. The automatic CB generation steps are described as following:

1) Compute the principle axis of the template model by Principal Component Analysis (PCA) [START_REF] David | Linear Algebra and Its Applications[END_REF] ;

2) Rotate and project the model into the principle plane, then obtain the contour polygon, see Fig. 2; 3) Automatic generation of the control polygon feature points from the contour; see Fig. 2b; 4) Semi-automatic generation of control box CB 0 by tensor production to the principle axis, see Fig. 2 c-d. It is noted that the suitable CB 0 should be construct carefully, which affects the deformable template and the final design. Meanwhile, control boxes CB 0 and the corresponding CB 1 also provide the feature-preserving of the template through space deformation method.

In this work, we use the projection method in order to simplify the user-interaction process, that is, designers can modify the shapes in 2D principle plane but in the complicated 3D model directly. Taking the advantage of 2D editing, our method can generate a new model just from an input image or image examples, which is so called image-driven panel design method. Fig. 3 illustrates a new control box generated by our method.

New control box generated from images

Due to editing the control box CB 0 in 2D while in 3D directly, our method can generate a new control box from a new contour with some constraints. So, an imagedriven modeling method is proposed based on the mesh space deformation method. This part is of the most importance in this framework, and will be given in Section 4.

Mesh deformation

In order to make use of surface-based techniques for deforming automobile sheetmetal panels, Masuda et al. [10][11] develop the soft and hard constraints on the mesh and propose a framework which can preserve the form features of the sheet-metal panel while deforming the model. Huang et al. [START_REF] Huang | Feature-preserved Morphing Method for Panel Design[END_REF] proposed a morphing method with feature-preserved for panel design, which use the DFFD method on NURBS surface directly. However, as described in Section 3, the rapid panel design method should provide different meshes to satisfy the need of subsequent CAE analysis. Thus, in this work, the mesh space deformation theory is used rather than the surface-based method.

New CB1 constructed from images

In this section, we introduce the detailed techniques to reconstruct new control box CB 1 which shares the same topological structure as the original one CB 0 , see Fig.

Contour extraction from images

First of all we introduce how to extract the exact contour information from an ideal panel image which is the key point to generate new control box.

As shown in Fig. 4, the input images should be pre-processed to obtain a contour with high precision firstly. There are two necessary operations to improve the extracted result in the pre-processing phase, which are image resizing and image smoothing respectively. In this work, for higher effencicy, the bi-linear interpolation method is used to resize an image for ideal; In order to obtain an initial contour from the input image, Guassian smoothing filter is used. Fig. 5 or an imput image example, and the Fig. 5 obtained after the pre-processing stage.

As the pipeline shown in Fig. 4, the following step is to extract the contour from the image, as shown in Fig. 5b-c. The improved gradient vector flow snake (GVF) [START_REF] Xu | Snake, Shapes and Gradient Vector Flow[END_REF] method is used in this paper. Now we introduce this method in brief. A GVF model is the vector field

( ) ( ) ( ) v , , , , x y u x y v x y = ⎡ ⎤ ⎣ ⎦ ,
which minimizes the energy functional ( )

2 2 2 2 2 2 v x y x y u u v v f f dxdy ε μ = + + + +∇ -∇ ∫∫ .
This variational formulation follows a standard principle, that of making the result smooth when there is no noisy data. In particular, when | | f ∇ is small, the energy is dominated by sum of the squares of the partial derivatives of the vector field, yielding a slowly varying field. On the other hand, when | | f ∇ is large, the second term dominates the integrand, and is minimized by setting

| | v f = ∇
. This produces the desired effect of keeping v nearly equal to the gradient of the edge map when it is large, but forcing the field to be slowly-varying in homogeneous regions. The parameter μ is a regularization parameter governing the tradeoff between the first term and the second term in the integrand. This parameter should be set according to the amount of noise present in the image (more noise, increase μ ).

Post-processing of contour

In the above subsection, the initial contour is obtained from an image, as illustrated in Fig. 4a-c. However, the initial contour cannot match with the contour of original control box CB 0 very well. There are two necessary operations to solve the problem, which are contour assessment and feature point assignment respectively.

Contour assessment

Once the template model is given, through the pre-processing operation in Section 3, a constant original contour is obtained. In order to generate a final control box CB 1 with the same topology and features as CB 0 , the original contour and contours extracted from images should be assessed exactly. Fig. 6 shows the geometric assessment result for a given carbody model, and Fig. 6b illustrates assessment result of the contour shown in Fig. 4c. It is should be noted that this step can be an automatic process for a given specific panel case.

Feature point assignment

There is another important point of contour post-processing, i.e., feature point assignment. It is obvious that there are no any apparent relationships between the original contours and contours extracted from images. Thus, to bridge this gap, we should assign some specific feature points on the contours which determine the panel shapes and main corresponding engineering meanings. In this paper, we assignment the feature points semi-manually. As shown in Fig. 7. Coding the feature points, the coding of the feature points for contour in Fig. 4c is given.

Reconstruction of new control box CB 1

By extraction from images and suitable post-processing, a coded contour with geometric assessment is obtained. In this step, a new control box CB 1 with the same topology with CB 0 will be constructed using the above contour information.

The geometric transformation between the contour from image and the contour associated with CB 0 is used. Due to the difference of geometric information between two contours, we adjust the control polygon of the contour extracted from image, according to the feature calibration and geometric information of the original contour. Fig. 8 illustrates an adjustment for the contour shown in Fig. 6 (in blue), and the red one denotes contour associated with the template. After this operation, the control polygon and contour of CB 1 are obtained. In order to construct a new control box CB 1 with high precision and the same topology with CB 0 , a resampling operation with high precision is necessary. However, the contour extracted from an image directly usually has a low-resolution. In this paper, cubic spline interpolation method [START_REF] Wang | Numerical Approximation[END_REF] is used for the initial extracted contour. Then the final contour of the CB 1 is obtained by resampling from the interpolated spline curve with the same number of points with CB 0 . Fig. 9 shows the final contour of the CB 1 corresponding to Fig. 5a-c.

Implementation and numerical examples

We have applied our method to a large variety of panel and carbody models, and obtained very ideal results. All models are computed on a double 2.80-GHz Pentium-R (2G RAM) machine, using VC++ 6.0 and KMAS/COMX development platform.

In this section, three images of cars from Internet are given, and the associated carbodies are computed rapidly by our framework. Fig. 10 is the template model used in this paper, and the final corresponding designs are given as following, such as Fig. 111213. Based on the template model shown in Fig. 10, a new carbody design from image in Fig. 5 is obtained as following figures using the new control box illustrated in Fig. 3 and Fig. 9. Fig. 11 bottom shows the final FEM mesh of the new design, and the others shows the shading one from different views. We also provide two carbody designs from images directly using our method, see Fig. 12 and Fig. 13.

Conclusions

In this paper, we proposed a framework for rapid panel design based on mesh space deformation. Furthermore, we also explored the approach to create any other kinds of man-made, engineered objects and realistic shapes. This approach generates so many different kinds of shapes due to image example input which is abundant from internet. Numerical examples show that our framework is effective and able to reconstruct visually pleasing objects. The final output finite element meshes can be utilized by CAE software. It should be noted that this study has examined only for auto-body design in the conceptual design stage.

For the future work, we will improve current contour-extraction method from images, in order to obtain the feature information with higher precision. A more automatic registration algorithm to reconstruct new control boxes CB 1 from images will be also considered. Furthermore, a more parametric framework will be constructed to generate panels or carbody self-adaptively to meet designer's needs. More applications under this framework will be also considered.

Fig. 1 .

 1 Fig. 1. Flowchart of our method.

Fig. 2 .

 2 Fig. 2. (a-d) Control box generation.

Fig. 3 .

 3 Fig. 3. New control box generation by projection.

4

 4

Fig. 4 .

 4 Fig. 4. Pipeline of CB 1 reconstruction from an image.

Fig. 5 .

 5 Fig. 5. Contour extraction from an image.

Fig. 6 .

 6 Fig. 6. Geometric assessment of contours.

Fig. 7 .

 7 Fig. 7. Coding the feature points.

Fig. 8 .

 8 Fig. 8. Adjustment of the control polygon.

Fig. 9 .

 9 Fig. 9. The final contour of the CB 1 .

Fig. 10 .

 10 Fig. 10. The template carbody model in this paper.

Fig. 11 .

 11 Fig. 11. The final design corresponding to Fig.5-9.

Fig. 12 .

 12 Fig. 12. New design generated using our method.

Fig. 13 .

 13 Fig.13. New SUV design generated using our method.

Acknowledgement

This work was funded by the Key Project of the NSFC (No. 10932003, u0935004), NSFC (No. 60873181), "863" Project of China (No. 2009AA04Z101), "973" National Basic Research Project of China (No. 2010CB832700) and the Fundamental Research Funds for the Central Universities. The model in Fig.10 is provided by DEP.


Xinzhong Wang
email: ndwangxi@163.com

Chun Wang

Weidong Zhuang

Study Study Study Study for for for for Organic Organic Organic Organic S S S Soybean oybean oybean oybean P P P Production roduction roduction roduction Information Information Information Information Traceability Traceability Traceability Traceability System System System System Based Based Based Based on on on on Web Web Web Web

Keywords: soybean, traceability system, internet, agriculture information

The paper analyzes the mode of organic soybean production supply chain and identifies the information needed to be recorded in each step of producing organic soybean through surveying the actual situation of organic soybean on the Red Star Farm combined with the mode of operation among enterprises, then it ensures integrity and consistency of information flow chain. At the same time, it establishes organic soybean production information traceability system, which adopts B/S architecture, ASP dynamic web technology and SQL Server 2005 database. By the system, enterprises can quickly inquire about the information of cultivation and processing of raw materials as well as the situation of products in time. Meanwhile, consumers can also inquire about the information of products by means of Internet quickly and easily, which enhances the transparency of production chain and ensures food safety.

Introduction Introduction Introduction Introduction

More and more people in our country even in the world have paid aettention to the topic of food quality since the 90s of last century, along with a series of mad cow disease, hoof-and-mouth disease and bird influenza which made people feel fear greatly and gave destructive strike on the related industries like export obstacle, sales decline. China is a major exporter that has more exports to the European Union, the United States and other countries after it has joined WTO, it cries out for implementing product tracking and tracing in order to meet the food quality requirements of developed country, serve the export for better, avoid technical barriers, improve the quality of agricultural products and enhance international competitiveness. Therefore, it is very important to build products safety traceability system which can promote information security system, ensure the safety of consumable and enhance the competitiveness of our country [START_REF] Shi | Agriculture Products Traceability System Study[END_REF] .

Taking organic soybean products which are produced by the Great Northern Wilderness People Organic Food Co., Ltd for example, it makes organic soybean production information traceability system true based on researching the process of organic soybean production and processing and analysis of tracing basic framework. The system can record information that related to product quality and safety while it produces and manages, which not only can supply the products information of quality and safety for consumers, but also quality control operations for business.

Analysis Analysis Analysis Analysis of of of of organic organic organic organic soybean soybean soybean soybean production production production production supply supply supply supply chain chain chain chain

The goal of analysis of organic soybean production supply chain is to enable producers to sell their products conveniently, making good quality and prices true and increasing revenue, enable administrators to understand the quality of products accurately in time, enable consumers to know product quality traceability and related information. It is meaningful to have the function of recording, transmission and management, which is good to improve products quality safety, increase the transparency of the whole process information and build products traceability system [START_REF] Honghua | Game Analysis of Agriculture Products Traceability System in Our Country[END_REF] .

The The The The meaning meaning meaning meaning of of of of soybean soybean soybean soybean production production production production supply supply supply supply chain chain chain chain

Soybean production supply chain refers to do some physical economic activities of soybean and its relevant information from producers to consumers in order to meet consumers' demand. It is the chain system that is composed of soybean growers enterprises and so on. Specifically, it is composed of seeding, fertilizer, stalks and labour control, mechanism management, harvest, processing and packaging, and making the soybean appreciated in the process.

2.2

The The The The basic basic basic basic structure structure structure structure of of of of organic organic organic organic soybean soybean soybean soybean production production production production supply supply supply supply chain chain chain chain Organic soybean production supply chain includes organic soybean plant, organic soybean processing, organic soybean packaging and so on. Every step is composed of different participants, among whom the constant distribution process makes organic soybean come true from planting, finished products to being bought by consumers at last, which makes up physical flow. In the process of distribution, participants from different steps send information to each other, which makes up information flow that has the characteristic of tracking and traceability. (1)Organic soybean plant farmers:the process of production supply chain plant, which supply materials for organic soybean.

(2)Organic soybean processing enterprises : the process of supply chains processing, packing and marketing, especially processing.

O O O Organization rganization rganization rganization modes modes modes modes of of of of o o o organic rganic rganic rganic soybean soybean soybean soybean production production production production supply supply supply supply chain chain chain chain

Organization modes of organic soybean production supply are composed of participants that interact with organic soybean production and communicate with each other, moreover enable to finish its communication function. As participants play different parts in the organic soybean production supply chain, organization modes of organic soybean production supply chain become different. It analyses organic soybean organization modes according to Heilongjiang reclamation area based on agricultural standardized production, laying stress on green organic food, making processing industry as centre shaft, aiming for improving core competitiveness of Great Northern Wilderness Group.

Farmer or base 一 >processing enterprises 一 >exports or marketing for wholesalers, retailers, processing agricultural products businesses export products which have been processed from our own base and farmer, or sell to wholesalers, or set windows for selling to consumer directly.

We must keep the information chain unblocked and integrated in order to achieve the need of organic soybean traceability. That means participants must keep detailed records about planting and processing, which can supply some information for quality problem and give consumers useful information. Participants of distribution step are responsible for recording the source of goods so that bad products can be recalled easily.

(1)Growing information: terrains numbers information, planting time information, planter information, the proposed crops information, soil information, seeding information, management information and harvest information.

(2)Processing information: crop source, time of arriving to factory, peeling processing.

(3)Packing information: traceability number, worktable, quality inspector, packing time.

(4)Products information: product name, production date, product strains, product specification, product categories, etc. Traceability system does not provide quality assurance, because it is only good at tracking, without improving the quality of products. It can define responsibilities of each participant through recording information, then reducing the incidence of counterfeit and improving the quality of products.

Traceability system combined with management system methods can paly a stronger part, such as GMP, HACCP and so on. These quality management system has their own record systems, which provide a good foundation for enterprise interior traceability [START_REF] Chunyan | Research on Agriculture Products Quality Security Information System Building[END_REF] .

Development Development Development Development platform platform platform platform

Under the software engineering ideas guidance, the study collects a great number of planting and producing information and designs two functional modules which include the background information management system and the foreground information enquiry system combined with the basic theory and methods of agriculture and computer subject. The background information management system is responsible for recording, modifying and deleting information, the foreground information enquiry system is responsible for tracking information of quality, planting and growing. It establishes a network of organic soybean products traceability system through using ASP object-oriented dynamic page, SQL serve 2000 database and B/S system structure, the overall design framework as in figure 2. The modern organic soybean traceability system has built the perfect management system, including crop seeds, fertilizer information, artificial management, machinery management, diseases management, pests management, harvest management and so on, it makes electronic filing true from planting to harvest and from producing to processing. The system which inquire about planting information through management system focus on the process of planting and processing corps.

Enterprises collect traceability information in detail, which can go back to the production process of each product. It can achieve functions by the query numbering system.

(1)Farm can inquire about all the planting situation using terrains code.

(2)Processor can inquire about processing chain information through products code, and then goes back to the production process of each product.

(3)Consumers who are using web can inquire about producing information of all organic agricultural products through products traceability numbers, which can guarantee consumers' legitimate rights and interests.

ASP ASP ASP ASP technology technology technology technology

ASP ( Active Server Pages) , dynamic server page, including three parts of the content : VB Script or JavaScript scripts the code, which is used to implement the logical control; Integrated server functionality components, which is used to passing on a information of the client and server, ADO( ActiveX Data Objects , ActiveX), which is used to access the database.

JavaScript is a type of light and interpretation programming language and has ability of oriented object. The language common core has been embedded Netscape, Internet Explorer and other web browser, it makes web process design rich and colorful expressing the web browser window and its content object. The client version of JavaScript executable contents to the web, in this case, the web is not static HTML, but including the process of interacting with the procedures, the process of controlling browser and the process of building HTML dynamically [START_REF] Pei | Web2.0 Development of Technology Solutions[END_REF] . ADO (ActiveX Data Object) a database access control of asp built in the server, it is the most efficient, simplest, powerful function method aiming at data operation that Microsoft software supports. It supplies database technology which can gain access to Internet easily for dynamic web page. ADO can supply main content including database information for users through accessing and operating data sheet in the database and can make user have access to background database through executive SQL command. In addition, ADO is almost compatible with all kinds of the database system, such as Microsoft Access, FoxPro, SQL Server and so on, ADO supplies the same mediashow for programmers [START_REF] Qingzhen | Implementation of Assignment System Based on Web Developed by ASP[END_REF] .

S S S System ystem ystem ystem o o o operation peration peration peration

Organic soybean quality security traceability system has been put in the actual application, including prevention crops pests query, organic agricultural products implementation standard query, organic crops production operation procedures query, prevention crops diseases query, organic agricultural products traceability system, etc. Organic soybean product traceability system can make us understand traceability information of single product production supply chain from planting to processing and make tracking products true, which ensures the quality safety of products, in this case, enterprises can inquire about materials information of production and inspection and understand the situation of agricultural production quickly. Consumers also can inquire about the information of products, it enhances the transparency of production and improves consumers' confidences. The red star farm organic soybean quality safety traceability information system, as shown in fig 3.

C C C Conclusions onclusions onclusions onclusions and and and and prospects prospects prospects prospects

It analyses organic soybean production supply chain concretely in the face of planting, harvest, processing and packing. combining with organic soybean production practice, it ensures the integrity of organic soybean quality safety information ,which is good for tracking information, and it also supplies data supporting for organic soybean quality safety information platform. It put forward the structure of B/S organic soybean traceability system which makes system have better operation, security and stability.

The problem of food quality safety is still concerned by people at home and abroad. The implementation of digital information management is more and more popular with the spread and development of information technology, transparency of production information. It researches on the multi-factor of organic soybean traceability combining with the characteristics of Great Northern Wilderness organic soybean. Currently, the system only involves the parts from planting on the farm to processing in the enterprise, but in the future, the system can reach more fields such as selling field.

  Fig Fig Fig Fig. . . . 1 1 1 1. . . . Structural model of supply chain

  raceability raceability raceability system system system system function function function function

Fig 3

 3 Fig Fig Fig Fig. . . . 2 2 2 2. . . . Architecture of the B/S

Fig.

  Fig. Fig. Fig. Fig. 3 3 3 3. . . . The overall structure of the system function

4 4 4 4 . 3 . 3 . 3 . 3

 43333 ASP+ADO ASP+ADO ASP+ADO ASP+ADO database database database database access access access access technology technology technology technology

Fig

  Fig Fig Fig Fig. . . . 4 4 4 4. . . . Traceability information system of organic soybean


Ting Zhao

Heru Xue

Regression Analysis and Indoor Air Temperature Model of Greenhouse in Northern Dry and Cold Regions

Keywords: Indoor air temperature model, Regression analysis method, Model parameters, Computer programming

The indoor air temperature of greenhouse is an important parameter in environmental monitoring. Existing research on it has many, and the mathematical models have been made. But the obtained models do not apply to the northern areas features of dry and cold. On the basis of previous studies, the indoor air temperature model of greenhouse will be re-modeling by using the regression analysis method in this paper. Analysis each convection heat transfer coefficient of the model and re-established them. In the case of known the outdoor weather conditions, the solution of the indoor temperature will be implemented by using the computer programming. And the data of a period is selected randomly from the observational data to validate the model is feasible and applied.

Introduction

There are many advantages in China's light greenhouse, but the environmental control level of greenhouse is still lower. The artificial experience is still the main force, and theoretical and practical guidance is lacking. The indoor air temperature of greenhouse is an important parameter in environmental monitoring. Predecessors have done a lot of research on the prediction model of the indoor air temperature of greenhouse. But most studies have been done for the greenhouse in Beijing. [START_REF] Fugui | Simulation and Analysis of Direct Light Environment in Typical Greenhouse in Beijing--Light Simulation and Analysis Facility Agriculture Research[END_REF][START_REF] Hong | Light Environment Simulation and Analysis of Greenhouse in Beijing[END_REF]And the obtained models do not apply to the northern areas features of dry and cold.

The prediction model of the indoor air temperature of greenhouse which can be used in the northern areas features of dry and cold can predict the daily change of temperature with time. According to forecasts, the staff can regulate the temperature actively, so that the crops can be provided the best growth temperatures.

The model to be build up on the basis of previous' work, and the indoor air temperature model of greenhouse will be re-modeling by using the regression analysis method. Analysis each convection heat transfer coefficient of the model and re-established them. The typical areas and typical greenhouse will be chosen for the study. And using the known conditions to validate the model is feasible and applied.

The indoor air temperature model of greenhouse

Heat balance differential equation of indoor air temperature

The heat balance differential equation used in this paper mainly by heat conduction differential equation. The model conditions have been simplified some. And according to the heat transfer principles and the law of conservation of mass and energy, the physical processes in the greenhouse have been considered comprehensively. The thermal change has much effect on thermal environment of greenhouse caused by heat change and moisture variation which come from solar radiation, heat convection, radiation heat transfer, heat exchange and natural ventilation. [START_REF] Wei | Theoretical Model of Thermal Environment in SolarPlastic Greenhouses with One-Slope[END_REF][START_REF] Qingyun | Dynamic Simulation of Thermal Environment in Solar Greenhouse[END_REF]

v c pf s r b h a a pa a Q Q Q Q Q Q Q dt dT v c 1 1 1 1 1 0          (1)
On the left of the equation is the heat change caused by the air temperature changes with time. And the right contains the energy of the indoor air temperature coming from hot-water heating-h Q ; the energy of the indoor air temperature wasted by natural ventilation and infiltration heat loss-v Q ; and the energy coming from natural-convection heat transfer--1 Q i . This is the derived formula of the indoor air temperature changes with time.

t a pa a v c pf s r b h a a a a             v c Q Q Q Q Q Q Q t t t t 1 1 1 1 1 0 0  (2)
Known by the Newton's law of cooling, the convection heat transfer energy from gas A to solid B can be shown as this.

) T T ( A Q A B A B A 1 B A B       (3)

In this equation, B A

A  is the surface area of the heat transfer from A to B; The indoor air is heated mainly by the hot-water heating pipes. And the heat transfer between heating pipes and the indoor air is carried out by convection.

The energy of the indoor air temperature coming from hot-water heating is h Q . It can be shown as this.

) T T ( h A Q a p h h h   (4)
In this equation, h A is the surface area of the hot-water heating pipes; h h is the coefficient of the heat transfer between the heating pipes and the indoor air; p T is the temperature of the hot-water heating pipes; a T is the indoor air temperature.

The energy of the indoor air temperature wasted by natural ventilation and infiltration heat loss is v Q . It can be shown as this.

) T T ( c Q o a v a a v     (5)
In this equation, v  is the air quantity of the indoor air; Through the equations of ( 2),( 3),(4),and ( 5), the heat balance differential equation of indoor air temperature can be simplified as this.

) T T ( c ) T T ( A ) T T ( A ) T T ( A ) T T ( A ) T T ( A ) T T ( h A T v c ) T (T 0 o a v pa a c a c a c pf a pf pf s a s a s r a r a r b a b a b h a h h a pa a a                             (6)

Heat convection coefficient-i



Heat convection is the heat transfer which happened on the liquid flows through the solid surface. In the greenhouse which has the small natural ventilation, rising or declining the indoor air temperature is mainly through the heat convection with the cover, the ground soil, plants and other heat transfer to achieve.

There are many essays about the heat convection in overseas. And the studies in this respect are also very mature. The arithmetic in these studies can be used in our country greenhouse. The heat convection coefficient can be shown as this. [

5]   ) T T ( h i j   4 5   (7)
Based on such expression of the heat convection, each heat convection coefficient of the heat balance differential equations can be explained to

25 . 0 i T T h j  .
Therefore, the problem of optimizing the equation is the problem of calculating the value of h.

Known conditions of this model

Structural parameters

To facilitate the calculation, the greenhouse structure can be approximately regarded as the following graphic which does not affect the results on the premise. 1) The 24-hour predicted values come from the weather forecast. They include the outdoor air temperature, the outdoor humidity, the wind velocity, the wind direction and the uitraviolet intensity. 2) Each average of the data calculated by hour is obtained. And the data of the cover temperature, the soil temperature, the rear wall temperature, the rear slope temperature, the crop canopy temperature and indoor air temperature is detected by the greenhouse monitoring system.

Experimental data is the observed data which can stand for the northern areas features of dry and cold. Use nonlinear regression analysis to optimize the parameters which comes from this heat balance differential equation of indoor air temperature. Nonlinear regression analysis is using least square method to estimate the parameters of non-linear model.

Interpretation of result

Validation data used the data of the weather including sunny, cloudy and cloudy.

Diagram one and diagram two shows that the variation trend with time of predicted values and measured values is consistent. And the temperature contrast between predicted values and measured values is small. The average relative error is less than 10%. Therefore, the simulation results which calculated by the mathematical model of are reliable. The two diagrams also show that the maximum between predicted values and measured values present to 9 o'clock and 10 o'clock after lifting up the quilt used to keep warm. Because the solar radiation light into the indoor, the indoor air temperature shoot up. But this prediction model cannot predict it. So this is the model should be improved.

Conclusion

The rebuilding heat transfer coefficients apply to the northern areas features of dry and cold. So the prediction model of indoor air temperature is feasible and applied. But the values predicted are not good after lifting up the quilt. And the problem will be studied next.

  of the heat transfer between A and B; A T and B T is the temperature of A and B.

 3 . 2

 32 The length of the greenhouse L5=50(m)  Leaf area index of the indoor crops La=0.3  The area of the covers L1*L5  The area of the ground L4*L5  The area of the rear slope L2*L5  The area of the rear wall L3*L5  The area of the crop canopy L4*L5*La  The volume of the indoor air V=506  The specific heat volume of the indoor air pa a c  =1185; kJ/(kg• ℃)  The temperature of the heating pipes during the day T=60+273;(K)  The area of the heating pipes is 9 m^2  The coefficient of the heat transfer between the heating pipes and the indoor air The air quantity of the indoor air Φ=0.001*V/3600 Known conditions (Temperature indicators)

  Formula six is used to the regression equation of non-linear model. The starting value of the parameter h chose the empirical value 1.25.The sample data chose the data from November and December of 2009 and January and February of 2010. Other required temperature used the observed data. Each heat transfer coefficient of the model was re-established by using SAS software. November 23 to 30, 2009 and January 1 to 6, 2010 is the tested date. And the forecasting module of the indoor air temperature has been verified by using these data. The two pictures below are the comparison diagram of predicted values and measured values using MATLAB subroutine to output.

Fig. 1 .Fig. 2 .

 12 Fig. 1. The test and prediction data inside air temperature of the solar greenhouse

Table 1 .

 1 Sign Description

	emblem description	unit	emblem description
	Q			Heat exchange	W	a	Indoor	air
							temperature
	V			Volume	m3	b	Rear wall
	A			Area	m2	c	Crop canopy
	T			Temperature	K	h	Heating
	Q	1	Convection heat transfer	W	o	Outdoor	air
				energy			Temperature
		cV	Heat capacity	Kg/s r	Rear slope
			p
					J/K	s	Soil
						v	Ventilation
						pf	Covers

Table 2 .

 2 The test and prediction data indoor air temperature of the greenhouse Jan.2,2010

		0	1	2	3	4	5	6	7
	Predictive value	9.5	9.1	8.8	8.4	8.1	7.7	8.7	9.3
	Measurements	9.3	8.7	8.6	8.4	7.9	7.9	9.1 10.3
		8	9	10	11	12	13	14	15
	Predictive value	9.8 10.2 11.1 13.6 16.9 19.3 18.4 16.4
	Measurements	10.8 12.2 13.9 14.4 18.2 19.5 19.1 15.2
		16	17	18	19	20	21	22	23
	Predictive value 14.3 13.7 13.1 12.6 12.3 12.1 11.9 12.1
	Measurements	13.8 13.6 12.9 12.7 12.6 12.6 12.6 12.5


Hongbin Zhang
email: hbzhang@caas.net.cn

Guixia Yang

Qing Huang

Gang Li

Baorui Chen

Xiaoping Xin

Study on spatial scale transformation method of MODIS NDVI and NOAA NDVI in Inner Mongolia grassland

Keywords: spatial scaling, grassland remote sensing, MODIS, NOAA

Based on MODIS NDVI and NOAA NDVI datum, covering the primary grassland types of Inner Mongolian in growing seasons from 2000 to 2003, this paper analyzes annual variation rule of the relationship between MODIS NDVI and NOAA NDVI datum. We use the theory of statistics to discuss the spatial scaling methods between different resolutions images of remote sensing in large-scale spatial extent. At the same time, we build spatial scaling model by MODIS NDVI and NOAA NDVI datum of July and August in 2002, and apply it to the 2003's NOAA NDVI datum, then take the survey datum in field to validate the precision of the model. The result indicates that this spatial scaling method is effective, and the model could be applied to other times. This method makes it scientific and effective to analyze and compare the result of monitor by NOAA NDVI and MODIS NDVI of different times in grassland.

Introduction

At present, remote sensing technology has already become the mainly acquisition method and important study means for macroscopic ecology subject. Because satellite sensor imaging has instantaneity and periodicity features coupled with weather factors (such as cloud, rain, snow and so on) affecting sensor imaging, any sensor can't supply the enough image datum to covered the whole grassland in any times (Li [START_REF] Xin | Progress and Foresight of Chinese land Data Assimilation System Study[END_REF]). That's say, shortage of time series image in the same region or spatial image data in the same time for macroscopical ecology system study. Especially in long time serial and large spatial scale ecology system monitoring study, this problem is the most serious. So how to effectively integrate multiple-source remote sensing datum and different resolution (spatial resolution, temporal resolution) image datum for macroscopical ecology monitoring study has become difficulties in current.

Especially in recent years, many experts and scholars at home and abroad have already developed search. Mayaux and Lambin adopted two-steps transformation method to make vegetation area scale transformation based on four spatial exponential relations of TM and AVHRR in 1995 [START_REF] Mayaux | Estimation of tropical forest area from coarse spatial resolution data: A two-step correction function for proportional errors due to spatial aggregation[END_REF]. Wang Kaicun and so on analyzed shortwave albedo of MODIS and AVHRR data and compared the differences between them in details in 2004(Wang kaicun et al

MODIS NDVI and NOAA NDVI data relation stability analysis

Because this study is involved with spatial scale transformation model applied in time scale, it is necessary to analyze stability of correlation between MODIS NDVI and NOAA NDVI data in long time scale. It is the basic premise to ensure stability and effectiveness of time scale transformation model. This paper selected monthly maximum compounding NOAA and MODIS NDVI data from April to October, in 2000 to 2003, and use ArcGIS 9.0 to calculate correlation of different months NDVI max between two types of data. Compare fluctuation of correlation in same term and study whether there is stable correlation between them in research area. The results as shown as below:

Spatial scale transformation method of MODIS NDVI and NOAA NDVI

Because MODIS sensor is superior to AVHRR sensor in spatial and spectral resolution, MODIS NDVI can reflect grassland vegetation condition more truly. So this paper selected down-scale transformation method that from NOAA NDVI to MODIS NDVI and built scaling model. The detailed method is as below.

Apply systematic sampling to select study samples in MODIS NDVI and NOAA NDVI remote sensing images. Sampling interval is 40 km. Sampling range of MODIS image was an circular region with 4km radius, take NDVI mean value as MODIS data sampled value. The Sampling range of NOAA image was a pixel (8km*8km), so we can obtain 533 MODIS and NOAA sample datum every month in research area. From fig 3 to 9, MODIS NDVI and NOAA NDVI data of grassland vegetation in April to October have well linear correlation which increase start at April, reached a peak in August and decrease start at September. As correlation was concerned, April was at lowest, May and October were almost the same, but October was slightly higher than May; June and September were almost the same, but September was slightly higher than June; July and Aug were almost the same, but Aug was slightly higher than July; May and October were significantly higher than April; June to September were significantly higher than April, May and October; July and August were significantly higher than June and September.

The change trend of linear correlation between MODIS NDVI and NOAA NDVI data was truly in response to grassland vegetation condition in research area [START_REF] Xu Bin | Monitoring by remote sensing of vegetation growth in the project of grassland withdrawn from grazing in countries of China[END_REF]. The grassland began to growing in April and at this time, the grassland vegetation coverage and biomass were the lowest in growth season. So NDVI index of grassland was very small, when the influence of soil background noise etc on AVHRR and MODIS sensors monitoring relatively the largest, more mistakes resulted. Especially NOAA NDVI data, because of uncertainty of calibration parameter, rang of mid-value and low value excelled 20%. All of the facts disturbed MODIS NDVI and NOAA NDVI data to obtain the parameters reflecting grassland vegetation condition to a great degree. So NDVI data has too much noise in April and correlation is the worst. In May grassland vegetation coverage and biomass were better than April. The influence of factors of soil background noise etc on AVHRR and MODIS sensors monitoring began to weaken. So correlation of MODIS NDVI and NOAA NDVI rose notably compared with April. In June, grassland vegetation coverage and biomass had a further marked increase and correlation continued to strengthen. July and august were the two months in which grassland vegetation condition were the best in the whole growth season. So correlation of July and August were higher than the other months and grassland vegetation condition in July was better than that in August. But because saturation effect of AVHRR and MODIS sensor bothered to obtain NDVI data reflecting truly grassland vegetation condition, correlation of August was a little higher than that in July. In September, grassland vegetation coverage and biomass began to decrease, but because there was a lot of hay in research area which decreased the influence of soil background noise on AVHRR and MODIS sensor monitoring, association relationship of September was just a little higher than June. In like manner, correlation of October was just a little than May.

Spatial Scale Transformation method application and verification

The NDVI values of July and August represented the maximum value all the year round in research area. So NDVI value of July and August were applied the most widely in biomass yield estimation. Because of shortage of ground surface survey data except July and August, this paper mainly validated the precision of spatial scale transformation model between MODIS NDVI and NOAA NDVI data of July and August in 2002, according to the spatial scale transformation discussed in 3.

∑ = = ∆ n i ;mean absolute bias: n / ) d ( V 2 1 ∑ = = n i
d is the difference of predicted value and measured value,x is measured value,n is sampling quantity Conclusions and Discussion 1. On the whole, MODIS NDVI was more than NOAA NDVI in value, and they both had high relativity in growth season. The relativity mainly affected by vegetation growth climate condition. That is because when climate condition was better, high coverage of grassland can reduce noise influence of soil background on AVHRR and MODIS sensors imaging. On the contrary, when grassland vegetation condition was bad, noise influence of soil background on AVHRR and MODIS sensors imaging would be increased, resulted correlation of MODIS NDVI and NOAA NDVI decreased. So in research area, correlation of July and August were the highest and April was the lowest.

2. Linear spatial transformation model built up based on MODIS NDVI and NOAA NDVI was stable in time dimension in some degree. After time scale transformation model was extrapolated application, the transformed NOAA NDVI data kept higher monitoring precision of grassland biomass to indicate the model could be extrapolated in time scale. Because vegetation condition in July and August were the best, association degree between MODIS NDVI and NOAA NDVI data were the highest. So stability of spatial scale transformation model built up based on MODIS NDVI and NOAA NDVI in July and August was the best when extrapolated in time scale.

3. This paper based on statistics method, making use of association relationship of 250m resolution MODIS NDVI and 8 km resolution NOAA NDVI data, spatial transformation model between MODIS NDVI and NOAA NDVI data was built up. But because this method self did not take 8 km resolution NOAA NDVI data big pixel inner heterogeneity in to account. So relative precision of 250mm resolution small pixel of NOAA NDVI data in 8 km large pixel region was not prompted and this was the shortcoming of the method. In so large spatial scale survey region, conflicted by so many basic data serious limits (such as high precision land utilization map shortage, big mistake in spatial adjustment of low resolution data, different type vegetation pure pixel shortage) It was difficult to adopt other scale transformation method taken spatial heterogeneity into account. Under the premise of ensuring total scale transformation precision, the method adopted by this paper was a scale transformation feasible scientific method in large space range different resolutions remote sensing data.

Fig. 2

 2 Fig.2 Analysis of the correlation between NOAA NDVI and MODIS NDVI, 2000-2003 From fig 2, we can see correlation of NOAA NDVI and MODIS NDVI data changed with regularity and the correlation was stable in long time scale between them in growing season from 2000 to 2003 except 2001.

Fig. 3 Fig5Fig. 7 2 =Fig. 9 The

 3729 Fig.3 Comparison of NOAA NDVI and MODIS NDVI in Apr Fig4 Comparison of NOAA NDVI and MODIS NDVI in May

  3, linear spatial transformation model was built up and extrapolated to NOAA NDVI data of July and August in 2003. Taken use of MODIS NDVI and NOAA NDVI data and survey data to validate and verify spatial scale transformation precision when extrapolated to time scale and practicability. Specific process as follows: Adopt systematic sampling method, draw 533 samples in 8km resolution NOAA NDVI and 250 mm resolution MODIS NDVI of July and August in 2002. According to linear correlation, built up spatial scale transformation model of NOAA NDVI and MODIS NDVI in July and August (table 1). Take advantage of this model and Erdas 9.0 to resample pixels (to 250 m) and linear process to obtain transformed 250m NOAA NDVI data of July and August in 2003. Use survey datum of July and August in 2002 and MODIS NDVI data of July and August in 2002 to build up regression model to get the regression formula: Y=11.2620+179.6915X (Y: dry biomass; X: MODIS NDVI). Input above transformed 250m resolution NOAA NDVI data to model and generate grassland vegetation biomass distribution map of July and August in 2003 separately. Use survey datum of July and August in 2002 and untransformed 8km resolution NOAA NDVI data to build up regression model to get the regression formula: Y=25.9050+129.6058X(Y: dry biomass;X:NOAA NDVI vegetation index). Input above uncorrected 8km resolution NOAA NDVI data in 2003 to model and get ground biomass distribution map in research area of July and August in 2003.

land utilization type map and different types of vegetation pure pixels and so on parameters. So we used the theory of statistics to build spatial scaling model of MODIS NDVI and NOAA NDVI datum, and applied the model to time scale application. 2 Survey region Overview

  transformation method among large scale grassland different resolutions remote sensing data. Because MODIS and NOAA data have lower spatial resolution features (Huang Jiajie et al., 2003; Potte, C et al., 2003; Steven, M. D et al., 2003 ), and survey region is too large, it's difficult obtain high precision

	meadow steppe, desert grassland, desert steppe and so on seven types of grassland and
	whole area is 781587.10 km2 accounting for 98.4% of Inner Mongolia grassland
	whole area. It is the main body of Inner Mongolia grassland autonomous region.
	Fig.1
	Inner Mongolia grassland is located in hinterland of Eurasian Continent. It is a vast
	land and has rich and colorful grass types occupying 22% of our country grassland
	whole area. at the same time it spans "Sanbei distraction" in our country. It's
	important animal breeding production base and a natural green protective screen for
	north china, it takes the role of protection and improvement our country ecology
	environment. According to Inner Mongolia autonomous region 1:10000000 grassland
	type map, whole grassland area is 794239.00 km2. The survey region makes up of
	Low-land meadow, up-land meadow, temperate steppe, steppe desert, temperate

., 2004). Kevin Gallo and so on analyzed differences of NOAA NDVI and MODIS NDVI in details and built up NOAA NDVI and MODIS NDVI data relational model of types of farmland, grassland, evergreen broad-leaf forest, shrubbery, city in 2005(Kevin

[START_REF] Gallo | Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data[END_REF]

. Bao Pingyong and so on analyzed surface albedo production of same time's ETM+ and MODIS data and compared differences of surface albedo of forest land, farmland, grassland, water, city, river shoal, exposed soil and exposed rock in 2007

[START_REF] Pingyong | Study on consistency of land surface albedo obtained from ETM+ and MODIS[END_REF]

. Zhang Wanchang and so on took on scale transformation study based on LAI index inverted from ETM+ data and attempted to bring forward a new more effective scale transformation method based on NDVI unmixed pixels in 2008(Zhang

[START_REF] Wanchang | Spatial scale transferring study on leaf area index derived from remotely sensed data in the Heihe river basin, China[END_REF]

. Wang Peijuan adopted ETM+ and MODIS data to investigate spatial scale transformation method of forest coverage net primary productivity in Changbai mountain nature reserve (Wang

[START_REF] Peijuan | Spatial scaling of net primary productivity based on process model in Changbai Mountain natural reserve[END_REF]

.

This paper took Inner Mongolia grassland as an example to study spatial scale

The Grass type of Inner Mongolia 3 Study Method 3.1 Data resources

	This paper used the first and second band of MOD09Q1 production provided by
	NASA. Because MOD09Q1 is 8 days reflectivity production composed by everyday

L2G surface reflectivity and affected by weather easily, it is very hard to get the remote sensing data which was not shaded by cloud in a short time (Yan

[START_REF] Jianwu | Application summary of EOS-MODIS data in the monitoring of grassland resources[END_REF][START_REF] Lianyi | Temporal changes of MODIS-NDVI vegetation index and forage biomass in Xilinguole grassland-taking the change from April to September in 2005 as a sample[END_REF]

. So this paper applied the maximum value compounding method to obtain monthly NDVI vegetation index data to analyze.

The NOAA data we used is 8 km resolution NOAA/AVHRR monthly maximum value compounding NDVI production provided by Global Inventory Monitoring and Modeling Studies.

ACKNOWLEDGEMENTS

This work is supported by Special Fund Project for Basic Science Research Business Fee, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences and The National Science & Technology Program (Grant No. 2006BAC08B0404 、 2007BAC03A10) and Project 863 of China: (Grant no.2007AA10Z230) and National Natural Science Foundation of China (Grant No: 30770327) and Commonweal Industry Scientific Research Special Funds Project (GYHY200906029-2). Acknowledgments This work is supported by The National Science & Technology Program (Grant No. 2006BAC08B0404 、 2007BAC03A10) and Project 863 of China: (Grant no.2007AA10Z230) and National Natural Science Foundation of China (Grant No: 30770327) and Meteorological science and technology of China, No.GYHY200906029-2 and Special Fund Project for Basic Science Research Business Fee and.

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Yunxia Luan
email: yunxia.luan@nercita.org.cn

Hua Ping
email: hua.ping@nercita.org.cn

Ligang Pan
email: ligang.pan@nercita.org.cn

Residue Dynamics of Phoxim in Pericarp, Sarcocarp and Kernel of Apple

Keywords: Apple, Phoxim, Sampling method, Residue dynamics, GPC-GC/MS

The temporal and spatial variations of phoxim residue in apples were studied. The insecticide phoxim was applied by spraying according to the recommended dosage. The phoxim residues in different parts of apple were extracted with acetonitrile, purified by primary secondary amine (PSA) and C18 filler, and then determined by gel permeation chromatography-gas chromatograph mass spectrometer (GPC-GC/MS). It was disclosed that the half-life of phoxim in apples was 1.64 days. The contents of the phoxim residue in different parts of apple could be ranked as follows : pericarp > entire apple > sarcocarp> kernel, and the ratio of residue in pericarp and the entire apple ranged from 6.241 to 9.262 in groups with different specific surface area. There were significant differences in the contents of residues between the pericarp and entire apple, which provided the evidence in the theory instruction and design basis to the sampling method of pesticide residue based on pericarp.

Introduction

Agricultural product quality and safety has been given higher requirements in recent years. Currently, the issues caused by excessive pesticide residues in edible agricultural products attracted the social attention to the safety and monitoring of vegetable production processes. Pesticides are used to control insects, weeds, and disease throughout the world [START_REF] Wen Yx | Studies on sensitivity and detection limit of phytoesterase on organophosphate pesticides[END_REF]. Although their use leads to increased crop yields and improvements to the food quality, pesticide residue levels in food are of increasing concern to the public. Due to the abuse and misuse of pesticide, pesticide residues severely influence people's health. Organophosphorus (OP) pesticides are the most widely used agricultural pesticide [START_REF] Jensen | Cumulative risk assessment of the intake of organophosphorus and carbamate pesticides in the Danish diet[END_REF]. More than 70% of that are hyper-toxic or high-toxic, and normally are forbidden to use in planting fruits and vegetables [START_REF] Mol | Determination of polar organophosphorus pesticides in vegetables and fruits using liquid chromatography with tandem mass spectrometry: selection of extraction solvent[END_REF].

OP insecticides are routinely applied to fruit crops (e.g., apple, peach, and pear) that are subject to consumption as single servings [START_REF] Zhou | Development and application of on-site fast detection techniques for food safety[END_REF][START_REF] Souza | Organophosphorous and organochlorine pesticides affect human placental phosphoinositides metabolism and PI-4 kinase activity[END_REF]. Consumption of individual fruits containing high OP insecticide residues may result in high exposure over a short period of time. Residue testing for the presence of pesticides, however, is routinely performed on composites of 5 to 10 individual or unit samples rather than samples of individual fruit [START_REF] Giddings | Fate of chlorpyrifos in outdoor pond microcosms and effects on growth and survival of bluegill sunfish[END_REF][START_REF] Andersson | Comparison of pesticide residues in composite samples and in individual units: the Swedish approach to sampling[END_REF][START_REF] Harris | How the variability issue was uncovered: the history of the UK residue variability findings[END_REF]. The use of data derived from composite samples may result in the underestimation of residues present in individual fruit because dilution may occur during compositing and, therefore, may not accurately represent exposure in single serving commodities [START_REF] Pennycook | Modeling of the dietary pesticide exposures of young children[END_REF][START_REF]Joint FAO/WHO consultation on food consumption and exposure assessment to chemicals in food[END_REF].

Variability factors have been established to indicate how much residue concentrations measured in individual samples vary from levels observed in composite samples [START_REF] Suhre | Pesticide residues and acute risk assessmentsthe US EPA approach[END_REF][START_REF] Ferna´ndez-Cruz | Field-incurred fenitirothion residues in kakis: comparison of individual fruits, composite samples, and peeled and cooked fruits[END_REF][START_REF] Ambrus | Within and between field variability of residue data and sampling implications[END_REF]. Variability factors have been calculated by dividing the maximum pesticide concentration observed in an individual or unit sample by the mean composite level [START_REF] Ambrus | Within and between field variability of residue data and sampling implications[END_REF]. In recent years, studies to establish residue levels observed in individual samples relative to composite samples have been performed in a variety of crops (e.g., apples, carrots, oranges, potatoes, and tomatoes) [START_REF] Boulaid | Effect of household processing and unit-to-unit variability of pyrifenox, pyridaben, and tralomethrin residues in tomatoes[END_REF][START_REF] Ambrus | Contribution of sampling to the variability of pesticide residue data[END_REF][START_REF] Lentza-Rizos | Residues of aldicarb in oranges: a unit-to-unit variability study[END_REF][START_REF] Lentza-Rizos | Residue levels of chlorpropham in individual tubers and composite samples of postharvest-treated potatoes[END_REF]. Residue data in unit samples of individual commodities are required to establish variability factors for comparison with default values currently being used. Recently, variability also has been estimated using the 97.5th percentile concentration data for individual samples, rather than maximum levels, where sufficient data exist. Sampling and sample preparation are critical steps in analysis, and cannot be treated separately from sample treatment; rather the processes must be conducted together. However, limited data have been reported that directly compare the sampling procedures against one another. Even less information is published regarding the most appropriate part of the material to use in conjunction with a sampling procedure for pesticides detection, which is particularly important to the portable rapid detector of contamination in fruits and vegetables.

The present study addressed how well pesticide residue levels measured in composite samples represent concentrations in different parts of apples, in order to improve sampling method of single serving foods. A within-laboratory trial was performed to ensure that insecticides were applied at known rates, times, and following label practices. By sampling from the selected apples, individual apples were not mixed with others during spray and distribution. The temporal and spatial variations of phoxim residue in apples could be established.

Materials and Methods

Chemicals and Reagents

Phoxim standard was purchased from National Institute of Metrology (China). The dispersive solid phase extraction sorbents of primary secondary amine (PSA) and C18 were purchased from Agela Technologies Inc. High-purity solvents cyclohexane, acetone, and acetonitrile were all purchased from Fisher Chemicals (Pittsburg, PA, USA). Analytical reagents magnesium sulfate (MgSO 4 ) and sodium chloride (NaCl) were purchased from Beijing Chemical Reagents Company (China).

Apparatus

The gel permeation chromatography (GPC) equipped with gas chromatography mass spectrometry system(GC/MS)was used. The GPC consists of two LC-10ADvp pumps, a SIL-10ADvp auto-sampler, a Shodex CLNpak EV-200AC column (2mm i.d.×150 mm) and CTO-10ASvp column oven, a SPD-10Avp UV detector, two FCV-12AH flow channel selection valves (RV.A, RV.B) and a SCL 10Avp system controller. GC/MS machine is a Shimadzu GC/MS-QP2010 instrumentation equipped with a PTV-2010 large-volume injection device.

GC/MS data analysis was triggered by a contact closure start signal from the HPLC controller. Data acquisition was performed using a C-R8A plus data processor. All these parts are the products of Shimadzu (Kyoto, Japan), except the Shodex CLNpak EV-200AC column (Shoko Co., Tokyo, Japan). Acetone/ cyclohexane mixing solvent (3/7, v/v) was used as the mobile phase of GPC, and the flow rate was set at 0.1 ml/min. The mobile phase was degassed using DGU-14A degasser (Shimadzu), and the GPC column was kept at 40℃ in the column oven.

Sample Preparation

All samples were purchased at local markets in Beijing. Samples were extensively collected to achieve good sample homogeneity. Phoxim was applied using an air blast sprayer and application rates guaranteed to contain 50% phoxim emulsifiable with the dilution multiple of 1:500. Apples were cored and sliced into 5 segments [pericarp(P), sarcocarp1(S1,S2,S3,S4) and kernel] separately, using a corer/slicer retailed and peeling machine for domestic use. The first and alternate slices of each apple were taken, chopped manually using a knife, and placed in a plastic bag for storage at -25℃ until extraction and analysis. The remaining segments of individual apples were retained in separate bags for preparation of composite samples. Composites were constructed from the apples prepared from each group by randomly selecting 8 apples for each treat, regardless of the style and weight of each apple. Composite samples were prepared by thoroughly mixing the chopped apple pieces from the bags containing the retained portions of the individual apples. Composite samples were frozen until extraction and analysis. To prepare each sample, 10 g of a previously homogenized food material was transferred into a suitable glass vessel. Then, 10ml acetonitrile was added to each sample using an adjustable-volume solvent dispenser. The glass vessels were capped before vortex mixing for 1 min at maximum speed. Once the initial sample mixing was completed, 1 g NaCl and 4 g anhydrous MgSO 4 were added and mixed immediately on a Vortex mixer for 1 min. It was important to note that this step must be taken immediately after the initial mixing step to prevent the formation of MgSO 4 conglomerates. To separate phases, samples were centrifuged for 10 min at 1570×g. Using an adjustable repeating pipette, 1.0 ml aliquot of upper acetonitrile layer was transferred into a 1.5 ml flip-top microcentrifuge vial containing 150 mg anhydrous MgSO 4 and 50 mg PSA sorbent. The vial was tightly capped and shaken on a vortex mixer for 1 min before extraction. Then the mixed extraction solution was centrifuged for 5 min to separate solids from solution. The solution was filtered through a 0.45μm syringe filter for GPC-GC/MS analysis.

Determination of Phoxim in Apple by GPC-GC/MS

Analysis was performed by GPC-GC/MS. The analytical GC/MS equipped with a PTV-2010 large-volume injection device was carried out using deactivated silica tubing (5m×0.53mm i.d.) , a Rtx-5MS pre-column (5m×0.25mm) and a Rtx-5MS column (30m×0.25mm×0.25μm film thickness; coated with 5% phenyl and 95% methylpolysiloxane; Restek Corporation, Bellefonte, Palo Alto, USA). The temperature of the PTV injector was set at 120℃ for the initial 5 min of sampling time, and then increased to 250 ℃ at 100 ℃ /min. The oven temperature was maintained at 80℃ for 5 min, subsequently increased to 280℃ at a rate of 8℃/min, and then held constant for 10 min. The quadrupole mass spectrometer was operated in the electron impact ion (EI) mode. Ion source temperature and interface temperature were set at 200℃ and 250℃, respectively. The mass spectrometer was operated in an ionizing energy of 70 eV. Injection volumes were 1μl for all analyses. Helium (99.999%)was used as the carrier gas.

Quality Assurance/Quality Control

Apples from market without phoxim pesticide were obtained, which were prepared the same as the rinsed and peeled apples, and used to prepare blank apple matrix for quality assurance testing. With each set of samples (8-10 samples per set) extracted and analyzed, two aliquots of blank apple matrix were prepared for separate extraction followed by extraction, cleanup, and analysis as for all other samples. Background levels of some analyses were periodically detected in the blank matrix samples and were used for background subtraction in the determination of recovery from spiked matrix only; residue concentrations in samples were not blank corrected. No traces of the phoxim were observed in any of the analyzed reagent blanks.

Results and Discussion

Degradation Dynamics of Phoxim in Apple

The phoxim pesticide was found to be beyond the detection limit in all the samples analyzed in the present study. In general, phoxim was found at elevated levels in entire and different parts of apples prepared from treatment relative to those control without sprayed by phoxim. Concentrations of phoxim were detected from the day applied to 28 days after treatment (Fig. 1). The degradation rate in the three days after treatment was 24.36%, 48.73% and 54.18%, respectively. The rate of degradation changed slowly between the 5th day to the 10th day, while the concentration of phoxim suddenly dropped after 11 days.

Phoxim residues were mainly found in pericarp and the outer laying sarcocarp. However, low levels of phoxim was also detected in the interior sarcocarp and kernel. Residue levels ranged from 0.0057 mg/kg to 0.1100 mg/kg in the entire apples, 0.0061 mg/kg to 0.4469 mg/kg in pericarp and reduced level in sarcocarp adjacent to pericarp (Tab. 1). Phoxim was not detected in any solvent blanks that were processed along with samples, indicating that background levels of this compound were not due to lab contamination.

Successive decline was observed during storage period (Fig. 1). It should be noted that the relative increase level of phoxim in sarcocarp and kernel after applied 10 days earlier (Tab.1). In apples analyzed after 28 days treatment, no residues were found in sarcocarp and kernel any more. It was assumed that the reduction of phoxim during the storage might be caused by its relatively high persistence to hydrolysis, and that the residues detected in kernel might be related to extensive vascular system around kernel.

Analysis of phoxim in different parts of apple

In order to investigate the phoxim level in different parts of apples, segments from different layer of pericarp (P), sarcocarp (S1, S2, S3, S4) and kernel of individual apples were separated by using the peeling machine to avoid errors caused by manual operation (Fig. 2). The residues observed in samples collected from different part of apples were shown in Figure 3. The concentration of the total apple and pericarp was consistent with the result above with the crude sampling method. The maximum phoxim concentration was observed in pericarp. The phoxim residue order in different positions of apple was: pericarp >total apple> the sarcocarp close to pericarp(S1)> kernel > the sarcocarp near kernel (S4) (Fig. 3). Contrary to expectations, no phoxim residue was detected in S2 and S3, which was the main edible part of apples. Therefore, 85% of the pesticide residues existed on the surface of the apple, and the degradation rate of pesticide residues in the entire apple was consistent with pericarp.

Relationship between residues and specific surface area

Specific surface area (SSA) is a material property of solids which measures the total surface area per unit of mass, solid or bulk volume, or cross-sectional area. It is a derived scientific value that can be used to determine the type and properties of a material. It is defined either by surface area divided by mass (with units of m²/kg).

The most representative sampling part of apple was the pericarp when phoxim applied by spray. Sample of pericarp instead of the whole homogenate would be efficient in pesticide residues analysis. Therefore, the ratio of residue in pericarp and the entire apple in apples was investigated in Fuji apples with different shapes and specific surface areas (Tab.2). Apples with higher weight had lower SSA, while the pesticide residue in pericarp was higher than the apples with lower SSA. The ratio of phoxim in pericarp and the entire apple ranged from 6.241 to 9.262 in groups with different SSA.

Discussion

The problem of pesticide residue has become a chief obstacle to the apply industry internationalization in the world. At present, samples tested for the pesticide residue stem mainly from the entire apple, because the results from the sample have been generalized. However, pesticide residue of apples mainly exists in the periearp, a large number of pulp mixed into analysis sample would dilute the pesticide residues in pericarp, which would reduce the accuracy of detection, especially in the rapid detection. Therefore, the careful research to pesticide residue of apple periearp provides the theory instruction and the design basis to the reasonable and efficient sampling method.

Fig. 2 .

 2 Fig. 2. Different sampling parts of apple P: pericarp of apples, S1: sarcocarp close to pericarp, S2: the second layer of sarcocarp, S3: the third of sarcocarp, S4: the sarcocarp near kernel, K: kernel or core of apples.

Fig. 3 .

 3 Fig. 3. Residues of phoxime in apple 6 days after treatment

Table 1 .

 1 Ratio of residue in pericarp and the entire apple in apples with different SSA.

	Days	after	Residues(mg/kg)
	treatment	Total apple	pericarp	sarcocarp	kernel
	0	0.1100	0.4469	0.0013	ND
	1	0.0832	0.3644	0.0015	0.0009
	2	0.0564	0.3572	0.0019	0.0011
	3	0.0504	0.3250	0.0025	ND
	5	0.0485	0.3071	0.0013	0.0008
	7	0.0449	0.3057	0.0033	0.0009
	9	0.0410	0.2810	0.0030	ND
	11	0.0391	0.2567	0.0012	ND
	13	0.0164	0.1173	0.0009	ND
	15	0.0087	0.0433	0.0008	ND
	20	0.0069	0.0219	0.0006	ND
	28	0.0057	0.0061	ND	ND

Table 2 .

 2 Ratio of residue in pericarp and the entire apple in apples with different SSA Weight

	Weight (g)	Surface Area (cm 2 )	SSA (cm 2 / g)	Cp/Ct
	169.605+4.666	170.750+6.583	1.007	6.241
	189.425+4.748	180.093+2.512	0.950	7.963
	209.032+4.015	190.377+2.926	0.911	9.262

Acknowledgments

. This work was supported by the public foundation from Beijing Municipal Rural Affair Committee (20080901), National High Technology Research and Development Program 863 (2010AA10Z403, 2007AA10Z202) and Beijing Municipal Science and Technology Commission Program (Z09090501040901).


Zhihong Zheng

Kai Zhang

Chengliang Liu
email: chlliu@sjtu.edu.cn

Greenhouse Temperature Monitoring System Based on Labview

Keywords: temperature, intelligent agriculture, labview, temperature sensor

The environmental temperature plays an important role in the growing crops. How to make use of computer technology to realize automatic control ambient temperature of greenhouse is a hot issue in the intelligent agriculture. The control of environmental temperature in modern greenhouses is collected and analyzed by monitoring system of greenhouse environment based on Labview software and wireless communications technology. NRF24L01 wireless receive-send model makes temperature collection come true. Moreover, managing computer makes data wireless communications become possible. Over-temperature alarm information is transmissioned to user timely. Finally system interface is designed on the Labview software platform.

Introduction

Facility gardening is a kind of production with the bad-effect condition in which the crops (flowers, fruit trees and vegetables) don't tend to grow normally in the cold or hot season .Thus people must utilize heat preservation, cold-proof, temperature reduction, defense and equipments in a man-made way to create an environment that favors the crop's growth without the effect of climate change. It is a comprehensive greenhouse technology, which meets the demand of ecological condition in plant including light, temperature, water, gas, soil and nutrition. Planting in the different seasons is able to yield high vegetables and fruits production in good quality. Comparing with foreign countries, overall technology ability of national facility gardening is poor due to the later start and shorter development time, and therefore environmental regulation should be enhanced. Diverse facility structure, automatic production management, mechanized operation, production intensification are the typical features in Holland, America, Japan, France and Israel. In those countries, modern industry, high technology and advanced management equip agriculture. We utilize computer and information technology to realize intelligent management of the temperature. The system adopts Labview software, wireless communications technology and GSM technology to realize temperature auto-monitoring and alarm. It is a good way to facility gardening automatic management.

Wireless Communications on NRF24L01

It is good for monitoring environmental from several spots due to large gardening scale, especially in a sunny exposure and at vents. Outside environment largely influence the temperature. So we should set more spots to collect and pay special attention to the important spots. It is the best to adopt wireless communications and use lower powered chip to timely send collected temperature to managing computer due to difficult allotting the positions. In this thesis, NRF24L01 wireless receive-send pattern as wireless communication equipment and set 6 sensors (all are 18b20) to attache to it. The place is that respectively two positions at vents, middle and side in the greenhouse. The collected environmental temperature is sent to store in the computer by NRF24L01 pattern in time. Under the Enhanced ShockBurst TM pattern, use first-in and first-out stack area, data is sent to micro controller with low speed. But it can save more energy under high speed. Therefore, using low speed micro controller get high sending ratio as well. All the high speed signals processing in the chip under ShockBurstTM receive -send pattern, NRF24L01 automatically process character and CRC checking code. When sending the data, character and CRC checking code is added. EC is high under sending pattern. It will take 10us to send over.

NRF24L01 Chip

Data Receive-send Process of NRF24L01

NRF24L01 adopts Enhanced ShockBurstTM pattern to send,it is shown as bellows: send the address and data to NRF24L01 as time sequences; configure CONFIG register and make it access to sending pattern; micro controller put CE higher(at least 10us

Sending Over-temperature Alarm Information to User

When temperature goes above the definited value, the monitoring system should notify the user so that take measure to adjust the temperature in the condition of no people guarded and low auto processing capability. Nowadays mobile is pretty popular, thus send timely information to user's phone by using GSM module is a good way. Here putting MCU into USB and then insert a SIM into MCU. When the temperature exceeds the defined value, the software platform will transfer such information to MCU. Finally MCU sends the information out via GSM network.

This system send messages to mobilephone via MCU, thus we should consider phone code's compatibility. PDA pattern is a common sending or receiving SMS information method. Most of the phones stand by this pattern. Each GSM SMS can transfer no more than 140b data. It is enough for transfer alarm information.SMS adopts a store-and-forward system and provides a guaranted two-way servise. Don't make call's connection and release. That's not collided with voice messege. We can receive the SMS even through the phone in the conversation state still receive SMS. It's convenient to transfer alarm information by SMS.

Computer and MCU's communications is based on virtual serial communications. On Labview software platform, attribute set of serial communications is: baud rate 9600, 8 data bits, one stop bits, no error check. Monitoring system writes down AT instructions via serial: + 861595177573 is mobile service center number in Nanjing area. The side double quotation mark should be sent. < CR > is enter mark. Each AT instrucion is end up with enter mark; Warning! That's the SMS content; <^Z > is CTRL + Z,SMS should be ended up with < ^Z >. Last line is readable SMS instructions, in which 6 is SIM index number. Once that instrucion is sent, GSM pattern sends bellow informations back from UART interface: + CMGR :″REC UNREAD″,″+ 8615951775730″,″19/ 05/ 10 ,15 :54 :00 + 00″Warning! It is easy to read out sender's mobilephone number,sending time and SMS conetent.

AT + CSCA =″+ 8613010710500″< CR > // set SMS centre number AT + CNMI = 2 ,1 ,0 ,0 ,1 < CR > //
The system writes AT intructions into virtual serial, which comes out by Labview software. There are 6 AT instructions to write down by sequence. So each time we respectively write down different instrutions with condition structure in the circular struction. Procedures are as follows:

Making Use of Labview Software Developing System Platform

Labview is a kind of developing environment of graphic program language. It is widely accepted by the industry and education and lab, which is considered as a standardized data collecting and equipment control software. LabVIEW integrates the hardware in the GPIB, VXI, RS-232 and RS-485 agreement and the entire functions from data collecting card communications. It also contains standard library function that is easy to apply for TCP/IP, ActiveX, etc. It makes user rapidly build self data and analyzed system, and then system over-temperature alarm information is sent to user via serial writing functions. TDMS data storage formats is mathematics model specially designed for data storage. It's characterized as steady read data API and self-configured data management tools that are used to manage data. The collected temperature each time is stored as binary in the TDM format folder. The value from the sensor can be find out by sequence. On the system interface, value can be traced through replaying the data and time input . Alarm light will turn to red to remind user that the current temperature has exceeded the defined figure. Thermometer shows the maximized temperature value. Below is monitoring interface. Fig. 3. The above waveform shows temperature change curve within one day. There are 48 points in the X, one temperature is collected per half an hour. It directly shows temperature change conditions in one day. But six numbers below waveform shows current temperature by 6 sensors. Obviously, the value of Sensor 1 and Sensor 3 at the center position is lower. But the value of Sensor 5 and Sensor 6 at the corner is a little bit higher.

Conclusion

Through the analysis of the monitoring results, it turns out that,when the change of environmental temperature is large in one day, the hysteresis of temperature adjustment mainly manifested in space. In the middle position near the thermostat, sensor detects the environment temperature unchanged, equal to the set temperature. But in the corner of the greenhouse external environment, the detected temperature is affected by the temperature of external environment, there exist certain volatility. Therefore, it is necessary to install the adjusting devices at the position where the temperature is vulnerable effectted by the external environment temperature, it can reduce the influence of temperature change for crops.

The control of environmental temperature in modern greenhouses is collected and analyzed by monitoring systems of facility gardening environment based on Labview software and wireless communications technology. NRF24L01 wireless receive-send model makes temperature collection come true. Moreover, managing computer makes data wireless communications become possible. Over-temperature alarm information is transmitted to user timely.

6

  set receive-send way via SIM card AT + CMGF = 1 < CR > //set Text pattern AT + CMGS =″15951775730″< CR > // send one short message, 15951775730 is target address Warning! <^Z > // SMS content AT + CMGR = 6 < CR > // read receiving SMS, suppose if new SMS's postion in the SIM is 6

Fig. 2 .

 2 Fig. 2. The program is to write AT instructions on Labview.

  CONFIG's EN_CRC). Send and receive the same protocol.

	Introduction
	NRF24L01 chip adopts 2.4 Ghz global ISM band comply with 126 multi-spots
	and skip band communications requirement. Build-in CRC hardware error detecting
	and one to many spots communications requirement. Module is able to set up address.
	If only receiving this computer address, the data can be sent out(offer interrupt
	directions). Directly connected with single chip to use, programming is very
	convenient. Receive-send module is categorized to Enhanced ShockBurstTM,
	ShockBurstTM and Direct ways, which is made up to device configuration. Four parts
	as bellows:
	Data width: declaring data occupied decimals in the radio frequency database. It
	renders NRF24L01 to tell data of receive-send database from CRC code;
	Address width: declaring address occupied decimals in the radio frequency
	database. It renders NRF24L01 to tell address from data;
	Address: receive data address, address of Passage 0 to Passage 5;
	CRC: yield CRC check code and decoding
	If use CRC technology within NRF24L01, CRC checking code should be used in
	the configuration(

  NRF24L01 pattern conect to sensors as data collecting . It is required to collect all the spots' environmental value per minute due to slow temperature change. In the other aspect, wireless data receive-send pattern sends temperature data to internal computer to store and analyze taking advantage of computer serial communications. NRF24L01 data is sent through single chip according to receiving address. Write the data to Labview's TDMS formats storage files by sequences. And show the temperature value collected from every sensor. The circuit drawing connected with single chip as bellows:

	CSN = 0; // Set CSN low, init SPI tranaction
	status = SPI_RW(reg); // Select register to write to
	and read status byte
	for(byte_ctr=0;byte_ctr<bytes;byte_ctr++)
	Enhanced ShockBurstTM pBuf[byte_ctr] = SPI_RW(0); //	), activate NRF24L01 and send
	EnhancedShockBustTM sending has four steps: (1) supply electricity to ratio front; CSN = 1;
	(2) Data packing ( adding character and CRC checking code ); (3) sending data package with high speed ; (4) send it over, NRF24L01 enters idle state. return(status); // return nRF24L01 status byte
	Sending BYTES data procedure via NRF24L01 pattern are as follows: }
	uchar SPI_Write_Buf(BYTE reg, BYTE *pBuf, BYTE bytes)
	{ 2.3	Computer Collecting data
	uchar status,byte_ctr;
	CSN = 0;
	status = SPI_RW(reg);
	for(byte_ctr=0; byte_ctr<bytes; byte_ctr++);
	SPI_RW(*pBuf++);
	CSN = 1; // Set CSN high again
	return(status); } GND 1 VCC 8 IRQ 7 MISO 6 MOSI 5 SCK 4 CSN 3 CE 2	VCC	AT89S52	VCC
	NRF24L01 adopts EnhancedShockBurstTM pattern receive-send procedure as follow: configure local address and be ready to receive data package; configure CONFIG register and make it access to receiving pattern; put CE high; 12M 4 9 after 130us, NRF24L01 enter monitoring state and waiting for data package; EA/VP 31 X 1 19 X 2 18 RESET 9 RD 17 WR 16 INT0 12 INT1 13 T 0 14 T 1 15 P10/T 1 P11/T 2 P12 3 P13 4 P14 5 P15 6 P16 7 P17 8 39 40 VCC J2 P00 38 nRF2 4L01Wireless Transceiv er Module P01 37 C7 U1 P02 P03 36 35 10u f S1 GND 15 16 VCC SW-PB P04 34 C4 C3 P05 P06 33 P07 32 P21 22 P22 23 P24 25 P25 26 P27 28 30p f 8 GND 20 C6 MAX232 3 P26 27 RR-IN2 8 7 R-OUT2 9 R-OUT1 12 2 RR-IN1 13 6 P23 24 RT-OUT2 7 1 T-IN2 10 T-IN1 11 J1 RT-OUT1 14 P20 21 PSEN 29 ALE/P 30 10K CAP 2-5 CAP 1-3 10u f 10u f TXD 11 R12 C2 C1 RXD 10 CAP 1+ 1 CAP-6 CAP 2+ 4 CAP + 2 10u f 10u f
	C7 When receiving right data package(right address and CRC checking code), 30p f CRYSTAL 5 DB9
	NRF2401 auto-character, address and CRC check position removal;
	NRF24L01 sets RX DR position of STATUS register through NRF
	Fig. 1. J2 is NRF2401 wireless communications pattern, AT89S52 single chip which uses P1 Read data out of NewMsg_ RF2401; foot's I/O to imitate SPI connector links NRF24L01 wireless receive-send pattern. Received Clean STATUS register after read over all the data. NRF2401 can enter one of four patterns. temperature value attach to max232 chip and then to computer as serial P3^0 and P3^1 pin.
	Procedure of art NRF24L01 pattern receiving end read bytes are shown as bellows:
	uchar SPI_Read_Buf(BYTE reg, BYTE *pBuf, BYTE bytes)
	{
	uchar status,byte_ctr;

Acknowledgment

This paper is sponsored by National High Technology Research and Development Program 863 (NO:2006AA-10A301).