Liu Yang
email: yangliu@cau.edu.cn

Yongjun Zheng

Zhijie Jiang
email: jiangzhijie@163.com

Zhijun Ren
email: zhijunr@gmail.com

Improvement of the Capacitive Grain Moisture Sensor

Keywords: Moisture Measurement, Precision, Factors, Radial Basis Function Never Network

Moisture is one of the most important factors affecting grain quality in storage. The grain must be dried as soon as possible after harvesting to lower moisture to a standard level. It is difficult to obtain satisfactory measurement effect on precision in capacitive grain's moisture measurement due to many influencing factors, such as temperature, species and weight. The data confusion method of Radial Basis Function nerve network is adopted with improved hardware of the measurement system. Tests show that the precision in moisture measurement of wheat has been improved.

Introduction

Safe storage of food is one of the most important strategic issues for a nation's economy and the people's livelihood. Moisture is a key factor to food storage. High grain moisture levels at harvest can result in heat, fermentation, deterioration and low burgeoning rate. To ensure safe storage, the grain must be dried as soon as possible after harvesting and is maintained at lower moisture levels.. It is, therefore, important to use an advanced sensor system to accurately monitor grain moisture levels. This paper examines the ways to improve accuracy and reliability of moisture sensors and to develop an advanced moisture monitoring system.

China urgently needs matching equipment with simple construction, low cost, high accuracy, reliable and good repetition of results. The capacitance sensor has advantages such as low cost, high sensitivity and good dynamic performance and it is suitable for online measurement. So the capacitance sensor is selected to measure the moisture.

1.1

The current situation of online moisture sensors

The principle of the capacitance sensor is that it measures the capacitance value to obtain moisture levels of the grain which chang with the relative dielectric constant. There are two types of capacitance sensors: double electrodes and cylindrical [START_REF] Zhang Cijun | Introduction on the Capacitor Sensors Method for a Moisture Measuring Instrument on Line in Grains[END_REF].

(1) Change the hidden area The capacitance value is proportional to the angle movement. The hidden area can be changed by using the line movement [START_REF] Wenshun | Principle and Application of Capacity Sensor.Modern Electronic Technique[END_REF]. At the beginning the hidden area is the biggest and it changes when the relative electrodes changes. It is hard to measure moisture levels of the grain.

(2) Change the distance of the electrodes This kind of sensor consists of two electrodes. One is grounded and the other is active with the measured object. The capacitance varies when the distance of the two electrodes changes. Accuracy of measurement is low when moisture levels of the grain are high.

(3) Change the dielectric constant [START_REF] Larry | [END_REF] One kind is cylindrical sensor probe. It has stable performance and high accuracy. But it is difficult to install this type of sensor on the dry machine and to achieve online measurement.

Research on affecting factors of the capacitive grain sensor

The capacitance value can be expressed by the following equations.

.

K0=

, LH is the front area and D is the distance between the two electrodes;

is the vacuum dielectric constant; M is the grain moisture; is the object dielectric constant; is the dielectric constant of water in grain; is the dielectric constant of the air between grain holes; is the object volume of grain; e is the rate of hole gaps.

From the equation we can derive the following affecting factors:

1 The temperature is an important factor and should be eliminated.

2 Different grains have different dielectric constants which will affect the capacitance. 3 The rate of grain gaps is the main factor that affects precision and should be eliminated.

3

Hardware Circuit

Moisture measurement

Fig. 1 is the basic circuit of capacitance moisture senor. The driving electrode is connected with the oscillation circuit and sensor electrode and protecting electrode are connected to the ground. The frequency f is decided by induction L and measured

capacitance x C 1 x f LC  . ( 2
)
Different grain moisture leads to changes in x C , and the frequency changes.

Thus we can obtain the moisture from the frequency.

Mathematics Model

The input vector is  

1 2 3 ,, x x x  X ; 1 2 3
,, x x x are temperature, frequency and compactness. The output vector is Y = y, y is grain moisture. RBF network hidden layer output (the ith node) [START_REF] Harder | Interpolation using surface splines[END_REF].

  ii u X t   . ( 3
)
In the sentence, RBF function (  ) is Gauss function, ti is the centre of ith hidden node,  is Euclidean Norm.

From the mathematical model, it is known that.

In the sentence, i w is the ith connection weight between the ith output i u and the system output y,  is respectively the neurons threshold of output.

W, as a Vector with I dimensions, is given by weights matrix composed of output connection weights.

 

12 ,,

iI w w w w  W LL . ( 5
)
To obtain RBF network [START_REF] Moody | Fast learning in networks of locally-turned processing units[END_REF] for a measuring system, which has N groups of inputs and outputs, can be described in mathematical model as: to get the optimum weights matrix W so as to achieve minimum error between output of RBF network and the expected output of the samples. The target function is.

2 2 1 11 min ( ) 22 N k k k k B d y       k dY . ( 6
)
k Y is kth output vector of RBF network, k d is kth expected output vector of training samples.

Training Algorithm of RBF Neural Network

There are 2 stages for this algorithm. Stage 1 is to determine the radial primary function for the hidden layer; stage 2 is the get the optimum weights matrix from nonlinear programming model.

Improved RBF network OLS algorithm

Three parameters are needed to be computed and learned: center vector in basis function, variance vector and weight value matrix. Structural optimization in network is a difficult. The general structure has large numbers of hidden nodes and leads to over-learn. The general orthogonal method is the traditional Gram-Schmidt which has rounding error [8][9]. The hidden network structure is optimized with the improved method in the article. The I eigenvectors are obtained by K-mean clustering [START_REF] Liu | Study on RBF Neural Network Improvement and its Application[END_REF]. Suppose the output testing samples column vector is.

[ , , , ]

I d d d  d L . (7)
The hidden layer output vector is.

12 , , ,

T I u u u    U L . (8)
The expected output vector can be expressed by hidden layer function output vector.

 d UW E . (9)

In the sentence, E is error vector. Make U as orthogonal triangular factorization with Gram-Schmidt orthogonal algorithm [START_REF] Youdu | en and Zhu shixin, Matrix Theory and its Application[END_REF].

 U QR . ( 10
)
The elements in upper triangular matrix R are computed in row but not in line in improved Gram-Schmidt method which lead to lower rounding error. For details, viewing 1 q as the result of ,

2 jj R u R R R j I             11 T (1) 1 j j j 1 qu q u u u q . ( 11
)
After the computation, , , , Then, Ortho-normalized (1) 2 u : ,,

,/ ,3

T j j j j j R u R R R j I             qu q u u u q . ( 12
)
Thus, The structure network is optimized with improved Gram-Schmidt method and detail solving process is designed in the article. The improved optimized network structure is obtained by sentence (7).

I TT i i g    d d E E . ( 13
)
In the sentence,

2 1 I T i i g    GG 12 ( , ..., ,..., ) T iI g g g g    G RW Q d Suppose the compression ratio is. 2 [ ] / 1 T ii err g i I    dd . ( 14
)
The above results show that as the rounding error is lower in the improved Gram-Schmidt, the numbers of the hidden layer nodes can be computed precisely. The computation amount is decreased and over learned problem is avoided.

Data Analysis and Processing

Comparing experiments have been done to verify the function of the improved RBF algorithm in this article, which is supposed to achieve higher moisture detecting accuracy.

BP network, traditional RBF network and improved RBF methods are used to compare for data fusion process. 300 groups of wheat samples are prepared for the experiments. Among which, 250 samples are used for learning algorithm, and 50 groups are used for measuring. Testing values and standard values of the two methods are listed in Fig. 5.

Conclusion

Factors such as temperature, species and weight affect measurement precision in capacitive grain moisture sensors are analyzed in this paper. The data confusion method of Radial Basis Function nerve network is adopted with improved hardware of the measurement system. Laboratory tests have been conducted on grains of wheat, rice and corns. The results demonstrate that the precision in wheat's moisture measurement has been improved, which shows that the proposed method is better than conventional methods.

Fig. 1 .Fig. 2

 12 Fig. 1. Moisture Measurement Circuit

Fig. 2 .

 2 Fig. 2. Temperature Measurement Circuit

Fig. 3 .Fig. 4 .

 34 Fig. 3. Weight Measurement Circuit

  Repeating the steps, the orthogonal matrix Q and upper triangular matrix R are obtained.

Fig. 5 .

 5 Fig. 5. Comparing Error of Three Kind of Grain With Four Methods 1 No RBF Algorithm 2 BP Algorithm 3 Regular Algorithm 4 Improved Algorithm


Keywords: Document service, Automatic composition,Semantic Web, Multi-agent

This paper proposes a method which composes document services over the Internet automatically based on multi-agent and semantic technology, and discusses the implementation of three document service functions (catalog browsing, document information browsing and document full version downloading) with that method. We use service delegate agent to encapsulate services on the internet; use service purchase agent to decide which service delegate agents should be used and then to generate a composition script describing how these services being purchased should be used; use service provider agent to call these services according to the composition script and merge the results, translate the merged result into the system predefined format, finally return it to the requesting user. By doing so, service provider can simply composite existed services on the internet and provide full functional service.

Study on the Automatic Composition of Document Service Based on Semantic and Multi-agent Method

Haiyan Hu 1 , Xianxue Meng 1 , Xiaolu Su

Introduction

Information services are becoming more and more specialized. There are many standalone indexing, searching, and online storage services on the Internet, provided by many providers respectively. Traditional online library is a multi-function system, provides catalog, indexing, and electronic document downloading services. This type of system appears obsolete today. Regarding that there are better search services like Google, better storage services like Amazon S3[1], etc, why not simply use them but do everything self? Develop an all in one service is quite a big challenge, even can be done, it must not be as cost effective as those specialized services from above providers, and can not achieve the high quality standard of them. Composing the existing specialized services are the best way to build online library on today"s Internet.

The core business of online library service provider is to give users the documents they want. To achieve this purpose, four fundamental functions are necessary, they are, document catalog and indexing, document searching, document metadata offering, and document full-text download. As the owner of the documents, providing document"s full-text version is the only job must be done by provider himself. If everything is ideal, the online library provider can use online storage service stores his documents, and create a database stores the download links and basic metadata of these documents, and need do nothing else but compose 3rd party services on the web. The basic metadata should include a unique identity for each document, and some candidate identity fields such as title, publish date, author, etc. These fields may be used by some service agents to identify documents, preventing duplication of document metadata from multiple sources.

Semantic and multi-agent based Web Services automatic composing have became new hot spot for Web Services [START_REF] Lian | Integrating semantics and agent technology to automatic Web service composition[END_REF][START_REF] Kumar | Multi-Agent Negotiation based Semantic Web Service Composition Models[END_REF][START_REF] Hao Tian | Agent-based semantic web service composition model[END_REF][START_REF] Falou | Automated Web Service Composition: A Decentralised Multi-agent Approach[END_REF][START_REF] Nouredine | Composition of web service based multi-agent[END_REF]. This article discusses the research based on multi-agent and semantic technology which automatically composes directories, document information and document texts in document services.

Technical Considerations

The best way to implement service composition system is multi-agent architecture. Using one single agent represents a service. Let these delegate agents promotes his service to the purchasers in a marketplace. After a process of competition, reasonable service consumption relations are established, from which the service cooperation framework can be derived out.

BDI model [7] is a multi-agent architecture that is widely used. A BDI model has three basic parts:

(1) The Belief. Belief is a set of theories about the world, which include the knowledge about the environment, the knowledge about other agents the agent may contact with, and the knowledge about the agent himself. It is the objective basis of the decision-making of the agent.

(2) The Desire. Desire is the motivation of agent, which represents the state the agent willing to achieve and to hold.

(3) The Intention. Intention is the current target of the agent. It is the most urgent desire among all the desires of that agent. It represents the direction of the agent"s mind, and guides the actions the agent currently taking.

The system takes JADEX as the basic framework of agent"s interaction [START_REF] Braubach | Jadex: A BDI Agent System Combining Middleware and Reasoning[END_REF]. Regarding that JADEX do not support ontology inference, it must be extended to meet our requirements. We created a new interface named "IMOWLBelief", two new classes named "MOWLBelief" and "MTypedOWLElement". The class "MTypedOWLElement" defines the attributes of the Ontology and their getters and setters, encapsulates OWL operations. The class "MOWLBelief" is extended from the class "MBelief". To make these newly defined classes accepted by JADEX framework, the JIBX binding file (binding.xml) must be changed. After doing that change, believes can be presented using OWL.

Architecture of Service Composition

Each kind of service is composited by a service provider agent, a service purchase agent, and a number of service delegate agents. Each service delegate agent delegates a single service source on the web. Service delegate agent "knows" every detail of the service it delegated, in another word, that service delegate agent encapsulates its service. The desire of service delegate agents is to sell its service to service purchase agent as much as possible. A service delegate agent can trade with more than one service purchase agents, sells different functions to each of them. For example, a service delegate agent delegates a service source which provides catalogs as well as document metadata, it can sell catalog function to catalog service purchase agent as well as sell metadata function to metadata service purchase agent. Each service purchase agent serves for a single function, so in our system there should be four service purchase agents, work for document catalog, document search, document metadata, and document download respectively. Like service purchase agents, service provider agents are also dedicated to those four functions respectively.

Service purchase agents decide which service the service delegate agents delegated should be used in service composition. While done, the chosen services can be composited to serve users. Service purchase agents will generate a composition script to describe how these services it purchased should be used. A service provider agent is responsible to calling these services according to the script generated by the service purchase agent who work for the same function as it does, and then merges the results, and translates the merged result into the system predefined format; finally return it to the requesting user. The script generation will consume a large amount of computation resources, so it shouldn"t be done frequently.

Functions of Service Composition

A typical document service provider provides catalog browsing; document searching; document information browsing; and document full version downloading. Regarding that the search service may returns a huge amount of data, and it is not fit for compositing, so we will only discuss composition of the other three functions.

Catalog Browsing

Catalog browsing service needs a catalog. In traditional way, catalog is created manually with some kind of classification method. Regarding that there may be different users expecting different classification methods, and the best practice is to provide each user what he needs. Creating catalogs manually is an exhausted task, and is apparently not cost effective. If we can reuse the catalogs currently exist on the web, it will become practicable to provide multiple catalogs with different classification method. The catalogs currently on the web are not created for your needs, so those catalogs must be customized.

In catalog service, the catalog itself is the belief of service delegate agent, and the desire of service delegate agent is to sell the service it delegated as much as possible. The service purchase agent will assess each delegate agent and calculate how much its catalog can complement the catalogs already purchased. The service purchase agent will purchase the services which can contribute the most content to current catalogs.

At the initial state, the service purchase agent does not purchase any service. The service delegate agents are added into system every time a new service is discovered. After created, each service delegate agent updates service information periodically to handles the possible changes of that service.

The periodical update of service information may introduce changes to existing service, and those changes may affects the purchase contract already signed. When changes occurred, the service delegate agent must notify the service purchase agent. While notified, the service purchase agent will expire the currently purchase contract, assess the changed service again, together with all other services not yet assessed, and decide how to purchase from all available services. Then the service purchase agent must regenerate service composition script.

The service purchase agent sorts the service delegate agents by the entry numbers in the catalogs their services provides, in descendent order, assesses them one by one. The catalog provides the more useful entries will be scored higher. After all services are assessed, the service purchase agent starts purchase from the service with highest score, until the service scored lower than threshold. The services with zero score will be excluded, until they got some changes and then triggered reassessment processes. The services scored above zero but lower than the threshold will not be purchased in the first round, and will be reassessed in the second round.

The second round purchase based on the result of first round purchase and composition. All the services scored above zero in the first round but not been purchased will be rescored in this round. There is no threshold of score in the second round, if a service is rescored more than zero against the new basis, it will be purchased. The initial score in the first round will be recorded for future use, but not the second round score.

The composition operation uses some simple rules. For example, there is a rule looks like this: "if equalsIgnoreCase (X.entryName, Y.entryName) and notEmpty(intersect (X.parents, Y.parents)) then X sameAs Y "; or "if equalsIgnoreCase (X.entryName, Y.entryName) and isEmpty(X.parents) and isEmpty(Y.parents) then X sameAs Y ". The catalogs the services provided may have multiple language versions. The same catalog entry or the same article title in different languages can be linked up automatically. The service purchase agent handles the merging of catalog entries, generates a script to tell the service provider agent how to composite these catalogs. When user requests, the service provider agent retrieves and composites catalogs according to the script generated by service purchase agent, then extracts out the part which meets the user"s requirement, converts them to standard format, and return to the user. Let the service purchase agent handle catalog composition and the service provider agent do the real composition job is based on such considerations: the service purchase agent can present each catalog entry as an OWLClass, and each document as OWL Individual, in its belief, to convenient inference of catalog entry relations; the service provider agent can treat all catalog entries and documents as individuals at the same time, to convenient inference of merge operation. These two believes can not be mixed up, because there is some thing in one is represented as class and in another is represented as individual; if put them together, the merged belief will violate the OWL DL constraint, and can not be used in any inference. The service provider agent reads the script generated by service purchase agent, and revises its own belief according to that script. Considering mapping multiple languages to each other, the link between them needs a new predicate, to express that they can be translated to each other. Two language versions of one document are handled as two linked documents; two language versions of one catalog entry are also handled as two linked ones.

Document Information (Metadata) Browsing

Metadata of one document can be acquired from multiple sources, and they may not consist with each other. Data from multiple sources may have multiple structures, and these structures must be identified correctly and mapped to a standard structure. Some of the data is acquired from online libraries, this part appears well arranged, there is some other data come from none academic sources such as online book stores, may be organized according to their business needs, and not comply with their logical meanings.

There is still some fragmental data distributed in online forums, bokees, and other online information sources. This part of data often published personally, and just organized by the intendancy of these people, but may have some in-depth opinions. Because this part of data often appears in html pages, and can not be acquired from some kind of services, it can only be collected from search engine service. But search engine services are never designed to provide such data, and they may return many irrelevant data as well as some useful data, some the results the search engine returned must be filtered first. Document metadata service composition is not the same as catalog service composition. The service purchase agent must organize the service delegate agents by each document, and then judge which source should be used for each document. There is no standard schema which all service delegate agent comply with. Fusion on attributes can only be done dynamically using inference.

There are 3 steps in the fusion process of each attribute:

(1) To judge whether current attribute can be merged with some attributes of another object.

(2) If two attributes from two objects can be merged, whether their value equal or compatible to each other.

(3) If both values not compatible to each other, determine which value should be kept.

There are two occasions that two different attributes can be merged. One is that they have the same meaning: attributes with the same meaning may have different names, for example, "title", "topic", and "caption" may be the same, and can be merged, another is that one attribute can be deduced from another, for example the attribute "age" can be calculated from the attribute "birth date" but not vice versa, so the attribute "age" can be removed. In the first occasion, a synonym table can handles the mapping well; in the second occasion, there must be a conversion function fit for that calculation, if such a function can be found by the service purchase agent, the merge will be allowed.

While mapped, compatibility between the values of attributes to be merged must be assessed. Because values are often digits, and mathematical calculation often needed, the assessment can not be completed by inference, but can only by dedicatedly designed functions. These functions are owned privately by the service purchase agent. The values to be assessed must have the data type which is acceptable to the assessment function. If some of the values are not in proper data type, data type conversion must be taken first, fail in conversion means not compatible. If compatible, the one with more accuracy will be kept, if not, there should be a method to choose the applicable one among them. If a value of attribute has more than one source, the value with the support of most of sources has the highest reliability; the less frequently changed values have higher reliability. For example, the "age" attribute must be changed every year, but the "birth date" attribute needn"t do that change, so "birth date" is more reliable than "age".

To get document metadata from search engine, the system need calling the search service with the title of that document as keyword. The result returned from search service includes the URL of target web page and a brief abstract of it. The search engine often break the title into words before search, so the result returned contains many irrelevant pages which just have some of the words we want to search. These entries are useless and should be eliminated first. While filtered, the remaining entries will be examined one by one, the target web page will be washed out peripheral contents and html tags, only core text content will be kept. The core text will be added to the description of that document.

Document Full Version Downloading

Document download service provides the binary versions of documents to users. There are many online store services on the web, they are often more reliable and accessible to common users than the server maintained by document provider itself. Most of the online storage services need user login first. Using multiple online storage providers to balance data flow and secure availability is also a good idea. So there is also motivation on composition of document storage services.

In document online storage service composition, the return of the composited service is the binary file of document, it needn"t further processing, and can return to user directly. So in this part service provider agent is not necessary. Service purchase is also simpler than the other two parts. The service purchase agent will give higher privilege to the services which can provide storage of larger number of documents, and which has the higher download speed. The privilege is dynamic, if a service already has a number of live calling, its privilege will be lowered to make it less possible to be called again. This dynamic privilege system will balance the load.

Traditional way of "all in one" document service system has its downside: waste resources on developing already exist functions on the internet; make the providers lost focus from their core business; and create obstacles to integrating them together.

This paper proposes a method a of automatically compositing existed document services based on multi-agent architecture and semantic technology, and discusses the composition of three common document service functions, and provides a solution to solve the problems traditional document services have.

Acknowledgement

The research was supported by the 12th five-year national key technology support program, super-class scientific and technical thesaurus and ontology construction faced the Foreign Scientific and technical literature(2011BAH10B01)and special fund of basic commonweal research institute project of information institute of CAAS.


Lili Yang

Qiaoxue Dong

Daoliang Li
email: li.daoliang@163.com

Utilizing model to optimize crop plant density: a case study on tomato

Keywords: Tomato, Plant density, GreenLab model, Yield optimization, PSO

Based on the functional-structural plant model GreenLab, a common plant category tomato was selected as a test case in order to analyze the impact of plant density on the yield. A yield optimization model was set up based on minimum ripe fruit weight constraints, fruit weight serve as the objective function for the bound-constrained optimization problem. Particle Swarm Optimization (PSO) algorithm was applied for maximizing the crop production, which will provide a theoretical reference for agronomy measures based on plant spacing.

Introduction

Tomato's economic benefit is related to fruit yield and quality, and these two factors are considerably affected by light condition [START_REF] Bertin | Competition for Assimilates and Fruit Position Affect Fruit Set in Indeterminate Greenhouse Tomato[END_REF] [START_REF] Papadopoulos | The influence of plant spacing on light interception and use in greenhouse tomato (Lycopersicon esculentum Mill.): A review[END_REF]. Manipulation of plant spacing is a possible means to change light interception for peasant . Traditionally, plant density is based on empirical knowledge from filed experiments, and this is quite time consuming and expensive. While plant growth models provide a very important tool for realizing optimal yields of crops with an efficient means [START_REF] Vos | Functional-Structural Plant Modeling in Crop Production[END_REF]. Functional-structure model GreenLab can simulate plant growth based on organ size, so it provides possibility for plant density optimization [START_REF] Qi | Application of the GreenLab Model to Simulate and Optimize Wood Production and Tree Stability: A Theoretical Study[END_REF]. Combining plant mathematical model with optimization method can provide guiding for the optimization of planting density and horticultural practice such as pruning and environmental control in special constrained environment.

2

The description of optimization problem

Greenlab model

In GreenLab model, time scale, called a growth cycle, is often the phyllochron between appearances of two successive leaves in main stem [START_REF] Guo | Parameter optimization and field validation of the functional-structural model GREENLAB for maize[END_REF]. Plant total product biomass at growth cycle n is Q(n).

( )

( ) 1 (1) S n A Q n AB         
A is the projection surface of single plant (m 2 /m 2 ), which is in response to plant density. B is plant growth potential multiplied by an empirical resistance, which can be computed by environment data. () Sn is total leaves surface at n th growth cycle. D(n) is all organ demands at the n th growth cycle

1 ( ) ( ) ( 1)
(2)

n o o o o t D n P f t N n t      
Where P o (unitless) is the sink of organ o ( o=internode (i), blade (b), petiole (p), fruit (f)), which is defined as the ability of organs to compete for biomass.

(

) 1
o N n t  is the number of organ o in t growth cycle. f O (t) is sink variation of organ o at t th growth cycle, which is described by an empirical Beta function. α o and β o are the coefficients of the Beta function, and α o is set constant. A, P o (unitless) and β o (unitless) are hidden parameter. These parameters were identified by fitting the periodical destructively-measured data and environment data with the corresponding model output.

Fruit weight calculation

At the end of n th growth cycle, biomass for one chronological aged j (growth cycle) fruit can be calculated as below:

1 ( ) ( , ) ( ) (3) ( ) j f f k Q n j k q n j p k D n j k       
k is fruit growth cycle, all organs including fruit compete for biomass in a common pool. The formula for calculating the total fruit weight of an aged n (growth cycle) plant as follows:

_ _ _ ( ) 1 ( ) ( ) (4) x f x f x f t n t k f k n t k Q Y p k D       
t x_f is fruit extension time, which is an observation value.

Model parameter estimation of Greenlab for tomato

For establishing the relationships between GreenLab model parameters and plant density, four gradient density tomatoes named by D1,D2,D3,D4 from low density to high density were planted in solar greenhouse at the Chinese Academy of Agricultural Science in Beijing (39.55°N, 116.25°E) [START_REF] Yang | Relation between Tomato Fruit Set and Trophic Competition-a Modelling Approach.Plant Growth Modeling and Application Procedding[A[END_REF]. The generalized least square method was used for fitting [START_REF] Yang | Relation between Tomato Fruit Set and Trophic Competition-a Modelling Approach.Plant Growth Modeling and Application Procedding[A[END_REF]. Four plant density model parameters are shown in table Model outputs show that increasing plant density results in a reduction of single plant fruit fresh weight (Figure is not shown) but in an increase of fruit fresh weight on a ground area (Fig. 1), which is consistent with measured data.

Mathematical solution for the tomato plant density optimization problem

Considering market economic benefit, we define a minimum harvestable fruit weight as constraint. A single object non-linear optimization function is built as follow,

Conclusion

Model output is well conformity with measurement. Results illustrate that model has the ability of reflecting plant density effect on yield in aspect of biomass production, distribution and organ number changing. We can infer model parameter, plant growth and development process in any environment from model calibration and parameter interpolation. Combining with optimization algorithm, we can get optimum plant conditions (in this paper plant density as an example). It provides a reference for decision-makers using limited experiment, and it can be used in plant growth prediction, manufacture management and so on.

Fig. 1 .

 1 Fig. 1. Fitting results on fruit fresh weight evolution for a ground area plant of four densities symbols-measurement, lines-model

  Optimal object is maximum yield Y. Plant density d is an optimizing object variable and it varies in real field. All the GreenLab parameters Ωare control variables. g min is a threshold value of tomato fruit referred to practice(100g). G is single fruit weight. The PSO(Particle Swarm Optimization) algorithm was implied to optimize. The fruit weight output with respect to different plant density is shown as Fig 2.Fruit yield variation with plant density can be divided into three phases. During the first phase (1 to 8.2 plants per square meter) fruit yield grow bigger with density increasing. During the second phase (8.2 to 10.5 plants per square meter) fruit yield grow less with density increasing. In the following, fruit yield drop to zero. Because fruits get smaller and smaller with later density increasing, even it can't up to harvestable standard. Optimal plant density is 8.2 plants per square meter, and the fruit weight is close to 5kg per square meter.

Fig. 2 .

 2 Fig. 2. Tomato yield evolution with planting density

Table 1 .

 1 1: Estimated parameter values of model

	Parameters	D1	D2	Values	D3	D4
	P i	0.47	0.46			0.58	0.56
	P p	0.65	0.57			0.83	0.72
	P f	23.4	16			15	8
	β i	2.41	2.24			2.76	1.67
	β p	1.91	1.86				2	1.35
	β b	1.48	1.28			1.83	1.46
	β f	1.58	1.03			1.48	1.44
	A	2228	2186			1783	1592
	An equation is used to describe model parameter variation trend with plant
	density. The supreme fitting degree function is shown as below:
			a b   	exp(	cx 	)	(5)

x is plant density, a, b ,c are constants which can be got by fitting, Ωis GreenLab parameters as table1 listed.

3 Tomato

optimal plant density solution 3.1 Tomato growth simulation at different plant density

		6000
				D1 sim D2 sim		fruit
		5000		D3 sim D4 sim
				D1 obs
				D2 obs
	fresh weight(g)	2000 3000 4000		D3 obs D4 obs
		1000
		0
		0	5	10	15	20	25	30	35	40	45	50
						growth cycle

Different density plants growth and development can be simulated using GreenLab model with parameters listed in table 1.

Acknowledgments. This research was financially supported by Hi-Tech Research and Development (863) Program of China (#2008AA10Z218), NSFC (31071330) and the Start Fund for Scientific Research, CAU (2011JS156).


Wenqian Huang
email: huangwq@nercita.org.cn

Chi Zhang

Baihai Zhang

Identifying Apple Surface Defects Based on Gabor features and SVM using Machine Vision 1

Keywords: defect identification, Gabor wavelet, SVM, apple quality grading

In this paper, a novel method to recognize defect regions of apples based on Gabor wavelet transformation and SVM using machine vision is proposed. The method starts with background removal and object segmentation by threshold. Texture features are extracted from each segmented object by using Gabor wavelet transform, and these features are introduced to support vector machines (SVM) classifiers. Experimental results exhibit correctly recognized 85% of the defect regions of apples.

Introduction

Tons of apples are produced, harvested and consumed throughout the world each year. Visual inspection of these apples is traditionally done by human experts. Even so, automation of this process is necessary to increase speed of inspection as well as to eliminate human inspection error and variation introduced. In recent years machine vision systems have been widely applied to evaluate external quality of apples [START_REF] Heinemann | Machine Vision Inspection of 'Golden Delicious' Apples[END_REF][START_REF] Paulus | Shape Characterization of New Apple Cultivars by Fourier Expansion of Digitized Images[END_REF][START_REF] Xiao-Bo | In-line detection of apple defects using three color cameras system[END_REF]. However, these systems can't provide robust and accurate results yet, because high variability of defect types and skin color as well as presence of stem/calyx areas increases complexity of the problem. Computer vision systems are mostly confused in discriminating defect regions of apples from stem/calyx regions due to their similarity in appearance. In most machine vision based automated apple grading and sorting systems, it is important to distinguish apple stem/calyx from defect in apple images because stem/calyx region in apple images often exhibit patterns and intensity values that are similar to defect region in apple surface and result in false alarms during defect sorting. Hence, accuracy of apple sorting decreased by false identification of defect regions of apples.

Several approaches have been introduced to recognize defects on apples using computer vision systems. T.G. Crowe et al. use structural illumination to detect apple defects, where concave dark spots are considered to be stem/calyx [START_REF] Crowe | Real-time defect detection in fruit part I: Design concepts and development of prototype hardware[END_REF][START_REF] Crowe | Real-time defect detection in fruit-part II: an algorithm and performance of a prototype system[END_REF]. Zhiqing Wen et al. develop a rules-based NIR system and histogram densities are used to discriminate stem/calyx from defect areas [START_REF] Wen | Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines[END_REF]. Recognition rates of stems and calyxes are 81.4% and 87.9%, respectively. Their system is less reliable when stem/calyx regions are closer to the edge of fruit. Wen Z. et al. use an NIR and a middle-infrared (MIR) camera for apple detection, where image from the latter is used to segment ROI and about 99% of them are correctly recognized [START_REF] Wen | Dual-camera nir/mir imaging for stem-end/calyx identification in apple defect sorting[END_REF]. However, cost of cameras, which is not discussed by the authors, is an important problem for practical implementation of this method. Kleynen et al. utilize correlation-based pattern matching technique to detect stem/calyx on apples in a multispectral vision system [START_REF] Olivier | Development of a multi-spectral vision system for the detection of defects on apples[END_REF]. Recognition rates for stems and calyxes are 91% and 92%, respectively. And 17% of defects are misclassified as stem/calyx. Pattern matching method has been widely applied for object recognition, but its main disadvantage is high dependency on the template used.

2-D Gabor wavelets have been successfully applied to face recognition. Its advantages are better time-frequency localization feature and better signal resolution in time and frequency domain which can be achieved by adjusting Gabor filter's direction and base-frequency width and central frequency. Tai Sing Lee extended 1-D compact wavelet to 2-D Gabor wavelet in 1996 [START_REF] Sing | Image representation using 2d gabor wavelets[END_REF]. Multi-channel filter technology, that is, a set of Gabor wavelets with different time-frequency characteristics are adopted in image transform, each channel can achieve some local feature from input image, so the input image can be analyzed on different-sized granularity. In all, Gabor wavelets optimally represent the texture structure with different locations and orientations. Because Gabor wavelets transform is better than others on texture feature extraction, it is adopted in this work for apple stem/calyx and defect characteristics extraction.

SVM is a strong classifier which can identify two classes [START_REF] Christopher | A Tutorial on Support Vector Machines for Pattern Recognition[END_REF], and defect identification is to tell the ROI image is defect or stem/calyx. Therefore SVM is suitable for this problem. In fact, SVM is also of some advantages. SVM is on the basis of Vapnik-Cheervonenkis (VC) dimension theory in SLT (Statistic Learning Theory) and structure risk minimization principle. In order to achieve the best generalization, SVM makes the compromise between model complexity and generalization. Compared to other classical learning methods, SVM can overcome traditional learning flaws, such as over-learning and less-learning and driven to local minimum as well. For the situation which input samples can't be separated in a linear space, SVM can carry on a non-linear transform and change this inseparable problem into a divisible question in a high-dimensioned space and figure out its optimal classification surface in this space. Classification can be realized through inner product computation with SVM core function in a high dimensioned space, but computation complexity is not increased.

Identifying Apple Surface Defects using Gabor Transform

This section describes the method for the identification of the defect on apple surface using Gabor transform. First, Gabor wavelet representation of apple images derives desirable features characterized by spatial frequency, spatial locality, and orientation. Second, the Gabor wavelet features of apple images are used as the input space in SVM.

Gabor Transform

The Gabor wavelets can be defined as follows:

(1)

Where z = (x,y) are the spatial coordinates, the parameter μ and υ define the orientation and scale of the Gabor kernels, ∥⋅∥ denotes the norm operator, and kμ,υ is defined as follows:

(2)

Where kυ = kmax∕f υ and ϕμ = πμ∕8. kmax = , and f = .

In most cases one would use Gabor wavelets of eight orientations, μ ∈{0,…,7}, and five different scales, υ ∈{0,…,4}. Fig. 1 shows the real part and magnitude of Gabor wavelets kernel under five scales and eight orientations. The Gabor wavelets exhibit desirable characteristics of spatial frequency, spatial locality, and orientation selectivity. In order to obtain the input feature vector, we concatenate all these representation results and derive an augmented feature vector X. Before the concatenation, we first downsample each Oμ,υ by a factor ρ to reduce the space dimension and normalize it to zero mean and unitvariance. We then construct a vector out of the Oμ,υ(z) by concatenating its columns. Now, let Xμ,υ denote the normalized vector constructed from Oμ,υ, the augmented Gabor feature vector X is then defined as follows: [START_REF] Wen | Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines[END_REF] The augmented Gabor feature vector thus encompasses all the elements (downsampled and normalized) of the Gabor wavelet representation as important discriminating information.

Defect Identification on Apple Surface

An image of apples is shown in Fig 2a . In order to obtain apple region, the background region has to be removed. In addition, each apple is a region of interest (ROI) and needs to be extracted individually. In this work, we use threshold method to extract apple image. That is, the R channel in RGB color channels is taken into account. The apple region can be obtained by using the following equation: In order to get the defect region in apple surface, we first convert the segmented color apple image into grayscale image by using the following equation: [START_REF] Olivier | Development of a multi-spectral vision system for the detection of defects on apples[END_REF] Where IRed, IGreen, and IBlue are the 8-bit color value of a given pixel in the segmented image.

Then a threshold is used to extract dark areas within apples, such as defects, stem, and calyxes, as region of interesting. However, in our experiments, we find this method of extracting ROI in apple surface can't obtain all dark areas within apples.

In this study, a new method is proposed to extract dark areas within apples. We first use the following equation to get the grayscale of each apple. [START_REF] Sing | Image representation using 2d gabor wavelets[END_REF] Then a threshold θ is used to get dark ROI in apple surface. [START_REF] Christopher | A Tutorial on Support Vector Machines for Pattern Recognition[END_REF] Finally, morphological operation is employed to refine the segmentation.

After segmentation operation is completed, we can utilize the central coordinate (x,y) of dark ROI to extract a 64× 64 region in original color apple image that centers on (x,y). The extracted color ROI in apple surface is show in Fig 3 :   Fig. 3 The segmented region of interesting in an apple image.

In this work, Gabor wavelet filter is used to extract Gabor wavelet features from ROI image. Two downsampling operations are employed to the features. So a 64×64 feature image becomes a 16×16 feature image. We then construct feature vector by concatenating its columns. The feature vector is used as input vector for SVM.

There are four following basis kernels of SVM: linear, polynomial, radial basis function (RBF), and sigmoid. In this paper, we use SVM with the linear function kernel as a black box classifier over the labeled set of feature vectors. Doing so yields support vectors in the feature space.

Experiments and Results

The machine vision system for apple quality inspection is shown in Fig. 4. It consists of a computer-controlled frame grabber that is based on the PCI Express interface and an image sensing system, which is a digital progressive scan color CCD cameras (JAI CV-A70CL) with a C-mount lens of 8mm focal length. The color images are grabbed and analyzed by a host computer equipped with a Dalsa X64-CL Express frame grabber. A lighting chamber was designed to provide uniform illumination for the CCD sensor. The chamber size is 90(W)*100(L)*100(H) cm. In order to provide lighting, two warm-white lamps with color temperature 5400K are mounted above the conveyor. The CCD imaging sensor is mounted inside on the two sides of the chamber. A roller conveyor belt is built to hold and move apples in one lane. All apple samples are manually placed on the conveyer belt with a random orientation. The apples are rotating and moving when they pass through the view of the two CCD cameras. The whole surface of each apple can be covered by the CCD camera during the apple rotation. A drive controller and speed controller are connected with a photoelectric switch that provides precise timing signals for both on-line mechanical and electrical synchronization. A total of 200 color apple images are acquired. From these apple images, we can obtain 90 stem regions, 90 calyx regions and 90 defect regions. The stem/calyx and defect images that are used in our experiments are show in Fig5: the first row is calyx image, the second row is stem image, and the last row is defect image. Fig. 5 The stem/calyx and defect images.

We first segment the ROI in apple surface and convert the color ROI to grayscale image. For each gray image, Gabor wavelet filter is used to extract the texture features, and these features is employed to the input of SVM. Finally, the identification results are given by SVM. In our experiments, the training dataset includes 50 stem images, 50 calyx images and 50 defect images. The testing set includes 40 stem images, 40 calyx images and 40 defect images. The overall defect identification rate is 85%.

Conclusion

External quality grading of apple fruits by machine vision is still an open, tedious and challenging problem. Accuracy of this task depends on several subtasks, one of which is precise recognition of defect areas.

In this paper, a defect identification method based on Gabor wavelet features is introduced. Firstly, this method accurately obtains the ROI in apple surface. Then Gabor wavelet filter is used to extract the texture features of ROI, and these features are employed to the input of SVM. Finally, in the experiments were conducted and the overall defect identification rate is 85%.

Fig. 1

 1 Fig. 1 Gabor Wavelets. (a) The real part of the Gabor wavelets at five scales and eight orientations. (b) The magnitude of the Gabor wavelets at five different scales.

( 7 )

 7 If I(x,y) is identified as an background pixel, we let I(x,y) be equal to 255. The segmented image is shown in Fig 2b. Each apple in a single image can be easily extracted from the segmented image individually.

Fig. 2

 2 Fig. 2 (a) Original image of apples. (b) The segmented image.

Fig. 4

 4 Fig. 4 The machine vision system.

Young Scientists' Foundation of Beijing Academy of Agriculture and Forestry Sciences (Project No. QN201119) and National High-Tech Research and Development Program of China (863 Program) (Project No.

2011AA100703)

Acknowledgments

. Our project is supported by Young Scientists' Foundation of Beijing Academy of Agriculture and Forestry Sciences (Project No. QN201119) and National High-Tech Research and Development Program of China (863 Program) (Project No. 2011AA100703).


Gang Wu

Yu Tan

Yongjun Zheng

Shumao Wang
email: wangshumao@cau.edu.cn

Walking Goal Line Detection Based on DM6437 on Harvesting Robot

Keywords: TMS320DM6437, Harvesting Robot, Line Detection, Hough Transform

As to aided driving by harvesting robot, there is the large amount of image data processing, meanwhile, harvesting robot requires real-time image processing and to calculate the linear parameters in a straight line detection of the walking target process. This paper presents a hardware processing platform to TMS320DM6437 digital signal processor as the core processing chip, and an improved Hough transform algorithm which is based on a determined point is proposed to complete line detection. A camera is to be fitted on the left top of the combine harvester in order to capture images of farmland scenes in the process of harvesting. At first, according to different color characteristics of harvested areas and non-harvested areas, improved methods of Maximum entropy threshold segmentation and morphological approach are employed to determine candidate points of walking goal line. Then the candidate points are selected as the point set. Finally, the improved Hough transform based on a determined point is applied to complete line detection. The algorithm simplifies binary map to unitary map. Comparing with the traditional Hough transform, it saves computing time, and reduces the parameter space greatly. After multiple images processing, tests show that this detection method can detect real-time parameters of harvesting robot's walking goal line, and the algorithm is well proved with respect to its speed, anti-interference and accuracy.

Line Detection Algorithm

The imaging system is designed to detect the navigation path line when harvest wheat by combine-harvester. As the combine harvester unloading grain port is on the left, so we usually choose the left part to start harvesting, and it is easy to unload the grain, even the right direction of driving is non-harvested area, and the left is harvested area. The most common route methods of harvesting are dextrorotation method and four sides harvest method [START_REF] Changan | Grain harvesting machinery Insider[END_REF], and dextrorotation method is suitable for longer but not wider farmland. After cutting out of road, the long side direction of the harvest is followed, then the combine-harvester needn't to head back when the harvest has finished, finally the combine harvester turns right around to the other long side of partition to continue harvesting until the end of harvest; four sides harvest method is suitable for large fields, after cutting out of road, the combine harvester harvests along the left side of terraces until the end, then turns right to continue to harvest, and so forth, until the harvest ends. Based on the above practices, the detected straight line is put on the left side of combine harvester.

Image Processing Steps

Images are taken by JAI CV-M77 camera, shooting frame rate of 25s-1, 640×480 color images. Test image was shot in wheat field of suburb in Luoyang City, the weather was sunny. Before the test, the camera is mounted on top of the center-left position combines, apart from the ground about 3m, camera and horizontal's angle about 40°. Camera adopted isolation pads and protected cover with a dust wiper for dust treatment.

Improved Maximum Entropy Thresholding Method

Because Gray-scale distribution of agricultural scene images is complicated, it is difficult to use a fixed threshold segmentation method, so we choose the adaptive threshold segmentation, currently, adaptive thresholding methods are: Otsu thresholding method [9~10], iterative thresholding method [START_REF] Ridler | Picture thresholding using an iterative selection method[END_REF] and maximum entropy thresholding method [START_REF] Haitao | Image Segmentation Using the Maximum Entropy of the Two-Dimensional Bound Histogram[J][END_REF]. By using three methods of image segmentation, the segmentation results are shown in Fig. 4. It is easy to see from four charts, Fig. 4d is the best, so we choose the one-dimensional maximum entropy thresholding method as thresholding method of this study.

Maximum entropy thresholding method put the concept of Shannon entropy into image thresholding, whose basic idea is that image information entropy is defined by gray-scale image distribution density function and according to different assumptions or different perspectives, there is different entropy criteria, and finally get threshold through optimization of the threshold criteria. Maximum entropy thresholding method has two types: one-dimensional maximum entropy thresholding method and two-dimensional maximum entropy thresholding method.

(1) one-dimensional maximum entropy thresholding method According to information theory, entropy is defined as

       dx x p x p H ) ( lg ) ( . ( 1
)
Where ) (x p is the probability density function of random variable x . For digital images, random variable x can be gray value or regional gray, gradient and other characteristics. The words one-dimensional gray-scale maximum entropy mean that the threshold which is chosen can split image into two parts, and the two parts' first-order gray-scale statistics has maximum amount of information.

The histogram is shown in Fig. 5, we assume that the original grayscale image has L gray levels, pixel points that are lower than t gray level constitute the target area O , and pixel points that are higher than t gray level constitute the background area B , so the probability distribution in its region is as follows:

area For digital images, the target area and background area of entropy are:

O : t i p p / t i , , 2 , 1   ; area B : ) 1 /( t i p p  L t t i , , 2 , 1     .
                   L t t t p p p p t H t i p p p p t H i t i t i B i t i t i O , , 2 , 1 )] 1 /( lg[ )] 1 /( [ ) ( , , 2 , 1 ) / lg( ) / ( ) (   . ( 2
)
So the entropy function is defined as:

                            L i i i p p H t i p p H p H H p H p p H H t i i i L i i i t t t L t t t t B O , , 2 , 1 lg , , 2 , 1 lg 1
)] 1 ( lg[ ) (    . ( 3
)
When entropy function obtains the maximum, the corresponding gray value  t is the desired optimal threshold, the calculation formula is as follows:

  ) ( max arg t t    . (4)
Because the process of finding

Improved Hough Transform

Linear equations in polar form as:

   sin cos     y x . ( 5
)
Where (x, y) is the Cartesian coordinates of image; θ is the angle between the x-axis forward direction and normal line, the range of angle is [-90°, 90°]; ρ is the vertical distance from the line to the coordinates origin. According to geometry, we can see that any two points can determine the line's parameters (ρ, θ).

) , ( y x a   which is supposed a determined point on a straight line, and b(x i , y i ) is a point from candidate set D, ρ and θ can use the formula (6) and formula [START_REF]Texas Instruments[END_REF] to calculate:

) ( tan 1 y y x x i i i         . ( 6
) i i i y x    sin cos     . ( 7
)
The value of θ is mapped to a set of accumulators, and each obtained θ i makes the corresponding accumulator value plus 1. Because the points on a same line have the same value of θ, so when there is a straight line, its corresponding accumulator has partial maximum, θ i which corresponded partial maximum become θ of the request line. According to θ and ) , ( y x a   , the corresponding ρ value can be got from formula [START_REF]Texas Instruments[END_REF].

Walking Goal Line Parameter Calculation

In

First, the accumulator A is defined in [-15°, 15°] range, and the range is divided into 100 equal parts A i (0<i<101), whose amplitude is 0.3°. Then ) , ( y x a   and the candidate point set D(x i ,y i ) as the basis, use formula (2) calculation θ i , then map θ i to 100 portions and make the cumulative device A i (0<i<101) value of the corresponding portions plus 1. When mapping has been completed, there must be an accumulator A i maximum value, and use the midpoint of this accumulator corresponding parts as the request linear parameter θ , according to the formula (3) and

Conclusion

In allusion to aided driving by harvesting robot, in order to make sure the entire image processing in real time and system can be working long-time stability, so this paper chooses TI's TMS320DM6437 as core processor for the system, meanwhile, the improved Hough transform based on a determined point is applied to complete line detection, the algorithm simplifies binary map to unitary map. Comparing with the traditional Hough transform, it saves computing time, reduces the parameter space greatly, and avoids invalid samples and cumulative problems. In addition, the algorithm is well proved with respect to its anti-interference and robustness etc. Tests show that the entire hardware system is capable of working stably, and the entire system operating time from the acquisition parameters to display is only 40ms or so, obviously, this design can meet the real-time processing requirements. Time is calculated by the time DSP debugging software CCS3.3.

1 Introduction

 1 Autonomous navigation research of Agricultural robot is the requirements of modern agricultural development, and it also has become a high-tech research direction such as intelligent control in the application of agricultural machinery and equipment. Currently, the ways of agricultural robot navigation are as follows: (1) Beacon navigation. Namely, beacons are set on the determined points of work environment; (2) Global Positioning System (GPS). The emergence of GPS is a major breakthrough of modern navigation technology, and it has high accuracy, unlimited users, all-weather work and other advantages. But it has a poor anti-interference ability. When it is obstructed by trees, houses and other blocks, the signal may be occasional lost; (3) Visual navigation. Visual navigation of agricultural autonomous walking robot, usually divides into two kinds: wireless guide and wired guide. Because of the complex operating environment of agriculture, we usually use wireless navigation, which robots accord a CCD camera's real-time detection of the surrounding environment to plan the required path, and it can follow the path without human intervention to move towards the target. Among them, due to its application is flexible and easy, visual navigation has become popular navigation way [1~3]. Agricultural robot's visual navigation requires not only an effective image processing algorithm, but also requires a stable platform of image processing with high executing speed and miniaturization. With the development of electronic technology, the signal processing hardware platform based on DSP [4~5] has been applied in image processing widely. This paper applies DSP hardware platform to complete image acquisition, image processing, external communications and other functions, at the same time, this hardware is very small, which applies improved Hough transform algorithm based on a determined point in the software.2 System Hardware Components System hardware consists of the signal processor TMS320DM6437, DSP program memory FLASH, and high-speed video A/D acquisition chip tvp5416. System hardware schematic diagram is shown in Fig.1. Firstly, TMS320DM6437 controls tvp5416 to do A/D conversion of the input analog video through the I2C bus and gets the digitized video signal. Then, image processing algorithms is used to detect linear parameters. Finally, the linear parameters by serial communication are sent to the steering control unit and the image is transmitted to the display unit.

Fig. 2 .Fig. 3 .

 23 Fig.2. Image processing flow chart

Fig. 4 .

 4 Fig.4. Image segmentation result

Fig. 5 .

 5 Fig.5. One-dimensional histogram

t

  is global search, so the calculation time is too long, so we did some improvement with one-dimensional maximum entropy thresholding method as follows: Since gray level distribution of the medium shot is between the close shot and long shot, we select gray-scale image from the 200 row to the 249 row as a processing unit after numerous tests, then calculate the average gray value T of this unit, greatly reduces the search space of  t and saves computing time.

  order to use the Hough transform based on a determined point[13~15], at first, we should confirm this point. As the long shot image has better effect, so we determine the method after many tests as follows: the first 40 points of the set of points D(x i , y i ) are taken, then we can determine a

Fig. 6 .

 6 Fig.6c is 269.831, 1.95; Fig.6d is 250.817, -6.75.

and then the resulting digital video signal is put into the DSP. 2.2 High-speed Image Processing Unit

  DM6437 video subsystem is composed by two parts, and one is video processing front end for digital video input data, which provides the input interface for a variety of standard digital video, at the same time, it also makes necessary pre-processing for the input digital video data. The other is the video processing back end, and it is used to output digital video data to drive the display video images.Therefore, here we use DM6437, external 27M crystal, by frequency to 600MHz. The program is written into FLASH, and then the program is loaded into the DSP by the power.

	DM6437 provides video-on-chip input/output interfaces which are called video
	subsystem VPSS.
			FLASH
		VIDEO IN	VIDEO OUT
		TVP5146	TMS320DM
		A/D	6437DSP
			COMM
		Fig.1. System hardware schematic diagram
	2.1	A/D Converter
	Among the AD converter, there is a professional video A/D chip tvp5146 [6]. The
	chips do not need to use others to separate horizontal and vertical sync signals of
	video signal, because it can get vertical sync signals from the composite video signal.
	By horizontal and vertical sync signals, it can collect A/D converted digital images
	correctly. The chip programs the input original image via I2C bus and controls image
	brightness, contrasts and outputs effective range. In practice, the original composite
	video signal of camera outputting is selected to do pretreatment according to field in
	the A/D chip, TMS320DM6437 from TI company is introduced in 2006, specifically for
	high-performance, low-cost video application development, frequency of 600MHz
	and 32-bit fixed point DSP Leonardo (DaVinci (TM)) technology processor family, it
	can be applied to automotive vision, robotics and other fields [7]. TMS320DM6437
	uses the TMS320C64x+DSP as its core which is TI's third-generation-long instruction
	set architecture (VelociTI.3), clocks at up to 600MHz, supporting eight 8-bit or four
	16-bit parallel MAC operations, and the peak capacity up to 4800MIPS. It can also
	handle H.264 encoding algorithm of 8 channels CIF or 3 channels D1 format.

Acknowledgements

The authors thank "Twelfth Five-Year" National Technology Key Project (Funding No.: 2011BAD20B00), for their financial support.


Song Xiaoyu
email: songxy@nercita.org.cn

Huang Wenjiang

Cui Bei

Zhou Jihong

Winter Wheat Seedtime Monitoring through Satellite Remote Sensing data

Keywords: words:Winter wheat, Seedtime, Remote sensing, Landsat5 TM, HJ-1B

Winter wheat seedtime is important for wheat growth. It affects the wheat yield and quality. The objective of this study is monitoring the winter wheat seedtime through remote sensing imagery. Two HJ-1B images and one Landsat5 TM image were used in this study. Three Vegetation Indices, DVI, SAVI and RDVI were calculated. The correlation about the wheat seedtime and VIs were analyzed. The result indicated that wheat growth was negatively correlated with seedtime. Wheat VIs during 40-60 days after sowing is best for seedtime monitoring. The correlation coefficient for DVI of November 22th HJ-1B and seedtime reached -0.51. The seedtime for whole wheat plant field of Beijing area was inverted through the model. The results show that area for seeded before Sept 30th was about 12.7 thousands ha, 31. 8 thousands ha between Oct 1th and Oct 8th, and 16.2 thousands ha seeded after Oct 8th during 2009-2010 year.

INTRODUCTION

Recent years, warm winter appears in Beijing area frequently since the Global climate warming. High temperature in winter will affects the growth of overwinter crop. Seedtime is important for the management of the wheat [START_REF] Ly | Satellite Remote Sensing of Winter Wheat Seedtime and its Application[END_REF]. Crop sowing date, sowing pattern, and nitrogen fertilization will affect on weed suppression and Yield [START_REF] Lars | Crop Density, Sowing pattern, and Nitrogen Fertilization.: Effects on Weed Suppression and Yield in Spring Wheat[END_REF][START_REF] Keyvan | The effect of sowing date and row spacing on yield and yield components on Hashem chickpea variety under rainfed condition[END_REF]. Sowing prior to October generally provides enough time for wheat to establish canopy and develop sufficient roots to anchor the plant in the soil [START_REF] Edwards | Factors affecting wheat Germination and stand establishment in hot soils[END_REF]. Proper late sowing can avoid wheat bloom before winter and help it safe live through winter Gao Q.L [START_REF] Gao | Studies on regulating sowing time of wheat under the warm winter conditions[END_REF][START_REF] William | Tillage Method and Sowing Rate Relations for Dryland Spring Wheat, Barley and Oat[END_REF]. Winter wheat in the Beijing area is typically sown from late September through middle October. During 2009-2010, there are almost 61.3 thousands ha wheat fields in Beijing suburb and the seedtime for different fields is varied. So it is necessary for the farmer or manager to master the wheat area of different seedtime.

This study tries to monitor the winter wheat seedtime in Beijing suburb through satellite remote sensing imagery. Firstly, the wheat plant area was extracted using the threshold of NDVI of three satellite remote sensing images acquired before 2010 spring. Then four Vegetation Indices (VIs) for 53 winter wheat fields were calculated and the relationship about the wheat seedtime and VI is analyzed. The study result indicated that the winter wheat growth was negatively correlated in wheat seedtime while it was positive correlated in wheat tiller number. The DVI of November 22th HJ-1B CCD2 image was highly related in the seeded time of wheat, and the correlation coefficient reached -0.51. Then winter wheat seedtime monitoring model was established based on the HJ-1B image DVI and the wheat sowing date. The seedtime for whole wheat plant field of Beijing area was inverted through the model.

The results show that in 2009-2010 year, winter wheat area for seeded before Sept 30th was about 12.7 thousands ha, while area for seeded between Oct 1th and Oct 8th was 31. 8 thousands ha, and 16.2 thousands ha wheat was seeded after Oct 8th.

MATERIALS AND METHODS

The study area

This study was carried on Beijing suburb which ranging from N 39°26′to 41°03′in latitude and from E 115°25′to 117°30′in longitude. During 2009-2010 the winter wheat growth season, 53 wheat fields distributed in Beijing suburb,including Shunyi, Tongzhou, Fangshan, Daxing, Changping, and Huairou were selected as the study samples area (Figure 1). One Landsat5 TM image and two Environment and Disaster Monitoring and Forecasting of small satellite Constellations 1B (HJ-1B satellite) CCD2 images were used in this study.

Study wheat fields

Data collection

Remote sensing data. One Landsat5 TM image acquired on 24 October, 2009, two Environment and disaster monitoring and forecasting of small satellite constellations 1B (HJ-1B satellite) CCD2 images acquired on 22 November, 19 December, 2009 were used in this study. HJ-1B satellite is equipped with a Charge-Coupled Device camera (CCD) and Infrared Camera (IRS).HJ-1B satellite are designed nadir symmetrical placement, split field and parallel observations [START_REF] Yi | Researches on HJ-1 satellite image quality and land use classification precision[END_REF]. HJ-1B satellite is loaded CCD2 cameral of four spectrum push-broom imaging with the ground swath width of 360 km, the ground pixel resolution of 30 meters. The main parameters of CCD2 camera are shown in table 1 [START_REF] Jia | Design and On-orbit application of CCD Camera on HJ-1A/B Satellites[END_REF][START_REF] Yang | The Evaluation and Application Study on CCD Multi-spectral Remote Data Characteristics of HJ-1A/B Satellites[END_REF]. Oct and the late is after 9 Oct.

Methods

The overall methods used in this study are shown as flow chart in Figure.

Fig.2. The flow chart of the study methods

The sequence involves:

(1) TM and HJ-1 data processing, which includes radiometric, atmospheric and geometric corrections;

RS data processing Radiometric Corrections

(3) Four Vegetation Indices (VIs) for 53 winter wheat fields were calculated and the correlation about the wheat seedtime, tiller number and VI were analyzed ,then winter wheat seedtime monitoring model was established.

(4) The seedtime for whole wheat plant field of Beijing area was inverted through the model.

ANALYSIS AND CONCLUSION

RS data processing

HJ-1B data was firstly processed by radiometric by convert the DN value of the raw image to the at-satellite radiance image using absolute calibration coefficients (table 2) [START_REF] Zhu | Analysis of System-level Radiometric Calibration Data for HJ-1A/B Wide Coverage CCD Camera[END_REF].

0 / L a DN L   (1)
where L represents at-satellite radiance, DN represents the DN value of the raw image, a

represents the absolute calibration coefficient of gain, and 0

L represents the offset. The unit of radiance converted is

1 1 2       m sr m W
 .Then the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model was used for image atmospheric correction (Vermote E., et.al, 1997). The band parameters for HJ-1B CCD2 are adopted that of 1 to 4 band of Landsat-TM5 image in this study.

After that, HJ-1B data was processed by geometry correction. The calibrated TM image in this study area was used as the base map, and the same point was selected in TM image and HJ-1B image for correction. Then the typical features such as road intersections adopted by the actual GPS handset were used as verification. Finally, the error of geometric correction was controlled within 1 pixel.

Winter Wheat fields extraction

The main plants in Beijing suburb are evergreen shrub and winter wheat in field from November to December. During the winter, the plant can easily differentiate from the water body, city land and bare land for the plant NDVI threshold great than that of others features. Firstly, we use the NDVI to extract plant form other land types. In this paper, the threshold great than 0.05 in 24, Oct, 0.

     R R R R AVI Huete, et al., (1988) RDVI B3,B4 ) ( / ) ( 3 4 3 4 R R R R RDVI    Rougean & Breon, (1995)
Then, analysis about the VI and the winter wheat growth investigation data are carried on. The sowing date has negative relationship with the winter wheat ground survey LAI. The correlation coefficient between sowing date and Oct LAI reached -0.23, increase to -0.54 in Dec then decrease to -0.12 in Nov.

Meanwhile, the relationship between wheat VI and sowing date also analyzed (Table 4).

Conclusion and Discussion

In

Fig. 1 .

 1 Fig.1. The location of the study area and the distribution of the study wheat fields.

  2. Conventional statistics were computed with Excel 2003. The remote sensing data processed and displayed with ENVI 4.1 (ITT 2003).

( 2 )

 2 Using three images NDVI thresholds to extract the winter planted area of 2009-2010 in Beijing suburb. Landsat5-TM, HJ-1B CCD images

  2 in 22, Nov, and 0.1 in 19, Dec is considered as the plant, which can easily differentiate the water body, city land and bare land. Then the winter wheat and the shrub were distinguished by the NDVI curve with the decision trees of ENVI. The evergreen shrub and the winter wheat have the different NDVI curve characters. The shrub NDVI curve shows obvious decrease from Oct to Dec while the winter wheat increases from Oct to Nov but decrease from Nov to Dec. Based on the difference of the NDVI curves, the winter wheat fields was extracted (figure 4).

Fig. 3 .

 3 Fig.3. The winter wheat extraction map

  seedtime monitored.the DVI of November 22th HJ-1B CCD2 image was highly related in the seeded time of wheat, and the correlation coefficient reached -0.510. Then winter wheat seedtime monitoring model was established based on the HJ-1B image DVI and the ground data (Figure4). Three classes of sowing calendar dates recognition namely: early, normal, late in this study. The date range for early is before 30 Sept, for normal is 1 Oct to 8 Oct and the late is after 8 Oct.

Fig. 4 . 4 ResultsFigure 5

 445 Fig.4. The scatter-plot of sowing date and HJ-1 CCD2 DVI

  this paper, the winter wheat seedtime monitoring model was established based on the HJ-1B image DVI and the ground data. The seedtime for whole wheat plant field of Beijing area was inverted through the model. The result shows a good agreement with the statistic data from Beijing agriculture technique popularizing station. It indicated that the winter wheat growth was negatively correlated with wheat seedtime in winter and the wheat spectral characteristics during 40-60 days after sowing is best for seedtime monitoring. The results of this study suggest that seedtime monitoring using RS data was reliable and feasible. The study is only one year's result, although the warm winter appears in Beijing area frequently in recent years, the winter of 2009-2010 is a typical cold winter. The average temperature from 2009 Dec to 2010 Feb is negative 3.5 ℃, it is lower than the recent 10 year's average temperature by 1.6 ℃. So it affects the growth of the winter wheat in different seedtime. More study about the different seedtime wheat growth characters in warm winter should be carried on in the future. Sowing date early No-Wheat fields Sowing date normal Sowing date late a b

Table 1

 1 Characteristics of HJ-1B CCD2Ground survey data. Meanwhile, three ground survey experiments were carried out when the two satellites flied over Beijing area from September to December. The winter wheat sowing date, plant area and seedling emergence numbers data are collected by investigators in all 53 fields. Meanwhile the crop growth information such as wheat winter main stalk number, tiller number and the plant height for each field were also calculated. In order to get the exact data, three 1 m2 study plots for each filed

	Satellite Sensor Bands	Wavelength	Spatial	Swath width	Sub-cycle (days)
				range (μm)	resolution (m)	(km)
	HJ-1B	CCD2	1	0.43-0.52	30	360	4
			2	0.52-0.60	30	360	4
			3	0.63-0.69	30	360	4
			4	0.76-0.90	30	360	4

where located in the middle of field were selected to calculate. Then the location of study plots was recorded by GPS. From the investigation, the sowing period for winter wheat of Beijing area lasts from 23 Sept to 15 Oct in 2009. Three classes of sowing calendar dates recognition namely: early, normal, late in this study. The date range for early is defined the seedtime before 30 Sept, normal is 1 Oct to 8

Table 2 .

 2 The absolute radiometric calibration coefficient of HJ-1B CCD2

	HJ-1B CCD2 bands	A (DN/W.m-2.sr-1.m-1)	L 0 (W.m-2.sr-1.m-1)
	Band1	0.5759	3.4608
	Band2	0.5488	5.8769
	Band3	0.7537	8.0069
	Band4	0.7753	8.8583

Table 3 .

 3 The vegetation index definition for Landsat5 TM and HJ-1B CCD2 image

	Vegetation Index	Bands														Definition	Author
	NDVI	B3,B4	NDVI		(	4 R		3 R	)	/(	4 R		3 R	)	Rouse, et al., (1974)
	DVI	B3,B4	DVI		(	R	4		R	3	)		Jordan, et al., (1969)
	SAVI	B3,B4	S			1 (	) 5 . 0	*	(	4	3	)	/(	4	3	) 5 . 0

Table 4

 4 The correlation coefficient between sowing dateThe table 4 indicated that wheat spectral characteristics during 40-60 days after sowing is best for

	Vegetation Index	sowing date	Vegetation Index	Sowing date	Vegetation Index	sowing date
	10/24 TM-NDVI	-0.136	11/22 HJ-NDVI	-0.458 **	12/19 HJ-NDVI	-0.142
	10/24 TM-DVI	-0.111	11/22 HJ-DVI	-0.510 **	12/19 HJ-DVI	-0.182
	10/24 TM-RDVI	-0.124	11/22 HJ-RDVI	-0.487 **	12/19 HJ-RDVI	-0.167

Acknowledgment

This work was supported by Beijing Natural Science Foundation (Grant No. 4092017) and Youth Scientific Research foundation of BAAFS.