Shuping Xiong
email: shupxiong@163.com

Lei Xi

Jucai Wang

Guanghui Xu

Xinming Ma
email: xinmingma@126.com

{shuping Xiong

Research on the Simulation Model of Above-Ground Organs Morphogenesis of Flue-Cured Tobacco

Keywords: Flue-cured Tobacco, above-ground organ, morphogenesis, model of simulation

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

With the dynamic development of information technology on agriculture, more and more researches on virtual plant have been taking. The morphogenesis model, a key technology in virtual plants, has aroused our attention. Ulam [START_REF] Ulam | On some mathematical properties connected with patterns of growth of figures [A[END_REF] and Cohen [START_REF] Cohen | Computer simulation of biological pattern generation processes[END_REF] were the pioneers on research to models of plant branches. These were followed by Lindermayer and his L-system [START_REF] Lindenmayer | Mathematical models for cellular interactions in development [J][END_REF], basing on the analysis to the plant organ growth, Hunt [START_REF] Hunt | Plant growth analysis: the rationale behind the use of the fitted mathematical function[END_REF] produced a plant organ-growing model. Besides, de Reffye, a France man, and his assistants developed a plant-growth system, named AMAP [START_REF] De Reffye Ph | A functional model of tree growth and tree architecture [J][END_REF][START_REF] De Reffye Ph | Modeling plant growth and architecture: Some recent advances and applications to agronomy and forestry[END_REF]. Combining the L-system and computer graphics, A.R. Smith and Prusinkiewicz made the visualization of plant morphology come true [START_REF] Indenm Ayer | Developmental models of herbaceous plants for computer imagery purpose[END_REF][START_REF]Visual models of plants interacting with their environment[END_REF][START_REF] Prusinkiew | A look at the visual modeling of plant using L-systems[END_REF][START_REF] Prusinkiew Icz | Animation of plant development[END_REF]. Considering the environment model (e.g. Light and disease and pest injury) and measured data from tri-dimension plant on the L-system, virtual plant model was built in the research center CPAI, Australia [START_REF] Room | Virtual plants: New perspectives for ecologists, pathologists and agricultural scientists[END_REF]. The virtual plant system GROGRA2.4 (Growth grammar Interceptor), which was basing on the L-system, was created by Kurth [START_REF] Kurth | Growth Grammar Interpreter GROGEA2.4: A Software tool for the 3 -dimensional interpretations of stochastic, sensitive growth grammars in the context of plant modeling[END_REF].

As a function-structure model, GREENLAB played an important role in the plant structure area [START_REF] De Reffye | Relevant qualitative and quantitative choices for building an efficient dynamic plant growth model: GreenLab case[END_REF][START_REF] Guo | Modeling of biomass acquisition and partitioning in the architecture of sunflower[END_REF][START_REF] Yan | A dynamic, architectural plant model simulating resource-dependent growth[END_REF]; it is brief and honest to mechanization. By building Recurrence relations among several Mathematical formulas, the feedback relationship was parallel simulated. It only needed a few certain parameters to simulate the growth of plants like trees. Virtual structure model of plant, like cotton, maize, wheat etc, was produced by de Reffye [START_REF] De Beffye | Study on plant growth behaviors simulated by the function-structural plant model-Greenlab[END_REF], Zhan Zhigang etc [START_REF] Zhigang | Morphological Architecture-Based Growth Model of Winter Wheat[END_REF], and GREENLAB. Technology frame on shape simulation and tri-demission visualization of corn, basing on growth model, was produced by Zhao Chunjiang etc [START_REF] Xinyu | Three-dimensional visualization of maize based on growth models[END_REF], and it created a new idea and method. After researching the static model on maize canopy structure, Ivanov [START_REF] Ivanov | Estimation of the height and angles of orientation of the upper leaves in the maize canopy using sterovision[END_REF] and Fournier [START_REF] Fournier | A 3D architectural and process-based model of maize development [J][END_REF] rebuilt the tri-dimension canopy and analyzed the way how the light is used by canopy with the help of oriented-object program, Jin Mingxian [START_REF] Ming-Xian | Simulation of Growth and Hydrotropism of Maize Roots [J][END_REF] built the tri-dimension model for root -growth of maize.

Besides, maize-visualization model, produced by Guo Yan and Li Baoguo [START_REF] Ma Yun-Tao | Evaluation of the Plant Growth Model GREENLAB-Maize [J][END_REF][START_REF] Guo | Parameter optimization and field validation of the functional-structural model GREENLAB for maize[J][END_REF], can be used to analyze tri-dimension structure character of maize canopy and the maize canopy space distribution under the effect of morphology. These researches above show that plant simulation has been developing fast in all the areas which it refers.

Tobacco is one of the very important cash crops. Researches about its under-ground organ such as root have been done. Ma Xinming [START_REF] Ma Xin-Ming | A Model for Simulating Root Morphological Development of Tobacco [J][END_REF][START_REF] Ma Xin-Ming | Dynamic changes of morphological parameters of tobacco root in field[END_REF] once described the dynamic relation between growth of root system and surrounding environment and built a mathematic model on morphological development of tobacco roots. However, researches on above-ground organs of tobacco are rare. Taking the existed researches [START_REF] Qiao Yuhui | Quantification of dry matter accumulation and distribution among different organs of winter wheat [J][END_REF][START_REF] Liu Xianzhao | A mathematical model of water stress and light condition effects on cotton dry matter and yield formation[END_REF][START_REF] Jingping | Crop growth simulation model and its application[END_REF] as references, basing on the measured data for building tobacco morphology, researching the dynamic relationship between tobacco morphogenesis regulation and the environment, visualization model about tobacco morphogenesis was built, with the help of morphology module produced by Song Youhong etc [START_REF] Song You-Hong | Virtual maize model II. Plant morphological constructing based on organ biomass accumulation [J][END_REF]. A purpose of this paper is to offer a theory for improving the quality of tobacco and increasing of production.

Material and Methods

Designing of Experiment and Materials in use

This experiment was made in the scientific and educational experimental area of Henan Agricultural University during 2006-2007. All the tobacco seeds, seeds K326 in this experiment, were cultivated in the pots, diameter 40cm, depth 45cm, filled with sandy and tidal soil. Content of nutrient in the soil were listed as Table1: The weight of soil which should be air-dried and sifted before being used in each pot was 25kg.

Chemical fertilizers used in this soil were AR (NH4) 2 SO 4 , KNO 3 and KH 2 PO 4 and all of these chemical fertilizers should be scattered off as base fertilizer at the first time. The content of fertilizer N in every kilogram soil was 0.2 gram, and N: P 2 O 5: K 2 O=1:1.5:3. Seeding growing with the float system was cultivated in artificial climatic box in the lab. Sixty days later, they were transplanted into the pot, laid in the soil under the shelter. In this way, the environment where they grow in can be similar with that of farmland. The amount of necessary water in different period of growing for tobacco is varied.

The supply of water can be controlled with the help of time-domain reflector (TDR) whose type model is TRIME-FM and shelter should also be open in the raining day to prevent the rainwater in. The management of tobacco, planted in row spacing 1.1m, plant spacing 0.6m in this experiment, has noting different with that of farmland except entering rosette stage in their 30 th day and pinching in 57 th day after being transplanted.

Measured Items and Measuring Methods

Measured Items for Internodes: Fresh Weight, Length and Diameter

Three typical plants from the experimental area should be taken every 10 days after seeding restitution stage. Then, cut them apart by internodes. Fresh weight of internodes can be measured on milli electronic balance. Length of internodes can be measured by ruler which is correct to 0.1cm. Diameter can be measured by vernier caliper within the accuracy of 0.001cm.

The length, width and area of leaf

The maximal width on the middle of leaf (Abb. W) and leaf length (Abb. L) can be measured with ruler, and the fresh weight of leaf can be measured with electronic balance. Then, the leaf area (Abb.

LA) can be figured out with this formula:

LA=L×W×0.6345 (1)
Besides, meteorological data can be achieved from meteorological bureau.

Model Testing

The dynamic model for above-ground organs of tobacco while growing can be built with data measured in 2006 and be tested with the data measured in 2007. The conformity between analog value and observed value can be made a statistical analysis with root mean square error (Abb. RMSE) and 1:1 graphing method. The RMSE can be counted in this equation:

Model on Growth Cycle of Tobacco

Growth cycle (Abb. GC) was defined as a time span caused by two adjacent me tamers which including internodes, node and leaf growing from the node. It has been proved that the topological structure of plant is obviously affected by cumulative effect which is caused by temperature change of environment [START_REF] Zhigang | Morphological Architecture-Based Growth Model of Winter Wheat[END_REF]. For that growing period of tobacco in field last for 120~130 days which is a pretty long time, the GC was defined as a time unit while modeling. Growing process can be predicted with the following formula (3) which reflect the relationship between tobacco growth and accumulated temperature.

Internodes morphology of tobacco

Growing of tobacco stem can be described in two aspects including prolonging and thickening.

Internodes morphology in different position should be built in different way. Leaf 1 to leaf 8, counted from the bottom of stem, are too short to measure when being transplanted. So, they were ignored in this experiment. Only internodes whose position was higher than that of the 8th internode were considered while building morphology.

As the shape of tobacco internode is like a cylinder, volume ( e V ) of the internode can be figured out with formula (4): The length and cross-sectional area of the tobacco internode change with the intercalary growth and primary thickening growth of the internode. Analyzing the experimental data, changing relationship can be built as following:

) , ( ) , ( ) , ( ) , ( ) , ( V i j s i j l i j i j q i j e
(

Computational formula on length and cross-sectional area of tobacco internodes can be figured out with formulas (4), ( 5) and [START_REF] De Reffye Ph | Modeling plant growth and architecture: Some recent advances and applications to agronomy and forestry[END_REF]. It turns out to be formula ( 7):

(7)

1 1 1               

q j i s j i

Value of the length and the cross-sectional area could be figured out in formula [START_REF] Indenm Ayer | Developmental models of herbaceous plants for computer imagery purpose[END_REF], only if knowing the biomass of internodes in a certain growing stage when morphology parameters of the internode b , e b and density  e were given. Then, simulation of internodes morphology can be carried out with biomass.

Morphology of Tobacco Leaf

What we need from tobacco is leaf. The leaf is not only a place for photosynthesis but also a place to store all kinds of chemical substances which directly decide the quality of tobacco. The area of leaf j, b s , in i th can be figured out in formula [START_REF]Visual models of plants interacting with their environment[END_REF] with its biomass b q .

(8) ξ b : Specific leaf weight, in other words, value of dividing leaf area into leaf fresh weight (g/cm 2 ). Showing from experimental data, the relationship between leaf length b l (cm) and its fresh weight b q (g) can be expressed with formula ( 9):

( , ) ( , )   r b i b l j i k q j i (9)
i k and r in formula (9) are fitting indexes. For that leaf area is always figured out through multiplying a certain parameter l with the product of leaf length and the maximum width of leaf in agronomy, the maximum width of leaf, b w (cm), can be figured out after the leaf area and leaf length being simulated with the following formula: [START_REF] Prusinkiew Icz | Animation of plant development[END_REF] So, the leaf area (Abb. LA), length and the maximum width of this plant can be figured out after the accumulated biomass of leaf in a certain time being simulated if parametersξ b , i k , r and l were given.

Ascertaining parameters

Parameter in models can be divided into three kinds: parameters being used to describe the growing process of plant, parameters being used to build the internode model and parameters being used in morphology model. Value of Parameters like growing speed, accumulated biomass, cross-sectional area of the internode, the internode length, leaf area, fresh weight of leaf, leaf width, and specific leaf

1 2 1 2 ( , ) ( , ) ( , ) ( , ) 1 ( , ) ( , )
                             e e
e e e e e e q j i l j i b j i 2.

q j i s j i b j i ( , ) ( , ) ( , )

Table 2. List of model parameters

The following Figure 1 is a relation graph between the measured number of tobacco metamer which was defined to be 7 when being transplanted and the accumulated temperature starting from seeding restitution stage. b  = 0.045(r=0.869 ** ,n=134) as shown in figure 5. Leaf length in different position in different GC and its fresh weight can be fitted in formula (9) (Fig. 6). Leaf area of tobacco used to be figured out through multiplying among leaf length, leaf width and leaf area index. Leaf area index l in formula [START_REF] Prusinkiew Icz | Animation of plant development[END_REF] is settled as 0.6345.

Conclusions and Discussion

The essential basis while building digital plant will be plant growing, structure simulation of plant morphology and union of the both. Interrelation between changing of above-ground organ morphology and GC during the growing process of tobacco was generally researched in this paper. Besides, basing on biomass-morphology relationship of tobacco organ, dynamic model on organ morphology was also built in this paper. It turns out that the model can express biological method well and that there is a high degree of uniformity between simulated results and measured values. They have made the model usable.

Growth period model of tobacco is a factorial index model which is using Rice Clock Model for reference and basing on mechanism of tobacco growing and to express growth process of tobacco [START_REF] Xiong Shuping | Study on Dynamic Simulation Model for the Relationship between Growth and Environmental Factors at Different Tobacco Growth and Development Stage[END_REF].

Virtual growth system of tobacco, coupled with dynamic organ model and growth period model, will be a new research direction in the next step. The system is necessary while digitizing the tobacco growth. Information on tobacco growth in different stage can be obtained from the system only if the agriculture technician input few certain parameters of growing environment. With that information, cultivating and control technology, fertilizer rate in necessary and so on will be guided well.

Preparing for building the morphology model of above-ground organ of tobacco, the simulation model which based on the model of physiological and ecological process of tobacco, under the control of outside environment, like temperature, was created in this paper. Taking the spatial angle change during the period of tobacco growth into consideration while researching and analyzing the dynamic change of tobacco morphology can comprehensively display the dynamic change information of tobacco, and this will be a research direction in the future. At last, that the simulation of the plant internodes morphology is not precise enough, caused by a low sampling rate, should be improved and perfected.

  Number of Growth Cycle DR(℃ -1 ): Developing ratio SumT ( ℃ ): Accumulated temperature which equals to the sum of temperature after being transplanted The number 7 in formula (3) represented the number of unrolled leaf when the tobacco plant being transplanted, in other words, 7 Growth Cycles has been finished when being transplanted.

:

  Accumulated biomass of the jth internode in its GC (g).

  The internode density (g/cm -3 ) ( , ) e l j i : The internode length (cm) ( , ) e s j i : Cross-sectional area (cm 2 )

ek

  , a : Fitting index which is possible to change with the change of the internode position If defining the morphology index of the internode b and e b as following:

  from experimental data. However, coefficients like fitting index e k and  of the internode morphology, morphology index  and e b of the internode, fitting index i k and r of leaf are implicit parameters. Parameters of model are listed as following table

Fig. 1 .

 1 Fig. 1. The relationship between number of tobacco and sum of temperature

Fig. 2 .Fig. 3 .

 23 Fig. 2. Relationship between length and cross-sectional area during its elongation(9 th internode)

Fig. 4 . 5 .

 45 Fig. 4. The density of tobacco internodes Fig. 5. The relationship between blade fresh weight and surface

Fig. 6 .

 6 Fig. 6. Relationship between leaf length and its fresh weight

Fig. 7 .

 7 Fig. 7. Comparison between simulated value and mesaured value of m number eta mer

Fig. 8 .Fig. 9 .

 89 Fig. 8. Comparison between simulated value and measured value in the 12th GC

Table 1 .

 1 Content of Nutrient in the Sandy and Tidal Soil

	Index of Soil	Organic Matter(g.kg -1 )	Available N(mg.kg -1 )	Available P(mg.kg -1 )	Available K(mg.kg -1 )	pH
	Content	7.8	35	9.86	80	7.6


Jing Wang

Jianping Hu

Qirui Wang

Xun Wang

Simulation on Magnetic Field Characteristics of Permanent-Magnet Seed-Metering Device

Keywords:

The cylindrical permanent magnet is the core part of the permanent-magnet seed-metering device, it can absorb single magnetic powder coated seed. The first, the magnetic induction intensity model of any point in the cylindrical permanent magnet external was established based on the equivalent current model of permanent magnet. The second, the mathematical formula was derived using the Biot-Savart law and the superposition principle of magnetic field. Then its magnetic induction intensity was calculated by multiple numerical integration function of MATLAB, and the characteristics and distribution were obtained. On this basis, the simulation and analysis of the permanent magnet with height 7mm and diameter 2mm was finished. The results showed that the closer the field point was to the permanent magnet end face, the stronger the magnetic induction intensity was, and the magnetic induction intensity at the edge of end face was stronger than at the center. So the diameter of the cylindrical permanent magnet should be less than magnetic powder coated seed, meanwhile, the air gap between the permanent magnet end face and the magnetic powder coated seed should be smaller as much as possible or the magnetic powder coated seed directly contacted with the permanent magnet end face.

Introduction

In recent years, concerning the problem of accurate sowing for the small seed, the literature [START_REF] Jianping | Design and experiment of precision magnetic cylinder seeder[END_REF][START_REF] Jianping | Analysis for Magnetic Field Characteristics of Magnetic Seed-metering Elements and Structure Optimization[END_REF][START_REF] Jianping | Magnetic field characteristic analysis for the magnetic seed-metering space of the precision seeder[END_REF] carried out the research about Magnetic Precision Metering Technology which used electromagnet as the seed-metering component of the metering device. In the literature [START_REF] Jianping | Simulation on Magnetic Precision Seed-metering Device[END_REF], a permanent-magnet seed-metering device was proposed and the simulation and experiment of sowing performance was studied. The structure diagram of the permanent-magnet seed-metering device was showed in Figure 1. The seed-metering component was a cylindrical permanent magnet. The permanent magnet was installed in the device which can make the permanent magnet to throw out and draw back. This device was installed in the drum. With the drum rotating, the magnetic powder coated seeds were absorbed by seed-metering components when it threw out, and were metered when it drew back. In order to analyze the characteristics and distribution of magnetic field about the cylindrical permanent magnet, the paper adopted the equivalent current model of permanent magnet to derive and establish the mathematical model which can be used for calculating the magnetic induction intensity of any point in the permanent magnet external. The mathematical model of magnetic induction intensity about the permanent magnet can be calculated by the multiple numerical integration function of MATLAB, and the distribution can be expressed by the visualization function. At present, the methods of studying the distribution of magnetic field of the cylindrical permanent magnet are magnetic dipole and empirical formula. The magnetic dipole method requires the radius of field point much larger than the cylindrical magnet's, so its error is large when the radius of the cylinder cannot be ignored. The empirical formula can only estimate the magnetic induction intensity on the cylinder axis, and the experience factor in the empirical formula can only be got by a large number of experiments, so the method is less accurate. In order to seek an easy and accurate algorithm for magnetic field of the cylindrical permanent magnet, based on the equivalent current model of permanent magnet, the mathematical model was derived to calculate magnetic induction intensity of any point in the cylindrical permanent magnet external. The equivalent current model of permanent magnet can be considered that the external magnetic field of the permanent magnet was generated from the bound surface current of magnet side surface. The current density of bound surface was:

0 r m B J   . (1)
Where: , and the coordinate system was established, as shown in Figure 2.

Considering the thin layer consisted of plane z and z dz  , the magnetic induction intensity of any point ( , , ) p x y z in its external space was generated by current loop m J dz to dB . According to Bio-Savart law and the superposition principle of magnetic field, the magnetic induction intensity of any point ( , , ) p x y z outside the cylinder was:

0 3 () 4 l Id l r r B rr              . ( 2
)
Where: r -the radius vector of source point   0 0 0 ,, x y z ; r -the radius vector of field point ( , , ) p x y z .

In cylindrical coordinate, a point

            . (3)
Order:

3 2 2 2 2 0 0 0 ( cos ) ( sin ) ( ) K x r y r z z             . (4)
Generated from current loop, the magnetic induction intensity

B  in x, y, z
directions were:

2 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 00 2 0 0 0 ( ) cos 4 ( ) sin 4 ( cos ) cos ( sin ) sin 4 m x m y m z J dz z z r dB d K J dz z z r dB d K r x r d J dz K dB r y r d K                                                            . ( 5
)
The space magnetic induction intensity of the cylindrical permanent magnet was:

h m x h m y h m z h J dz r z z Bd K J dz r z z Bd K r x r d J dz K B r y r d K                                                   . (6)
substituted (1) into (6)

K Brdz r z z Bd K r x r d Brdz K B r y r d K                                                  . (7)

Calculation of the magnetic induction intensity using MATLAB

When the integral expression of function was known, it can be solved with Newton Leibniz formula in theory, but it was not practical in the calculation of the magnetic field, for integral functions of most functions can not be found. A lot of command functions of the numerical integration can be provided by MATLAB, and they can be called based on the actual situation, not only convenient but also accurate. The integral command used in this paper was dblquad. Actually, the dblquad was achieved by calling quad repeatedly. The principle of quad was adaptive Simpson quadrature method. If using the quadratic interpolation polynomial--the area of the curved trapezoidal surrounded by the parabola y=g(x) approximate instead of the area of the curved trapezoidal surrounded by y=f(x), the integral formula was called Simpson Formula:

( ) [ ( ) 4 ( ) ( )] 62 b a b a a b I f x dx f a f f b       . (8)
The interval   , abwas divided to K equally and 2 k n  subinterval was obtained, then the composite Simpson formula further improved was:

1 1 1 1 1 0 0 ( ) [ ( ) 4 ( ) ( )] 6 2 i i n n x ii n i i x i i xx h I f x dx s f x f f x                 . ( 9
)
When the variable step Simpson integration method was used to calculate the double integral, the numerical integration approach was to divide the double integral

In MATLAB, the model in [START_REF] Hui | Experimental Study on Magnetic Field Characteristic of Magnetic Marker[END_REF] was written into M-file of fun   0 z , , and the integral of the model was realized by B=dblquad(fun,a,b,c,d,tol). Where a, b was the upper and lower limit of θ; c, d for was the upper and lower limit of 0 z separately; tol was integration accuracy (default accuracy was 10e-6 ).

Simulation and analysis the magnetic induction intensity of the cylindrical permanent magnet

The magnetic coated and pelletized seeds, about 3mm in diameter, were used in the permanent-magnet seed-metering device. In order to further simulate, a cylindrical magnet with diameter 2mm and length 7mm was selected.

Conclusions

1) The paper adopted the equivalent current model of permanent magnet and the Bio-Savart law to derive and establish the mathematical model which can be used for calculating the magnetic induction intensity of any point in the permanent magnet external. The calculated value would not produce errors for the method itself, because the magnetic induction intensity of field point only depended on all source points and the magnetic parameter of permanent magnet was constant. Compared to the magnetic dipole and the empirical formula, this method was more accurate.

2) Using adaptive Simpson quadrature method and the numerical integration function of MATLAB, the precise calculation about the magnetic induction intensity of any point in the permanent magnet external was realized. Meanwhile, the complex derivation process of the discrete integral formula was eliminated and the problems of stability, accuracy and overflow were avoided in the integration formula of recursive process.

3) The simulating calculation results showed that the space magnetic induction intensity of the cylindrical permanent magnet was concentrated on the end face of the permanent magnet. The magnetic induction intensity in z direction achieved maximum value on the end face of the permanent magnet, decreasing rapidly with the distance increased; the magnetic induction intensity in x, y directions achieved maximum value at the edge of the permanent magnet end face, and zero at the center. So, considering the effects of seeds filling, the diameter of the cylindrical permanent magnet should be less than magnetic powder coated seed, meanwhile, the air gap between the permanent magnet end face and the magnetic powder coated seed should be smaller as much as possible.

1 .Fig. 1 .

 11 Fig. 1. Structural diagram of roller-type metering device

mJFig. 2 .

 2 Fig. 2. Parameters of cylinder permanent magnet

Figure 3 (Figure. 3 .

 33 Figure 3(a) showed the gradient changes in the x direction of the magnetic induction intensity with the cylindrical permanent magnet in the plane with y=0,-5mm <x<5mm, 7mm <z<10mm; Figure 3(b) showed the gradient changes in the x direction of the magnetic induction intensity with the cylindrical permanent magnet in the plane with z=7mm,-5mm<x<5mm, -5mm<y<5mm; Figure 3(c) showed the gradient changes in the z direction of the magnetic induction intensity with the cylindrical permanent magnet in the plane with y = 0,-5mm <x<5mm, 7mm <z<10mm ; Figure 3(d) showed the gradient changes in the z direction of the magnetic induction intensity with the cylindrical permanent magnet in the plane with z=7mm,-5mm<x<5mm, -5mm<y<5mm.

Acknowledgements

This work was financially supported by Three Engineering of Agricultural Machinery,Jiangsu province(NJ2009-41),sponsored by Qing Lan Project of Jiangsu Province (Su teacher(2010)no.27) and A Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (Su financial teacher(2011)no.8).


Wencheng Lv

Zhihong Li

Xingxia Wu

Wenlong Ni

Weiwei Qv

Maximum entropy niche-based modeling (Maxent) of potential geographical distributions of Lobesia botrana (Lepidoptera: Tortricidae) in China

Keywords: Lobesia botrana, potential geographical distribution, Maxent, ArcGIS

Lobesia botrana (Denis & Schiffermüller, 1775) (Lepidoptera: Tortricidae) is one of the most destructive pests of grape in the Palearctic region. The potential geographical distribution of this pest is important to agriculture security. In this study, Maxent and ArcGIS were used to project the potential geographical distribution of L. botrana in China under the current climate. The result indicated that L. botrana was suitable in most parts of middle and southern China and the Maxent model was highly accurate for the AUC value of 0.970. Jackknife analysis revealed that low temperature influence the potential distribution of L. botrana. This study would be a decision-support of surveillance and quarantine measures.

Introduction

The grape berry moth, Lobesia botrana (Lepidoptera: Tortricidae) is an important pest of vineyards in many countries of the world [START_REF] Avidov | Family Tortricidae: Leaf roller moths[END_REF][START_REF]Distribution Maps of Pests, Series A, Map[END_REF]. It was first described in 1775 in Vienna, from samples collected in Italy. At present, it has spread to several European countries, North and West Africa and the Middle East. It was introduced into Japan before 1974 and recently into Chile [START_REF]Distribution Maps of Pests, Series A, Map[END_REF]. In September 2009, it was reported for the first time from Napa County, CA in North America [START_REF] Monica | European Grapevine Moth:a New Pest of Grapes in California Farm advisors 20-28[END_REF]. It is considered an economic pest of grape because the damage to the grape yield is twofold, direct damage by larvae feeding on all parts of the flower cluster in early spring, on green berries in mid summer, and on ripening fruit in late summer continuing through harvest, and indirect damage by fungi and bacterial growth [START_REF] Louis | Mating disruption of Lobesia botrana (Lepidoptera: Tortricidae) in vineyards with very high population densities[END_REF]. In response to differences of climate, the number of generations completed by L. botrana within a year differs geographically. In general, two generations occur annually in northern areas of Europe, such as Austria, Germany, Switzerland and northern France, whereas three generations occur in southern Europe, including Mediterranean countries [START_REF] Badenhausser | Contributions of Monte-Carlo test procedures for the study of the spatial distribution of the European vine moth, Lobesia botrana (Lepidoptera, Tortricidae) in European vineyards[END_REF][START_REF] Venette | Mini Risk Assessment, Grape Berry Moth Lobesia botrana (Denis & Schiffermuller) [Lepidoptera: Tortricidae].USDA-APHIS[END_REF]. High temperature and low humidity provide optimal conditions for L. botrana, while rainy in conjunction with low temperature conditions seem to reduce the frequency of mating and, subsequently, egg production [START_REF] Deseo | Observations on the biology and diseases of Lobesia botrana Den. and Schiff. (Lepidoptera: Tortricidae) in central-north Italy[END_REF]. Optimal conditions for its activity occur at temperatures over 20°C and at 40%-70% relative humidity [START_REF] Roditakis | Effects of photoperiod and temperature on pupal diapause induction of grape berry moth Lobesia botrana[END_REF]. The Chinese government classified L. brotrana as one of the quarantine pests [START_REF] Aqsiq | Plant inspection and quarantine[END_REF][START_REF]Crop Protection Compendium[END_REF]. Although to date there is no distribution of L. brotrana is unknown in China, the potential risk of it spreading from its original introduction [START_REF]Distribution Maps of Pests, Series A, Map[END_REF] or being repeatedly introducted into China is increasing as the international grape trade increases. It is important to predict the potential geographical distribution of L. brotrana in China, which will present a decision-support of surveillance and quarantine measures.

There are several methods to predict the potential geographical distribution of organisms. The most commonly used methods are secological niche model such as CLIMEX, GARP and Maxent [START_REF] Wang | Application of ROC curve analysis in evaluating the performance of alien species' potential distribution models[END_REF].

Maxent is a machine learning method, which estimates the distribution of a species by finding the probability dispersal of maximum entropy, subject to constraints representing incomplete information about the distribution. The constraints are the expected value of each environmental variable that should match its average over sampling locations derived from environmental layers. The algorithm evaluates the suitability of each grid cell as a function of environmental variables of that cell [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF].

Maxent has been extensively used though it was just recently released [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF][START_REF] Perkins | Infections of the malaria parasite, Plasmodium floridense, in the invasive lizard, Anolis sagrei, in Florida[END_REF][START_REF] Wang | Potential Geographic Distribution of Radopholus similis in China[END_REF][START_REF] Bigg | Ice-age survival of Atlantic cod: agreement between palaeoecology models and genetics[END_REF][START_REF] Loiselle | Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes[END_REF][START_REF] Zhao | Prediction of potential geographic distribution areas of the maize downy mildew in China using MAXENT[END_REF][START_REF] Feng | Potential Suitability Analysis of Hemiberlesia pitysophila Takagi in China[END_REF]. In this study, Maxent software is used to predict the potential geographical distribution of L. botrana in China.

Material and Method 1.1 Material

Occurrence Data of L. botrana

Fig. 1. The known distributions of Lobesia botrana in the world

The species distributional data are gathered from literature published, the specimen records in the museum or herbarium, and authoritative electronic resources. In addition, the data are also obtained through on-the-spot investigation [START_REF] Zeng | Prediction of potential geographic distribution of Microcyclus ulei in the world using MaxEnt[END_REF]. In this study, the data came from the distribution of papers published abroad [START_REF] Sharon | The effect of grape vine cultivars on Lobesia botrana (Lepidoptera: Tortricidae) population levels[END_REF][START_REF] Eghtedar | Biology of Lobesia botrana in Fars province[END_REF][START_REF] Casta±eda-Samayoa | Evaluation of some Trichogramma species with respect to biological control of Eupoecilia ambiguella Hb. and Lobesia botrana Schiff[END_REF][START_REF] Burov | A new preparation for orchards and vineyards[END_REF][START_REF] Baumgartner | Phenological model of the flight of Lobesia botrana Den. & Schiff. (Lep. Tortricidae) in relation to the environmental conditions of Emilia-Romagna[END_REF][START_REF] Fischer-Colbrie | Present pests in viticulture and modern methods of control[END_REF][START_REF] Pavan | Damage of the grape berry moths, Lobesia botrana (Den. e Schiff.) and Eupoecilia ambiguella (Hb.), on late-harvested cultivars in north-eastern Italy[END_REF][START_REF] Pongracz | Swarming dynamics of Lobesia botrana Schiff. in the Sopron wine-growing region[END_REF][START_REF] Russo | Flight dynamics of the grape berry moth in the vineyards of eastern Sicily[END_REF][START_REF] Schieder | Investigations on the swarming dynamics of grape berry moths in Fejer County, Eastern Transdanubia[END_REF]. Localities were geo-referenced based either on coordinates provided directly in papers or on the official gazetteers (GeoNet, http://gnswww.nga.mil/geonames/GNS/index.jsp). The 95 unique records in Fig. 1 were obtained after checking the location. According to Maxent software requirements, the reference points were saved in csv format by species name, longitude, and latitude in order.

Software Tools

Maxent: Downloaded from http://www.cs.princeton.edu/~schapire/maxent/, ver. 3.3.3e.

ArcGIS: Purchased by the Plant Quarantine and Invasion Biology Laboratory of China Agricultural University (CAUPQL), ver. 9.3.

Environmental Variables

Nine biological climate variables (Table 1) were used as the main environmental data forprediction.

The climate data with a resolution of 5arc-minutes in 1950-2000 were downloaded from http://www.diva-gis.org/climate.htm, and converted to asc format according to Maxent software requirements.

Basic Geographic Data

The 1:400 million maps of national boundaries and province boundaries used in this paper were downloaded from the national fundamental geographic information system (http:∥nfgis.nsdi.gov.cn/).

Methods

The distributional and environmental data were imported into the Maxent, and 75% of the distribution points were selected at random as the training dataset, the remaining points as the test dataset. Other parameters were the software defaults. The analysis resulted in the logistic format were outputted by ASCII type of file. The file was imported into ArcGIS and showed the results map. ArcGIS was also used to show the known distributions of L. botrana in the world.

The predicted results were usually classified based on the suitable values. The area under the curve (AUC) were directly calculated by Maxent. In addition, Maxent has a jackknife option through which the importance of individual environmental variables can be estimated (Fig. 4).

Results and Analysis

We graphed the potential geographical distribution of L. botrana in China under current climate conditions (Fig. 2). In the predicted results, the appropriate values of known regions of occurrence were more than 0.006; the values of Hokkaido in Japan were 0.107, It was reported that this pest had occurred in this region [START_REF]Crop Protection Compendium[END_REF]; the values of the regions all had severe occurrences were more than 0.17.

Therefore, the suitable area was divided into 4 grades. applications, and values > 0.9 indicate high accuracy [START_REF] Swets | Measuring the accuracy of diagnostic systems[END_REF].The AUC value reached 0.970 in this study (Fig. 3), indicating that the Maxent model performed well and that its predictions of the potential distribution of L. botrana were highly accurate.

Discussion

In order to make more accurate predictions using the Maxent model, we required a certain amount of accurate and representative geographical distribution of species known point data, but also needed to select the key environmental variables to establish a relatively accurate model. The foundation depends on having a good knowledge of the biological and ecological characteristics of species [START_REF] Chang | Suitability analysis of Karnal bunt in China based on MaxEnt model[END_REF].

Fig.4. Jackknife test for environmental variable significance performed by Maxent

From Fig. 4, the environmental variable with the highest value is the mean temperature of coldest quarter, which contains the most valuable information. The Maxent background showed the min temperature of coldest month had some information which other variables did not have. This meant low temperatures had a great influence on the occurrence of this pest, which was consistent with previous studies, and also indicated that the study selected the correct environmental variables.The main host of L. botrana is grape which grows in most part of our country, so L. botrana is easy to establish itself once introduced into China. Therefore, the following management measures should be strengthened: (i)

Fruit or planting material from areas where the pest has occurred should require a licensing system and strictly quarantined to prevent future spread of this pest; (ii) the regions predicted to be suitable for L.

botrana should be monitored more rigorously to strengthen quarantine; (iii) long-term monitoring system should be established and the monitoring results should be reported regularly.

The process of invasive species entering and establishing in a new region is complex. In this study, we just considered the altitude and climate factors in environmental variables. Soil type, vegetation and climate warming on the distribution of this pest have not been considered for lack of corresponding digital base map. More research on the influence of environmental variables on survival of L. botrana are needed.

Fig. 2 .

 2 Fig.2. The potential geographical distribution of Lobesia botrana in China under current climate conditions, as modeled using Maxent. White = unsuitable (Appropriate value≤0.006); yellow = marginal (0.006<Appropriate value≤0.1); brown = suitable (0.1<Appropriate value≤0.17) and red = optimal (0.17<Appropriate value≤1).

Fig. 3 .

 3 Fig. 3. Sensitivity vs. 1-Specificity for Lobesia botrana

Table 1 .

 1 Environmental variables used in potential distribution prediction of L. botrana

		Environmental variables
	BIO6	Min temperature of coldest month
	BIO19 Precipitation of coldest quarter
	BIO11 Mean temperature of coldest quarter
	BIO18 Precipitation of warmest quarter
	BIO8	Mean temperature of warmest quarter
	BIO2	Mean diurnal range
	BIO4	Temperature seasonality
	BIO3	Isotherm
	BIO1	Annual mean temperature

Acknowledgments

The authors thank Prof. Stauffer at Pennsylvania State University for his valuable suggestions and modifying the draft. We thank all members of the Plant Quarantine and Invasion Biology Laboratory of China Agricultural University (CAUPQL). This study received financial support from the 973 project (2009CB119200).


Jun Wang

Gang Liu

A Design of Greenhouse Remote Monitoring System Based on WSN and WEB

Keywords: Greenhouse monitoring, Wireless sensor networks, WEB, Sensor, ARM7

Wireless sensor networks (WSN) are increasingly popular in the field of greenhouse environment monitoring. In this paper, a new design of greenhouse remote monitoring system based on WSN and WEB is presented.

The system has a three-tier structure of node layer, distributed management layer and WEB service layer. Experiments showed that the node layer has excellent self-organization and communication ability. The maximum distance of one-hop is 856 m. The distributed management layer can acquire and manage the data of temperature, humidity, light and the level of the carbon dioxide, and simultaneously send the data to WEB server database through the Internet. The WEB service layer can complete format storage, statistical analysis and conditional query for database, also ordinary users can obtain the graphic display of the greenhouse environment data through web browser.

Normal working time of system reaches 141 days in the experiment. Meanwhile, normal operation rate exceeds 92%. Packet transfer rate of each sensor node is higher than 89.6% and lower than 95.4%.The greenhouse remote monitoring system based on WSN and WEB can realize an organic combination between distributed management and remote data sharing throughout different regions.

Introduction

Greenhouse has played a key role in the development of agriculture. Due to the increased demand of food, people are trying to put extra efforts and special techniques to multiply the food production. Use of different technologies towards greenhouse is one of such efforts. Apart from scientific technologies in greenhouse, information technology is now being heavily exercised to improve decision making capabilities in this area. Recently, wireless sensor networks (WSN) attract intensive research efforts due to easy deployment and a large variety of application scenarios. Wireless sensor networks provide the capability of self-organizing, self-configuring, self-diagnosing and self-healing which enable un-interrupt, in-field sensing, measurement, and control [START_REF] Xu | A survey of sensor network applications[END_REF][START_REF] Messer | Environmental monitoring by wireless communication networks[END_REF]. Greenhouse production commonly features as remote, variable, harsh, and sometimes hard to access. Hence, continuous monitoring can be difficult to realize.

With the advancement of wireless communication, micro-electronics, and computer technology, wireless sensor networks show advantages in these applications, and make it possible to acquire spatial and temporal knowledge about greenhouses [START_REF] Serodio | A networked platform for agricultural management systems[END_REF][START_REF] Li | Greenhouse environment monitoring system based on wireless sensor network[END_REF].

The main purpose of wireless sensor networks in greenhouse is to keep the process of real-time monitoring and control the most suitable environment for growing crops in accordance with expert knowledge [START_REF] Wang | Wireless sensors in agriculture and food industry-Recent development and future perspective[END_REF]. A large number of nodes can collect and transmit data cooperatively in different regions. However, the essence of wireless sensor networks is a distance-limited distributed system. Data management is the key problem of application. Different regional greenhouses need a remote background system to support the task. In recent years, greenhouse remote monitoring technology based on WEB has received more and more attention with the rapid development of the Internet [START_REF] Wei | Development of wireless intelligent control terminal of greenhouse based on ZigBee[END_REF]. The combination of WSN and WEB is a very positive direction in greenhouse practices. In this paper, we describe the features and architecture of the greenhouse remote monitoring system, and the approach of distributed data processing by Web application services.

System architecture

Fig. 1 shows the system architecture, which comprises three basic tiers as below.

The sensor application tier: This tier contains the wireless sensor nodes and the sink node. For environment monitoring, sensor nodes are usually distributed to certain greenhouses determined by geographical constraints or user requirements. These nodes can self-organize themselves into a WSN, which continuously generates and delivers environment data to the sink node. The information may include temperature, humidity, light and carbon dioxide. Issues about the manner of sensing, reporting and communication protocol are critical in designing this tier.

The distributed management tier: The distributed management tier obtains each greenhouse group data from the sink node through the serial cable. The environment data is forwarded to local processing program for analysis, calculation and visualization. For more comprehensive data processing, some professional platform could be introduced, which is powerful in handling large amount of data.

The WEB service tier: The distributed management tier uploads environment data to the WEB server tier via the Internet. As an important feature of the system, remote users can access the data via Web interfaces. Following this way, users are able to gather, visualize the data from the server. In consideration of the WSN security, users are only provided with data surveillance but assumed having no authority to change parameters of the fundamental tier.

Sensor application tier

Sensor nodes should have abundant functions, such as data acquisition, data transmission and friendly interface [START_REF] Mckinion | Wireless local area networking for farm operations and farm management[END_REF]. In order to realize the above functions, sensor node mainly includes four units. First, sensor unit completes information collection and data transformation. Second, microprocessor unit is responsible for nodes control, data processing and the collected data transmission. LPC2131 is selected as processor. The typical greenhouse environment is comprised of temperature, humidity, light, gas and soil, etc. Considering the impact on crop growth, the collection information includes temperature, humidity, light and carbon dioxide. Obviously, sensor is the basis of data gathering, the accuracy of sensor has an important effect on the whole system. The sensor selection should follow the principle of actual demand.

The technical parameters of each sensor show in Table .1.

T T T T T T      (1)

Fig.2 Period of single node collection

The communication testing of sensor nodes is carried out as follows: 1) first, sink node respectively sends 50 control frames to every node according to the interval of 5 s; 2) compute the time T, which is from sending control frame to receiving data packet, 215ms ≤ T ≤ 1453ms. In addition to ensure the stableness of wireless communication modules, the sensor application tier still should have a reliable communication protocol. The data packet structure of monitoring system is designed, which is shown in Fig. 3. The application message is consisted of network segment ID, node ID, type and information of sensor node, battery capacity. By using this packet structure, the sensor node periodically gathers sensing data and transmits through RF channel.

Fig. 3 Data packet structure

Distributed management tier

The core of greenhouse remote monitoring system is distributed management tier, which is responsible for searching sensor nodes, sending instructions, receiving data, information management, processing alarm information and uploading data to WEB server, etc. The software of distributed management tier adopts C # language as the tool in Visual Studio 2008, and uses SQL Server 2008 to build database. In this tier, dynamic data analysis and other possible algorithms could be easily proposed and directly applied, reinforcing the software's extensibility. The software consists of three modules: This module controls sink node to send instructions to sensor nodes in point-to-point pattern. All the nodes respectively respond in master-slave pattern.

(2) Information management module: In the application process, the module completes a number of process functions, such as choosing greenhouses, setting working frequency, connecting the database, inquiring data information, visualization, statistics and so on.

(3) Data transmission module: The module sends the collected data to WEB server through the Internet. The steps for upload process are: 1) the distributed management tier establishes a Socket; 2) set up the WEB server IP address and service port; 3) issue a connection request; 4) start the data communication with WEB server after receiving a confirmation.

WEB service tier

The WEB service tier's hardware includes network connection equipments (routers, switches, etc) and server. The WEB service tier's software consists of WEB services program and database program [START_REF] Zhongfu | GPRS and WEB based data acquisition system for greenhouse environment[END_REF]. In order to facilitate remote monitoring, the WEB service tier provides remote users with real-time data accessing, by broadcasting refined data packets over the Internet. In the opinion of data security, any user should first get permission from the server before browsing any information.

In the system design, a user should first send a request to the server conveying its user name, password and its own Internet address. Then, this information will be checked by an embedded program to confirm whether the name and password are right. If it is permitted, an acknowledgement message will be sent back to this user. Fig. 4 shows the database table relationship of the tier.

Fig.4 Database table relationship

As an end-user interface, the WEB service tier generally accomplishes four functions:

(1) Basic information maintenance: Maintain the basic greenhouse data (crop species, planting history, unit yield, etc.), and manage user registration.

(2) Data collection and storage: Use TCP Socket to receive data from the distributed management tier, and then compare with standard data format to judge the legitimacy of data, finally storage the correct data.

(3) Data process and inquire: Manage the upload data, then output statistical analysis of the real-time monitoring data, and provide the service of data inquire.

(4) Dynamic data browse: Authorized users can access WEB server via browser, and obtain the dynamic information of greenhouses through clicking hot-points on the electronic map.

System implementation and test 4.1 System deployment

In the experiment, 20×sensor nodes and 5×sink nodes are involved. All the nodes are assigned to five greenhouse groups respectively. Each greenhouse group only equips with a sink node. 6×PCs with one for the server and the other five for five greenhouse groups are used to test the ability of system. This experiment lasted for four months (August 2010-December 2010) and all the equipments ran to monitor in the greenhouse groups. Normal working time of system reaches 141 d. Meanwhile, normal operation rate is higher than 92%. The collection frequency of each node was set to 30 s. Sensor nodes were fixed on the wall in greenhouses. In order to prevent the influence of wall, the antennas of wireless communication modules extended 5 meters to the top of greenhouses.

Calculate the theoretical propagation distance of wireless communication in free space according the following equation:

f d L fs lg 20 lg 20 44 . 32 ] [    (2)
Where [L fs ] is transmission loss. d is transmission distance and f is working frequency. The parameters of wireless communication module are: 1) working frequency is 2.425 GHz; 2) transmission power is 25 dBm; 3) antenna loss is 3 dBm per meter; 4) receive sensitivity is -94 dBm. The equation ( 2) can be expressed as the following equations: The transmission distance d is 1.57 km through equation ( 4). Due to different kinds of interference, the actual propagation distance is less than the distance of theoretical calculation in practical application. The maximum distance of one-hop is 856 m.

Test performances

Fig. 5 shows the dynamic curve of humidity, which is displayed in distributed management tier by Dundas Chart. As shown in Fig. 5, the humidity range is 47.8% to 49.4% in a short time. Considering the sensor precision, the changes of data accord with greenhouse characteristics. These results demonstrate that sensor application tier has the excellent capability of self-organizing in the greenhouse, and effectively obtains the greenhouse environment data. Meanwhile, light does not appear remarkable changes in the whole time. Through the above analysis, it can be found that the remote monitoring technology can help users to make rapid response and reasonable management methods by collecting real-time data.

Fig. 6 Data monitoring curves

During the experiment period, the packets which are sent from each node to WEB service layer should be up to 2880 each day, according to packets received by the server, the packet transfer rate can be calculated through the following equation:

% 100   t r N N N (5)
Where N is packet transfer rate. N r is actual number of received packets and N t is ideal number of received packets. Each sensor node should send 2880 packets every day and the experiment lasted for 141 d. N t can be expressed as 2880×141 = 406080. N r is the received packets of each node in the server during the time. According to the equation ( 5), packet transfer rate of each sensor node can be calculated. The maximum rate is 95.4%, the minimum rate is 89.6%, and the average rate is 93.5%.

Conclusion

In this paper, a system based on WSN and WEB is presented, which has the capability of environment monitoring and data processing. Experiments are then demonstrated, which shows the system's well performance and the convenience to obtain data analysis. The system has made several achievements in: (1) The system is a practical environment monitoring application that sensor nodes periodically sense their ambience and the information can be eventually uploaded to the server. [START_REF] Messer | Environmental monitoring by wireless communication networks[END_REF] Sensor information is extracted and visualized accounting for its authenticity. (3) The data from the server can be forwarded to all the users over the Internet which makes remote monitoring system feasible. Future work will employ more nodes and powerful sink nodes to provide long-term monitoring of five greenhouse groups. Besides, this system can be reinforced by introducing more efficient hardware working styles to make it less aware of environment disturbances.

  Fig.1 System architecture

12-

  Bit serial A/D converter MAX186 is used as the external A/D chip for conversion precision. Third, wireless communication unit adopts SZ05 wireless data communication module produced by shanghai shuncom network technology Co., LTD. The unit can effectively organize wireless network, send the collected data and receive instructions from distributed management tier. Liquid crystal display unit chooses graphic dot matrix unit LCM12864ZK, which undertakes the task of displaying greenhouse environment information.

( 1 )

 1 Wireless communication control module: The module mainly operates RS232 interface to realize serial communication via the System.IO.Ports.SerialPort class.

Fig. 5

 5 Fig.5 Dynamic curve of humidity Fig.6 shows the data monitoring curves of a greenhouse on August 27, 2010 through WEB browser. As shown in Fig.6, Temperature is more than the maximum 38 ℃ at almost 12:00 pm. Due to ventilation and thermal transpiration, humidity in the greenhouse shows a slow decline trend. Carbon dioxide maintains a good level.

Table . 1

 . The sensor technical parameters SZ05 wireless data communication module is developed according to the standard ZigBee protocol. The topology structure of sensor application tier is designed as mesh network by considering transmission distance. Mesh network is a type of network where each node must not only capture and disseminate its own data, but also serve as a relay for other sensor nodes. All the nodes must collaborate to

	often more than one path between a source and a destination in the network. In the
	sensor network, all the nodes have been set in advance. There is only one sink node in
	a greenhouse group, which controls the data transmission. Relay routing nodes can
	forward other nodes' data to sink node through the way of multi-hop, and work as
	ordinary nodes. In order to avoid network congestion, sink node uses tour collection
	pattern [8]. The detailed steps of communication process are: 1) sink node sends data
	transmission command to relay routing nodes one by one; 2) relay routing node
	transmits environment data after receiving the command; 3) sink node uploads data
	packet to the distributed management tier, if data obtained within a period of single
	task	b	reg	trans	route	sleep
	Types and models			Technical parameters
	Temperature& humidity sensor DHT85		Temperature:-40~123.8℃ Humidity: 0~100%RH Sensor accuracy: 2.0%RH, 0.3℃@25℃
	Light sensor			Measurement range: 0~200KLUX
	SC-GZ			Sensor accuracy: ±5%
	Carbon dioxide sensor			Measurement range: 0~2000ppm
	T6004			Sensor accuracy: ±40ppm + 3% of measured value
	propagate the data in the network. The self-healing capability of mesh network
	enables a routing based network to operate when one node breaks down or a
	connection goes bad. As a result, the network is typically quite reliable, as there is

node collection; 4) if not, then sink node skips over the node and sends instruction to the next node. As shown in Fig.

2

, one period of single node collection can be approximately described as the following equation:

Acknowledgements

. This research is funded by National High Technology Research and Development Program of China (No.2011AA100704) and Major National Science and Technology Programs of China (No.2009ZX03001-019-02).


Liu Yang
email: yangliu@cau.edu.cn

Xinrong Cheng

Zhijie Jiang
email: jiangzhijie@163.com

Zhijun Ren
email: zhijunr@gmail.com

Research On the Monitor and Control System of Granary Temperature and Humidity Based on ARM

Keywords: Monitor, Alarm, Sensor, ARM

The temperature, humidity, air composition and pH value are very important parameters in the granary that keep the grain safely store and good quality. Nowadays only temperature and moisture are used in most granary monitor and control system.Air composition, pH and insect pest are not monitored because classic industrial personal computer or microcomputer are not able to handle these real-time multiply tasks at the same time.So it is necessary to design a monitor and alarm system to monitor these parameters in the granary to minimize or curtail damage or loss. Applying the advanced embedded technology and sensor technology, a set of granary temperature and humidity monitor and alarm system is designed based on the ARM. Temperature sensor, humidity sensor, air composition sensor and pH value sensor are selected to monitor the parameters in the granary. Based on the ARM microprocessor LPC2290, the hardware system consists of serial communication module, LCD display module and alarm module. Based on the embedded operation system, the software completes the allocation design and testing of the tasks and realizes the functions such as pulse frequency signal acquisition, analog signal A/D convert, serial digital signal communication, alarm in different grades and LCD display. All the parameters can be real-time displaying on the LCD screen. The system meets the requirement of the monitor and alarm functions in the granary.

Introduction

Grain safety storage is the important strategy relating to peoples' livelihood and has its social meaning and economic value. The temperature and humidity in the granary varies when the environments and ventilation situation changes during the storage that leads to the grain becoming worse and insect damage. At the same time, the quality of the grain influenced by the air in the grain and microorganism. According to the specific of the grain storage, the temperature and humidity are the main parameters which should be monitored in the granary and the air composition and pH value are the auxiliary parameters [START_REF] Tieliu | The Design of Intelligent Granary Monitoring System Based on Wireless Sensor Network[END_REF].

In the old days the temperature and humidity in the granary are controlled by the workers. The granary workers check the temperature and humidity in the granary periodically in order to make decision how long and how strong the wind is supported to ventilate the granary. It is hard to control the temperature and humidity accurately in the granary just by the workers. With the developments in the electric and electronic technology, the sensors are used to monitor the temperature and humidity in the granary. At the beginning granary measure and control system consisted of only some electronic temperature measurement and several controlled fans [START_REF]Simulation of Temperature Measurement System forGrain Storage Based on ZigBee Technology[END_REF]. Later on industrial personal computer and semiconductor PN temperature components are used in big and middle granary to control the limited parameters [START_REF] Songfeng | The Development and Application of the Granary Temperature Measure System[END_REF]. And then the digital sensors and industrial personal computer with windows operation were used in the control system [START_REF] Zhiguo | Wireless Oxygen Content Monitor Design in Metallurgy Gas Processing[END_REF]. Nowadays wireless technology is used in the monitor and control system [START_REF] Xiaowei | Research and Design of Smart Online pH Detector and Recorder[END_REF].

Although in the 20 years monitor and control system have developed and applied greatly and achieved economic benefits, many problems still exist.In wireless system, it it hard to improve the system accuracy and reduce the repeated error because of the useful detected signal being added by surrounding wireless signals.In the wireless system communication data temporary disappearing with the environment temperature varied leads to unreliability [START_REF] Tao | pH Detection's Realizer and Control Based on Transcendental Knowledge of Neuron Model[END_REF]. On the other hard,nowadays only temperature and moisture are used in most granary monitor and control system.Air composition, pH and insect pest are not monitored because classic industrial personal computer or microcomputer are not able to handle these real-time multiply tasks at the same time [START_REF] Tianbo | The pH Detecting System Based on nRF9E5 Wireless Transmission[END_REF]. However the ARM monitor system in this article solved the problems above. It can process multiple signals at the same time using the multiple tasks and real-time functions.

Scheme of the Hardware System

The monitor system mainly consists of controller, signal acquisition module, LCD display module, alarm module, testing module and power module. The controller makes the data acquisition and compares the values to the boundary condition values.

The alarm beeps and at the same time lights flash if the real value is greater than the boundary ones. Every parameter can be real-time displaying on the LCD screen. This project is based on the LPC2290 belonging to ARM microcomputer of Zhi Yuan company. According to the functions, hardware part bases on the LPC2290 microprocessor and connects to inputs and outputs through the bus and interface circuit. The diagram of hardware system is shown in Fig. 1.

Mini ARM Microcontroller LPC2290 Signal Acquisition Module Power Module

Testing Module

LCD Display Module

Alarm Module Design

The alarm system is divided into three levels. When the acquisition data reach to the critical value, the system judge the type of the alarm level and output the related results. Also the alarm result is divided into three levels. Green light will be flashing in the first level, reminding the granary clerk to monitor the parameters carefully. Yellow light will be flashing while beeping in the second level, reminding the granary clerk to deal with the dangerous situation in twenty four hours. Red light will be flashing while beeping in the third level, reminding the granary clerk to deal with the emergency immediately.

LCD(Liquid Crystal Display)Module

The LCD module is designed base on M240128-1A1 made by Xin Li company to display the temperature, humility, air composition and pH values. The LCD matrix dot is 240×128.The LCD display module consists of controller, line driver, row driver, display memory and LCD display screen assembled in one low-cost PCB board. The module can be real-time displaying the data monitored by the temperature sensor, humidity sensor, air composition sensor and pH value sensor. The LCD display module is connected with the microcontroller by input and output ports [10][11]. The circuit is shown in the Fig. 4. The more powerful functions and system calling, the more effective and simple supports can be provided by the applying program and the less amount of maintain. TheμC/OS-Ⅱ only provides a task scheduling kernel. In order to realize a complete and reality online multiple task operating system, the basic common task is built such as serial ports, LCD and A/D according to the function acquirement of the monitor and alarm system. The bottom layer consists of the alarm hardware system whose kernel is LPC2290 (RM7 serial chip) and some surrounding devices such as LCD and alarm module. The middle layer consists of signal supported by operating system, mailbox and information line etc. communication mechanism, driving program library and API functions. The top layer is the customer applied program which is established in the main task and operated through calling API function by the customers to satisfy their requirements. Customers can establish their own tasks in their own applied programs. Multiple tasks complete the cooperative communication depending on the system information line.

System Task Modules and Priority Level Division

The tasks division is the important step of the real-time software system development. whole system will be low effective, poor real-time and less capacity if the division is unreasonable. According to the design requirement, the program establishes six tasks: the main task, A/D acquisition task, the switch signal I/O acquisition task, serial communication task, alarm task and LCD display task. The allocation of the tasks is shown in the table 1. Every task has its own stack space depending on the variety numbers and interruption numbers in the task after the priority is decided.

In theμC/OS-Ⅱ system, the static and active ways are used to allocate the stack space. The static stack allocation is completed when compiling while the active stack allocation is completed when operating. The static allocation is used in the design that keep the priority in the process of system operation is the same as before. The experiments methods and steps are designed and operated in the lab and in the granary. Take wheat as the sample. Fig. 6 shows the LCD system.

Conclusion

Applying the advanced embedded technology and sensor technology, a set of granary temperature and humility monitor and alarm system is designed based on the ARM he ARM microprocessor LPC2290. Temperature sensor, humidity sensor, air composition sensor and pH value sensor are selected to monitor the parameters in the granary. The hardware system consists of serial communication, LCD display and alarm module. The software is based on the embedded operation system that completes the allocation design and testing of the tasks. The system realizes the functions such as pulse frequency signal acquisition, analog signal A/D convert, serial digital signal communication, alarm in different grades and LCD display. The system meets the requirement of the monitor and alarm functions in the granary.

Fig. 1 .Fig. 2 .

 12 Fig. 1. The diagram of hardware

Fig. 3 .

 3 Fig. 3. Alarm Module

Fig. 4 .

 4 Fig. 4. LCD Display Interface Circuit

Fig. 5 .

 5 Fig. 5. Main Program Diagram

Fig. 6 LCD

 6 Fig.6 LCD Module in the Monitor and Control System

Table 1 .

 1 Tasks Allocation

	Task Name			Priority	Task Stack	Function Description
	Main Task			17	256	Initialized Established Other Tasks Display	Interface,
	A/D Acquisition Task		18	128	Measure Temperature, Humidity
	Switch Acquisition Task Signal	I/O	19	128	Acquire I/O Signals
	Serial Task	Communication	20	256	Send Air composition Signals, pH Value Signals To Serial Ports
	Alarm Task			21	128	Beep and Light Alarm
	LCD Display Task		22	512	Display Measurement

Table 2

 2 Compared Measured Value with Actual Value of the Granary Parameters

				Air
	Parameters Name	Moisture( % )	Temperature(°C)	Composition	pH Value
				( % )
	Measured Number 1	51.45	22.56	3.78	6.89
	Actual Value	51.81	23.00	3.28	6.77
	Measured Number 2	52.32	22.89	3.02	6.71
	Actual Value	52.67	23.13	3.35	6.67
	Measured Number 3	51.87	23.09	3.15	6.80
	Actual Value	51.31	22.79	3.77	6.74
	Measured Number 4	52.04	22.11	3.20	6.69
	Actual Value	52.56	23.43	3.82	6.79
	Measured Number 5	51.78	22.67	3.32	6.81
	Actual Value	51.99	23.99	3.57	6.90
	Measured Number 6	51.37	23.00	3.90	6.83
	Actual Value	51.09	22.89	3.11	6.47
	Measured Number 7	52.67	22.62	3.65	6.29
	Actual Value	52.01	23.00	3.22	6.47
	Measured Number 8	51.81	22.47	3.92	6.59
	Actual Value	51.57	23.13	3.06	6.70
	Measured Number 9	52.83	23.28	3.46	6.39
	Actual Value	52.32	22.90	3.94	6.37
	Measured Number 10	51.34	23.06	3.26	6.44
	Actual Value	51.67	22.97	3.80	6.61


Lei Shi

Qiguo Duan

Xinming Ma
email: xinmingma@126.com

Mei Weng
email: wengm@163.com

The Research of Support Vector Machine in Agricultural Data Classification

Keywords: Support Vector Machine, Agricultural Data, Classification

The agricultural data classification is a hot topic in the field of precision agriculture. Support vector machine (SVM) is a kind of structural risk minimization based learning algorithms. As a popular machine learning algorithm, SVM has been widely used in many fields such as information retrieval and text classification in the last decade. In this paper, SVM is introduced to classify the agricultural data. An experimental evaluation of different methods is carried out on the public agricultural dataset. Experimental results show that the SVM algorithm outperforms two popular algorithms, i.e., naive bayes and artificial neural network in terms of the F 1 measure.

Introduction

As a very promising field with a huge growth potential, agricultural data classification is a hot topic in the agriculture and computer science communities. In recent years, many popular algorithms in the machine learning field have been applied in the agricultural data classification, such as decision tree [START_REF] Kirchner | The analysis of simulated sow herd datasets using decision tree technique[END_REF], kNN [START_REF] Rajagopalan | A k Nearest Neighbor Simulator for Daily Precipitation and Other Weather Variables[END_REF], artificial neural network [START_REF] Chedad | Recognition System for Pig Cough based on Probabilistic Neural Networks[END_REF][START_REF] Schatzki | Defect Detection in Apples by Means of X-ray Imaging[END_REF], etc. Support vector machine (SVM) [START_REF] Vapnik | On the uniform convergence of relative frequencies of events to their probabilities[END_REF][START_REF] Vapnik | Statistical Learning Theory[END_REF], introduced by Vapnik and Chervonenkis in 1971, is a machine learning algorithm based on statistical learning theory. By using nonlinear kernel functions, SVM can map original input data into a high dimensional feature space to seek a separate hyperplane, and then it can perform classification by using the constructed N-dimensional hyperplane that optimally separates the data into two categories. For the past few years, SVM has been widely used in different fields and it can obtain high performance in many real world classification applications such as image retrieval [START_REF] Tao | Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval[END_REF], cancer recognition [START_REF] Giorgio | Cancer recognition with bagged ensembles of support vector machines[END_REF], text classification [START_REF] Hyunsoo | Dimension Reduction in Text Classification with Support Vector Machines[END_REF][START_REF] Simon | Support Vector Machine Active Learning with Applications to Text Classification[END_REF] and credit scoring [START_REF] Bellotti | Support vector machines for credit scoring and discovery of significant features[END_REF][START_REF] Lee | Application of support vector machines to corporate credit rating prediction[END_REF][START_REF] Huang | Credit scoring with a data mining approach based on support vector machines[END_REF].

In this paper, SVM is introduced to classify the agricultural data. Experiments on real agricultural dataset have been conducted and the experimental results indicate that the SVM algorithm outperforms two popular algorithms, i.e., naive bayes and artificial neural network in terms of the F 1 measure. Thus, SVM is an effective method for agricultural data classification.

The remainder of the paper is organized as follows: Section 2 gives an introduction of SVM in detail. Section 3 reports and discusses the experimental results and finally Section 4 states the conclusions of our work.

SVM

As a popular machine learning algorithm, SVM is a new generation learning system based on recent advances in statistical learning theory. It realizes the theory of VC dimension and principle of structural risk minimum to constitute an objective function and then find a partition hyperplane that can satisfy the class requirement. The basic idea of SVM can be described as follows. Firstly, search an optimal hyperplane satisfies the request of classification. Secondly, use a certain algorithm to make the margin of the separation beside the optimal hyperplane maximum while ensuring the accuracy of correct classification. Then, the separable data can be classified into classes effectively [START_REF] Vapnik | Statistical Learning Theory[END_REF]. As a kind of structural risk minimization based learning algorithms, SVM have better generalization abilities comparing to other traditional empirical risk minimization based learning algorithms. An illustration of the SVM is shown in Fig. 1.

Fig. 1. An illustration of SVM

In a SVM classifier, let the training set be {(x 1 , y 1 ), (x 2 , y 2 ), …, (x n , y n )}, where x i is an input vector and y i its label. The partition hyperplane can be defined as [START_REF] Vapnik | Statistical Learning Theory[END_REF] 0

   xb . ( 1
)
where b is the offset of hyperplane;  is the normal vector of the partition hyperplane. A partition hyperplane to make the bilateral blank area, i.e., 2/ || ||  , maximum must be found to make the partition hyperplane as far from the point in training dataset as possible, which can be defined as follows.

2 1 Minimize ( )= || || 2    . (2)
A constraint condition must be met, which is defined as follows.

( ) 1

    ii y x b . ( 3
)
Support Vectors

Maximal margin hyperplane

The lagrange function can be defined as:

1 1 ( , , ) ( ) ( ( ) 1) 2               n i i i i L b y x b . ( 4
)
Subject to the following two conditions, i.e., , then the following formula can be defined for seeking the minimum of lagrange function.

1 , 1 1 max ( ) ( ) 2          nn i i j i j i j i i j Q y y x x . (5)
The optimal class function can be defined as follows.

* * * * 1 ( ) sgn(( ) ) sgn( ( ) )          n i i i i f x x b y x x b . (6)
An important advantage of SVM is that it can be analyzed theoretically using concepts from computational learning theory, and obtain state-of-the-art performance. Recently, it has also been applied to a number of real-world problems such as handwritten characters recognition, information retrieval and the classification of biomedical data. In this paper, SVM is introduced to classify the agricultural data for improving the classification performance of agricultural data.

Experimental Results

To study the effectiveness of the SVM method for agricultural classification, we test it on agricultural data in this section. One agricultural dataset obtained from agricultural researchers in New Zealand, i.e., the white-clover dataset [14], is used in experiment. The objective of the white-clover dataset is to determine the mechanisms which influence the persistence of white clover populations in summer dry hill land.

We used the F 1 measure to evaluate the performance of algorithm. A confusion matrix contains information about actual and predicted classifications done by a classification system. The table 1 shows confusion matrix for two class classifier [START_REF] Yang | An evaluation of statistical approaches to text categorization[END_REF].

Table 1. Cases of the classification for one class Class C

Result of classifier

Belong

Not belong

Real classification

Belong TP FN

Not belong FP TN

Several standard terms can be defined for the two class matrix. The recall is the proportion of positive patterns that were correctly identified, as calculated using the equation:

  TP recall TP FN . ( 7
)
Precision is the proportion of the predicted positive patterns that were correct, as calculated using the equation:

  TP precision TP FP . ( 8
)
Then, the performance of the classification can be evaluated in terms of F 1 measure.

1 2    precision recall F precision recall (9)
For SVM, we used the LIBSVM [16] for SVM implementation and set linear function as default kernel function of SVM. To evaluate the effectiveness of SVM in agricultural data classification, two popular algorithms, i.e., naive bayes [START_REF] Lewis | Naive (Bayes) at forty: The independence assumption in information retrieval[END_REF] and artificial neural network [START_REF] Bishop | Neural networks for pattern recognition[END_REF], are implemented and used as benchmarks for comparison. Performance is evaluated by 10-fold cross validation.

Fig. 2 shows the classification results of SVM, naive bayes and artificial neural network in terms of F 1 measure on the dataset. The F 1 value of SVM is 67.3%, which is approximately 6.9% higher than that of naive bayes algorithm and 4.8% higher than that of artificial neural network algorithm.

Conclusion

The classification of agricultural data is an important application of information technology in agriculture. SVM is a powerful state-of-the-art classifier and has been applied in many fields. In this paper, SVM is introduced to classify the agricultural data for improving the classification performance. The experimental results show that the SVM is an effective method for classification of agricultural data.

Fig. 2 .

 2 Fig. 2. Comparison of the F 1 of classification on dataset


Tao Hu
email: hutao@hit.edu.cn

Hui-Lan Wu

Guodong Liu

Study of micro-vision calibration technique based on SIFT feature matching

Keywords: Vision measurement, Calibration, Feature Matching

In the micro-vision system, precise calibration of the pixel equivalent is a prerequisite for accurate visual inspection. Traditional calibration process not only needs to take standard parts as the base, but also has strict requirements on their shape, manufacturing precision, placement and so on. Because of such shortcomings of traditional method, this paper gives an improved SIFT calibration algorithm for the pixel equivalent. Firstly, get the image's characteristic points before and after its' moving, using SIFT algorithm. Then filter the mismatched points through the second filtering algorithm so that the matching accuracy can be greatly improved. Compare moving distances of the characteristic point pixels to the reference distance of optical grating. Then accurate pixel equivalents can be obtained. Experiments show that the calibration algorithm is more accurate than traditional methods. So this algorithm can completely replace the standard-parts method in micro-vision system.

Introduction

Measuring object size using CCD pixels as the scale is the core of vision measuring technology. So we need to acquire the actual size of CCD pixel. And because of the existence of optics system, the pixels' actual application size should be the one which is calculated through the optics system. During the application process of the vision system, an accurate enlargement of the optics system is hard to obtain because of many factors, just like the camera's relative position with CCD, and the camera's assembly quality differences. So, calibration of CCD pixel equivalent is the precondition and foundation of the measurement based on image [1~2] .

Currently, standard-parts method is commonest among the calibration of CCD pixel equivalent. E.g.: figure 1(a) Standard mask. Test the standard part's geometrical shape, and then transmit its precise size to digital image. The precision of this method is subject to the standard part's accuracy of manufacture and the picture border's inspection quality. Moreover, it has strict requirements on the shape of standard part. In addition, the standard part's border must be parallel to the direction of CCD pixel.

Or the distance between parallel lines will correspond to slant image's pixel number. And there will be angular misalignment in the demarcated pixel equivalent, as the picture 1 (b) shows. To optimize the calibration method, this paper presents an improved SIFT-based method to demarcate the pixel equivalent of Microscopic inspection system, according to its calibration characteristics. SIFT-Feature point matching method is based on image's local gradient features. It has better adaptability to image's lighting change, fuzziness and distortion. It doesn't need to detect the object's border. And it's not sensitive to object's shape.

Calibration Technique

Micro-vision system, which is based on SIFT-algorithm, can accomplish the calibration process with only the measured object. It doesn't have to draw support from standard part, which changes the traditional method that acquires the number of pixels of the reference distance. Detail calibration steps as follows: 1) Lay the object upon the one-dimensional translation stage; 2) Move the object. And collect the images of it separately before and after the moving; 3) Read the object's displacement L through the numerical reading device (optical grating or other displacement output unit); 4) Extract SIFT characteristic points of the collected images; 5) Match SIFT characteristic points; 6) calculate the number of pixel's corresponding distance the characteristic points moved; 7) L/N is the pixel equivalent of the vision system. What is key in the calibration method base on SIFT algorithm is the extraction and matching of SIFT characteristic points.

Method of Abstracting SIFT Characteristic Points

The SIFT algorithm extracts extreme points of Difference of Gaussian scalespace(DoG) as characteristic points, which mainly contains the following 4 steps [START_REF] Pan | Detecting image region duplication using sift features[END_REF][START_REF] Bastanlar | Improved SIFT matching for image pairs with scale difference[END_REF][START_REF] Li | A comprehensive review of current local features for computer vision[END_REF][START_REF] David | Distinctive image features from scale-invariant keypoints[END_REF][START_REF] Mikolajczyk | A performance Evaluation of Local Descriptors[END_REF][START_REF] Hui-Lan | Study on the circle center fast accurate-locating technique based on the SIFT[END_REF] : 1) Build a DOG and extracts its extreme points; Gaussian image. k is a scale scaling factor. σ is a Gaussian scale. The extreme point is the one that has the biggest change in grayscale value compared with its surrounding 26 pixels.

) , , ( ) , , ( ) , ( * )) , , ( ) , , ( ( ) , , (      y x L k y x L y x I y x G k y x G y x D     (1) (a) (b)
2) Precisely position extreme value points through fitting 3-d quadratic function.

3) Direction distribution of characteristic points Calculate the images' gradient of characteristic points in the DoG. Determine the characteristic points' main direction by the way of accumulating their gradient. The amplitude and direction angle of gradient are calculated as follows:

                               ) , 1 ( ) , 1 ( ) 1 , ( ) 1 , ( tan ) , ( )) 1 , ( ) 1 , ( (
)) , 1 ( ) , 1 ( ( ) , ( 1 2 / 1 2 2 y x L y x L y x L y x L y x y x L y x L y x L y x L y x m  (2)
4) Build descriptor of the characteristic point Turn the coordinate axis of Gaussian image L(x,y,σ) to the main direction of characteristic points. The characteristic points being the center, divide its surrounding 16×16 pixel window into 4×4 windows. In each small window, accumulate the Gaussian image's gradient projection in 8 directions. Then a characteristic vector F of 4×4×8=128 dimensions can be obtained.

Characteristic Points Matching and Mismatch Filtering

After detecting the SIFT characteristic points of the object's image, the points obtained after moving need to be matched with those before moving, thus confirm their one-to-one correspondence. The SIFT characteristic point matching methods that are in common used mainly include: NN(Nearest Neighbor), Exhaustive method, K-D(K-Discriminator Tree), BBF(Best-Bin-First) and so on. Above methods are all based on traditional SIFT characteristic point descriptor, all getting too much error matching.

In the calibration process of micro-vision system, even a few mismatches can bring great deviation to the result. In order to overcome the influence of the mismatches, this paper does a second filtering to the mismatches, using the space coordinate information of characteristic points, which is based on NN(Nearest Neighbor) method. Suppose

} ,... , { ) ( 2 1 test M test test test k k k I K 
is the set of characteristic points of the image before object's moving. And

, { ) ( 1 temp temp k I K  } ,... 2 temp M temp k k
is the set of characteristic points of the image after object's moving.

Calculate the nearest and the second nearest distance (MD i and SMD i ) between each couple of characteristic points' descriptor (before and after the image's moving). And then calculate the value: SMD i / MD i as follows: .

))) ( ), ( ( ( min

1 temp j test i M j i k F k F dis MD   (3)
))) ( ), ( ( ( min sec th 1 is an appointed threshold value. With this step, usually 80% mismatches can be filtered. The larger th1 is, the less the number of matching couples is, and the better the stability is. But when th 1 is too large, the number of matching point couples decreases so sharply that correct positioning is impossible, and not all mismatches can be eliminated. In practice, in most cases the value of th1 is around 1.5.

1 temp j test i M j i k F k F dis ond SMD   ( 4
) i i i MD SMD prop  (5)
After initial characteristic point matching, point pairs can be obtained:

), , {(

)) , ( ( min sec , 1

test j test i L i j j test i s s dis ond sm d ≠   (7)
)) , ( ( min , 1

temp j temp i L i j j temp i s s dis md ≠   (8)
) are smaller than the appointed threshold value th 2 ,with md i >10pixel ,then this couple of matching points be kept. If not, delete it. The value of th 2 is about 0.9 in common case. As is in picture 2, k 1 and k 3 ,k 2 and k 4 are all considered correct match before the second filtering. But actually, only k 1 and k 3 are the correct couple. k 2 and k 4 are the mismatches. After the second filtering, only the couple of k 1 and k 3 will be retained.

Lots of experiments show that after the second time filtering, correct correspondence (before and after the object's moving) of object's SIFT characteristic points can be gained. And through affine transformation between images, the number of pixels corresponding to the SIFT Fig. 2 Filter out the mismatches using space coordinates

Experiments and Analysis

In order to examine above ideology, this paper designed two experiments.

Experiments of Standard Parts' Calibration and Comparison

Firstly, lay a standard part (masking layer) upon the onedimensional electric transition stage which has a grating readout device. Resolution of the optical grating is 0.2μm, using backlit lighting. CCD's type is Japan's Watec 902H, with a nominal pixel size value of 8.6*8.3μm. To make a contrast, pixel equivalent calibration of the standard parts is done separately with the traditional method (including border detection, subpixel subdivision, parallel fitting) and this paper's method. Figure 3 shows the result of SIFT characteristic point matching. And table 1 shows the result of calculation.

From table 1, the mean value of pixel equivalent is 11.8036 μm/pixel with this paper's method. While with traditional method, the mean value is 11.8106 μm/pixel. Being influenced by border detection's degree of precision, the result of traditional methods has less stability.

In order to check the accuracy of this paper's method further, move the standard part to a certain distance. The displacement can be read out by Renishaw ML10 twofrequency laser; Calculate the displacement's number of pixels with this paper's SIFT algorithm. And then calculate the actual displacement separately with above two calibration results. Table 2 shows the result.

Tab.1 Calibration result of pixel using mask

As can be seen in table 2, the measuring result of this method is better than traditional method in application.

Experiment of Workpiece's Self-Movement

One obvious advantage of this calibration method is that the calibration algorithm is independent from object's shape. So the calibration can be done without standard parts, only making use of workpiece's self-moving. Keeping the same optical magnification as previous experiment, table 3 gives the results of vision system's calibration by workpiece with hole. Two images' matching result of SIFT characteristic points is given in figure 4. In table 3, pixel equivalent error 1 is the deviation of the two calibration results (the one that makes use of workpiece based on this paper's algorithm and the other by traditional method). Pixel equivalent error 2 is the deviation of the two results (the one that makes use of workpiece and the other that makes use of standard part, which all take this paper's algorithm).

Under different magnification, table 4 gives the calibration result of micro-vision system by workpiece with circular hole. In table 4, pixel equivalent error is the deviation of the two calibration results----the one that makes use of workpiece and the From table 3 and table 4, we can see that the calibration result by workpiece itself can maintain better repeatability with the method that uses mask. This further again proved this paper's method.

Conclusion

In micro-vision measuring system, calibration's precision of pixel equivalent applying traditional methods is subject to standard part. To overcome these shortcomings, this paper proposed a method based on improved SIFT local characteristic point matching to demarcate pixel equivalent. The experimental results show that the method presented in this paper has better stability than the traditional method. While reaching more stable calibration precision, the vision system's positioning accuracy is improved too. With this paper's calibration method, standard part is no more necessary in the process of micro-vision-detecting system's pixel equivalent calibration, overcoming the shortcoming that standard parts need to be positioned precisely.

Fig. 1

 1 Fig.1 Traditional method of calibration

  moving distance can be calculated. With the reference displacement given, the pixel equivalent of micro-vision system can be demarcated.

Fig. 3

 3 Fig.3 SIFT matching point of the mask

Fig. 4 0013 Tab. 3

 400133 Fig.4 SIFT matching point of the workpiece

Tab.4 Calibration result 2 of work with a hole