Shijuan Li
email: lishijuan@mail.caas.net.cn

Yeping Zhu

Evaluating Simulation Model Based on Generated Weather Data

Keywords: Weather generator, Dry and wet spell, Growth simulation

The daily weather series simulated with observed weather data and weather data generated with conventional WGEN model taking dry and wet day as stochastic parameter and DWS model were statistically compared and analyzed. These three weather data were respectively used to drive maize growth simulation model. The corresponding yield results were compared. The results showed there was no significant difference between observed weather data and weather data generated with DWS model. The difference between yield simulated with DWS model and yield simulated with WGEN model was not significant.

Introduction

Crop growth simulation is to analyze the growth and development mechanism and the quantitative relationship with environment with the help of computer [START_REF] Williams | The EPIC Crop Growth Model[END_REF][START_REF] Mccown | APSIM: a novel software system for model development, model testing and simulation in agricultural systems research[END_REF][START_REF] Gao | Rice Cultivational Simulation-Optimization-Decision Making System (RCSODS)[END_REF][START_REF] Zhao | Study on intelligent expert system of wheat cultivation management and its application[END_REF]. The dynamic processes of crop growth and development are expressed by mathematical methods. Crop simulation model evolves with the development of computer technique, systemic analysis theory and agricultural research achievements. It is the core of precise agriculture and information agriculture, furthermore is the foundation of agricultural production management and resources optimization.

Agriculture is a complicated huge system with many controllable objects. The development and application successively of crop models promoted the change from qualitative description to quantitative analysis for crop growth and development discipline, established good foundation for development of crop growth decision system. The mature models of developed countries had been applied in many fields such as yield estimation, drought evaluation and variety breeding under deferent environments. As a research tool, crop simulation model plays an important role in the domains of environment, resource, continuous development and climate change impacts etc.

Besides solar radiation and temperature, crop simulation considers water and fertilizer restrict too. Disease, insect pest, weeds and other biological factors will be evolved in great simulation model. Many of the factors are linked with weather and management measures. Lack of accurate and timely weather data restricts the regional and large-scale application of crop simulation model.

Weather generator provides an effective measure and tool for weather data simulation. Weather simulation is to generate weather data in common use such as daily maximum temperature, daily minimum temperature, rainfall and solar radiation based on the observed historical weather materials. The common methods applied are stochastic process theory, probability theory and statistics. When there is no enough observed weather data in certain region, weather generator can provide daily meteorologic materials with parameter interpolation method.

International researches on weather generator began in the 1960s. For China it was in the 1980s [START_REF] Chen | Stochastic simulationmodeland its verification of precipitation, temperature and sunshining hours[END_REF][START_REF] Wu | Study on the creation of daily climatic variation scenarios with a stochastic weather generator and various interpolations[END_REF][START_REF] Zhu | Stochastic Modeling of Daily Weather and Its Implementation in Java[END_REF]. The main works were to introduce the foreign weather models to test their simulation effects and improve the model parameters. Weather Generator of NCC/RCC-WG that can simulate four parameters of rainfall, maximum temperature, minimum temperature and sunshine duration was developed by National Climatic Data Center, China Meteorological Bureau. Now only the simulation effect about rainfall was reported, and there was no comparison with other weather generators [START_REF] Liao | Precipitation simulation in China with a weather generator[END_REF]. Shen et al exploited their own weather generator to drive vegetable disease popularity model and construct insect dynamic diversification [START_REF] Ma | Visual programming stochastic weather generator and its applications to ecological study in future[END_REF]. This weather generator is limited to laboratory, and has not been applied and test in a large scale.

2

Weather Generators Used in this Study

WGEN Model

The WGEN model is a common used weather generator based on the time series stochastic process constructed by Richardson et al [START_REF] Richardson | WGEN: A model for generating daily weather variables[END_REF]. The precipitation component of WGEN is a two-parameter Markov chain and gamma distribution model. The occurrence probability of wet or dry days is determined, and the precipitation is generated independently of the other variables for a given day. The residual of the three variables of daily values of maximum and minimum temperature and solar radiation are generated using a multivariate normal generation procedure. The WGEN model offers daily values for rainfall, maximum and minimum temperature, and solar radiation for an n-year period at a given location.

DWS Weather Generator

For WGEN model a given day is wet or dry depends on the Markov chain. So the bigger statistic error of dry and wet spell is gotten. DWS model adopts dry and wet spell but not dry and wet day as stochastic parameter. This is the essential difference with WGEN model. Researcher had studied the statistical discipline of dry and wet spell [START_REF] Jorgenson | Persistency of rain and no-rain periods during the winter at San Francisco[END_REF][START_REF] Gabriel | On distribution of weather cycles by length[END_REF]. The modeling approach and test results were listed in reference [START_REF] Wang | Stochastic simulation for dry and wet spell[END_REF].

Maize Growth Simulation Model

Making use of programming language Visual C++ and Visual Basic 6.0 on the operation system of Windows 2000 server, Digital Maize Planting and Management System was designed in accordance with maize growth and development discipline using technologies of system engineering theory, software engineering theory, computer and animation and image processing [START_REF] Li | Study on digital maize management system based on model[END_REF]. The system consists of cooperative models, database and interface etc. The system simulates several kinds of production fashions, i. e. experiential irrigation, optimal irrigation, no fertilization, no irrigation, rainfall agriculture, potential crop production. This paper uses the production fashion of experiential irrigation to test the weather generation. We run the system according to the irrigation and fertilization amounts commonly applied by farmer, then compared the deference between simulation yield and observed yield.

Results Analysis

For this study, two sites were selected that have weather stations with long-term reliable historical daily data. They include Beijing and Zhengzhou. For each location, daily solar radiation, precipitation, and maximum and minimum temperature were available for 31 years . The day with daily precipitation greater than 0.1 mm is set as wet day, contrarily as dry day. At first, two 100-year daily weather data generated by WGEN and DWS models respectively was analyzed. Then Digital Maize Planting and Management System was run making use of the observed, generated weather data by above two models to compare the yield difference.

Weather data simulation results analysis

DWS model simulated weather series well. There was no significant difference between simulated monthly statistics and observed values at the level of 0.01. Table 1 showed that the absolute error for monthly maximum temperature generally was less than 0.2℃ at two sites. The highest simulation error was in January, Zhengzhou, but didn't exceed 0.8℃. The absolute errors for monthly minimum temperature generally were less than 0.2℃ at two sites. The simulation errors of January, July and August in Zhengzhou exceeded 0.4℃, but were less than 0.6℃. In general the absolute errors for monthly precipitation day were less than 1 day. For monthly total solar radiation, the simulation effect difference of DWS and WGEN models is minor. Overall, simulation results for Beijing site were better than Zhengzhou site.

Table 1 also showed that DWS model got the similar effect with WGEN model. All the absolute errors for monthly maximum and minimum temperature between DWS model and WGEN model was less than 0.4℃. The absolute errors of monthly precipitation day were less than 1.5 d, and the errors of average monthly precipitation day were less than 0.5 d. The absolute errors of monthly precipitation were less than 3 mm at Beijing site except for July and August. For Zhengzhou site the value was 5 mm, but didn't exceed 10 mm. the average monthly precipitation difference between the two models was less than 3mm. the difference of solar radiations generated by DWS and WGEN was under 0.3KJ•m 2 .

Analysis of Simulation for Maize Yield

We run the digital maize plant and management system with the observed and generated weather data by DWS and WGEN to test the influence of irrigation period on maize yield. Four irrigation periods were set, i. e. 1st July, 10th July, 20th July, 30th July. The irrigation amount was 100 mm. The simulation sites were Beijing and Zhengzhou. Maize variety of Nongda 108 was used. Table 2 is the t value (p=0.05) between yield simulated with observed weather data versus generated weather data based on DWS or WGEN. We can see that there was no significant difference between observed yield and simulated yield by DWS or WGEN. From figure 1 we got the yields simulated with generated weather data by DWS or WGEN were all higher than that with observed weather data. The yields simulated with generated weather data by WGEN were very similar with observed weather data. With the delay of irrigation period, all the maize yields simulated with three sources of weather data increased gradually. Jointing stage in July is the key stage for maize growth. Irrigation in this stage is advantageous to grain increase. But July belongs to rainy season, so rainfall may weaken the yield increase extend.

Conclusion

This paper introduced the DWS model taking dry and wet spell as stochastic parameter, compared statistically the weather data generated with DWS and WGEN.

There was no significant difference between observed weather data and generated data. Three source of weather data were used to run the digital maize plant and management system to simulate maize yield. And different irrigation periods were set to test the influence on maize yield. The results showed there was no significant difference between observed yield and simulated yield by DWS or WGEN. The yields simulated with generated weather data by DWS or WGEN were all higher than that with observed weather data. The yields simulated with generated weather data by WGEN were very similar with observed weather data. With the delay of irrigation period, all the maize yields simulated with three sources of weather data increase gradually.

Fig. 1 .

 1 Fig. 1. Response of grain yield to irrigation period for maize simulated with observed weather data and weather data generated with DWS or WGEN.

  The main cooperative models contain maize growth model, maize development, water balance model, nitrogen balance model, and 3D visualization model which lay out maize growth and yield formation process. Database was built in Access 2000, and forms and graphs output was carried out by Teechart control unit. In addition, databases, variety parameter test module and weather creation module programmed by Visual C++ are included. Basic parameter database consists of information about location, fertilizer type, fertilizer management, irrigation management, cultivation variety, phenology data, soil texture and soil parameter. Weather data day by day are kept in weather database. The simulation and predicting results of models were transported into result database. Interface functions implement the data transfer between models and databases.

Table 1 Significance test of difference between observed daily weather data (O) and simulated with DWS

 1

	(D) and WGEN (W)

Table 2 t values between yield simulated with observed weather data versus generated weather data based on DWS or WGEN (t 0.05 =2.595,df=129)

 2

	Sites	Irrigation period	DWS-Observed	MG-Observed
		1 st July	0.2	0.936
		10 th July	0.154	0.843
	Beijing	20 th July	0.103	0.557
		30 th July	0.944	0.341
		1 st July	0.011	0.392
		10 th July	0.011	0.318
	Zhengzhou	20 th July	0.022	0.301
		30 th July	0.027	0.256

Acknowledgments. This research is kindly supported by Special Fund of Basic Scientific Research and Operation Foundation for Commonweal Scientific Research Institutes and Beijing Nova program. 1 st July 10 th July 20 th July 10 th July


Ergen Liu

Qing Zhou

Wei Yu

On Integral Sum Numbers of Cycles

Keywords: Integral sum number, Integral sum graph, Graph n C

In this paper we determine that integral sum number of graph n C , namely for any integer 5  n , then 0 ) (  n C  , therefore we prove that the graph ) 5 (  n C n is an integral sum graph.

Introduction

The graph in this paper discussed are undirected、no multiple edges and simple graph, the unorganized state of definitions and terminology and the symbols in this paper referred to reference [START_REF] Harary | Graph Theory[END_REF], [START_REF] Bondy | Graph Theory with Applications[END_REF]. F.Harary [3] introduce the concept of integral sum graphs. [START_REF] Xu | On integral sum graphs[END_REF] as followss: Conjecture [4] : Is it ture that any old cycle is an integral sum graph?

      . 4 when , 0 4 when , 3 ) ( n n C n ;  Proof. It is immediate from references [3, 4] that 0 ) ( 3  C  and 3 ) ( 4  C  . And it is clear that } 1 3 2 1 2 { 5     , , , , G C and } 3 4 3 7 5 2 1 { 7 , , , , , ,     G C .
Next we consider two cases: For all ) 3 ( 2

 j C j and ) 4 ( 1 2   j C j
, we will show that the two classes of cycle are integral sum graph.

Let the vertices of ) 3

( 2  j C j and ) 4 ( 1 2   j C j
be marked as the methods in Fig. 1 and Fig. 2.

    u u u , , ; 2 3 1 3 3 3 2 3 1     v v v , , .
      u u u u , , , ; 8 3 5 2 4 4 4 3 4 2 4 1      v v v v , , , . Then } 1 3 2 5, 1 4 { 6 , , , ,     G C and } 2 , 5 , 3 8 1 1 9, 2 7 { 8      , , , , G C , therefore 0 ) ( 6  C  and 0 ) ( 8  C  . When 5  j
,we give the labels of j C 2 as follows:

Let ) 5 ( 2 1 1 1 1      j u u u j j j ; ) 5 ( 2 2 1 2 2      j u u u j j j ; ) 5 ( 2 1 1 1 1      j v v v j j j ; ) 5 ( 2 2 1 2 2      j v v v j j j . and ) 4 3 ( 1 2 j k u u u j k j k j k , , ,       ; ) 4 3 ( 1 2 j k v v v j k j k j k , , ,      
. By labeling of above, we know

) 5 ( 0 ) ( 2   j C j  .
The labeling of above is illustrated in Fig. 3.

Fig.3. labeling of ) 3 ( 2  j C j Case 2 When ) 4 ( 1 2    j j n
We first give the labels of 9 C and 11 C as follows:

Let ) 4 3 2 ( 5 2 4 1 4 2 4 4 1 4 0 , , , ,        k u u u u u k k k ; ) 4 3 ( 7 5 4 1 4 2 4 4 2 4 1 , , ,         k v v v v v k k k . and ) 5 4 3 2 ( 8 3 5 1 5 2 5 5 1 5 0 , , , , ,        k u u u u u k k k ; ) 5 4 3 ( 11 8 5 1 5 2 5 5 2 5 1 , , , ,         k v v v v v k k k . Then } 5
       , , , , G C , therefore 0 ) ( 9  C  and 0 ) ( 11  C  . When 6  j ,we give the labels of 1 2  j C as follows: Let ) 6 ( 2 0 1 0 0      j u u u j j j ; ) 6 ( 2 1 1 1 1      j u u u j j j ; ) 6 ( 2 1 1 1 1      j v v v j j j ; and ) 3 2 ( 1 2 j k u u u j k j k j k , , ,       ; ) 3 2 ( 1 2 j k v v v j k j k j k , , ,       . Where j j u v 0 0  for 6  j .
By labeling of above, we know

Fig. 1 . 2 Fig

 12 Fig.1. marking of

  above is illustrated in Fig.4.

Fig. 4 C

 4 Fig.4. labeling of ) 6 ( 1 2  j C j

Main Results and certification Theorem 2.1. For any integer

	Definition 1.1. If a graph is isomorphism graph	G 	(S	)	, then we call graph
	G is Integral sum graph, denoted by	G	G	(S	)
			n		3	, then

  . Definition 1.2. For graph G, if it exists nonnegative integer S such that 1 sk G  is an integral sum graph, then we call number s is integral sum number of G, denoted by s G  ) (  . 2


Jianmin Gao
email: jmgao@ujs.edu.cn

Yongchao Jin

Soil-cutting Simulation and Test of Oblique Rotary Tiller

Keywords: oblique rotary tilling, simulation, SPH, specified power consumption, throwing soil

A virtual prototype of oblique rotary tilling based on SPH(Smooth Particle Hydrodynamics) was developed. Digital simulation and field test of oblique rotary tilling were conducted. The digital simulation suggested that soil mainly was torn to failure during oblique rotary tilling. Therefore, oblique rotary tilling could save energy. The given power consumption comparison of digital simulation and indoors test verified the virtual prototype .The optimization value of oblique angle and phase angle was given when rotary velocity equals 200rpm and working velocity equals 0.4m/s based on simulation. By simulating throwing soil during rotary tilling, the result suggested that the velocity of soil thrown was not uniform but lean to tangent blade's direction. This simulation result should be taken into consideration while designing oblique rotary tiller. Furrow bottom shape was figured out by simulating, too. The convex height was about 9.6% of tilling depth. All of the research result suggested that oblique rotary tilling might be a very potential tilling method which could save tilling energy greatly.

Preface

In the oblique rotary tillage, there was a sub-movement which made edge of the subexercise to apply force to the soil along the axis in the lateral when rotary blade was around the axis of rotation.

If the soil in the certain width direction along with the lateral force completely or partially lifted constraints (had been cultivated), then lateral edge of a lateral tear of the role of the soil causing more soil would be broken due to tension. From this perspective, oblique rotary tillage may reduce energy consumption, coupled with a better performance off the grass, it may be a promising new cultivation. The structure of cultivated soil was complex and very different in time and space and the movement of oblique rotary blade was more complex, so it was difficult to quantitative study of soil oblique rotary tilling mechanism by physical prototype testing of soil causing great difficulties in the development of the relevant equipment.

The Research of Oblique Rotary Virtual Prototyping System

The soil, nonlinear material, was damaged in high-speed collision was involved in this process of oblique rotary tilling. Soil high-speed cutting process has been an important and difficult problem with the method computer simulation in the study of tillage mechanics. Because this involves cultivated soil which is multi-phase, loose and scattered in the working parts of soil failure under high speed and describes the state of soil movement after the destruction process. Theory of continuous physical and numerical solution (finite element method or boundary element method) are difficult to describe the process(such as finite element method for large deformation conditions mesh distortion occurs, leading to failure calculated).Therefore, traditional numerical simulation methods based on the continuous mechanics could not do highspeed cutting mechanism of the soil. SPH (smoothed particle hydrodynamics)is without grid and no mesh distortion problems, so it could be dealt with under the Lagrange large deformation. Meanwhile, the method of SPH permits material interface, so you could simply and accurately get the complex constitutive behavior and the method of SPH also applies to materials at high loading rates of fracture and other issues.

SPH does not use discrete units, but use a fixed point of the movable mass particles or nodes. The quality is fixed on the particle's coordinate system, so that discretization is closer to the physical properties of cultivated soil. SPH do not use mesh and do not have mesh distortion problems, so it could be accurately described the object as well as destruction of nonlinear large deformation process. Hence, SPH is very suitable for the dynamic description of the process of farming.

By the basic equation of dynamic, we could get the acceleration of Lagrange particle volume of soil under stress.

          x x dt u d        2 ) (  (1)
In the formula: ρ is a relevant variable for the density,  u  is velocity vector and   is stress tensor. The spatial coordinates  x and time t were the independent variables. The acceleration of point x could be estimated by the formula (1) and the neighboring information. SPH's weight function w was multiplied on the right of the formula (1) and the result was integral in the domain of whole space.

x d x w x x x x d x x w x dt u d               ) ( ) ( ) ( ) ( ) (            (2)
If the adjacent information was only valid in the discrete point j and the volume element was shown by j j m  , then formula (2) could be got by formula [START_REF] Weimin | Grass Sliding Cutting Angles and Their Equations of Oblique rotary Blades[END_REF].

ij j i j j ij j j j j i i w m x w m x dt u d                  2 
(3) A simple soil and crushable foam model was advanced by Krieg in 1972.If the yield stress was too low, this model feature was close to the fluid.

According to equation ( 3), combined with the pressure -volume deformation relationship, it's easy to calculate the force between soil particles.

Because this test was mainly carried out in indoor soil, SPH model of the soil was established according to indoor soil tank model. Because the structure of soil which has time and space diversity is extremely complex, it is difficult to observe the process of cutting soil by Oblique rotary in the physical prototype test. The numerical simulation could accurately describe the damage process of cutting soil by Oblique rotary and the force of machine, and then the mechanism of Oblique rotary tilling soil could be revealed.

The Research on Lateral Tear of Soil

Tillage machinery approach to design was best "destroyed pull" and followed by "destroyed by cutting" and avoided "was destroyed by pressure" as far as possible to reduce energy consumption and improve the economy. In Oblique rotary, the soil was torn lateral that was possible main reason for oblique rotary saving energy. Through the physical prototype of lateral tear of the lateral edge effects on soil was more difficult, but the numerical simulation could be more clearly reveal the process. The particle of soil no.166,544 which was to be cutting by the rotary blade was selected (Figure 4) .The coordinates of x, y, z were 0.045, 0.226, -0.05 and unit forward speed was 0.5m / s and rotate speed was 200 rpm,then the acceleration in X direction was shown in Figure 5. Soil particle no.166,544 generated acceleration of 1370m/s 2 in the X positive, that was to say, the lateral movement of rotary blade torn soil along the positive of X when rotary blade was cutting soil in the soil near the particle no.166,544. Because the rotary cutter blade lateral torn the lateral effect to the soil, the soil was destroyed by "pull" was verified from the perspective of numerical simulation. From this perspective, oblique rotary tillage could greatly reduce energy consumption. The soil particles no.166562 (Figure 6) and no.166482 (Figure 7) which were near the two other lateral edge cutters were chosen. Soil particles no.166,562, and no.166,482 had the same coordinates of the no.166 544 in Y, Z directions and its acceleration in X direction was shown in Figure 8. It could be seen from Figure 8: Soil particles no.166544, no.166562, and no.166482 all had a instantaneous acceleration in the same direction which was along lateral edge of the direction of lateral movement indicating that the soil near the three cutter disc cutter were destroyed by the lateral movement of soil caused by tearing.

Soil particles no.166544, no.166562, and no.166482 of the coordinate of Y were consistent, but the failure time was not the same. This was because the tool axis was inclined to make that the soil produced ordered cut.

The tensile strength of soil particles no.166544, no.166562, no.166,482 reduced in correct order. This is because the tool axis was inclined, cutter head near soil particles no.166544, no.166562 and no.166482 cut soil in proper order to make the follow-up sword plate achieve all or part of the unconfined cut. From the literature, we could get that, unconfined soil strength is much smaller than confined soil strength. The cutter head near the soil particles no.166,544 first cut soil was confined soil cutting, so the tensile strength of it was more larger than the other two cutter heads. On the contrary point of view from the numerical simulation, the correctness of the theory of the side of the limit on soil strength which was introduced in the literature 4 was proved.

The Research of Specified Power Consumption

The specified power consumption was important parameter to measure the performance of a rotavator. To the oblique rotary tiller, the computing formula of specified power consumption is:

m s Av R B N N ) sin 2 cos (     (4)
In the formula (4): N-output (N.m) B-width (cm) α-Oblique angle (°) R-Rolling radius of the blade. In the Figure 9,connection in specified power consumption, phase angle and oblique angle were obtained through laboratory test when the rotational speed was n = 200rpm and V = 0.4m / s. It could be seen from these figures: Measured and simulated values were basically the same and the maximum relative prediction error is 6% indicating that the accuracy of the virtual prototype system could meet the analysis requirements. Whether actual or simulated results showed that when the phase angle was 60° and oblique angle was 25°, the specified power consumption was minimum when speed was n = 200rpm and V = 0.4m / s. It could be used as design reference designing oblique rotary tiller.

The Analysis of Oblique Rotary Tillage Throwing Soil and the Shape of the Ditch after Plow

Throwing soil simulation image was shown in Figure 13 when unit forward speed was 0.5m / s and oblique angle was 25°and rotate speed was 200 rpm and deep tillage was 25 cm. By the statistics of thrown SPH soil particles, the sub-speed X of 93.4% of the thrown soil particles were greater than zero. That was the same as the lateral edge of the axial speed indicating that the velocity of soil thrown was not uniform but lean to tangent blade's direction by simulating throwing soil. We should fully consider the factors when design shell oblique rotary equipment. The shape of ditch after plowing was shown in Figure 14. As could be seen from Figure 14: when forward speed of the unit was 0.5m / s and oblique angle was 25°and rotate speed was 200 rpm and deep tillage was 25 cm, the maximum raised height was 2.4 cm which was 9.6% of the tillage depth and it was accounting for 7.1% of the whole ditch area indicating that after oblique rotary ditch was basic level could meet the farming requirements.

Conclusion:

Through this study, we could obtain the following conclusions: The result of digital simulation indicates that soil mainly was torn to be destroyed during oblique rotary tilling. The failure modes made more soil "destroyed pull" significant savings in energy work. This could also explain the phenomenon that oblique rotary tilling soil was easier in testing process. The correctness of the simulation system was verified by the comparison of the test of oblique rotary tilling in specified power consumption between indoor and numerical simulation. At the same time, the best oblique angle and the optimum phase angle was given when the speed was n = 200rpm and V = 0.4m / s. By simulating throwing soil, the velocity of soil thrown was not uniform but lean to tangent blade's direction. We should fully consider the factors when design shell oblique rotary equipment. When forward speed of the unit was 0.5m / s and oblique angle was 25°and rotate speed was 200 rpm and deep tillage was 25 cm, the maximum raised height was 2.4 cm which was 9.6% of the tillage depth and it was accounting for 7.1% of the whole ditch area indicating that after oblique rotary ditch was basic level could meet the farming requirements.

Fig. 1 .

 1 Fig.1. Saltus curve the SPH model of soilIn this paper, soil and crushable foam model is as a soil model. The mechanical model about the tool contacting with soil was shown in Figure2.Virtual prototyping system of oblique rotary was developed was shown in Figure3. Rotary blade shaft was consists of three cutter head and each cutter head install two rotary cutter blade, so a total of six rotary blade was installed on the cutter shaft. Variables contains tillage depth, oblique angle, phase angle could be controlled in virtual prototyping system.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Saltus curve the contact mechanical model between soil and cutter

Fig. 4 .Fig. 5 .

 45 Fig. 4. The location of soil element no.166544

Fig. 6 .Fig. 7 .

 67 Fig. 6. The location of soil element no.166562

Fig. 8 .

 8 Fig. 8. The acceleration of soil element no.166544, 166562,166482

Fig. 9 .Fig. 10 .Fig. 11 .Fig. 12 .Figure 10 ,

 910111210 Fig.9. The specific power of test

Fig. 13 .

 13 Fig.13. Throwing Soil simulation of oblique rotary tilling

Acknowledgements

This work was financially supported by Science and Technology research project of Jiangsu Province (BE2007355), by Agricultural Science and Technology project of Zhenjiang City (NY2008003) and "Qinglan" Project of Jiangsu Province


Guanlin Li

Zhanhong Ma

Haiguang Wang
email: wanghaiguang@cau.edu.cn

Image Recognition of Grape Downy Mildew and Grape Powdery Mildew Based on Support Vector Machine

Keywords: Grape downy mildew, grape powdery mildew, image recognition, support vector machine

In order to realize automatic disease diagnosis and provide related information for disease prediction and control timely and accurately, the identification and diagnosis of grape downy mildew and grape powdery mildew was conducted based on image recognition technologies. The method based on K_means clustering algorithm was used to implement unsupervised segmentation of the disease images. Fifty shape, color and texture features were extracted from the images of the diseases. Support vector machine (SVM) classifier for the diseases was designed based on thirty-one effective selected features. The training recognition rates of these two kinds of grape diseases were both 100%, and the testing recognition rates of grape downy mildew and grape powdery mildew were 90% and 93.33%, respectively. The recognition results using the SVMs with different kernels indicated that the SVM with linear kernel was the most suitable for image recognition of the diseases. This study provided an effective way for rapid and accurate identification and diagnosis of plant diseases, and also provided a basis and reference for further development of automatic diagnosis system for plant diseases.

Introduction

Grape downy mildew caused by Plasmopara uiticola (Berk.dt Curtis) Berl. Et de Toni and grape powdery mildew caused by Uncinula necator (Schw.) Burr., are two kinds of common grape diseases in greenhouse [START_REF] Li | Occurrence and Damage of the Main Diseases of Grape in Greenhouse During Growth Period and Shelf Life (in Chinese)[END_REF]. They could cause serious damage via affecting grape quality and yield. The pathogens causing plant diseases are always difficult to be identified by using naked-eye observation method directly and disease symptoms are always influenced by various factors. So it is difficult to diagnose plant diseases accurately and effectively by using the traditional plant disease diagnosis method that is mainly dependent on naked-eye observation. Instead of using naked eyes, plant diease images could be processed using computer vision. This method could eliminate the image noise caused by environmental factors and image capture elements, and could diagnose the plant diseases by computer automatically. And then diagnosis results could be output directly. Thus the intuitive, practical, reliable and accurate information of plant disease diagnosis could be provided to the agricultural technicians and the farmers, and then could be used to guide plant disease control.

Real-time diagnosis of plant disease could be conducted quickly and accurately by using computer vision. Computer image recognition technologies have been widely used in plant disease studies. Image recognition of plant diseases is usually conducted based on shape features [START_REF] Sasaki | Automatic Diagnosis of Plant Diseaserecognition Between Healthy and Diseased Leaf[END_REF], [START_REF] Zhao | Research of Maize Leaf Disease Identifying System Based Image Recognition (in Chinese)[END_REF], color features [START_REF] Sanyala | Pattern Recognition Method to Detect Two Diseases in Rice Plants[END_REF], [START_REF] Cui | Image Processing and Extracting Color Features of Greenhouse Diseased Leaf (in Chinese)[END_REF], [START_REF] Tian | Study on Plant Disease Recognition Using Support Vector Machine and Chromaticity Moments (in Chinese)[END_REF], [START_REF] Tian | Applied Research of Support Vector Machine on Recognition of Cucumber Disease (in Chinese)[END_REF], [START_REF] Cen | Discrimination of Cucumber Anthracnose and Cucumber Brown Speck Based on Color Image Statistical Characteristics (in Chinese)[END_REF] or texture features [START_REF] Pydipati | Identification of Citrus Disease Using Color Texture Features and Discriminant Analysis[END_REF] by using the methods, such as artificial neural network (ANN) [START_REF] Pydipati | Statistical and Neural Network Classifiers for Citrus Disease Detection Using Machine Vision[END_REF], support vector machine (SVM) [START_REF] Tian | Study on Plant Disease Recognition Using Support Vector Machine and Chromaticity Moments (in Chinese)[END_REF], [START_REF] Tian | Applied Research of Support Vector Machine on Recognition of Cucumber Disease (in Chinese)[END_REF], [START_REF] Camargo | Image Pattern Classification for the Identification of Disease Causing Agents in Plants[END_REF] and discriminant analysis [START_REF] Pydipati | Identification of Citrus Disease Using Color Texture Features and Discriminant Analysis[END_REF], [START_REF] Wang | Maize Leaf Disease Identification Based on Fisher Discrimination Analysis[END_REF].

SVM is a new kind of pattern recognition algorithm based on VC dimension theory and structural risk minimization principle, which can solve the small sample, nonlinear problems, high dimension and local minimum points and other practical issues [START_REF] Vapnik | The Nature of Statistical Learning Theory[END_REF]. SVMs have been used for image recognition of plant diseases, such as corn leaf diseases [START_REF] Tian | Study on Plant Disease Recognition Using Support Vector Machine and Chromaticity Moments (in Chinese)[END_REF], [START_REF] Song | Corn Leaf Disease Recognition Based on Support Vector Machine Method (in Chinese)[END_REF], cucumber leaf diseases [START_REF] Ren | Research on Plant Disease Recognition Based on Linear Combination of the Kernel Function Support Vector Machine (in Chinese)[END_REF] and cotton diseases [START_REF] Camargo | Image Pattern Classification for the Identification of Disease Causing Agents in Plants[END_REF]. However, the SVM based on multiple feature parameters has not yet been applied to image recognition of grape diseases.

In order to provide quick and reliable identification and diagnosis methods for grape downy mildew and grape powdery mildew in greenhouse, image recognition of these two kinds of grape diseases was conducted by using the method based on SVM and multiple feature parameters according to the properties of the disease images in this study.

2

Image acquisition and image processing

Disease Image Acquisition and Image Preprocessing

Disease images were acquired from grape leaves that were naturally infected by downy mildew and powdery mildew. In order to obtain clear vertical projection images of diseased single leaves, the diseased grape leaves which surfaces were clean were selected, and then disease images were taken by using common digital camera that was perpendicular to the leaves. Fifty images of grape downy mildew were obtained and thirty-five images of grape powdery mildew were obtained. The size of the original images obtained was 2592×1944 with format of jpg, 24 bitmap. To improve the operation speed of computer programs, the images were compressed from 2592×1944 to 800×600 in the same proportion without changing the image resolution using the nearest neighbor interpolation method. And then the plant disease images were denoised with median filter algorithm.

Disease Color Image Segmentation

The obtained grape disease images were composed of diseased regions and normal leaf regions. In order to extract effective features, disease color image segmentation was conducted to segment diseased regions from the images. It included binary segmentation and color segmentation. The former was to extract shape features from the segmented images and the latter was to extract color features and texture features from the segmented images. Currently, the methods used for plant disease image segmentation are edge detection method [START_REF] Zhang | A Study on the Segmentation Method in Image Processing for Plant Disease of Greenhouse (in Chinese)[END_REF], fuzzy C-means clustering method [START_REF] Mao | Segmentation of Crop Disease Leaf Images Using Fuzzy C-means Clustering Algorithm (in Chinese)[END_REF], segmentation method based on statistical pattern recognition [START_REF] Tian | Color Image Segmentation Method Based on Statistical Pattern Recognition for Plant Disease Diagnose (in Chinese)[END_REF], etc. In this study, K_means clustering algorithm was used to segment plant disease images. This algorithm is an unsupervised real-time clustering algorithm proposed by Mac Queen [START_REF] Selim | K-means-type Algorithm: a Generalized Convergence Theorem and Characterization of Local Optimality[END_REF]. The basic idea of this algorithm is to identify the regions where the different colors belong to based on the different colors in the image color space, so as to achieve the purpose of image segmentation. The implementation process of this segmentation method has been used in [START_REF] Li | Segmentation of Color Images of Grape Diseases Using K_means Clustering Algorithm[END_REF]. Using the K_means clustering algorithm, binary segmentation and color segmentation of the images of grape downy mildew and grape powdery mildew were conducted, and the results were as shown in Fig. 1.

The results indicated that this method could segment the diseased regions from the color images of the diseases with good accuracy and robustness, and that the binary segmentation images and the color segmentation images of the grape diseases could be used for subsequent image feature extraction.

Disease Image Feature Extraction

Fifty features including four shape features (Table 1), twenty-one color features (Table 2) and twenty-five texture features (Table 3), were extracted from the segmented images of the diseases. These features were regarded as candidate features for subsequent classification and recognition of grape diseases. Among these features, color features and texture features were based on RGB color model and HSV color model.

    N y x y x f S 0 , 0 ) , (
in which, N is lesion area and f(x,y) is unit coordinate of lesion area.

S refers to the total number of pixels contained in the lesion areas in the segmented image.

Perimeter (L) 1     N i i l L 1
in which, N is lesion area, and △l i is unit coordinate at the edge of the contour of the lesion area.

L refers to the total number of pixels at the edge of the contour of the lesion areas in the segmented image.

Circularity (C) 1 2 4 L S C  
C describing the extent of lesion area close to the circle is between 0 and 1.

Complexity (E) 1

S L E 2  E describing the complexity and dispersion degree of lesion area is greater than 1.

Disease image recognition

Disease image recognition was conducted using the SVM classifier designed in MATALAB 7.6. Fifty images including thirty images of grape downy mildew and twenty images of grape powdery mildew which size was 800×600 were randomly chosen as the training set. The remaining thirty-five images including twenty images of grape downy mildew and fifteen images of grape powdery mildew were regarded as the testing set. After preprocessing, segmentation and feature extraction of each disease image, each feature, groups of features and all fifty features were used as inputs to the SVM classifier, respectively. And then the SVM used the features with significant contributions as input nodes to optimize the weights. In all cases, cross validation was used as the evaluation method to evaluate the contribution of each feature to the recognition effect of the SVM. The features with negative contributions to the recognition effect were removed. Finally, the optimal combination of the features was selected to achieve the image recognition of grape downy mildew and grape powdery mildew. Based on the optimal combination of the features, the SVMs with the four basic kernels, i.e. linear, polynomial, radial basis function (RBF) and sigmoid, were used to recognize the disease images with fifty images as the training set and thirty-five images as the testing set, in order to determine which kernel function was the best suitable for image recognition of grape diseases.

Table 2. Twenty-one candidate color features and their corresponding calculation formulas.

Features Number of measurements Calculation formulas Comments

Mean of gray values of R/G/B component 3

) ( 1 1 j G j j f p f G    
in which, G is the total number of pixels contained in the diseased region, f j is an arbitrary coordinate in the diseased region, and p(f j ) is color value of the point at f j . This feature is the mean of color values in the diseased region in color segmentation image.

Variance of gray values

of R/G/B component 3 ) ( ) ( 1 1 2 2 j G j j f p f G      
in which, G, f j and p(f j ) are the same as above.

This feature is the square difference of color values in the diseased region in color segmentation image.

Skewness of gray values

of R/G/B component 3 ) ( ) ( 1 1 3 3 j G j j f p f G      
in which, G, f j and p(f j ) are the same as above.

This feature is the cubic difference of color values in the diseased region in color segmentation image.

Color ratio in RGB color model: r, g, b

3 ) /( ) /( ) /( B G R B b B G R G g B G R R r          Mean of gray values of H/S/V component 3 ) ( 1 1 j G j j f p f G    
in which, G, f j and p(f j ) are the same as above.

Variance of gray values of

H/S/V component 3 ) ( ) ( 1 1 2 2 j G j j f p f G      
in which, G, f j and p(f j ) are the same as above.

Skewness of gray values of

H/S/V component 3 ) ( ) ( 1 1 3 3 j G j j f p f G      
in which, G, f j and p(f j ) are the same as above.

Table 3. Twenty-five candidate texture features and their corresponding calculation formulas.

Features

Conclusions

In this study, the binary segmentation and the color segmentation of diseased regions from the color images of grape downy mildew and grape powdery mildew were implemented using K_means clustering algorithm. This method could segment the diseased regions from the color images of the diseases with good accuracy and robustness. Using the SVM classifier with linear kernel based on the thirty-one features selected from the fifty shape, color and texture features extracted from the images of grape downy mildew and grape powdery mildew, the best recognition rate for the training set including fifty disease images was 100% and the best recognition rate for the testing set including thirty-five disease images was 91.43%. The average recognition rate of the training set and the testing set was 95.83%. For these two kinds of grape diseases, the SVM classifier with linear kernel had the best and stable classification performance. This study provides an approach for rapid and accurate identification and diagnosis of grape downy mildew and grape powdery mildew under the greenhouse environment.

Discussion

The symptoms are always different during the development of plant disease. And the symptoms on the upper and lower surfaces of a diseased leaf are not always the same. For example, the grape leaves infected with downy mildew shows chlorotic at the early stages, and the color changes of the lesions are not obvious, so it is difficult to carry out image processing. Without taking into account the symptoms at the early stages, this study only dealed with the images with typical symptoms on the upper surfaces of the diseased leaves. Image acquisition is often influenced by many kinds of factors including ambient lighting conditions, the differences between samples collected, the differences between acquisition devices, etc. In recent years, the common low-resolution digital cameras and even cell phones are used to capture images of plant diseases during the research on image recognition of plant diseases [START_REF] Li | Research on Identify Technologies of Apple's Disease Based on Mobile Photograph Image Analysis[END_REF]. The common digital cameras or cell phones that are more portable and widely used, are very important to promote practical application of image recognition technologies to the plant diseases.

The methods of plant disease image segmentation in many studies are based on gray differences. The method bases on K_means clustering algorithm used in this study had good accuracy and robustness, and it could be an effective method to segment plant disease images. However, this method demands high image quality and needs computer to run a long time. Therefore, it is important to control the impact of complex environmental factors in the process of collecting disease images, and it is better to compress the images in the same proportion without changing the image resolution in order to improve the running of computer programs.

The features of the lesions of different plant diseases are not the same. However, only one feature or no enough features were used in many studies on image recognition of plant diseases, and it is difficult to meet the needs of identification and diagnosis of different targets. It is necessary to utilize shape, color and texture features to image recognition of plant diseases. Fifty shape, color and texture features extracted from the images of grape downy mildew and grape powdery mildew were regarded as candidate features for image recognition in this study. Guan et al [START_REF] Guan | Study on Recognition Method of Rice Disease Based on Image (in Chinese)[END_REF] extracted sixty-three parameters of shape, color and texture features from disease images for image recognition of rice diseases.

With the development of agricultural informatization, more and more agricultural technicians and farmers could get access to the internet and solve the problems in agricultural production using web-based expert systems. With the popularity of digital photography products, the acquisition of plant disease images is easier. Web-based systems for image recognition of plant diseases should be developed to meet the needs of the agricultural technicians and the farmers. When the users upload digital images of plant diseases to the systems, the names of plant diseases and related information could be provided for plant disease control.

Fig. 1 .

 1 Fig.1. Results of image segmentation of grape downy mildew and grape powdery mildew.

  P is the total number of pixels contained in the diseased region, (i,j) is an arbitrary coordinate in the diseased region, and p(i,j) is color value of the point at (i,j). which, P, (i,j) and p(i,j) are the same as above.Correlation is used to measure the degree of similarity of the elements of gray-level co-occurrence matrix (GLCM) at the row direction which, P, (i,j) and p(i,j) are the same as aboveP, (i,j) and p(i,j) are the same as above. I is the total number of pixels contained in the diseased region, r is an arbitrary pixel in the diseased region, and N r is box's size.D is used to measure the fractal characteristics. Box's size is 4×4.

Table 1 .

 1 Four candidate shape features and their corresponding calculation formulas.

	Features	Number of measurements	Calculation formulas	Comments
	Area (S)	1

Table 5 .

 5 Results of image recognition using the SVMs with different kernel functions.

	Kernel function	Disease	Sample size Training Testing	Number of correct recognition Training Testing	Recognition rate/% Training Testing
			set	set	set	set	set	set
	Linear	Grape downy mildew	30	20	30	18	100	90
	function	Grape powdery mildew	20	15	20	14	100	93.33
	Polynomial	Grape downy mildew	30	20	30	20	100	100
	function	Grape powdery mildew	20	15	2	1	10	6.67
	Radial basis	Grape downy mildew	30	20	30	19	100	95
	function	Grape powdery mildew	20	15	20	13	100	86.67
	Sigmoid	Grape downy mildew	30	20	30	19	100	95
	function	Grape powdery mildew	20	15	19	13	95	86.67

Note: The feature with positive contribution to the recognition effect was recorded as "+". The feature with no contribution to the recognition effect was recorded as "○". And the feature with negative contribution to the recognition effect was recorded as "-".

Acknowledgments. The work was supported by National Key Technology R&D Program (2007BAD57B02).

Results and Analysis

Optimization Results of the Features and Analysis

Based on the SVM classifier with the default RBF as kernel function in MATALAB 7.6, optimization of the features and image recognition of grape downy mildew and grape powdery mildew were conducted using the method described above. The results were as shown in Table 4. While each feature was used as input to SVM classifier, the obtained recognition rate was regarded as individual recognition rate. While group of features was used as inputs to SVM classifier, the obtained recognition rate was regarded as group recognition rate. While all fifty features were used as inputs to SVM classifier, the obtained recognition rate was regarded as total recognition rate. And while the optimal combination of the features was used as inputs to SVM classifier, the obtained recognition rate was regarded as best recognition rate.

The results indicated that every individual recognition rate was not exactly the same and that group recognition rate was different with each other. In the groups of color features, recognition effect of the SVM with the first-order moments of color or the second-order moments of color as inputs was better than that with the third-order moments of color as inputs. In the groups of texture features, recognition effect of the SVM with parameters of gray-level co-occurrence matrix (GLCM) in HSV color model as inputs was better than that with parameters of GLCM in RGB color model as inputs.

The results showed that there were eight features with no contributions to recognition effect, eleven features with negative contributions to the recognition effect and thirty-one features with positive contributions to the recognition effect. Using these thirty-one features as inputs to SVM classifier, the best recognition effect was obtained. Based on these thirty-one features, optimal parameters were obtained after training using SVM classifier, i.e. c=128 and g=0.0625. The best recognition rate for the fifty images in the training set was 100%, and the best recognition rate for the thirty-five images in the testing set was 91.43%.

Results of Image Recognition Based on the SVMs with Different Kernel and Analysis

Based on the SVMs with different kernel functions, the images of grape downy mildew and grape powdery mildew were recognized using the selected thirty-one features as inputs and the comparison of corresponding recognition effects was conducted. The results were as shown in Table 5. The results showed that, for grape downy mildew and grape powdery mildew, the SVM with linear kernel had the best recognition effect and the average recognition rate of the training set and the testing set was 95.83%. The average recognition rate using the SVM with RBF kernel was 95.42% and that using the SVM with sigmoid kernel was 94.17%. The average recognition rate using the SVM with polynomial kernel was only 54.17%. Therefore, the SVM with linear kernel was the most suitable method for image recognition of grape downy mildew and grape powdery mildew based on multiple feature parameters. Using the SVM with linear kernel, the recognition rates of these two kinds of grape diseases were both 100% for the training sets, and for the testing sets, the recognition rates of grape downy mildew and grape powdery mildew were 90% and 93.33%, respectively.


Haiguang Wang
email: wanghaiguang@cau.edu.cn

Jiebin Guo

Zhanhong Ma

Monitoring Wheat Stripe Rust Using Remote Sensing Technologies in China

Keywords: Wheat stripe rust, remote sensing, monitoring

Many studies on remote sensing monitoring of plant diseases have been conducted. Remote sensing (RS) has played an important role in monitoring some kinds of plant diseases and making decisions for the management of the diseases. Progress on remote sensing monitoring of wheat stripe rust in China was summarized from four aspects including remote sensing monitoring stripe rust of single wheat leaves and monitoring this disease using ground, aerial and space remote sensing technologies. The phenomena of same object with different spectra and different objects with same spectrum, the lowest threshold of disease prevalence for remote sensing monitoring, the spectral information distilling technologies, and the methods to develop inversion models based on spectral information were also discussed. Moreover, the development trends of multi-pest remote sensing, space remote sensing and integrated utilization of RS, geographical information system (GIS) and global positioning system (GPS) in monitoring wheat stripe rust were prospected.

Introduction

Remote sensing (RS) is to acquire spectral information or images of an object in a far distance by receiving electromagnetic waves that the object reflects or radiates using either recording or real-time sensing devices. Since the 1960s, remote sensing technologies have being developed quickly. In the 1980s, with the rise of hyperspectral remote sensing, more and narrower wavebands of electromagnetic waves could be used to acquire related information from certain object, and then the application areas of remote sensing technologies were broadened further [START_REF] Pu | Hyperspectral Remote Sensing and Its Applications[END_REF]. Now remote sensing technologies have been widely applied in many disciplinary fields, such as geography, geology, meteorology, ecology, oceanography and agronomy.

When plant disease occurs, biochemical components and organization structure inside plant leaves would be induced to change. Accordingly, the changes of the spectral characteristics of the plants and remote sensing images would be caused, and would show some specificity, which provides a basis for remote sensing monitoring of plant diseases [START_REF] Liu | Preliminary Feasibility Exploration of Prediction of Crop Pests Using Remote Sensing Technologies (in Chinese)[END_REF], [START_REF] Chen | Research Progress on Hyperspectral Remote Sensing in Monitoring Crop Diseases and Insect Pests (in Chinese)[END_REF]. Generally, either crop plant pests, fruit tree pests, forest pests or grassland pests that could cause discoloration or deformation of the leaves, or produce residues on the leaf surfaces, could be monitored using remote sensing technologies [START_REF] Li | Application of Remote Sensing Technologies in Investigation of Plant Pests (in Chinese)[END_REF]. There have been many reports about remote sensing monitoring of plant diseases in the world [START_REF] Greaves | Identification of Barley Yellow Dwarf Virus and Cereal Aphid Infestation in Winter Wheat by Aerial Photography[END_REF], [START_REF] Malthus | High Resolution Spectroradiometry: Spectral Reflectance of Field Bean Leaves Infected by Botrytis fabae[END_REF], [START_REF] Adams | Yellowness Index: an Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation[END_REF], [START_REF] Steddom | Remote Detection of Rhizomania in Sugar Beets[END_REF], [START_REF] Nutter | Quantifying Alfalfa Yield Losses Caused by Foliar Diseases in Iowa, Ohio, Wisconsin, and Vermont[END_REF], [START_REF] Guan | Quantifying the Intrarater Repeatability and Interrater Reliability of Visual and Remote-sensing Disease-assessment Methods in the Alfalfa Foliar Pathosystem[END_REF], [START_REF] Nakane | Assessment of Pine Forest Damage by Blight Based on Landsat TM Data and Correlation with Environmental Factors[END_REF], [START_REF] Wu | Effects of Rice Leaf Blast on Spectrum Reflectance of Rice (in Chinese)[END_REF], [START_REF] Liu | A Remote Sensing Monitoring System for the Damage Caused by Forest Insects and Diseases Using TM Images (in Chinese)[END_REF], [START_REF] Qiao | Influence of Fusarium Wilt to Different Resistance Cultivars on Spectrum of Cotton (in Chinese)[END_REF]. The reports about remote sensing monitoring of wheat stripe rust caused by Puccinia striiformis f. sp. tritici, were almost published by Chinese researchers.

In China, stripe rust is the most important disease of wheat [START_REF] Li | Wheat Rust in China[END_REF]. The four most destructive epidemics of this disease occurred in 1950, 1964, 1990 and 2002, which caused yield losses up to 6.0, 3.2, 1.8 and 1.3 million metric tons, respectively [START_REF] Li | Wheat Rust in China[END_REF], [START_REF] Wan | Wheat Stripe Rust Epidemic and Virulence of Puccinia striiformis f. sp.tritici in China in 2002[END_REF]. Prevention and control of the disease is very important for wheat production and even for food security in China. In order to control wheat stripe rust effectively, it is necessary to carry out disease monitoring to obtain the information on the incidence of the disease timely and accurately. Traditionally, monitoring of this disease in China mainly relies on field surveys by human power, which is time-consuming and energyconsuming. The subjectivity of the monitoring results and a certain hysteretic nature of the acquired information seriously affect the accuracy of disease forecast. The development of remote sensing technologies could make quickly, conveniently, economically and accurately real-time monitoring wheat stripe rust come true. In recent years, a lot of funds have been invested in the studies on remote sensing technologies for monitoring wheat stripe rust in China and a lot of results have been obtained. In this paper, the progress on remote sensing monitoring of wheat stripe rust in China was reviewed from four aspects, namely, remote sensing monitoring stripe rust of single wheat leaves and ground, aerial and space remote sensing monitoring of wheat stripe rust. Furthermore, some problems in the studies were discussed and the development trends of remote sensing monitoring wheat stripe rust were prospected.

Monitoring Single Wheat Leaves Infected by Stripe Rust Using Remote Sensing Technologies

The studies on monitoring single wheat leaves infected by stripe rust using remote sensing technologies could provide theoretical basis for remote sensing monitoring the prevalence of this disease. The results obtained by Huang et al [START_REF] Huang | Spectral Reflectance Feature of Winter Wheat Single Leaf Infected with Stripe Rust and Severity Level Inversion (in Chinese)[END_REF] indicated that the wavebands in 446-725 nm as well as 1380-1600 nm were sensitive to the severity level of wheat stripe rust. In their study, using the established mathematical models (shown in Table 1) with designed spectral angle index (SAI), designed absorption area index (AAI), absorption depth and absorption area as independent variable, respectively, the disease severity level of winter wheat leaf could be inversed with high accuracy. It has been found that spectral reflectance of diseased areas of wheat leaves was positively correlated with disease severity when the wavelength was in 350-1600 nm [START_REF] An | Preliminary Study on Spectral Characteristics of Single Leaf Infected by Puccinia striiformis (in Chinese)[END_REF]. On the wave band from 670 nm to 690 nm where reflectance rate was correlated with disease severity significantly, regression model (P<0.0001) (shown in Table 1) elucidating the relationship between wavelength and disease severity was established [START_REF] An | Preliminary Study on Spectral Characteristics of Single Leaf Infected by Puccinia striiformis (in Chinese)[END_REF]. Wang et al [START_REF] Wang | Application of Hyperspectral Data to the Classification and Identification of Severity of Wheat Stripe Rust (in Chinese)[END_REF] applied support vector machine (SVM) to process hyperspectral data obtained from 88 leaves including healthy leaves and infected leaves over a range of disease severity levels. The identification model was built based on 44 proof-read samples to estimate 44 proof-test samples and the identification accuracy was totally 97%. The results indicated that SVM could be used in the classification and identification of severity of wheat stripe rust based on obtained spectral data. R 685nm and R 690nm mean the reflectance rates on 685 nm and 690 nm, respectively. Note: The first four models were established by Huang et al [START_REF] Huang | Spectral Reflectance Feature of Winter Wheat Single Leaf Infected with Stripe Rust and Severity Level Inversion (in Chinese)[END_REF] and the last one was established by An et al [START_REF] An | Preliminary Study on Spectral Characteristics of Single Leaf Infected by Puccinia striiformis (in Chinese)[END_REF].

Monitoring Wheat Stripe Rust Using Ground Remote Sensing Technologies

There have been many studies on monitoring wheat stripe rust using ground remote sensing technologies. They mainly focused on canopy spectral properties, inversion of disease index (DI), changes and inversion of biochemical parameters of wheat infected by stripe rust. Some spectral indexes (shown in Table 2) were used to analyze spectral properties, and some inversion models of wheat stripe rust were built based on spectral information (shown in Table 3). The inversion effects of the models were relatively insensitive to wheat varieties [21], [22]. Therefore, these models could be applied to monitor stripe rust on different wheat varieties. The studies clarified the mechanism of remote sensing monitoring of wheat stripe rust and provided the theoretical basis for monitoring this disease by using aerial and space remote sensing technologies.

The relationship between canopy reflectance and DI of wheat infected by stripe rust has been analyzed. Different results were obtained in different studies. Huang et al [START_REF] Huang | Hyperspectral Character of Stripe Rust on Winter Wheat and Monitoring by Remote Sensing (in Chinese)[END_REF] found that 630-687 nm, 740-890 nm and 976-1350 nm were sensitive bands to DI of winter wheat stripe rust and the red edge position of diseased wheat shifted to the blue. The study conducted by Cai et al [START_REF] Cai | Remote Sensing Research on Monitoring Technology of Wheat Stripe Rust (in Chinese)[END_REF] showed that 769-938 nm was the sensitive band to DI. It has been found that DI of wheat stripe rust was highly correlated with the first derivative data of the canopy reflectance of winter wheat in the regions of 432-582 nm, 637-701 nm and 715-765 nm [22], [START_REF] Jiang | Using Hyperspectral Derivative Indices to Diagnose Severity of Winter Wheat Stripe Rust (in Chinese)[END_REF]. The results obtained by Li et al [START_REF] Li | Using Hyperspectral Derivative Index to Identify Winter Wheat Stripe Rust Disease (in Chinese)[END_REF] and Jiang et al [START_REF] Jiang | Using Hyperspectral Derivative Index to Monitor Winter Wheat Disease (in Chinese)[END_REF] showed that the first derivative values of winter wheat infected by stripe rust increased at the green edge (500-560 nm), while decreased at the red edge (680-760 nm) with DI increasing. The ratio of the sum of derivative within the red region (SDr) and that within the green region (SDg) and the ratio of SDr′ and SDg′ were both highly correlated with DI of wheat stripe rust, and could be used to identify the disease effectively twelve days before symptoms appearing [START_REF] Li | Using Hyperspectral Derivative Index to Identify Winter Wheat Stripe Rust Disease (in Chinese)[END_REF], [START_REF] Jiang | Using Hyperspectral Derivative Index to Monitor Winter Wheat Disease (in Chinese)[END_REF]. The study conducted by Guo et al [21] indicated that DI has positive correlation with canopy reflectance in visible region and that it has significant negative correlation in the near infrared region and has stable negative correlation with the first derivative in the region of 700-760 nm. Zhang et al [START_REF] Zhang | Correlation Analysis of Multi-temporal Canopy Reflectance and Disease Index of Wheat Stripe Rust (in Chinese)[END_REF] found that with DI increasing, canopy reflectance in 600-703 nm decreased distinctively and that in 770-930 nm increased distinctively. Wang et al [START_REF] Wang | The Application of Partial Least Square to the Inversion of Severity of Winter Wheat Stripe Rust Disease (in Chinese)[END_REF] used partial least square (PLS) to build a regression model for disease severity inversion based on canopy reflectance, and analysis results of regression coefficients of PLS showed that the first derivatives on the two sides of the chlorophyll absorption valley (505-550 nm, 640-670 nm, 680-700 nm) were very important for the assessment of disease severity.

SDb

Sum of first derivative within blue region [21], [22], [START_REF] Jiang | Using Hyperspectral Derivative Indices to Diagnose Severity of Winter Wheat Stripe Rust (in Chinese)[END_REF]

SDg

Sum of first derivative within green region [21], [22], [START_REF] Jiang | Using Hyperspectral Derivative Indices to Diagnose Severity of Winter Wheat Stripe Rust (in Chinese)[END_REF], [START_REF] Li | Using Hyperspectral Derivative Index to Identify Winter Wheat Stripe Rust Disease (in Chinese)[END_REF]

SDr

Sum of first derivative within red region [22], [21], [START_REF] Jiang | Using Hyperspectral Derivative Indices to Diagnose Severity of Winter Wheat Stripe Rust (in Chinese)[END_REF], [START_REF] Li | Using Hyperspectral Derivative Index to Identify Winter Wheat Stripe Rust Disease (in Chinese)[END_REF]

SDr′

Sum of first derivative within red peak region [22], [START_REF] Jiang | Using Hyperspectral Derivative Index to Monitor Winter Wheat Disease (in Chinese)[END_REF]

Dx

First derivative value of reflectance in x nm [22], [START_REF] Jiang | Using Hyperspectral Derivative Indices to Diagnose Severity of Winter Wheat Stripe Rust (in Chinese)[END_REF]

Dr

The maximum of first derivative in red region [22], 23] Dg

The maximum of first derivative in green region [22]

SDg′

Sum of first derivative within green peak region [22], 25] Rx Reflectance in x nm [21], [START_REF] Cai | Remote Sensing Research on Monitoring Technology of Wheat Stripe Rust (in Chinese)[END_REF], 27] DSr Thinness of the first derivative of red edge spectra [START_REF] Wang | Two New Red Edge Indices as Indicators for Stripe Rust Disease Severity of Winter Wheat (in Chinese)[END_REF] Ar Asymmetry of the first derivative of red edge spectra [START_REF] Wang | Two New Red Edge Indices as Indicators for Stripe Rust Disease Severity of Winter Wheat (in Chinese)[END_REF] REP

Red edge position [29] YEP

Yellow edge position [START_REF] Jiang | Using the Distance Between Hyperspectral Red Edge Position and Yellow Edge Position to Identify Wheat Yellow Rust Disease (in Chinese)[END_REF] Wang et al [START_REF] Wang | Two New Red Edge Indices as Indicators for Stripe Rust Disease Severity of Winter Wheat (in Chinese)[END_REF] put forward two new red-edge indexes (DSr and Ar) to predict wheat stripe rust. DSr, an index describing thinness of the first derivative of red edge spectra (Dred) of winter wheat infected by stripe rust, was defined as the ratio of maximum value of Dred and sum of Dred. Ar, an index describing asymmetry of Dred, was defined as (S 2 -S 1 ) /(S 2 +S 1 ), where S 1 and S 2 were the sums of Dred in 680-700 nm and 701-775 nm, respectively. Jiang et al [START_REF] Jiang | Using the Distance Between Hyperspectral Red Edge Position and Yellow Edge Position to Identify Wheat Yellow Rust Disease (in Chinese)[END_REF] analyzed the relationship between DI and two spectral features, namely, the red edge position (REP) and yellow edge position (YEP) of the first derivative values. The results showed that with disease severity increasing, REP gradually shifted to the short-wave band, and the YEP gradually shifted to the long-wave band, however, the variable (REP-YEP) quickly became smaller. The model built using REP-YEP as independent variable has the best estimation precision for DI than that built using REP or YEP. Table 3. Some inversion models of wheat stripe rust for monitoring wheat stripe rust using ground remote sensing technologies.

Inversion model

The studies on monitoring the changes of biochemical components of wheat infected by stripe rust were also conducted by using ground remote sensing technologies. The canopy reflectance spectral data of winter wheat at the wavelength of 550-1160 nm had good correlation with DI of stripe rust and some physiological parameters such as chlorophyll content and water content, and chlorophyll content and water content can be used as two important indicators in remote sensing monitoring of the disease [START_REF] Huang | The Physiological Changes of Winter Wheat Infected with Stripe Rust and the Remote Sensing Mechanism of Disease Incidence (in Chinese)[END_REF]. Chlorophyll fluorescence sensing based on Fraunhofer lines could be used to detect wheat stripe rust [START_REF] Zhang | Chlorophyll Fluorescence Sensing to Detect Stripe Rust in Wheat (Triticum aestivum L.) Fields Based on Fraunhofer Lines (in Chinese)[END_REF]. The model established by Jiang et al [START_REF] Jiang | Study on Hyperspectra Estimation of Pigment Contents in Canopy Leaves of Winter Wheat Under Disease Stress (in Chinese)[END_REF] could be used to estimate the contents of chlorophyll a, chlorophyll b and carotenoid in the diseased wheat with good accuracy. The regression model with satisfactory accuracy was established between the content of wheat chlorophyll and the hyperspectral data of wheat infected by stripe rust [START_REF] Jiang | Estimation of the Content of Winter Wheat Chlorophyll Influenced by Yellow Rust with Hyper Spectrum (in Chinese)[END_REF]. The inversion model with the first derivative index (D 750 -D 550 )/(D 750 +D 550 ) as independent variable could be used to estimate canopy chlorophyll densities (CCDs) [START_REF] Jiang | Using Hyperspectral Remote Sensing to Estimate Canopy Chlorophyll Density of Wheat Under Yellow Rust Stress (in Chinese)[END_REF]. Jiang et al [START_REF] Jiang | Hyperspectral Estimation Models for LTN Content of Winter Wheat Canopy Under Stripe Rust Stress (in Chinese)[END_REF] found that the leaf total nitrogen (LTN) of the wheat infected by stripe rust gradually decreased with disease aggravating, and there was high correlation between LTN and first derivative data of canopy reflectance in 430-518 nm, 534-608 nm, 660-762 nm and 783-893 nm, and that the model established with the ratio of sum of the first derivative within red edge and sum of the first derivative within blue edge (SDr/SDb)had satisfactory accuracy. The canopy spectral reflectance of winter wheat under stripe rust stress gradually decreased in the near-infrared region (900-1300 nm) with the reduction of relative water contents, while that gradually increased in the short-wave-infrared region (1300-2500 nm) [START_REF] Jiang | Using Canopy Hyperspectral Ratio Index to Retrieve Relative Water Content of Wheat Under Yellow Rust Stress (in Chinese)[END_REF].

Monitoring Wheat Stripe Rust Using Aerial Remote Sensing Technologies

The studies focusing on aerial remote sensing monitoring of wheat stripe rust have been done in recent years. A study was done to obtain the reflectance data of the wheat infected by stripe rust in field at a fire-balloon flight and near ground, respectively [START_REF] Cai | Comparison Research of Hyperspectral Properties Between Near-ground and High Altitude of Wheat Stripe Rust (in Chinese)[END_REF]. The results showed that the reflectance data from the fire-balloon were dramatically higher than that from near ground in the visible region, while the sum of first derivatives of the reflectance data from the fire-balloon was lower than that from near ground in 537-582 nm and 700-1000 nm. This study provided some certain basis for further studies on monitoring wheat stripe rust by using aerial remote sensing technologies. Liu et al [START_REF] Liu | Monitoring Stripe Rust Disease of Winter Wheat Using Multi-temporal Hyperspectral Airborne Data (in Chinese)[END_REF] analyzed the multi-temporal hyperspectral airborne image data acquired from winter booting stage to milking stage using pushbroom hyperspectral imager (PHI) on Yun-5 plane. The results showed that compared with normal wheat, the image spectral reflectance of diseased wheat was higher in 560-670 nm bands, lower in near infrared bands and the absorption depth of chlorophyll well in red band and the height of chlorophyll peak in green band were relatively reduced. The model built to inverse DI was fitted well to the practical disease index and occurrence ranges. Yang and Guo [START_REF] Yang | Analysis of Hyperspectral Features and Extraction of Disease for Vegetation (in Chinese)[END_REF] analyzed the multi-temporal PHI airborne wheat image data, and the information of wheat stripe rust was recognized in PHI images in terms of wheat spectral features and spectral angle mapping (SAM) technique. The analyses of the PHI images conducted by Luo et al [START_REF] Luo | Monitoring Stripe Rust of Winter Wheat Using PHI Based on Sensitive Bands (in Chinese)[END_REF] showed that red bands (620-718 nm) and near infrared bands (770-805 nm) were the sensitive bands to wheat stripe rust. With the inversion model established using the mean reflectance of red bands and near infrared bands as independent variables and the observed disease indexes as dependent variable, wheat stripe rust was monitored successfully based on PHI images.

Monitoring Wheat Stripe Rust Using Space Remote Sensing Technologies

Fruitful results have been obtained in monitoring forest pests and crop insect pests by using space remote sensing technologies in the world [START_REF] Guo | Utilization of Remote Sensing for Detecting Forest Damage Caused by Insect Infestations or Diseases (in Chinese)[END_REF], [START_REF] Ni | Progress in Application of Remote Sensing and GIS to the Study of Locust Prevention and Control (in Chinese)[END_REF]. However, the studies on monitoring crop diseases using space remote sensing technologies are less, especially for wheat stripe rust. Huang [START_REF] Huang | Monitoring of Winter Wheat Stripe Rust Using Hyperspectral Remote Sensing Data[END_REF] reported that the sensitive spectral bands to DI were similar for three wheat varieties with different susceptibility including Jing 411, 98-100 and Jingdong 8, and that the bands included TM2 (520-600 nm), TM3 (630-690 nm) and TM4 (760-900 nm) of Landsat/TM. Thus, it is possible to monitor wheat stripe rust using the Landsat/TM remote sensed data, and meanwhile the wheat variety parameter could be neglected. Preliminary studies on remote sensing monitoring of wheat stripe rust based on SPOT5 image have been conducted by the researchers from China Agricultural University [START_REF] Zhang | Relativity Research on Nearground and Satellite Remote Sensing Reflectance of Wheat Stripe Rust (in Chinese)[END_REF], [START_REF] Guo | Preliminary Study on Remote Sensing Monitoring Wheat Stripe Rust Based on SPOT5 Image (in Chinese)[END_REF]. They found that SPOT satellite remote sensing spectral information could be used to monitor wheat stripe rust, and the third band of SPOT image or normalized difference vegetation index (NDVI) and RVI calculated using the reflectance values extracted from SPOT image were useful for further studies. Zhang et al [START_REF] Zhang | Monitoring of Winter Wheat Stripe Rust Based on the Spectral Knowledge Base for TM Images (in Chinese)[END_REF] used three PHI images that included different severity levels of stripe rust as a medium to establish the spectral knowledge base of relationships between DI and the simulated reflectance of TM bands by using the empirical inversion model of DI and the relative spectral response (RSR) function of TM-5 sensor. And then Mahalanobis distance or SAM was used to monitor and identify winter wheat stripe rust. The results showed that the infected pixels could be identified accurately from the simulated TM pixels in pustulation and milk stages of winter wheat. Liu et al [START_REF] Liu | Monitoring and Evaluation of the Diseases and Yield Winter Wheat from Multi-temporal Remotely-sensed Data (in Chinese)[END_REF] used multi-temporal remote sensed data to monitor and evaluate the diseases including stripe rust and powdery mildew and the yield of winter wheat. Four Landsat TM images used to analyze were acquired at erecting stage, booting stage, anthesis stage and grain filling stage. The results showed that both the spectral reflectance of diseased wheat at visible and short infrared regions and the red edge position decreased, and that the spectral reflectance at near-infrared region increased. An early yield prediction model developed using the Landsat TM images acquired at the erecting and booting stages was used to evaluate wheat yield loss.

6

Problems in the Studies on Remote Sensing Monitoring of Wheat Stripe Rust

The Phenomena of Same Object with Different Spectra and Different Objects with Same Spectrum

The acquisition of remote sensing spectral data of wheat stripe rust is affected by various conditions. The stability of the spectrum of the disease is often influenced by the device(s), wheat density in the field, soil types, meteorological conditions and other pests. Therefore, the phenomena of same object with different spectra and different objects with same spectrum are caused easily. That is a difficult problem for remote sensing monitoring. To solve this problem, it is important to obtain the specific spectrum or the diagnostic spectrum via basic studies on spectral properties of the wheat infected by stripe rust under different conditions.

The Lowest Threshold of Disease Prevalence for Remote Sensing Monitoring

It is an important objective of remote sensing monitoring of wheat stripe rust to realize early monitoring of the disease and even monitoring the disease under its economic injury level or in incubation period. Reflectance spectra of the diseased wheat in incubation period showed different with that of normal wheat and could be used to identify wheat stripe rust [START_REF] Li | Using Hyperspectral Derivative Index to Identify Winter Wheat Stripe Rust Disease (in Chinese)[END_REF], [START_REF] Cai | Remote Sensing Research on Monitoring Technology of Wheat Stripe Rust (in Chinese)[END_REF]. However, could the disease be found out using remote sensing technologies when a small quantity of wheat leaves infected by stripe rust in the field? Is there the lowest threshold of disease prevalence for remote sensing monitoring of wheat stripe rust? If yes and it is determined, it would be very useful for guiding the early control of wheat stripe rust by using the remote sensing information.

6.3

The Spectral Information Distilling Technologies

The distilling of spectral property information is the premise of application of spectral data of wheat stripe rust and DI inversion. The common methods to distil spectral property information include the methods based on K-L transform, separability criterion, nonlinear criteria and spectral recomposition [START_REF] Tong | Hyperspectral Remote Sensing and It's Multidisciplinary Applications[END_REF]. For information distilling from remote sensing images, the technologies such as image enhancement and image classification seem a bit rough and poor because of the affects of sun elevation angle, meteorological conditions and seasonal variation on the remote sensing images at different time. The image processing methods such as Gramm-Schmidt transformation, principal component analysis (PCA), tasseled cap transformation (TCT), spectral mixture analysis and change vector analysis (CVA), and remote sensing image classification methods such as maximum likelihood classification, artificial neural network (ANN) and SVM, could be used to improve information distilling from remote sensing images.

The Methods to Build Inversion Models Based on Spectral Information

The ultimate goal of remote sensing is the inversion and the use of inversion results for decision-making. For the inversion of wheat stripe rust using remote sensing technologies, it is the key to determine the relationship between disease prevalence and the changes of original spectra, vegetation indexes, derivative spectra and so on, and then to determine sensitive bands and sensitive period for monitoring the disease.

In the studies on remote sensing monitoring, generally, spectral reflectance in sensitive bands or the model parameters obtained from its differential transformation are used to build the inversion models of wheat stripe rust by using the methods such as linear regression and nonlinear regression. These methods always have the shortcomings such as the deficiency of sample size and the instability of the errors in application and extension. In order to overcome these shortcomings, it is better to use the methods such as nearest neighbor algorithm, ANN and SVM, to establish the inversion models with minor error, wide application scope and high stability.

Future Prospects of Remote Sensing Monitoring of Wheat Stripe Rust

The Studies on Remote Sensing Monitoring of Multiple Pests of Wheat

There are many kinds of wheat pests, and there are more than eighty wheat diseases in China, of which there are more than twenty diseases causing severe losses [START_REF] Liu | Introduction to Plant Pathology[END_REF]. Some different wheat pests cause similar symptoms that make it difficult to distinguish between the pests. Moreover, there are always obvious differences between the symptoms caused by the same wheat pest in different wheat growth stages. So it is very important to carry out the studies on remote sensing identification of multiple pests and the pests in multiple growth stages of wheat. There is still much work to be done in this area. The damages caused by wheat powdery mildew, wheat stripe rust and aphid have been successfully identified by using stepwise discriminate analysis and hierarchical clustering based on the canopy reflectance data [START_REF] Qiao | Identification of Damage by Diseases and Insect Pests in Winter Wheat (in Chinses)[END_REF].

Further Studies on Monitoring Wheat Stripe Rust by Using Space Remote Sensing Technologies

Now space remote sensing has been widely used in various fields. High resolution remote sensing satellites such as IKNOS, QuickBird, SPOT-5 and ORBVIEW-3, promoted the development of space remote sensing. In China, remote sensing monitoring of many kinds of great natural disasters such as flood disasters, drought disasters, fire disasters of forests and grasslands, sandstorms and red tides, is used to keep abreast of emergency disaster information and make timely decisions. The space remote sensing technologies used in other areas are worth making use for reference in development and exploration of space remote sensing of wheat stripe rust. If the studies on multi-platform, large-scale, multi temporal and multi-band remote sensing are conducted successfully and relationship models between different platforms are built, remote sensing monitoring of wheat stripe rust will come true finally.

The Integrated Application of 3S Technology in Remote Sensing Monitoring of Wheat Stripe Rust

The term, 3S technology, refers to RS, geographical information system (GIS) and global positioning system (GPS). GIS is a computer decision support system to display, manage and analyze geographic spatial data and to show the processing results in the means of maps, graphics or data. GIS has been widely used in military, agriculture, environmental protection, traffic and transportation, urban planning, and so on. GIS has been well applied in the analysis of spatial dynamics of plant pathogen population, monitoring, forecasting and risk analysis of plant diseases [START_REF] Shi | The Application of Geographical Information System in Plant Protection (in Chinese)[END_REF]. GPS could be used to realize the localization in the investigations of plant diseases. Thus, the investigations of some plant disease in fixed locations in many consecutive years could be realized. It is very useful for the studies on epidemic dynamics of plant disease during many years and more precise management of plant disease. The integration of RS, GIS and GPS could be implemented in real-time monitoring of plant disease [START_REF] Everitt | Using Airborne Digital Imagery for Detecting Oak Wilt Disease[END_REF], [START_REF] Yang | Application of Remote Sensing in Monitoring and Management of Forest Disease and Insect Damage (in Chinese)[END_REF], [START_REF] Orum | Using Predictions Based on Geostatistics to Monitor Trends in Aspergillus flavus Strain Composition[END_REF], [START_REF] Verreet | Regional Monitoring for Disease Prediction and Optimization of Plant Protection Measures: the IPM Wheat Model[END_REF]. The localization using GPS is absolutely necessary for the realization of aerial and space remote sensing monitoring of wheat stripe rust. The essential information could be distilled from the remote sensing images more precisely via the localization of target objects. A monitoring and warning information system for remote sensing of wheat stripe could be established via the integrated application of 3S technology in future.

Table 1 .

 1 Remote sensing inversion models of disease severity (S) of wheat leaves infected by Puccinia striiformis f. sp. tritici.

	Selected wave band	Inversion model	Independent variable
			SAI =7.289×R 666nm -R 758nm , in which R 666nm and
	666 nm, 758 nm S=1/tgθ×SAI	R 758nm mean the reflectance rates on 666 nm
			and 758 nm, respectively.
	540-740 nm	S=-3.494 Depth + 2.827	Depth means absorption depth in 540-740 nm.
	540-740 nm	S=-0.014 Area + 1.532	Area means absorption area in 540-740 nm.
	540-740 nm	S= 2.459AAI-0.019	AAI means absorption area index.
	685 nm, 690 nm	S=-0.5768+0.7953R 685nm -0.6036R 690nm

Table 2 .

 2 Some spectral indexes used in the studies on monitoring wheat stripe rust using ground remote sensing technologies.

	Spectral index	Definition	References
	NDVI	Normalized differential vegetation index	[20], [21]
	RVI	Ratio vegetation index	[20], [21]
	TVI	Transformed vegetation index	[20]

Acknowledgments. This work was supported by National Natural Science Foundation of China (Grant No.: 30671341, 31071642)


Junwan Hu

Changlai Gong

Zhigang Zhang

Dynamic Compensation for Impact-Based Grain Flow Sensor 1 Dynamic Compensation for Impact-Based Grain Flow Sensor

Keywords: impact-based grain flow sensor, system identification, transfer function, dynamic compensation

The impact-based grain flow sensor is widely used in the combine harvester yield monitor systems to measure the crop yield .but its accuracy wrecked by false signal of excessive natural vibration. A step response experiment was carried to identify the math function of the sensor. Experiment found that the math function of the sensor was a second-order vibration system, its Damping Ratio was 0.037, and its natural frequency was 125Hz, the overshoot reached 20%.To reduce the effect of the overshoot and natural vibration signal on the measurement accuracy, a dynamic compensation algorithm was designed with the series correction method. Step response experiment showed that the overshoot of the compensated sensor was reduced from 20% to 2%.

Introduction

As an important element of the combine yield monitor system The impact-based grain flow sensor measure the grain flow of the harvester by detecting the impact of grain. With the characteristics of simple structure, and low cost, the impact-based grain flow sensor is widely used in the combine harvester yield monitor systems to measure the crop yield [1~11] , But its accuracy was influent by vibration and tilt [12~15] .

With a high velocity, the grain jet out of the crop outlet of the combine harvester, and impact the detect plate of the sensor and reflect quickly. The yield monitor system acquires the impact force and calculates the grain flow rapidly. But, after impacting, a false signal last a while by overshoot and nature vibration.

A Damping material was adopted to enhance Damping Ratio of sensor, and then eliminate the affection of overshoot and nature vibration [16~18],but reduce the sensitivity.

To investigate the dynamic characteristic, a step response experiment was carried to identify the math function of the sensor. And a dynamic compensation algorithm was designed with the series correction method in this paper.

The impact-based grain flow sensor

As show in Fig. 1,. the single plate impact-based grain flow include bracket. elastic body, impact plate.

The impact plate is made of a Plexiglas board with the dimensions 260mm ×120mm×4mm. It fixed at the end of elastic body which bean made of a hard aluminum-Alloy with the dimensions of 80mm ×12mm×12mm, and 2 parallel through-holes in its center.

As show in the Fig. 2, a Wheatstone bridge with 4 electrical-resistance strain gages with 320 Ω bean pasted at the most sensitive surface of the elastic body.

The grain impact the plate of the sensor when the combine harvester working, and then, the output signal of the sensor have a liner relationship with the impact force. The signal is amplified from 0~5mV into 0~5V by a AD623 instrumentation amplify circuit. In this way, we can calculate the grain flow by the output voltage of the sensor.

The output voltage of the sensor can be acquired by a embed ADC of C8051F040 MCU,and send to a computer by RS232 Bus.

System Identifications

The impact-based grain sensor belong A Mass-elastic-Damper system. According to the basic theory of automatic, the impact-based grain flow sensor is a typical attenuate vibration system of second order, its transfer function should be: Where:  --Free Vibration Frequency, rad/s;  --Damping Ratio, No dimension.

The parameter of transfer function can be identified by Step Response test.

Step Response Test

As the grain flow sensor belongs a Mass elastic system, the nature frequency could be changed by put a mass-block load. So step signal can be conducted by unload a pre-load suddenly.

As show in Fig. 3, fix the sensor in level, and hang a 2kg mass-block as pre-load with a slender thread. Adjust the zero point; make sure the output of the sensor is 0V.

The step signal happened by unload the preload with a pair of shears suddenly.

Test Results

The output voltage under step signal excitation was acquired by C8051F040, and recorded by PC. As show in Fig. 4, the step response curve indicates that the grain flow sensor is a typical Second Order System.

Fig3 Step load method

According to the basic theory of automatic,there is:

2 1      n d ( 2
)
) ln( 1 The overshoot reached 20%, the accuracy of the sensor could be damage by overshoot and free vibration. o Fig. 4 Step response of the sensor t/s

Dynamic Compensation

basic theory of compensation

For a second order system with small Damping Ratio,it's necessary to compensate the Damping Ratio up to 0.6~0.7 for the stability. Take an additional Damping material can improve the Damping Ratio in some degree, but it is not enough. Series/parallel correction methods are widely used in sensor dynamic compensation [15~16].

If the original transfer function is

) (s G ,the series correction system is ) (s G c ,the after correction system should be: ) ( ) ( ) ( ' s G s G s G C  (5)
According to the formula (1), If the appropriate Damping ratio is 0.6,and keep the nature frequency, then the ideal corrected system should be:

616225 942 616225 ) ( ' 2    S S s G (6)
By Matlab simulate, we got the step response curve of the original system and the corrected system. As show in Fig. 5, the simulation result showed that the overshoot of the corrected system can be limited significantly. The corrected system should keep more stability.

According to the formula ( 5)and (6),the series correction system should be:

616225 942 616225 58 ) ( 2 2      S S S S s G c (7)
Fig. 5 Step response by simulation

Compensation Algorithm

It is difficult to reality the formula(7)by create a suitable hardware circuit . But it is easy for software. For that, the correction system must describe in discrete transfer function. Make a Z Transform by Matlab function named C2D with a 0.001s sampling period,the formula (7) transform into discrete transfer function:

03898 . 0 01 . 1 619 . 0 9246 . 0 6849 . 0 ) ( 2 2      z z z z z G c ( 8
)
According to formula [START_REF] Chaplin | Comparison of impact plate and torque-based grain mass flow sensors[END_REF], If the input of the correction system is x,the output is y, the second order difference equations of the correction system is:

) (
k y k y k x k x k x k y          (20)
Where k-positive integer; initial condition of the iterative equation:

compensation result

Step response test carried again after compensation. Show as at Fig. 6.

The response curve of the compensated showed that the overshoot been limited from 20% to 2%,the dynamic characteristics improved significantly.

Conclusion

The impact-based grain flow sensor system was identified by a step response test. The step response curve indicated that the transfer function of the grain flow sensor is a under Damping second-order system with 125Hz free frequency and 0.037 damped Ratio.

Fig. 2 Fig. 1 .

 21 Fig.2 Data acquiring circuit for sensor

1 A 2 A

 12 angular frequency under damped vibration,rad/s; T--period of damped vibration, s; --the first peak voltages,V; --the second peak voltages,V。 From the curve,T=0.008s,

  the compensation formula[START_REF] Strubbe | Mass flow measurements with a curved plate at the exit of an elevator[END_REF] and[START_REF] Vansichen | An impact type flow rate sensor for combines[END_REF] into fire ware of C8051F040.

AFig

  Fig.6Step response after compensation

  Where y* --discrete value of output variable y; x* --discrete value of input variable x; T -sampling period,0.001s。 According to formula[START_REF] Schrock | A diaphragm impact sensor for measuring combines grain flow[END_REF],get the iterative equation:

	y	*	( t		2 T	)		01 . 1	y	*	( t		T	)		03898 . 0	y	*	( t	)	(9)
		6849 . 0	x	*	( t		2 T	)		9246 . 0 x	*	( t		T	)		619 . 0	x	*	t
				*													*				*
				(		) 2		6849 . 0	(			) 2	9246 . 0	(	) 1
								*									*				*
				619 . 0		(	)	01 . 1	(		) 1	03898 . 0	(	)

Dr.Junwan Hu, Lecturer, Reserch on agricultural automation,E-mail : hujunwan @jyu. edu.cn