Caiyun Lu

Zhijun Meng

Xiu Wang
email: cwangx@nercita.org.cn

Guangwei Wu

Nana Gao
email: egaonn@nercita.org.cn

Jianjun Dong
email: fdongjj@nercita.org.cn

Application Feasibility Analysis of Precision Agriculture in Equipment for Controlled Traffic Farming System: A Review

Keywords: Controlled traffic farming system, equipment, precision agriculture

Equipment includes planter, field management equipment and harvester for controlled traffic farming system is elaborated in the paper. And the analysis results shows that main problems of equipment for controlled traffic farming system contains poor ability to navigate, uneven and bent wheel lanes. To solve the problem, the thought that precision agriculture is introduced into equipment for controlled traffic farming system is put forward. For this purpose, the key technology of precision agriculture is introduced, and the feasibility of combination of precision agriculture and controlled traffic farming system is discussed, which provides theoretical support for the development of equipment for controlled traffic farming system.

Introduction

Controlled traffic farming system, as a new agricultural production system, combines the conservation tillage and controlled traffic technology. The principle includes that crop zone is independent of wheel lane, which means wheels of tractor and machines run only on permanent lanes, and crop is planted on other soil except wheel lanes. So the crop will not be compacted by wheels, which can keep better environment for crop growth. Controlled traffic farming system requires few tillage to keep the lanes, no tillage and straw cover to realize water saving, waterlogging, tillage reduction and residue management before planting. Controlled traffic farming system was developed from Mexico, and has developed in many countries, which suffer drought and waterlogging.

Researches show that controlled traffic farming system has many effects [START_REF] He | Study on permanent raised beds in irrigation areas of Northern China[D[END_REF][START_REF] Jin | Current status of permanent raised beds equipment[J][END_REF]. Wheel traffic can increase soil compaction which has negative influences on some critical soil properties, like saturated hydraulic conductivity, porosity and bulk density [START_REF] Mathieu Lamande | Changes of pore morphology, infiltration and earthworm community in a loamy soil under different agricultural managements[END_REF][START_REF] Pagliai | Soil structure and the effect of management practices[END_REF][START_REF] Radford | Changes in the properties of a Vertisol and responses of wheat after compaction with harvester traffic[END_REF][START_REF] Green | Advances and challenges in predicting agricultural management effects on soil hydraulic properties[END_REF]. Bai et al. (2008) showed that 9 years controlled traffic farming system reduced bulk density 11.2%, and increased porosity in 0-15cm soil layer. In addition, the saturated hydraulic conductivity was greater under controlled traffic farming system [START_REF] Yuhua | Traffic and tillage effects on wheat production on the Loess Plateau of China: 2. Soil physical properties[END_REF]. Research by Chen et al. (2008) indicated that controlled traffic farming system prominently improved microbial biomass, soil organic matter and total N in 0-5cm soil layer and the yield increased 10% greater than conventional tillage [START_REF] Hao | Traffic and tillage effects on wheat production on the Loess Plateau of China: 1. Crop yield and SOM[END_REF].

The development of controlled traffic farming system bases on mechanization, for example, key operations such as ridge reforming and reshaping and planting can't be completed by manual. Extending controlled traffic farming system will be meaningless without mechanization, and mechanized controlled traffic farming system is the inevitable trend. The equipment for controlled traffic farming system includes planter, field management equipment and harvester etc. There must be any method to guide the tractor and agricultural equipment running on the wheel lane rather than crop zone due to the separated crop zone and wheel lane. The development of controlled traffic system is still in its infancy, and the corresponding equipment is not mature. The guidance is mainly completed by artificial marking, and the method only suits short field, and the ability of guidance is bad, and wastes time and energy.

Based on the positioning and navigation, precision agriculture manages each growth process and controls agriculture products, such as fertilizer, pesticide and seed, etc. to fulfill a full potential of soil and crop. Precision agriculture is comprised of 10 systems, including global positioning system (GPS), geographic information system (GIS), remote sensing (RS), field information collecting system, intelligent agricultural system, expert system, system integration, environmental monitoring system, network management system, and communication system. And GPS, GIS, RS and computer control system are the core of the precision agriculture [START_REF] Yaqin | The necessity analysis of the development of precision agriculture in China[END_REF][START_REF] Maohua | Development of precision agriculture and innovation of engineering technologies[END_REF][START_REF] Zhao Chunjiang | Advance and prospects of precision agriculture technology system[END_REF][START_REF] Pusheng | On the precision agriculture technological system[END_REF]. The attribute data, which comes from the analysis of soil and crop information by GIS, and map data can make field management information system. On the basis, input is adjusted according to actual conditions on each unit to reduce waste, increase income and protect environment. GPS, one of the cores of the precision agriculture, is used for accurate positioning of agricultural machine. The application of precision agriculture in controlled traffic farming system can realize accurate orientation, which is conducive to the demonstration and application of controlled traffic.

Basis on the literature at home and abroad, the paper introduces equipment for controlled traffic farming system and the existing problems, and elaborates the key technology of precision agriculture. On the basis, application feasibility of precision agriculture on equipment for controlled traffic farming system is discussed.

Equipment for Controlled Traffic Farming System

The research of equipment for controlled traffic farming system focuses on machines, and aims to avoid blockage by mulch on the field. However, the research on how to ensure accurate wheel layout has rare research, which is more important to controlled traffic farming system [START_REF] Hao | Design of row-followed no-till wheat and maize planter under controlled traffic farming system[END_REF].

2.1

Planter

(1) No-tillage maize and wheat seeder for controlled traffic farming system 2BMDF-2/7 no-tillage maize and wheat seeder for controlled traffic farming system is used for wheat or maize planting in double cropping area. The planter can complete straw chopping, furrowing, fertilizing, planting and compacting in one time. Furrowing and fertilizing are completed by opener, and the mulch is chopped by high speed cutter. Combination of high speed cutter and double-disc opener is designed to avoid blockage by straw and weed. The cutter can put into sides of openers, and take away downed straw and weed to avoid blockage. The sharp tip of tine opener can cut off or hitch stubble, and the cutter chops the stubble hitched by tip to avoid blockage. (2) No-tillage fertilizing planter for controlled traffic farming system 2BMFSG-3/6 no-tillage fertilizing planter for controlled traffic farming system can plant wheat and maize with the mating power 300HP and working speed 3~5km/h. The planter works on sunken controlled traffic, which means permanent raised bed farming system. Installation position of land wheel can be adjusted according to the depth of furrow to satisfy the requirements of sowing depth. The stubble and straw are cut by the rotary cutter. The diameter of land wheel is larger than that in flat field, and the installation position is lower, so that the furrow by opener satisfies the agronomy requirements when land wheel runs on the traffic lanes. The openers are distributed into two rows to avoid blockage [START_REF] Bin | Design of 2BMFSG-3/6no-till fertilizing planter under controlled traffic farming system[END_REF].

Field Management Equipment

Mating equipment of controlled traffic farming system is developed from planters to sprayers and other field management equipment with the wide application of controlled traffic farming technology in the world.

Figure 3 shows the sprayer for sunken controlled traffic farming system in Mexico. The spraying is finished during crop growth season to control weed and insects; furrow is used for guide, which improves the spraying with overlap and misses [START_REF] Jin | Current status of permanent raised beds equipment[J][END_REF].

Harvester

To improve efficiency of crop harvest, harvester for controlled traffic farming system is researched in the world, such as hanging harvester developed by China Agricultural University and two-row ridge harvester in Mexico. These two harvesters can not only ensure the harvest quality, but also keep ridge bed [START_REF] Jin | Current status of permanent raised beds equipment[J][END_REF].

Key Technology of Precision Agriculture

Precision agriculture was put forward in the 1990s by soil scientists in the University of Minnesota, America. After years of development, precision agriculture has developed in many aspects, including variable rate fertilization, nutrient detection, and yield monitoring, etc. The key technology of precision agriculture mainly includes:

Global Positional System

Global positioning system (GPS) refers to a satellite navigation system based on space, which can provide spatial and temporal information in all climatic situations, and anyplace where has a clear line of sight to at least four GPS satellites. This system can provide key capabilities for military, civilian, and commercial users all over the world. It was established and maintains by the United States government, and it is freely accessible to anyone who has a receiver. In 1973, the United States began the GPS project to overcome the restrictions of previous navigation systems, integrates advanced technologies from a few predecessors, and includes some classified engineering design studies in the 1960s. The system initially used 24 satellites. And it was completely operable in 1995 [15].

Global positional system has following functions in precision agriculture: measurement controlling, field information sampling positioning and navigation control [START_REF] Yuchun | Application of geographic information technologies in precision agriculture[END_REF]. The research of GPS mainly focuses on field information sampling and intelligent agricultural machinery system with GPS receiver. For example, GPS receiver is installed on rotary tiller, planter, field sampler, fertilizing sprayer and harvester, which can obtain accurate positioning information navigation monitoring, and get crop growth information and associated spatial position information [START_REF] Blackmore | An information system for precision farming[END_REF][START_REF] Shen | Research on application of 3S in precision agriculture[J][END_REF].

Geographic Information System

Geographic information system (GIS) is the core of precision agriculture, which is used for field data management, query of soil, natural condition, crop growth and yield etc., and agriculture theme maps drawing, as well as spatial data collecting and analyzing. In addition, GIS system can combine various data into new data sets, for example, GIS system deals with soil type, terrain, and mulch, and establishes the connection among them.

GIS system is software for dealing with spatial information for organizing, analyzing and showing all types of spatial information in the same area. Each kind of information can form one GIS layer, and information from different layers can form new layer by analysis. GIS system can combine various element maps into new map, and analyze the effect of various factors on crop. Take variable fertilizing for example; fertilizer rate is made in different site according to soil nutrition and yield, which guides the fertilizing [START_REF] Xu | Application of geographic information system in precision agriculture[END_REF].

Remote Sensing

Remote sensing (RS) refers to remote probing techniques. It uses sensors detecting electromagnetic waves, visible light and infrared rays from distant, and obtains detection and identification information by data analysis. Remote sensing technology is the key tool for large area obtaining field data rapidly in precision agriculture. It can support vast field spatial change information.

Remote sensing technology is an advanced probing techniques based on modern physics, space science, electronic computer technology, mathematics and earth scientific theory [START_REF] Diofantos | Advances in remote sensing and geo-information for the environment[END_REF]. Various kinds of sensors were developed since 1900s, and both the probing ability and applying range have been significantly expanded.

Remote sensing mainly provides two types of crop and field spatial distribution information. One is basic information which has little change during crop growth season. The information mainly includes farm infrastructure, field contribution etc. And the other is dynamic change information of time and space, including yield, crop nutrition and insects, etc. [START_REF] Zhou | Status and prospect of agricultural remote sensing[J][END_REF]

Application Feasibility Discussion of Precision Agriculture in Equipment for Controlled Traffic Farming System

The research of equipment for controlled traffic farming system mainly focus on working performance with the assumption that wheel lanes exists and works well, and the tractor and equipment run on the wheel lanes. However, it is difficult to control the distance of two lanes constant, and the wheel lane is easy to bend during operation. In China, when the equipment works on the soil, it is guided by the distance location method of pulling a rope, which means that the ropes are pulled on the rim of lanes, and a person stands in the middle of the two traffic lanes at the end of the rows, and the operator takes the person as the orientation. This method guides the equipment staying on the traffic lanes. But it needs many labors, and is very inconvenient to pull a rope, so it only fits to the small-scale fields. In addition, the size of the overlap and the potential reduction by using controlled traffic farming system depends to a large extent on the individual driver's experience and skills with the precise handling of equipment and tractor tools prior to the introduction of auto steering. To solve the problems, precision agriculture is introduced to controlled traffic farming system [START_REF] Mark | Using conservation agriculture and precision agriculture to improve a farming system[M[END_REF].

Precision agriculture is a new agricultural technique to realize accurate fertilizing, sowing, irrigation and harvesting etc. with the GPS, GIS and RS. Farmers try hard to improve efficiency by increasing field size and machinery. Precision agriculture adoption on this scale has been made possible due to the enormous decrease of GPS equipment cost in recent years. Yield mapping can be accessed by the GPS system, and farmers could see the yield variation in real time occurring on the farm. Precision agriculture helps farmers to balance management with land capability to improve profitability and protect environmental resources [START_REF] Hans | Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Demark[END_REF].

Conclusions

Traditional equipment for controlled traffic farming system including planter, field management equipment and harvester is elaborated and the analysis results shows that main problems contain poor ability to navigate, uneven and bent wheel lanes. To solve the problems, the thought that precision agriculture is introduced into equipment for controlled traffic farming system and key technology of precision agriculture is introduced. Precision agriculture is a new agricultural technique to realize accurate fertilizing, sowing, irrigation and harvesting etc. with the GPS, GIS and RS. Farmers try hard to improve efficiency by increasing field size and machinery, which promote precision agriculture adoption. In addition, precision agriculture can help farmers to balance management with land capability to improve profitability and protect environmental resources. From this perspective, the application of precision agriculture in equipment for controlled traffic farming system is feasible.

Fig. 1 .

 1 Fig. 1. 2BMDF-2/7 no-tillage maize and wheat seeder for controlled traffic farming system

Fig. 2 .

 2 Fig. 2. 2BMFSG-3/6 no-tillage fertilizing planter under controlled traffic farming system

Fig. 3 .

 3 Fig. 3. Mexico sprayer for sunken controlled traffic farming system

Fig. 4 . 5 .

 45 Fig. 4. Hanging harvester in China Fig. 5. Two-row ridge harvester in Mexico

Acknowledgments

This work was financed by the Postdoctoral Science Foundation of Beijing Academy of Agriculture and Forestry Sciences of China (2014002) and 863 Program (2012AA101901).


Qiyu Zhang

Yingyi Chen

Zhumi Zhen

Jing Xu

Ling Zhu

Liangliang Gao

Yanzhong Liu

Liu Yanzhong

Aquaculture Access Control Model in Intelligent Monitoring and Management System Based on Group/Role

Keywords: group, role, access control, aquaculture, permission

Aquaculture intelligent monitoring and management system (AIMAMS) is a web information system covering businesses, home users, and management of aquaculture information, which have many users. The information security and safety equipment of different users is an important issue that we have to consider. Role-based access control introduces the roles concept into user and access rights, and its basic feature are that divide roles depending on the security policy, assign operating license for each role, and assign roles to each user. The user can access the specified object based on their respective roles. In order to achieve different user needs for security, aquaculture access control model based on group/role is developed which is based on analysis of the role-based access control model. Aquaculture intelligent monitoring and management system consists of system administrators, business users, family farming users and technicians, each category contains a number of other users who have different permissions. For such a complex distribution of competences, the user groups are introduced here on the basis of idea of role-based access control model. Users are divided into four groups which correspond to four categories of users, each group is given the largest collection of operational authority, and the users of each group are assigned different roles, which have all or part of privileges of the current group. Technicians can access business and family farming user's information, but can not view some sensitive data, such as price, therefore, technicians group is controlled by field-level permissions of data tables, the sensitive fields of business and family farming user data tables is shielded. This model can effectively reduce the overhead of rights management, and can improve system's scalability. Along with the needs of business development, the system can easily add new user groups and assign permissions and roles for them. This aquaculture access model is universal, it is not only suitable for aquaculture intelligent monitoring and management system but also has reference value for other systems.

Aquaculture intelligent monitoring and management system(AIMAMS) real-time online monitoring the important environmental factors of water quality such as water temperature, dissolved oxygen, PH value in the aquaculture process, by the integration of water quality sensors, Wi-Fi, GPRS and other technologies. This system provides monitoring and management functions which is data query, data collection, curve analysis, and provides more functions which is aquaculture profiles, map browsing, disease prevention and control, feed fed decision, application configuration, provides comprehensive information intelligent automation services for aquaculture users。AIMAMS is a java-based web information system which covers business and many home users. How to ensure information security and the safety equipment of different users? Role-based access control (RBAC) is a good choice. As a security mechanism,RBAC has been widely accepted, which can greatly reduce the cost and complexity of large networked and Web-based systems [START_REF] Ferraiolo | Role-Based Access Control[END_REF] .

Role-based Access Control

The concept of RBAC began in the 1970 s,accompanied by multi user and multi application online systems [START_REF] Ravis | Role-Based Access Control Models[END_REF] . The basic idea of RBAC is that introduce the concept of role between the user and the permission and link users and roles, and then control user to access system resources by giving permission to role. Role is a set of permission, a user has a role means the user can have all permission owned by the role. A user can have many roles and a role can be assigned to many users; A role can contain multiple permission and a permission can be included more than one role. A user is not directly associated with permission but get permission by role. [START_REF] Shin | UML-Based Representation of Role-Based Access Control[END_REF] To facilitate understanding of the various aspects of RBAC, Sandhu et al [START_REF] Ravis | Role-Based Access Control Models[END_REF] defines four models, their relationship are shown in Figure 1, Figure 2 describes the basic features.

Figure1 Relationship among RBAC models 1.1 RBAC 0 [2] RBAC 0 is the base model of RBAC, which contains the most basic requiremets. RBAC 0 model is composed of various parts of Figure 2. RBAC 0 has the following components:  U, R, P, and S(users, roles, permissions and sessions),

 R P PA  
, many-to-many assignment relation to permission and role,

 R U UA  
, many-to-many assignment relation to user and role,  user: U S  , function mapping user (s i ) of each session s i to the single user (user

      PA r p p i s roles r    ,
In this model, a user is an organization's staff. A role is a function of a task or job title within the organization. An permission is an approval that users access and operate data and resources in the system. As a mapping from one user to many roles, session can be created during the user activates some subset of their roles. [2] Based on RBAC 0 , RBAC 1 increases the concept of role hierarchy that roles can inherit permissions from other roles.

RBAC 1

The

          UA r s user r r r i      
, (which can change with time) and session s i has the permissions

          PA r p r r p i s roles r          , 1.3 RBAC 2 [2]
Based on RBAC 0 , RBAC 2 increases constraints mechanism(that is, configuration is limited between different components in RBAC). These constraints are needed to determine the values of various parts in RBAC 0 are acceptable or not. Only those values which are acceptable is permitted. 1.4 RBAC 3 [2] RBAC 3 combined with RBAC 1 and RBAC 2 , provides both role classifications and hierarchies.

Aquaculture Access Control Model Based on Group/Role

The paper studies the role-based access control model, and combines with the characteristics of aquaculture intelligent monitoring and management system, then proposes the aquaculture access control model based on group/role. The model divides into four major groups based on the nature of users, and each large group divides into several different groups by per unit or duties. Each group has a largest collection of identified operational authority. A manager is assigned to a group, who assigns different roles to each user group, the user has all or part of the privileges of the current group.

User Groups

AIMAMS has many users. Depending on the subordinate units, the users divide into four categories which are system administrators, business users, home users and technicians. System administrators are staff who work in system website operator unit, business users are enterprise who use the system, home users are individual users who use the system, and technicians are users who provide technical guidance to business users and home users, for example, technicians of fishery technical extension station and so on. Except system administrators, each type of user has users coming from different units, these users also contain a number of users who have different functional privileges. For such a complex distribution of competences, the paper introduces the user groups basing on the basic idea of role-based access control model, and divides the four categories into four groups. Each users group divides into different groups by unit.

The group of users G={GA,GE,GF,GT},where GA={GA 0 , GA 1 , GA 2 ,…, GA m } is system administers group. GA 0 is super administrator group whose function is to manage other system administrators group; GA 1 ~GA m is system administrator group whose function is to manage business user, home users and technicians by region, area can be north, northeast, east and other large areas, can also be provincial, municipal and autonomous regions; each region's the administrator user is responsible for the maintenance work which contain managing business users group, home users group and technicians group and initializing the system. GE={GE 1 , GE 2 ,…, GE n } is business users group, GE 1 ~GE n is specific business user groups. GF={GF 1 , GF 2 ,…, GF s } is home users group, GF 1 ~GF s is specific home user groups. GT={GT 1 , GT 2 ,…, GT t } is technicians group, GT 1 ~GT t is specific technician groups who guide business users and home users.

Users

Because a lot of units involved, the system administrator is impossible to establish an account for each user. A better way is grouped by team management, that is, administrators can specify an administrator user for each group of business users, home users and technicians, and the group administrator can add new user in oriented group.

The user U applies the same packet format as users group G, that is , U={UA,UE,UF,UT},where UA={UA 0 , UA 1 , UA 2 ,…, UA m },UE={UE 1 , UE 2 ,…, UE n } , UF={UF 1 , UF 2 ,…, UF s }, UT={UT 1 , UT 2 ,…, UT t }。 As many user groups, one certain business user group UE i (1≤i≤n) as an example to illustrate users representation. UE i ={ue i0 ,ue i1 ,ue i2 ,…,ue ik },k is the number of non-administrator users of this group, ue i0 is the administrator user of this group.

Roles

Defining roles is associated with specific applications, these is no fixed pattern, the model defines roles using the "two-step method" [START_REF] Wei | Research on Role-Based Access Control and ITS Application in Court System[END_REF] . The first step is to determine the class of role, according to the business processes of the business, the commonality of the various stages of the business is abstracted as function terms, in order to define each class of role. The second step is to determine the subclass of role, the model makes sure that the class of user group has permissions ( like query, add, update, update, startup, shutdown) to specific data or equipment, according to type of business, users powers, as well as the current state of the system, that is the true sense of role.

They are predefined roles of system, which are determined using above method. For each group, the system pre-defined roles are not directly used, the role of each group must is associated with the data and equipment of current group, and must create a new role by inheriting existing roles or according to the special needs of current group.

The role R applies the same packet format as users group G, that is, R={RA, RE, RF, RT} , where RE={RE 1 , RE 2 ,…, RE n } , RT={RT 1 , RT 2 ,…, RT t }. As many user groups, one certain business user group RE i (1≤i≤n)as an example to illustrate roles representation. RE i ={ re i0 , re i1 , re i2 ,…, re ih }, h is the number of non-administrator roles of this group, re i0 is the administrator role of this group.

Permissions and Representation

Permissions

Depending on the circumstances of AIMAMS, the permissions are divided into six kinds, they are menu permissions, page access permissions, button permissions, access permissions to data area, field-level access permissions and device permissions.

(1) Menu Permission In order to make different levels of minimum user access to their own user interface, the menu is managed as an authority [START_REF] Liaofei | The Application of Role-Based Access Control Technology in Union Equipment Management Center System[END_REF] . The system grants the menu permissions, and organizes flexibly application's menu composition based on the user's permissions, then each role user has a better human-computer interface [START_REF] Jianrui | Research of Permission Management Based on Role and Discretionary Access Control[END_REF] . When the lack of user permissions, the menu item is grayed out [START_REF] Haitao | Research and application of Role-Based Access Control in Privilege Management[END_REF] or not displayed. The latter is better. There are two menus in AIMAMS, they are main menu and tree menu for on-line monitoring. The main menu consists of a menu and submenu, and grants permission for the role by clicking the check box of two-dimensional table composed by a menu and submenu, online monitoring tree menu grants permission for the role by clicking the check box of the tree menu. And these two menus dynamically generated according permission.

(2) Page Access Permission In order to prevent unauthorized access to the page, the system must set permission for each page, so the page verifies access permission when it is loaded initially to allow or ban the user from accessing the page [START_REF] Jianrui | Research of Permission Management Based on Role and Discretionary Access Control[END_REF] . The system is modular in design, and creates a file for each module, where stores the relevant page files. IO operations traverse through all folders in the root directory of the system, and get each module JSP pages. The tree structure with check box grants permission for the role, which is composed by module and page.

(3) Button Permission Button operation is an important operating mode bearer service processing actions. By controlling button permission, applications can achieve transaction processing for multiple users at different stages of the same transaction on the same page, then make enterprise information systems more intuitive and efficient [START_REF] Jianrui | Research of Permission Management Based on Role and Discretionary Access Control[END_REF] . In fact, the button permissions correspond with the SELECT, INSERT, DELETE, UPDATE operation of the data table. When a user has some permissions, the corresponding button is clicked, otherwise it is gray, that can not be clicked.

(4) Access Permission to Data Area Each group operating pages of business users and home users are the same, but they can only operate their own data and equipment. According to the business process, the page data are divided by group, so that different user can only handle the data that the user has the permissions on the same page [START_REF] Jianrui | Research of Permission Management Based on Role and Discretionary Access Control[END_REF] .

(5) Field-Level Access Permission When the different users use the same type of permission, there is a difference that they can access resources and use functions [START_REF] Fan | RBAC Based Classified Application System Access Control Method Design[END_REF] . In this case, the system uses the field-level access control method in data table. Technicians can access information of business users and home users, but can not view sensitive information, such as price and value. During view, each field must determine whether the current role be prohibited to display , only field that is not prohibited can be displayed.

(6) Device Permission Online monitoring system can control aerators, feeding machines and other equipment according to monitor status of the system. In order to ensure the safety of equipment, only the current group user who have permission can startup and shutdown operating position of the devices.

Representation of Permission

The authorization status of role can be described by access control matrix [START_REF] Peng-Xiang | DESIGN AND IMPLEMENTATION OF A PRACTICAL ROLE-BASED ACCESS CONTROL MODEL[END_REF] . In the access control matrix, the row is a role, and each column is an authorized ACL( Access Control List), which is a Boolean value, as shown in Table 1. The matrix value of the i-th row j-th column is represented by RP ij , RP ij =TRUE means that the role of R i has permission P j , otherwise it does not have the permission.

Table 1 Access Control Matrix

P 1 P 2 … P j … R 1 TRUE TRUE … FALSE … R 2 TRUE FALSE … TRUE … … … … … … … R i FALSE FALSE … TRUE … … … … … … …
Role permission can be saved in HashMap, and only the permission of the access control matrix whose value is true can be saved. There are six permissions, the menu permission separates main menu and online monitoring tree menu, so there are seven access matrix. Button permission, access permission to data area and field-level access permission are combined into a HashMap, which all operate the data table. Therefore, the system can devise five HashMap to save all permissions of a role.

Constraints

In a model, the constraints are divided into relation constraint, precondition constraint, cardinality constraint, time constraint, address constraint and so on.

(1) Relation Constraint Conflicting role can not be granted to the same user, for example, in order to prevent fraud, the purchaser role and accounting role can not be granted to the same user; conflicting role can not be activated in the same session [START_REF] Zhuo | Specify RBAC Constraints Using Object Constraint Language[END_REF] , for example, the administrator role and other role can be granted to a user, but can not be activated at the same time.

(2) Precondition Constraint A user wants to be granted the role A, it must already have the role B, that is a role wants have a permission, the precondition is that it must have another permission [START_REF] Zhuo | Specify RBAC Constraints Using Object Constraint Language[END_REF] . If a user already has a technician role, it can be granted a technical director role. If a user already has teh corresponding menu permissions, it can be granted the submenu permissions.

(3) Cardinality Constraint Cardinality constraint is a upper bound of users number, these users are granted the same role. n represents the maximum number of users assigned this role [START_REF] Hong-Qi | Research of Extension of Static Constraints Mechanism in RBAC Model[ J][END_REF] . The value of n is specified by the group administrator, and the default value is 1.

(4) Time Constraint Roles or permissions can be only activated in certain time range [START_REF] Guang-Yu | Role-Based Authorization Constraint with Time Character[ J][END_REF] . When a role need to grant a user temporary, the role can be setted time constraint, and it can be activated only in the specified time range.

(5) Address Constraint Address constraint is divided into IP-role [START_REF] Wu Xian | Web Applications of Role-Based Access Control[END_REF] and MAC-role constraint, that is , address constraint binds the IP address, MAC address and the role together. The administrator has a very high permission. The extent of damage to the system caused by their operation is far higher than the average user. If the administrator permissions is to be attacked, the damage to the information system is inevitably fatal [START_REF] Jin-Sheng | Design of extended role-based access control model and its implementation[END_REF] . So the administrator can be granted the IP-role constraint, MAC-role constraint or IP-MAC-role constraint, and only users whose IP, MAC address is required can activate the administrator roles.

Access Control Process

(1) User login, verify the user name and password. If the user passes the verification, the user is prohibited access system. Once authenticated, users get all the role information, and check relationship constraints, time constraints and address constraints, then get the effective role of the user. If a time constraint of a role in time constraint after the current time, this role is an effective role, but there are time constraints.

(2) If the user has multiple valid roles, the six kinds of permissions for each user role represents a seven access control matrix, access control matrix between the different roles is a logical or operation, the user can get the access control matrix. If the user has a valid role, the user's access control matrix if a role's access control matrix. If the user does not have a valid role, the system gives tips and prohibits the user access.

(3) The access control hash table, which is structured according to the user's access control matrix, is added to the user class, and the user's complete object is added to the session. At the same time, the user name is added to the online user hash table, where the scope is application, the key means user name, value is the flag vale of corresponding seven access control matrix. value=zero means the corresponding access control matrix does not change, value=1 means change. Each time a user generates access control hash table, the value of the corresponding online user hash table is set to "0000000". If the user is logged, the administrator will determine which access changes when the user roles or permissions changes. The value is set to 1, if the matrix is changed.

(4) When the user has access or operation request, the administrator firstly check the online hash table to see whether role or permission is change. If there is no change, turn (5); if there are changes, the system generates access control matrix again, and generates the hash table.

(5) By querying the corresponding access control hash table, the system judge whether the user has permission. Only the user has permission, its access or operation will be response.

Conclusion

Based on the research of role-based access control model, the paper put forward the aquaculture access control model based on group/role, combined with the safety requirements of AIMAMS. The model can simplify the complex permission, reduce the system permissions management overhead effectively, and improve the expansibility of the system. With the business development, the system can easily add new user groups and grant permissions and roles to them. The model is general, it is not only suitable for AIMAMS, but also has reference significance to the other system.

3 RBAC 2 RBAC 1

			RBAC 1 model has the following components:
	 U, R, P, S, PA, UA, and user are unchanged from RBAC 0 ;
		RH		R		R	is a partial order on R called the role hierarchy or role dominance
		relation, also written as  ;

constraints

RBAC

Acknowledgements

This paper was supported by Shandong Province Self-innovation Projects (2012CX90204) and the Shandong ProvinceKey Research & Development Program "Research on aquaculture management and application platform technology based on big data" (No. 2015GGC02066).


Chenglong Huang

Lingbo Liu

Wanneng Yang

Lizhong Xiong
email: dlizhongx@mail.hzau.edu.cn

Lingfeng Duan
email: eduanlingfeng@mail.hzau.edu.cn

Rapid Identification of Rice Varieties by Grain Shape and Yield-Related Features Combined with Multi-class SVM

Keywords: computer vision, rice varieties identification, grain shape, rice yield, multi-class SVM

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

Introduction

Rice is one of the most significant cereals in the world, especially for china (zhu et al. 2011). Thousands of rice varieties could be produced daily by modern breeding technique [START_REF] Bagge | Functional markers in wheat: technical and economic aspects[END_REF]. And a large number of rice germplasm-resources need to be exploited by breeders for the rice improvement [START_REF] Xing | Genetic and molecular bases of rice yield[END_REF]. However, the characterization for the various rice varieties are technically challenging due to the slight difference (Takanari et al. 2012). Rice variety is also regarded as one of the most important factors related to cooking and processing quality, which was resulted by the variations in size, shape, and constitution [START_REF] Zhang | Strategies for developing green super rice[END_REF]. Therefore rice variety identification is of great significance. Since the identification of rice varieties is so important for rice-related research. A lot of work had been reported about it. Namaporn Attaviroj tried to identify the rough and pure rice varieties using fourier-transform NIR [START_REF] Attaviroj | Rapid Variety Identification of Pure Rough Rice by Fourier-Transform Near-Infrared Spectroscopy[END_REF]. Liu Hongyun had tried to indentify rice varieties by tolerance and sensitivity to copper [START_REF] Liu | Identification of Rice Varieties with High Tolerance or Sensitive to Copper[END_REF]). Liu Feng tried to identify rice vinegar variety using visible and near infrared spectroscopy [START_REF] Liu | Variety Identification of Rice Vinegars using visible and near infrared spectroscopy and multivariate calibrations[END_REF]. However, the above study only focused on a few of special rice varieties, and the identification for the massive ordinary rice varieties were still an urgent problem. Machine vision was a practical technology and had recently been widely applied in the agriculture. Dual-camera rice panicle length measuring system was proposed by Dr. Huang [START_REF] Huang | Rice panicle length measuring system based on dual-camera imaging[END_REF]. A machine-vision-facility was developed for rice traits evaluation (Duan, 2011). A hyperspectral imaging system was designed for biomass prediction [START_REF] Feng | A hyperspectral imaging system for an accurate prediction of the above-ground biomass of individual rice plants[END_REF]. Yang. Et.al applied x-ray computed tomography for rice tiller measurement [START_REF] Yang | High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography[END_REF]. Duan et.al had counted filled/unfilled spikelets using Bi-modal imaging (Duan, 2011). Support vector machine (SVM), first proposed in 1995 by Cortes and Vapkin, has a lot of advantages, such as nonlinear, smallsample, and high dimensional pattern recognition and can be easily extended to other machine learning problems. However, since it is originally used for binary classification [START_REF] Cortes | Support-vector networks[END_REF][START_REF] Vapnik | an overview of statistical learning theory[END_REF], it requires extra algorithm support to meet practical needs. This research aimed to propose a feasible method for rapid identification of rice varieties. In this study, the features of grain shape and yield-related traits were extracted by image analysis. And the specific Muti-SVM classifier was developed to discriminate the rice variety.

Materials and Methods

The Rice varieties used in this study are selected from the Chinese core-germplasm resources. 79 rice varieties were tested and each variety had four samples. Three quarter of the rice samples were taken as training set, meanwhile the other were testing set. The rice grains were threshed from the panicles manually. And the filled spikelets were selected out by wind separator. The technical method for rice variety identification is described as Fig. 1. Firstly, the rice grain were imaged and analyzed for shape and weight parameters. Then features of the training set were applied to build the SVM model. With the SVM model, the testing set was applied to evaluate the rice variety identification accuracy.

Fig. 1. The technical method for rice variety identification

Rice feature extraction

The features of each rice sample were obtained as shown in Fig. 2. The rice grains per sample were spread on the scanner manually (Fig. 2a). And the image was acquired and transferred to the computer. Then grain image was analyzed for grain number (GN), grain width (GW), grain length (GL), grain area (GA) (Fig. 2b,2c). And the grain weight was obtained by the electro-weighing device (Fig. 2d).

Fig. 2. Rice grain features acquisition

Multi-SVM Classifier

Since we focus on the classification of rice varieties, we should know the biological classification criteria of rice. Generally, different type of rice will have different genotypes, which usually leads to different phenotypes. Obviously, one of the important problems is how to divide the training rice samples into different subsets by binary tree based SVM-BTA algorithms. When it comes to plants classification, we wish the two subsets will have at least one totally different genotype. Knowing the fact that disparate varieties all have different genotypes, one possible way is using the K-Means clustering method, which will resign each data to the nearest cluster repeatedly, just like combining analogous genes. The problem is to determine the evaluation function. In order to reduce the algorithm running time of the partitioning process, it's necessary to improve the KMeans clustering. Since we always want to divide the input set into two clusters, a pretreatment of the data using average threshold algorithm will work. In the next section, we introduce one partition function for evaluation, and then propose MBT-SVM based on the K-Means clustering with optimal partition function.

Partition function

Suppose the problem's center problem c as the alldata mean value in the i.th column of the input of a non-leaf node. The following partition function can be adopted to split the node [9] :

2 12 11
( , ) ()

l i problem ji i d c c PF I I     U (1)
Where ( , ) i problem d c c is the Euclidean distance and i  is the variance of column i.

The larger PF is, the better it works. So we have also determined our termination criterion, just to traversal all the possible combinations to find the largest PF, or to reassign the object one by one until the value PF won't become larger in a whole round.An initial partition is needed to reduce the algorithm running time. The judging function using average threshold algorithm was described as followed:

1 1 1 1 1, () 1, v l v i ij i j i xx l Jx else              ( 2
)
Where v is the number of features. And li x is the i.th feature of the j.th sample in the training set. The judging function compares the average value of all the features of a sample with the average value obtained by the whole training set. Since different features of the rice will have different magnitude, we have to standardize the input data. The standardization includes data integrity check, linear unification and repacking.

Kernel function

The efficency and accuracy of SVM is determined by the kernel type and parameters, as well as the parameter c. To determine the best type of kernel function, we can try the three basic kernel functions and pick the one with the best accuracy according to cross-valid. In general, the Gaussian kernel with a single parameter γ is a good choice. The c and γ is usually calculated by a grid searching method, in which the cross validation is applied, then we will pick the one with the highest accuracy, such as . The final model, will then training set was applied by the chosen type of kernel function and with the optimized parameters [10] . As is shown in Fig. 3, an inappropriate combination of kernel type and parameters will cause under-fitting or over-fitting problems.

Results and Discussion

Grain traits extraction accuracy

Totally, 79 copys of rice grains were measured automatically and manually, and the parameters of the GN, GL, GW, GA, grain weight were all obtained for each copy. The measurement accuracy for each traits were shown in Fig. 4, the MAPE were caculated according tho the eq.1. The measurement results showed that the average MAPE for grain number measurements was 1.33%; the average MAPE for grain length measurement was 1.25%; the average MAPE for grain width measurement was 2.20%. The results demonstrated tha the automatical measurements performed a good relationship with manual measurements.

Rice varieties identification

The rice feature measurements were shown above. From the results, it was proved that the difference between outer-varieties and inner-varieties were both slight. In the SVM identification, for each tree node, an RBF-kernel SVM was first adopted after partitioning the input data by a clustering method and the linear function was the last one to try. A grid search based on cross-validation was employed for parameter optimization. The result for the whole experiment is shown in table1. The training time includes partitioning, tree construction and SVM training. It represented the CPU running time (millisecond), for a quad core computer, the real-time approximately equals to a quarter of the CPU time.

The standard data from UCI and the rice sample data were all tested and the classifying results were shown in table 1. It was proved that the algorithm had high classification accuracy when processing these standard data. Since the testing samples are randomly selected, every kind of data set was tested only three times and the worst result was recorded in order to avoid anthropogenic interference (like continually running the algorithm until it gets a good output). From the data associated to rice in table 1, it was seen that building a classification binary tree for a set of data with many classes needed a relatively long time.

Rice-s79-1 is a basic pre-experiment focusing on verification of the algorithm compatibility. There was not a linear kernel function and the number of attributions was fixed to 13. Rice-s79-2 was a grid-search experiment aiming at finding a suitable number of attributions for the next experiment. Since the algorithm needed to traverse all the possible combinations of the attribution, and there were four kernel functions to be examined for each combination, it's naturally to have a very large training time (nearly ten hours of real-time). After tracking the misclassified samples, we find that about half of the errors occurred in the tree nodes with a relatively high cross-valid accuracy other than the low ones. Clearly, the model is over-fitted. It is necessary to improve the optimization function. We needed to pick the SVM classifier with an appropriate cross-valid accuracy instead of the ones with the highest. Rice-s79-3 was the result of the formal experiment. As was mentioned above, we repeat the experiment three times and record the result with the worst accuracy rate. And the average accuracy was about 79.74%. In this paper, a support vector machine working in the multi-space-mapped mode (MBT) was proposed for a rice multi-class classification task. The result showed that this study performed high accuracy for the grain traits extraction and also proved a good performance for the rice varieties classification. In future work, we will further analyze the data of tree nodes from experiments to develop more effective algorithms. The range of the parameters will change according to the size of the input training set which will greatly reduce the computation time, which will reduce the time complexity of the algorithm. Therefore we could apply this method for larger rice sample sets for varieties recognition. The results also demonstrated that this method would provide new knowledge for automated rice-vision-evaluated system.

Fig. 3 .

 3 Fig. 3. Classification models generated by different kernel type and parameters

Fig. 4

 4 Fig. 4 System measurements accuracy evaluation. (a) Grain number, (b) Grain length, (c) Grain width.

Table 1

 1

			the class identification results by Muti-SVM
	data	class dimensions size training set testing set accuracy	Training time (CPU time ms)
	iris	3	4	150	120	30	100%	0.875
	wine	3	13	178	133	45	97.78%	28.03
	seeds	3	7	210	100	110	96.67%	161.03
	vehicle	4	18	846	746	100	86%	213.07
	glass	6	9	214	166	48	72.91%	321.04
	rice s79-1	79	13	316	237	79	64.56%	1226.6
	rice s79-2	79	13	316	237	79	25.32%	124457
	rice s79-3	79	13	316	237	79	79.74%	55108

Acknowledgment

This work was supported by the Fundamental Research Funds for the Central Universities (2662014BQ036, 2662015QC006, and 2662015QC016), the Natural Science Foundation of Hubei Province (2015CFB529), the National High Technology Research and Development Program of China (2013AA102403), the National Natural Science Foundation of China (30921091, 31200274).


Weixin Zhang

Hongxin Cao
email: caohongxin@hotmail.com

Wenyu Zhang

Yanliu

Daokuo Ge
email: egedakuo@163.com

Chunhuan Feng

Weitao Chen

Chuwei Song

Rapeseed (Brassica napus L.) Primary Ramification Morphological Structural Model Based on Biomass

Keywords: biomass, FSPMs, rapeseed, primary ramification 1

Primary ramification morphogenesis has a significant influence on the yield of rapeseed. In order to quantify the relationship between rapeseed architecture indices and the organ biomass, a rapeseed primary ramification structural model based on biomass were presented. Intended to explain effects of cultivars and environmental conditions on rapeseed PR morphogenesis. The outdoor experiment with cultivars: Ningyou 18 (V1, conventional), Ningyou 16 (V2, conventional) and Ningza 19 (V3, hybrid), and designed treatment of cultivar-fertilizer, cultivar-fertilizer-density, and cultivar tests in 2011-2012 and 2012-2013. The experimental result showing that the leaf blade length of PR, leaf blade width of PR, leaf blade bowstring length of PR, PR length, and PR diameter from 2011

to 2012 were goodness, and their d a values and RMSE values were -1.900 cm, 5.033 cm (n=125); -0.055 cm, 3.233 cm (n=117); 0.274 cm, 2.810 cm (n=87);-0.720 cm, 3.272 cm (n=90); 0.374cm, 0.778 cm (n=514); 0.137cm, 1.193cm (n=514); 0.806cm, 8.990cm (n=145); and -0.025cm, 0.102cm (n=153), respectively. The correlations between observation and simulation in the morphological indices were significant at P<0.001, but the d ap values were < 5% for the second leaves length and the third leaves length, leaf blade bowstring length, PR length, and PR diameter, which indicated that the model's accuracy was high. The models established in this paper had definite mechanism and interpretation, and the impact factors of N, the ratio of the leaf length to leaf dry weight of primary ramification(PRRLW), and the partitioning coefficient of leaf blade dry weight of primary ramification(PRCPLB) were presented, enabled to develop a link between the plant biomass and its morphogenesis. Thus, the rapeseed growth model and the rapeseed morphological model can be combined through organ biomass, which set a reference for the establishment of FSPMs of rapeseed.

Introduction

Rapeseed is a very good oil crop with high economic and nutritional values. In the ten years from 2004 to 2014, the production and consumption of rapeseed have increased significantly, and the growth rates of the plant area and total production of rapeseed were35% and 54%, respectively [1]. Plant morphological structure simulation and visualization is one of important content of agro-informatics in nowadays, and its latest trend is to establish Functional-structural Plant Models (FSPMs). Some crop morphological structure models had been developed based on GDD [START_REF] Xinyu | Study on the 3-D visualization of leaf morphological formation in corn [J][END_REF][START_REF] Zihui | Study on simulation model of morphological development in wheat plant[END_REF][START_REF] Shi Chunlin | Study on morphological model and visual growth in rice[END_REF][START_REF] Juan | Study on morphological model of stem, branch and leaf in cotton[END_REF][START_REF] Yue Yanbin | The morphological structural model and visualization of rapeseed (Brassica napus L.) plant[END_REF][START_REF] Aizhen | A visualization simulation of leaf shape elongation process in rice based on accumulated temperature change [J][END_REF], which described the main stems, branches, leaves, leaf sheaths, and internodes of the morphological model of the crops, and realized the quantitative simulation of the crop morphogenesis process. With the maturity of the research conditions, crop morphological structure and physiological ecological process will become the new focus. In order to analyze the relationships between the morphological parameters of the organs of crops and biomass, some related studies had been reported [START_REF] Youhong | Virtual maize modelⅡ. Plant morphological constructing based on organ biomass accumulation [J][END_REF][START_REF] Kang Mengzhen | The derivation of sink functions of wheat organs using the Greenlab model[END_REF][START_REF] Yan | Main geometrical parameter models of rice blade based on biomass[END_REF][START_REF] Hongxin | Biomass-based rice (Oryza sativa L.) aboveground architectural parameter models [J][END_REF]. Rapeseed morphology directly influences its biomass production. In recent years, studies of the rapeseed morphological structure model had also been proposed [START_REF] Hongxin | Biomass-based rapeseed (Brassica napus L.) leaf geometric parameter model[END_REF][START_REF] Wenyu | A biomass-based leaf curve model for rapeseed (Brassica napus L.) [J][END_REF][START_REF] Zhang Weixin | Morphological structure model of leaf space based on biomass at pre-overwintering stage in rapeseed (Brassica napus L.) plant [J][END_REF]. A rapeseed leaf geometric parameters model based on biomass presented quantified the relationship between biomass and rapeseed leaf geometric parameters, a leaf curve model based on biomass for rapeseed was established, which described the relationships between the leaf curve and the corresponding leaf biomass for rapeseed on main stem, and a morphological structure model of leaf blade space based on biomass at pre-overwintering stage in rapeseed plant presented revealed the relationships between biomass and rapeseed architecture indices. These studies laid a good foundation for FSPMs of rapeseed. By combining LEAFC3-N with the FSPMs, rapeseed functional structural plant model was established, which could respond to the environmental conditions [START_REF] Groer | A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes[END_REF][START_REF] Müller | Towards universality and modularity: a generic photosynthesis and transpiration module for functional structural plant models[END_REF].

However, the rapeseed primary ramification morphological structural model based on biomass has not been reported.

Vegetative organs of rapeseed including leaf, stem, ramification, and root [START_REF]Oil crops research institute[END_REF]. Leaf is the crucial organs of photosynthesis, ramification leaves play gradually an important role in the rapeseed mid and late growth stage, and the ramification becomes a vital source. Therefore, how to accurate quantitative description of the rapeseed morphological variation is vitally important and difficult.

The objectives of this paper were to link primary ramification architectural parameters of rapeseed plant with biomass, by analyzing field experimental data from 2011-2012, and 2012-2013, to develop finally the rapeseed primary ramification morphological structural model, and to lay a foundation for rapeseed morphological structure model and visualization.

2

Experiments and Methods

Experimental Samples

Three varieties Ningyou 18 (V1) (conventional), Ningyou 16 (V2) (conventional), and Ningyou 19(V3) (hybrid)were used in experiments, and they all belong to brassica napus. Canopy morphology structure of the three cultivars had following traits:V1 with the overwintering half-vertical cultivars, and medium height, had higher rankof branch,compacter in plant type; V2 with the overwintering half-vertical cultivars,medium height, andcompact plant type;

V3 with the overwintering half-vertical cultivars,had broader and thicker leaves, the leaves light green, and edge of leaves with saw teeth.

Experimental Methods

Experimental conditions: Field experiment was conducted at the experimental site (32.03° N, 118.87° E) of JAAS, China during the 2011 to 2013rapeseed growing period. The soil type is the yellow-brown soil, and its basic nutrient status: organic carbon, 13.8 gkg -1 ;available Nitrogen, 58.95 mgkg -1 ; available Phosphorus, 29.25 mgkg -1 ; available Potassium, 109.05 mgkg -1 ; and pH 7.84. Three experiments were designed to implement.

Experiment on Cultivar and fertilizer: By split-plot design, fertilization levels ((N 180 kghm -2 , P 2 O 5 120 kghm -2 , K 2 O 180 kghm -2 borax 15 kghm -2 and CK) was assigned to the whole-plot, and cultivars (V1,V2,V3) to the sub-plot. Six treatments were repeated three times, and the 18 subplots were arranged randomly. The area of each plot was 7.0 m×5.7 m = 39.9 m 2 , the density design is 30 cm of row spacing and 17-20 cm of distance between plants, respectively. The13 rows were planted in each subplot, and a blank line were stayed in the inter-plot. The sowing date and the transplanting date wasOctober 15 and November 4respectively, for both 2011 and 2012.The total amount of N-fertilizer application was 3.26kg in the fertilization area, and the basal: seedling: winter ratio was 5:3:2 respectively. Basalrate of N+P 2 O 5 +K 2 O-compound fertilizer was at 16.3kg (mass fraction≥25%), rate of calcium superphosphate was at 7.3 kg (mass fraction 12%), and rate of agricultural potassium sulphate was at 6.4 kg (mass fraction33%). the manure for seedling and winter dressing were all urea and its application amount was 1.41 kg, 2.12 kg, respectively (mass fraction of total N ≥46.2%). Other management activities followed local production practice.

Experiment on Cultivar, fertilizer, and density: By split-plot design, fertilization levels (N, P 2 O 5 , K 2 Oeach of 180 kghm -2 , 90kghm -2 kghm -2 and CK) was assigned to the whole-plot and cultivars (V1, V3) and density levels (D 1 (6×104 planthm -2 ),D 2 (1.2×105 planthm -2 ) and D 3 (1.8×105 planthm -2 ) )to the sub-plot. Eighteen treatments were repeated three times, and the 54 plots were arranged randomly. The area of each plot was 3.99 m×3.5 m = 13.97 m 2 , the density design is 42 cm of row spacing and the distance between plants was calculated by density. The 9 rows were planted in each subplot, and a blank line was stayed in the inter-plot. The sowing date and the transplanting date was October 8 and November 9 respectively, for both 2011 and 2012.

The basal: seedling: winter ratio of fertilizer application was 5:3:2 respectively. Folia application of borax was at a rate of 15 kghm -2 after bolting of rapeseed. Other management activities followed local production practice.

Experiment on Cultivar experiment:

The experiments were a randomized complete block design, with three cultivars (V1, V2, V3), under the same fertilization level,N90(N, P 2 O 5 , K 2 O each of 90 kghm -2 )and density level,D 2 (1.2×105 planthm -2 ),3 replications, and the 9 subplots. The area of each plot was7.98 m×3.5 m = 27.93 m 2 , the density design is 42 cm of row spacing. Other treatments with the Cultivar, fertilizer and density experiment.

Measurements: we selected 3 plants with similar growth status in each treatment, and determined the blade length, blade width, and blade bowstring length at various leaf ranks on primary ramification more than 2.5 cm, and the length and the diameter of the primary ramification. Then, leaves were separated from the ramification, and into the paper bags, then put in oven, the temperature of green removing in 105℃ for 30 minutes, in 80℃until reaching a stable weight.

Blade

Si Oi i X X d   𝑑 𝑎 = 1 𝑛 ∑ 𝑑 𝑖 𝑛 𝑖=1 𝑋 ̅ 𝑂 = 1 𝑛 ∑ 𝑋 𝑂𝑖 𝑛 𝑖=1 𝑑 𝑎𝑝 = 𝑑 𝑎 /𝑋 ̅ 𝑂 × 100%
With i=sample number, X oi =observed values, X si =simulated values, n=total number of measurements.

Model Description

Leaf is an important organ of photosynthesis, and the blades of PR are the main organ of photosynthesis in the mid and late period of rapeseed growth stages, which directly determines the photosynthetic capacity and the final yield. The morphology of the rapeseed leaves have characteristics with complexity, variability, and difficult to obtain, so it is an important part of the rapeseed plant model. According to the status of the petiole, the leaf for brassica napus can be divided into long-petiole, short-petiole, and sessile leaf on the main stem, and the leaves of PR are similar to the sessile leaf [START_REF] Houli | Practical rapeseed cultivation[END_REF].

Leaf Blade Length Model

The production of the effective PR usually occurs in the axillary buds above the tenth leaf in the upper on the main stem.

According to observation in 2011 to 2012 experiment, the leaf blade length with the leaf dry weight was close to proportional increasing trend (Fig. 1). Hereby, the models can be expressed as follows:

PRLL jk (i) = DWPRLB jk (i)×PRRLW jk (i) (1) DWPRLB jk (i) = PRCPLB jk (i)×DW SP (i) (2) DW SP (i)=MDW SP (i)±SDW SP (i) (3) MDW SP (i)=DW CP (i)/DES(4)
where, PRLLjk(i)is the kth leaf blade length of the jth primary ramification(cm), DWPRLB jk (i)is the kth leaf dry weight of the jth primary ramification(cm), PRRLW jk (i) is the ratio of the kth leaf length of the primary ramification to leaf biomass (cm g -1 ), PRCPLB jk (i) is the ratio of the kth leaf biomass of the primary ramification to the biomass of upper plant part (g The data in the 2011 to 2012 and 2012 to 2013 experiment showed that the number of the ramification leaf has a definite relationship with ramification rank. Generally effective ramification was on the middle and upper part of the main stem, all can reach four leaves, and some ramifications was up to six or more but relatively less. Therefore, the first four leaves were studied in this study, and the other blades were not measured as it is difficult to determine the relationship with biomass.

The data in the 2011 to 2012 experiment showed that the values of PRRLW jk (i) with the leaf rank on primary for the fourth leaf (eq. ( 5) ,Table 1). (Table 1). 4, eq. ( 6),( 7),( 8), Table 2).

The F-values, t-values, all model parameters apart from B 5 were at P<0.001 (Table 2).

3.3Maximum Leaf Blade Width Model

The experiment on Cultivar and fertilizer in the 2011 to 2012 showed that the PRLW jk(i) with the leaf length were described by a growth function (eq. ( 9) ). R=0.914, n=262, P<0.001, R (260, 0.001) =0.206, R 2 =0.835 (Fig. 5).

The F-values, t-values, c 5 , d 5 all at P<0.001 (Table 1).

PRLW jk (i)=c 5 PRL jk (i)+d 5 , 11≤j≤23 (9)

3.4Leaf Blade Bowstring Length Model

Apparently, the maximum PRBL jk (i)=PRLL jk (i). The experiment on Cultivar and fertilizer in the 2011 to 2012 showed that variation of leaf bowstring length with the leaf length could be represented by the linear function. R=0.846, P<0.001, n=262, R (260, 0.001) =0.206, R 2 =0.715 (Fig. 6).

The F-value ,t-values, c 6 , d 6 all were t at P<0.001 (Table 1).

PRBL jk (i)=c 6 PRL jk (i)+d 6 , 11≤j≤23 (10)

Stem Length Model of Primary Ramification

From the experimental data in the 2011 to 2012 we can see that the jth stem length of primary ramification (cm), PRSL ji , changes with the dry weight could be described by a power function. R=0.776, n=106, P<0.001, R (104, 0.001) =0.314, R 2 =0.603 (Fig. 7).

The F-value, t-values, c 7 , d 7 all were at P<0.001 (Table 1).

3.6StemDiameterModel of Primary Ramification

The experimental data in the 2011 to 2012 showed that the jth stem diameter of primary ramification, PRSD ji , changed with the dry weight could be described by a linear function with significant R=0.501, n=96, P<0.001, R (94, 0.001) =0.331, R 2 =0.251 (Fig. 8).

The F-value, t-values, c 8 , d 8 all were at P<0.001 (Table 1).

3.7Validation

we used the independent experimental data to validate the biomass-based rapeseed plant primary ramification morphological structure model proposed in this study. The RMSE, and the d a in rapeseed primary ramification morphological parameters, leaf blade length, the maximum leaf blade width, the leaf blade bowstring length, stem length of PR, and stem diameter of PR were 5.033cm,-1.900cm (n=125);3.233cm, -0.055cm (n=117);2.810cm, 0.274cm (n=87);3.272cm, -0.720cm (n=90); 0.778cm, 0.374cm (n=514); 1.193cm, 0.137cm (n=514); 8.990cm, 0.806cm (n=145); and 0.102cm, -0.025cm (n=153), respectively. The r values in rapeseed primary ramification morphological properties all at P<0.001 or P<0.01, but the ratio of d a to the average observation (d ap ) values were less than 5% for the second leaves length, the third leaves length, leaf blade bowstring length, the PR length, the PR diameter, PRRLB values for V2 and V3,which indicated these model's accuracy is high (Table 3).The 1:1 line in rapeseed primary ramification were represented in Fig. 9. were between 5%-10% for PRCPLB with fertilizer (j>19) and PRCPLB no fertilizer (11≤j≤19), which indicated that these model's accuracy is good. The dap values of more than 10% which indicated that the model had a lower accuracy, but the d a values and the RMSE of PRCPLB and PRRLB were small (Table 3). The 1:1 chart of measured values and simulated values in PRCPLB and PRRLB are represented in Fig. 9.

Notably, we had seen that the first leaf blade length and PRRLB value(no fertilizer)models had obvious errors from Table 3, and Fig. 9, which showed that the two models still needed to be improved and perfected in the further. Studies on crop morphological structural models, such as rice, wheat, cotton, corn have been many reported. Chang et al. [START_REF] Liying | A simulation model of leaf elongation process in rice [J][END_REF] constructed the simulation model of leaf elongation process in rice, analyzed the variation of leaf blade geometric morphology indices of rice with the growth process and environment conditions, and provided facilitate to digital and visualization of rice. Zhanget al. [START_REF] Wenyu | Dynamic simulation of wheat stem-sheath angle based on process [J][END_REF] established a process-based model with the methods of system analysis and dynamic modeling techniques. Fournier et al. [START_REF] Fournier | A 3D architectural and process-based model of maize development [J][END_REF]by using L-systems proposed a detailed description of the relationship between leaf functions and chlorophyll content of leaf. The concept of the relative leaf area index (LAI) and the relative accumulated temperature were put forward, and fitted parameters of the model by using MATLAB, the dynamic simulation model of leaf area index of corn was established [START_REF] Xinlan | Study on dynamic simulation model of leaf area index of corn in Northeast[END_REF].By the computer model based on combining crop physiological and ecological process with visualization, a digital and visualization techniques of cotton growing system was presented [START_REF] Xuebiao | Digital and visualization techniques of cotton growing system[END_REF].

Through there were some studies on rapeseed growth models and morphological models [START_REF] Liangzhi | The basis of agricultural model[END_REF][START_REF] Weixing | Crop System Simulation and Intelligent Management[END_REF], there are many research on growth-development law and structural characteristics of rapeseed [START_REF] Hongxin | Researches of simulation models of rape (Brassica napus L.) growth and development [J][END_REF][START_REF] Groer | A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes[END_REF]. However, the combination of rapeseed growth model and morphological model is lack of further study. Yue [START_REF] Yue Yanbin | The morphological structural model and visualization of rapeseed (Brassica napus L.) plant[END_REF] conducted a rapeseed ramification morphological structure model based on GDD, but the combined with the growth model was not mentioned.

In this paper, the leaf length models can be linked with the rapeseed dry matter production models through biomass, and the models also can be linked with the rapeseed growth models by biomass. The maximum leaf blade width model and the leaf blade bowstring length model are represented by a linear function with leaf blade length well. Stem length model and stem diameter model of primary ramification are represented by a power function and a linear function, respectively. One of the most important factors is the angle between primary ramification and main stem, and we will complete it in the coming.

The research on morphological structure of rapeseed primary ramification hasa certain complexity, and influenced by the external environment and difficult to obtain morphological indices accurately, tends to appear some experimental error. We will improve measurement methods, such as digital camera, and 3D laser scanner etc., in order to get the whole morphology of plant leaves and ramifications, exploration of the way to enhance the model accuracy.

4Conclusions

In this paper, we developed a rapeseed PR structural model, which indented to explain the response mechanism of the PR morphogenesis to environmental conditions and varieties. Validation of the model with the independent experiment data indicated a good fitness between the simulated and observed in rapeseed.

The PRRLW was first put forward, and the relationships between rapeseed plant primary ramification morphological structure and the organ biomass to be established by PRRLW. It is a morphological structural parameter of rapeseed with a biological significance, and enhanced mechanism of this study.

Thus, the rapeseed plant primary ramification morphological structure model in this paper is feasible. We expect the proposed to be useful for other morphological indices of rapeseed in the further.

  Leaf blade length: measuring the length of the blade straight state from the leaf tip to the leaf base; Leaf blade width: measuring the maximum length of the leaf width value (in the middle of the blade),average value was gained by multiple measurements. Leaf blade bowstring length: as the elongation of the blade, due to gravity and other effects, the leaf is deformed, and bends into an arc downwardly. So the leaf blade bowstring length is from the leaf base to the leaf tip of the linear distance of space, average value was gained by multiple measurements.Primary ramification (PR)Lengthof PR: the length of the straight state which is the distance from the basal of the PR to the top of the PR; Diameter of PR: measuring the base, middle and top of PR several times using the vernier caliper and average value was gained by multiple measurements. Data Analysis: We used the MS EXCEL 2007 and SPSS version 19 to analyzed the experiment data. Part of the data of different cultivars and fertilizer levels were for the modeling and parameter determination in 2011-2012. The remaining independent data were for model testing and inspection. Model validation: We used the root mean squared errors(RMSE), the correlation (r), the average absolute difference (d a ), the ratio of d a to the average observation (d ap ), and 1:1 chart of measured values and simulated values properties to validate the models developed in this paper (Cao et al. 2012). The smaller RMSE, d a , and d ap values, the better consistency of simulated and observed values, and the deviation will be small, the simulation results of the model proved to be accurate and reliable. So the d a , and d ap are defined as:

  Fig.1 Variation of the primary ramification leaf length by the leaf dry weight for different treatments in 2011-2012

  ramification were close to quadratic function. The significant R=0.768 (n=74, R (72,0.001) =0.375, P<0.001) and R 2 =0.589 for the first leaf; R=0.584 (n=77, R (75,0.001) =0.367,P<0.001) and R 2 =0.341 for the second leaf; R=0.489 (n=32, R (30,0.01) =0.452, P<0.01) and R 2 =0.240 for the third leaf; R=0.557(n=36, R (34,0.001) =0.525, P<0.001) and R 2 =0.310 (Fig.2)

  The F-values, t-values, c 1 , d 1 , e 1 , c 2 , d 2 , e 2 , c 4 , and e 4 all were significant at P<0.001, apart from c 3 , d 3 , d 4 , and e 3

Fig. 2

 2 Fig.2 Variation of PRRLW values of different treatments of the first four leaf blade length by primary ramification rank in 2011-2012.

,

  the fourth leaf, 13 ≤ 𝑗 ≤ 20 1≤k≤4[START_REF] Juan | Study on morphological model of stem, branch and leaf in cotton[END_REF]

Fig. 3 7 )Fig. 4

 374 Fig.3 Variation of the PRCPLB ji values by primary ramification rank in 2011-2012 𝑃𝑅𝐶𝑃𝐿𝐵 𝑗𝑖 = { 𝐴 3 𝑃𝑅𝑅 𝑗𝑖 + 𝐵 3 , 11 ≤ 𝑗 ≤ 19 𝐴 4 𝑃𝑅𝑅 𝑗𝑖 + 𝐵 4 , 19 < 𝑗 ≤ 24 Fertilizer (6) 𝑁𝑃𝑅𝐶𝑃𝐿𝐵 𝑗𝑖 = { 𝐴 5 𝑃𝑅𝑅 𝑗𝑖 + 𝐵 5 , 11 ≤ 𝑗 ≤ 19 𝐴 6 𝑃𝑅𝑅 𝑗𝑖 + 𝐵 6 , 19 < 𝑗 ≤ 23No fertilizer[START_REF] Aizhen | A visualization simulation of leaf shape elongation process in rice based on accumulated temperature change [J][END_REF]

Fig. 5

 5 Fig.5 Variation of PRLW jk (i) values by leaf length in 2011-2012

Fig. 6

 6 Fig.6 Variation of PRBL jk (i) values by primary ramification leaf blade length in 2011-2012

Fig. 7

 7 Fig.7 Variation of PRSL ji values of different treatments by the dry weight in 2011-2012

Fig. 8

 8 Fig.8 Variation of PRSD ji values of different treatments by the dry weight in 2011-2012

Fig.9Comparison of

  Fig.9Comparison of the observed with the simulated in 2011-2013

Table 1 .

 1 The determination of model parameters and significance test

	Eq.			n	Parameter Symbolic	Unstandardized Coefficients	t
					c1	6.364	8.554 ***
	PRRLWj1(i)= c1PRR j2(i)	2 +d1PRRj2(i) +e1	74	d1	-97.389	-7.69 ***
					e1	451.118	8.752 ***
					c2	2.446	5.563 ***
	PRRLWj2 (i)= c2PRR j2(i)	2 +d2PRRj2(i) +e1	77	d2	-36.179	-5.913 ***
					e2	221.418	10. 806 ***
					c3	3.732	2.074 *
	PRRLW j3(i)= c3PRR j3(i)	2 +d3PRRj3(i) +e3	32	d3	-40.823	-1.616
					e3	230.506	2.684 *
					c4	5.608	3.068 ***
	PRRLW j4(i)= c4PRR j4(i)	2 +d4PRRj4(i) +e4	36	d4	-46.053	-2.528 *
					e4	214.337	4.933 ***
					c5	0.528	36.285 ***
	PRLW jk(i)=c5PRL jk(i)+d5	262	d5	-0.941	-5.356 ***
					c6	0.703	25.536 ***
	PRBLjk(i)=c6PRLjk(i)+d6	262	d6	1.120	3.369 ***
					c7	37.098	20.956 ***
	PRSLji=c7PRSDWji d7	106
					d7	0.571	12.563 ***
					c8	0.082	5.619 ***
	PRSDji=c8PRSDWji+d8	96
					d8	0.508	30.897 ***
	P<0.001, R (54,0.001) =0.428), R 2 =0.363 and R=0.568 (n=34, P<0.001, R (32,0.001) =0.539), R 2 =0.323; no fertilizer with
	cultivars significant R=0.567 (n=39, P<0.001, R (37,0.001) =0.507), R 2 =0.321 and R=0.599(n=32, P<0.001, R (30,0.001) =0.554),
	R 2 =0.359(Fig.3). The fitting precision for PRRLB jk (i) in various treatments were: fertilizer with cultivars significant
	R=0.592(P<0.001, n=159, R (157,0.001) =0.264), R 2 =0.351; no fertilizer with cultivars significant R=0.466(n=102, P<0.001,
	R (100,0.001) =0.321), R 2 =0.217(Fig.

*** , ** , and * denotes P<0.001, P<0.01, and P<0.05, respectively.

The data in the 2011 to 2012 experiment showed that PRCPLB jk (i) and PRRLB jk (i) with primary ramification rank were linear function model and exponential function model, respectively.R19 was the inflexion under different treatment levels, and the fitting precision for PRCPLB jk (i) in various treatments were: fertilizer with cultivars significant R=0.603 (n=56,

Table 2 .

 2 The determination of model parameters and significance test

	Eq.	n	Parameter Symbolic	Unstandardized coefficients	t
			A3	0.008	5.549 ***
	PRCPLBji=A3PRRji+B3	56
			B3	-0.085	-3.513 ***
	PRCPLBji=A4PRRji+B4	34	A4	-0.015	-3.915 ***

  The r values in rapeseed PRCPLB all at P<0.001 or P<0.01, but the ratio of d a to the average observation (d ap ) values

	𝑃𝑅𝑆𝐿 𝑗𝑖 = 𝑐 7 𝑃𝑅𝑆𝐷𝑊 𝑗𝑖	𝑑 7
	𝑃𝑅𝑆𝐷 𝑗𝑖 = 𝑐 8 𝑃𝑅𝑆𝐷𝑊 𝑗𝑖 + 𝑑 8

Table 3 .

 3 Model validation results in 2012-2013

				Statistic parameters of measured values and simulated values
	Model parameters
			r	da	dap (%)	RMSE	n	Sig.
	LL1(cm)		0.6520 ***	-1.900cm	21.11	5.033cm	125 r(123,0.001)=0.291
	LL2 (cm)		0.6846 ***	-0.055cm	0.70	3.233cm	117 r(115,0.001)=0.300
	LL3 (cm)		0.6169 ***	0.274cm	4.54	2.810cm	87	r(85,0.001)=0.347
	LL4 (cm)		0.5275 ***	-0.720cm	11.56	3.272cm	90	r(88,0.001)=0.341
	LW of PR(cm)		0.9071 ***	0.374cm	11.90	0.778cm	514 r(512,0.001)=0.146
	LBL of PR (cm)		0.8555 ***	0.137cm	2.21	1.193cm	514 r(512,0.001)=0.146
	PRL(cm)		0.8325 ***	0.806cm	2.73	8.990cm	145 r(143,0.001)=0.273
	PRD(cm)		0.4476 ***	-0.025cm	4.64	0.102cm	153 r(151,0.001)=0.264
		11≤j≤19	0.9091 ***	-0.004 gg -1	10.81	0.021g/g	50	r(48,0.001)=0.451
	V1-V3PRCPLBji(gg -1 )	j>19	0.5079 **	-0.003gg -1	5.41	0.021 g/g	26	r(24,0.01)=0.496
	NV2-NV3PRCPLBji(gg -1 )	11≤j≤19	0.6364 ***	0.003gg -1	5.85	0.030 g/g	52	r(50,0.001)=0.443
		j>19	0.6500 ***	-0.013 gg -1	29.55	0.028g/g	30	r(28,0.001)=0.570
	V2-V3 PRRLBjk(i)( gg -1 )	0.7440 ***	-0.0001 gg -1	0.458	0.008g/g	352 r(350,0.001)=0.175
	NV1-NV3 PRRLBjk(i)( gg -1 )	0.2385 **	0.004 gg -1	17.39	0.012g/g	185	r(183,0.01)=0.190
	N0V1-V1 with no fertilizer, N90V1-V1 with normal fertilizer, N180V1-V1 with high fertilizer; N90V2-V2 with normal fertilizer;
	N0V3-V3 with nofertilizer, N90V3-V3 with normal fertilizer, N180V3-V3 with high fertilizer

Acknowledgment

This work was supported by NFSC (31171455, 31201127, and 31471415),Jiangsu ProvinceAgricultural Scientific Techn ology Innovation Fund [CX(12)5060],National High-Tech Research and Development Program of China (2013AA10230 5-1), and JAAS Basic Scientific Research Work Special Fund[ZX(15)4001, ZX(15)2008].


Jie Bai

Heru Xue

Yanqing Zhou

The Milk Somatic Cell Image Segmentation Method Based on Dimension Reduction and Fusion

Keywords: milk somatic cell image, milk somatic cell image, dimension reduction, k-means algorithm, image fusion

Milk somatic cell image segmentation has a very important effect on milk quality analysis. In this paper, a new segmentation method is proposed for color milk somatic cell image. Aiming at the milk somatic cell image, a new method for image segmentation is proposed. First of all, selecting the appropriate color components by reducing the dimension of RGB color image ; Then using the k-means algorithm to segment the low dimension image; Finally, the segmented images are fused by the region splitting and merging process. The experimental results show that the proposed method is better than the original three-dimensional(3D) color space segmentation method. The method improves in the performance and running time, the correct segmentation rate reached 98.4%. Therefore, this method has certain feasibility.

Introduction

With the development of computer application technology,graphics and image processing has penetrated into all walks of life. the research on somatic cell bovine milk is a very important research field. The number of milk somatic cells is an important index to judge the quality of milk [START_REF] Hai-Xia | Screening test of somatic cell count in bovine milk[END_REF]. The more somatic cell counts, the higher the incidence of mastitis is. But the image segmentation is the first step of the milk somatic cell counting, which has very important significance to improve the quality of milk cow mastitis detection and diagnosis.

At present, domestic and foreign scholars for the segmentation of cell image are also put forward a series of effective methods, such as Threshold segmentation method, Edge detection method and Region growing method. Threshold segmentation method has the advantages of simple algorithm and is easy to realize, which can use boundary definition closed and connected overlapping regions. The disadvantage is difficult to obtain the accurate object boundary for the small difference between object and background [START_REF] Si-Qi | A Survey of Thresholding Methods for Image Segmentation[END_REF]. The advantage of edge detection method is precise contour location, and the disadvantage is the inability to guarantee the closed contour [START_REF] Hanene | A Surrey and Evaluation of Edge Detection Operators Application to Medical Images[END_REF]. Region growing method can get a closed contour, but it is hard to determine the growth of termination conditions [START_REF] Chang | Adaptive image region-growing[END_REF]. In biological cells, the cell shape varies, so there is no universal method for all cell images.

By comparing the existing algorithms, selecting reasonable low dimensional space and using K-means algorithm and image fusion technology to segment the milk somatic cell images can get better segmentation results, and it provides a basis for the further analysis of milk somatic cell image.

The remaining of the paper is organized as follows. Section 2 describes how to select the appropriate color components and implement the segmentation in the low dimensional space. Section 3 presents a fusion of these segmentation results and gets a final segmentation. Section 4 evaluates the segmentation results. conclusion is given in Section 5.

The milk somatic cell image segmentation base on the low dimensional space

The color image contains more information than the gray image, so in many applications of computer vision and pattern recognition,the color image can obtain better results [START_REF] He-Ru | Color Image Segmentation Based On Mathematical Morphology and Fusion[J][END_REF]. This paper uses the color milk somatic cell image. The processed color image will produce the problem of large computation and slow processing speed. So in order to overcome the above problems, it is necessary to reduce the dimensionality of 3D color space. In this paper, the 3D color space is projected to the 2D color space [START_REF] Kurugollu | Color image segmentation using histogram multithresholding and fusion[ J][END_REF], and the k-means segmentation is implemented in 2D color space.

Select The Low Dimensional Space For The Milk Somatic Cell

In this paper, the milk somatic cell image is RGB color image. Therefore, it is necessary to analyze its color components, In the process of analysis, through calculating the standard deviation and the histogram of each color component , to select the spectral component with the most abundant informations. In the final analysis, the R component of the standard deviation is 0.1865, G is 0.1469, B is 0.0216. The histogram of different color components is shown in Fig. 1 can be say that the R and G components have the significant difference in the nucleus, cytoplasm and the background. In this paper, the segmentation is performed in RG channel and RB channel by using k-means method.

Using K-means to segment the milk somatic cell in low dimensional space

Using the k-means algorithm, first of all, selecting the initial cluster centers, and then classifying all data points. In the end, the average value is calculated to adjust the cluster center for the each cluster, and continuous loop iteration, which makes the evaluation of clustering criterion function to achieve optimal performance, in order to reach the compactness within class and the independence in class of each cluster [START_REF] Zhou | The Research about Clustering Algorithm of K-Means[END_REF].

The image segmentation steps are described as follows:

1. Select the k sample points in the sample data sets N, and the k sample values are assigned to the initial clustering center(

) 1 ( ) 1 ( 2 ) 1 ( 1 ,..., , k    );
2. When the j iteration, to all points in the sample points t P (t=1,..., n), followed by calculating the Euclidean distance d(t, i) of each cluster center

) ( j i  ; 2 ) ( ) ( ) , ( d j i t P i t    (1)
3. Find out the minimum distance of

1  i E 2 1 1 1 | |       k i n t j i it i i P E  (3) If    | E - E | i 1 i
, the algorithm ends, or go to (2) iterative again. The segmentation results are shown in Fig. 2 2 shows that it has different segmentation results by using k-means algorithm in the RG space, RB space and the original three-dimensional space. And the original image segmentation results (Fig. 2 (f)) compared to in RG space segmentation results (Fig. 2 (b)) the background is misclassification into cytoplasm, in RB space segmentation results (Fig. 2 (d)) the nucleus is misclassification into cytoplasm. It can be seen that the effect of segmentation in the two two-dimensional subspaces is not the best, which is due to loss a color component. Therefore, In order to overcome the above problems, it is need to use the image fusion technology to fusion the segmentation results of subspace, and obtain the more accurate segmentation results.

Fusion Results of Low Dimensional Space Segmentation

Image fusion is the spatial matching of image data from different sources, and using complementary organic combination and producing new image data. This new data has a description of the research object and better representation of information, compared with the single information source, it can reduce or inhibit the ambiguity, incompleteness, uncertainty and error to explain the perceived object or environment and maximize the use of the information provided by the various sources of information [START_REF] Xue | Color image segmentation using fuzzy sets and fusion[END_REF][START_REF] Xiao-Chuan | Modern digital image processing technology to improve and Applications Comments (MATLAB version )[END_REF].

The fusion of milk somatic cell image is divided into the following steps. The first step is to apply the k-means segmentation process in two 2D subspaces, and then use the region splitting and merging process to segment again. Finally it gets a better segmentation image. The specific segmentation process as shown in Fig. 3 I  . Computing the respective regional of the average pixel components R, G, B which is reclassified, to compute the average pixel components is assigned to all the pixels belonging to this class. At the end of the splitting process, the number of sub class may be larger than L. So it is necessary to carry out the above segmentation image region merging process. In this paper, by comparing the two color points of Euclidean distance

2 2 2 ) 2 1 ( ) 2 1 ( ) 2 1 ( b b g g r r D      
, the smallest color distance of two classes are first merged and the mean color values for new regions are updated. This process is iterative, until the image to achieve a specified class number date. In this experiment, the milk somatic cell image should be divided into three classes, namely the nucleus, cytoplasm and background.

The experiment is performed in Matlab2011b environment to achieve the above algorithm. This paper uses the milk somatic cell image that comes from the Inner Mongolia Agricultural University laboratory animal pathology. The image using Newman's staining and it is magnified by 40x oil lens, which is stored as a 24 bit color image. In this experiment, it can be divided into three categories, namely the nucleus, cytoplasm and background. The final result using k-means and fusion approach in two 2D subspaces as depicted by Fig. 4 Fig. [START_REF] Chang | Adaptive image region-growing[END_REF] The results of subspace images segmentation and fusion

Experimentation and Results

In this paper, through computing the image segmentation correct rate, the quantitative results are evaluated. Firstly, a standard and stable method is used to segment the image as a reference image segmentation. The FCM [START_REF] Mei-Fang | The Application of FUZZY C-Means Algorithm in the Segmentation of Milk Somatic Cell Color Image[END_REF] method is used to segment the milk somatic in the experiment, then the comparison calculation of segmented image and the reference image are made. If the nucleus is divided into the pixel numbers of nucleus is a, the cytoplasm is divided into the pixel numbers of cytoplasm is b, and the background is divided into the pixel numbers of background is c. The accuracy will be calculated: (a+b+c)/n, and n is the total number of the image. Table 1 is the time comparison with the milk somatic cell image by k-means method in different color spaces. Table 2 and table 3 is the accuracy of the segementation which is used the paper method and directly segment in 3D space using k-means method. As table 2 shows ,the segmentation accuracy is 98.4%, and the table 3 shows that the segementation accuracy is 95.4%. Through many times of experimenting, we can know that both segmentation methods in 3D color space and in 2D subspace can get the correct results. However the correct rate of 2D method is higher than using 3D method directly and the running time is shorter. So the method proposed in this paper has some maneuverability and practicability.

Conclusions

Milk Somatic cell image segmentation is an important part of milk somatic cell image processing, due to color image with large amounts of data, segmentation directly in 3D color space will produce the problem of slow processing speed. However in this paper, we have presented an efficient method for the milk somatic cell image segmentation. Firstly, through analyzing the effect of each color component, the 3D color space is projected onto the low dimensional space.

3D method

2D method Runni ng time(s) Secondly, the k-means algorithm is implemented in the low dimensional space to segment. Finally using region split and merge methods of the image fusion to obtain the final segmentation result. By comparison the method, the method proposed in the paper not only overcomes the disadvantages of the large amount of calculation and lower speed in 3D color space segment directly, but also improves the accuracy of segmentation. Therefore, the proposed method is feasible and has certain theoretical and practical significance.

  (a)Histogram of the R component (b)Histogram of the G component (c)Histogram of the B component

Fig. 1

 1 Fig. 1 Histograms of different color components By observing the color component histogram and the compute results of the standard deviation, the standard deviation of R is the largest, B is the smaller. It can be concluded that the distinguishing effect is from strong to weak is R、G、B. It also

5 .

 5 Calculate the square error i E of all the points in the data sets N, and compared with previous error

2

 2 Fig.2shows that it has different segmentation results by using k-means algorithm in the RG space, RB space and the original three-dimensional space. And the original image segmentation results (Fig.2(f)) compared to in RG space segmentation results (Fig.2 (b)) the background is misclassification into cytoplasm, in RB space segmentation results (Fig.2 (d)) the nucleus is misclassification into cytoplasm. It can be seen that the effect of segmentation in the two two-dimensional subspaces is not the best, which is due to loss a color component. Therefore, In order to overcome the above problems, it is need to use the image fusion technology to fusion the segmentation results of subspace, and obtain the more accurate segmentation results.

Fig. 3

 3 Fig.3 The flow chart of milk somatic cell image segmentationThe region splitting and merging process is briefly described as follows:Inputting the 3D color space image I, Extracting it 2D subspace and implementing k-means algorithm in two 2D subspaces, the segmentation image are denoted by RG I and

  ..,

																																	RG L and	I	RG ) 1 (		I	RG ) 2 (	 ... 	I	RG l ) (		I	RG	,
	I	) RG l (		I	) RG ( t			( l		t	)	, The RB subspace can also be obtained in segmentation
	result			and								expression			of	symbols.	for	l  	 1 ,	,..., 2	RG L		and
	t		 t 1	t , 2	,...,	t	k		  1 , 	,..., 2	RB L		, The region	I	RG ) l (	is subdivided into
	I	(	l	)		I		(	1 t	)	,	I	(	l	)		I		(	t	2	)	,...,	I	(	l	)		I	(	t	k	)	and
		RG				RB				RG			RB					RG	RB
	I	RG l ) ( 	I	RG t ) (					 I	RB t 1		I	RB t 2		...		I	RB k t		. Above is the splitting process, this process
	has realized the secondary segmentation of	I relative to	I , the RG subspace
																																	RG	RB
	image is further divided into many subclasses, the results of segmentation is denoted
	by			RG		RB

Table 1 .

 1 Comparison between different methods

Table 2 .

 2 Segmentation accuracy using 2D method

Table 3 .

 3 Segmentation accuracy using 3D method

Acknowledgment

Funds for this research was provided by the National Natural Science Foundation of China(Grant No.61461041 )