Hao Zhang
email: zhanghaohnnd@126.com

Qiang Wang

Hui Zhang

Yali Ji

Xinming Ma
email: xinmingma@126.com

Lei Xi

Wheat Three-dimensional Reconstruction and Visualization System *

Keywords: Wheat, Plant morphological, Virtual, Visualization

The study object is wheat. Through the observation and analysis on the morphological structure of wheat plant, the concepts of macrostate, substate and microstate are put forward, the wheat growth mechanism model is established, and thus the description and control of wheat morphogenesis process could be realized. Based on the branch structure characteristics of plant, the wheat morphological data model based on axis structure is proposed to realize the structural storage of plant topological structure and organ morphological feature data. On this basis, based on the thought of "growth model -morphological feature model -geometric modeling -display model", the technical framework of wheat form visualization is established and on the VC++ platform, the virtual wheat growth system is built with OpenGL. The running result of the system shows that wheat morphological feature can be well simulated with the system to realize the virtual display of the growth process in the individual growth period of wheat.

Introduction

Virtual plant has been the research hotspot in digital agriculture, virtual reality and other interdisciplinary fields. It is aimed at revealing the complex relationship among plant growth, physiological process and environmental factors with a dynamic and visualized virtual scene, therefore, it has bright prospect in agriculture, forestry, ecology and remote sensing. A lot of researches have been carried out on virtual plants at home and abroad. Among them, in terms of the reconstruction of plant morphological structure, L system established by the University of Calgary, Canada [START_REF] Prusinkiewicz | The Algorithmic Beauty of Plants[END_REF], the AMAP system by CIRAD in France [START_REF] De Reffye | A Functional Model of Tree Growth and Tree Architecture[END_REF] and the dual-scale automata proposed by Sino-French Laboratory of Institute of Automation, Chinese Academy of Sciences [START_REF] Xing | Dual-scale Automaton Model for Virtual Plant Development[END_REF] and so on are included. L system focuses on the expression of plant topological structure, and describes plant morphology and growth pattern with abstract rules, but its defect lies in that it is too complex, and it is very difficult to extract and define the growing rule of a specific type of plant. AMAP and dual-scale automata can well describe the plant physiological feature and growth process, however, AMAP is suitable for modeling tall plants, and dual-scale automata cannot describe the organ growth state, thus, it is not fully applicable to the reconstruction of wheat plant morphology. In terms of the organ morphological modeling based on process, Mengjun, et al. described leaf vein curve with leaf specific weight, leaf inclination angle and leaf length, they could accurately describe the curl and distortion of wheat leaves in combination with the leaf shape and the geometric modeling of leaf edge; Wu Yanlian constructed the geometric model based on the characteristic parameters of organ morphology, including leaf, stalk and spike [START_REF] Yanlian | OpenGL-based Visual Technology for Wheat Morphology[END_REF][START_REF] Guoqing | Modeling Leaf Sheath and Internode Growth Gynamics in Wheat[END_REF], which could dynamically describe the formation process of organ. In terms of the research on wheat three-dimensional visualization system, Chen Guoqing et al. proposed wheat threedimensional visualization technical framework based on growth model [START_REF] Guoqing | Morphogenesis Model-based Virtual Growth System for Organs and Plant of Wheat[END_REF], but the structural coupling of morphological model, growth model and visualization model was not taken into consideration. In addition, the structural storage of wheat plant topological structure and organ morphology characteristic data has not been reported.

On the basis of referring to the existing research findings and the observation and analysis on morphological structures of wheat plant, the concepts of macrostate, substate and microstate are put forward, the wheat growth mechanism model is established, and thus the formalized description of wheat morphogenesis process is realized. Through the analysis on the branch structure of wheat plant, the wheat morphological data model based on axis structure is proposed to realize the structural storage of plant topological structure and organ morphological feature data. On this basis, according to the thought of "growth model -morphological feature model -geometric modelingdisplay model", the visualization framework of wheat morphology is designed. On the VC++ platform, the virtual wheat growth system is built with OpenGL to realize the three-dimensional visualization of the morphological changes of organ and individual plant of wheat in the whole growth period.

The Technical Framework of Wheat Form Visualization

To accurately reconstruct wheat three-dimensional morphology, the technical framework of morphological visualization of wheat is designed by following the thought of "growth model -morphological feature model -geometric modeling -display model". The framework consists of virtual environment, growth model base, the model base of organ morphological characteristics, growth engine, visual engine, and wheat morphological data model and other components, as shown in Fig. 1. 1) The virtual environment mainly includes weather data, soil information, variety data and cultivation management data. 2) Wheat-Grow is adopted for wheat growth model, and the system mainly uses the output growing degree days, leaf area index, nutrients, water and other influencing factors [START_REF] Meichun | A Mechanistic Model of Phasic and Phenological Development of Wheat I. Assumption and Description of the Model[END_REF][START_REF] Meichun | A Simulation Model of Above-ground Organ Formation in Wheat[END_REF][START_REF] Tiemei | A Simulation Model of Photosynthetic Production and Dry Matter Accumulation in Wheat[END_REF][START_REF] Weixing | Simulating Organgrowth in Wheat Based on the Organ-weight Fraction Concept[END_REF]. 3) Organ morphology characteristic parameter model is mainly composed of 4 parts, morphology characteristic parameter models of leaf, stalk, sheath and wheat head [START_REF] Guoqing | Modeling Leaf Sheath and Internode Growth Gynamics in Wheat[END_REF][START_REF] Zihui | Modeling Spike Growth Dynamics in Winter Wheat[END_REF][START_REF] Guoqing | Modeling Leaf Growth Dynamics in Winter Wheat[END_REF]. 4) The growth engine is made up of macrostate trigger, blade element trigger, growth engine drive, and branching (tillering) trigger and organ extension trigger. 5) Visualization engine is composed of engine drive, organ geometric modeling model and visualization model. 6) Wheat morphological data object is used to store plant topological structure and organ morphological characteristic data.

Fig.1. Technical framework of morphological visualization of wheat

Driven by the growth engine, the system invokes the growth model and morphological characteristic model of organ, calculates the physiological age of the wheat according to the virtual environment data, and then saves the plant topological structure at the physiological age and the related morphological characteristic parameters of leaves, stalks, nodes and spikes into the wheat morphological data model. The visualization engine receives the visual message sent by the growth engine, conducts precise geometric modeling of the organ according to the wheat morphological data model and splices the organs according to the topological structure of the organ to form an individual so as to realize the demonstration of three-dimensional configuration and visual computing function of wheat organs and individuals.

Wheat Growth Mechanism Model

Wheat growth mechanism model is composed of macro automata, child automata and micro automata.

The growth process of wheat is a unidirectional and irreversible evolutionary process, which passes through a number of distinct physiological stages, namely growth elements. In the model, macrostate represents the growth unit. Blade element, the meristematic unit of plant, is represented by sub-state. Since the growth unit is composed of blade elements, macro-state is constituted by sub-states, moreover, the blade elements in the same growth unit are in the same physiological stage. Changes of blade elements are embodied by the meristematic organs forming blade elements, and macro-state represents the growth state of the meristematic organs making up of blade elements.

According to the analysis mentioned above, the topological structure of wheat is generated by the growth mechanism model of wheat through the combination of macro-state, sub-state and microstate and cycle simulation of growth process of wheat. In the system, the growth mechanism model is realized by the growth engine.

A. Macro Automata

Macro automata could represent the state transition among the growth units of wheat. It is defined as a hexahydric group: AP ::= <Q p ,π p , S GU ,C p ,δ p ,F p >. Where, Q p is the set of the finite state of macro automata, πp is the initial probability vector(π pj ) j = ( p ( Q pi = j)) j ;S GU is the clock cycle that is needed for the growth of a growth unit; C p is the time of macro-state circulation;

p p p Q Q   
is the transition condition between states; Fp is the termination condition. The parameters in the automata are determined with the aforementioned wheat growth model in the system.

B. Child Automata

Child automata could express the state transition relationship among blade elements within a growth unit. Blade elements of wheat are classified as ml type and mt type; the former is composed of leaf, sheath and internode and the latter is constituted by spike and internodes under spike.

Child automata are defined as a hexahydric group, including AC::= < Q c ,π c , S ME , C c ,δ c and F c >. Q c indicates the set of finite state of child automata and πc is defined as a vector of initial probability ( π cj ) j = ( p ( Q ci = j)) j . S ME is the clock cycle during which a blade element outgrows. Cc expresses the cycling times of child automata.

c c Q Q   c 
indicates transition conditions between states and F c is the termination condition. Parameters of child automata could be determined by phyllochron and growth model.

As wheat grows in cluster with a main stem and several lateral stems (tillering), 2 types of mechanisms need to be introduced in order to reflect wheat morphogenesis truthfully. 1) One is branching (tillering) mechanism which could decide time, quantity and disappearance of tillering and effects of external environment on tillering. A great number of studies have been carried out on this aspect [START_REF] Mcmaster | Simulation of Shoot Vegetative Development and Growth of Unstressed Winter Wheat[END_REF][START_REF] Yushan | A Staistic Analysis of the Development of Tillerings in Winter and Its Use in Production[END_REF][START_REF] Tiemei | Simulation on Wheat Tillering Dynamic[END_REF]. 2) The other is synchronous mechanism. Lateral axis of wheat (tillering) has synchronous growth relation with its parent axis and its physiological age is the same to that of parent axis. However, the number of its meristematic units can not be greater than that of its parent axis. Therefore, when parent axis reaches a certain physiological stage, its lateral axis will also reach synchronously.

C. Micro Automata

Micro automata show transition relationship between specific states of organs within the blade element and are defined as a hexahydric group, including AM::= < Q m , π m , S OG , C m , δ m and F m >. Q m indicates the set of finite state of micro automata and πm is defined as a vector of initial probability ( π mj ) j = ( p ( Q mi = j)) j . S OG is a clock cycle in which the micro automata could change.

m m Q Q   m 
indicates transition conditions between states and F m is the termination condition. Micro automata could be decided by rule of synchronous growth of wheat organs.

Plant Morphological Data Model of Wheat Based on Axis Structure

Plant morphology of wheat is manifested as branch structure composed of axes with the main axis and lateral axes of all levels included (tillering). Essence of plant morphological description of wheat is to reveal the following aspects: 1) Branch network, namely axis information, such as branch numbers of axis (tillering), blade element number, organ set, arraying order, etc.. Logical relation between axes and morphological description are also included, such as mapping relationship between branching (tillering) and parent axis, angles between axes, etc.. 2) Morphological characteristics of organs, such as length and width of leaf organ, angle between stem and leaf, length and thickness of internode organ, etc.. On this basis, plant morphological data model of wheat is constituted by set of axis objects, axis objects and organ objects, which describes relationship among them and morphological characteristics of organs from three aspects of axis, blade element and organ.

A. Set of Axis Objects

Set of axis objects is showed in Fig. 2. As elements of a set, axis objects of all levels are ordered according to their occurrence time.

B. Axis Object

Structure of axis object is shown in Fig. 3. 1) Identity of axis object is its key which reflects logical relations between axes. 2) Branching (tillering) number indicates number of branches in an axis. 3) Blade element number indicates number of blade elements in an axis. 4) Angle of parent axis shows the angle between an axis and its parent axis. 5) Leaf object set is the set of leaf organs constituting the current axis and its elements are leaf objects. 6) Sheath set is the set of sheath organs constituting the current axis and its elements are sheath objects. 7) Internode object set is the set of internode organs constituting the current axis and its elements are internode objects. 8) As to spike object, axis which could grow into a spike only has one spike organ.

In actual application of this model, organs constituting the blade elements could be added according to practical requirements. Morphological characteristic data of every organ object are recorded, which is not described in detail here. Twisted modeling of blade could be regarded as rotating the centertop part of blade around curve of leaf vein under the condition of unchanged vein curve. In terms of NURBS surface, it is manifested as rotating control points in 2nd and 3rd rows on blade around curve of leaf vein. As shown in Fig. 5, l1, l2, l3 and l4 are lines connected by the control points in last 4 rows (control points in 5th row are all at the highest point of blade) and p1, p2, p3, p4 and p5 are 5 control points in curve of leaf vein. Through rotating 14 and 13 around tangent line of curve of leaf vein, twisted modeling could be achieved, namely, rotating 14 around the vector which passes point p4 and is parallel to p3p5 with an angle of ro and rotating 13 around vector which passes p3 and is parallel to p2p4 with an angle of ro. Twisting angle of r is within 0-180 o . In order to ensure smooth connection between blade and sheath, y coordinate of control points of blade in first row could be set to be equal to that of control points in the highest part of sheath. Thus, control points in the 10 rows and 7 columns could form a NURBS surface to simulate the modeling of whole leaf, as shown in Fig. 7.

B. Geometric Modeling of Internode

Wheat internode is manifested as a cylinder which grows to be thick and long gradually during the growth process and this is simulated by cylinder in quadric surface. Based on internode length and thickness output by morphological characteristic model of aforementioned organ, length and diameter of cylinder are determined.

C. Geometric Modeling of Wheat Spike

Structure of spike is relatively complex and could be divided into grain, axis, peduncle and awn. Therefore, method of combining several basic graphics was adopted to construct the three-dimensional geometric model of spike. Axis and pedunde of spike were modeled by cylinder in quadric surface. For species which had awn, model construction of awn adopted cylinder and ellipsoid was used to construct grain model. Visualization effects of spike are shown in Fig. 8.

Example Analysis and System Implementation

Based on the technical frame of wheat morphological visualization technology, virtual growth system of wheat was developed using VC + + and OpenGL. With the variety of "Yumai 34" used as the test materials, morphological characteristic parameters of "Yumai 34" were extracted based on meteorological data, soil characteristics, variety parameters, cultivation measures, etc. during wheat growth in Science and Educational Garden of Henan Agricultural University in 2008 and 2009. With variety parameters and model parameters extracted, plant topological structure and morphological characteristic parameters of all organs were generated using virtual wheat growth system. Threedimensional morphology of all organs was drawn and threedimensional morphological reconstruction of wheat individual was achieved, as shown in Fig. 9. 2) Concepts of macro state, child state and micro state were put forward in this study from the perspective of botany and wheat growth automata model was constructed. Through introducing organ growth state, this method could reflect changes of blade element during growth precisely, which overcame the deficiency of dual-scale automata in this aspect. Besides, growth mechanism of wheat was also considered in this model and it combined with the agricultural knowledge effectively, facilitating simulation of wheat morphological process. This model had certain universality and could offer ideas and methods for studies on morphology and structure of other cereal crops.

3) Plant morphological data model of wheat based on the axis structure was put forward in this study and this achieved structural storage of topological structure of wheat plant and morphological characteristic data of organ. Combination of all organs could be controlled conveniently and effectively using this model and virtual display of the wheat individual was achieved. This method was also applicable to structured storage of morphological data of other cereal crops.

4) This study did not involve the crop root. Therefore, further studies should be carried out on how to construct the morphological model of crop root so as to achieve the complete visualization of crop organ, individual and population.

Fig. 2 .

 2 Fig.2. Set of axis objects

Fig. 3 .

 3 Fig.3. Axis object

Fig. 4 .

 4 Fig.4. Geometric schematic diagram of untwisted blade

Fig. 5 .

 5 Fig.5. Geometric schematic diagram of twisted blade Sheath of wheat is in an open cylindrical shape. Its modeling is mainly determined by radius R and height H. Control points are set to be included in 5 rows and each row has 7 control points. The first row is set to be a square with a side length of 2 x R which is defined as a circle with a radius of R. The x and z coordinates of control points in row 2, 3 and 4 are equal to those of row 1 and y coordinate has an equidistance rising. The y coordinate of control points in row 5 increases with the same space and x and z coordinates form into an open square, thus defining an open circle, as shown in Fig.6.

Fig. 6 .

 6 Fig.6. Geometric schematic diagram of sheath.

Fig. 7 .

 7 Fig.7. Morphological visualization of blade and sheath of wheat

Fig. 8 .

 8 Fig.8. Morphological visualization of spike of wheat

Fig. 9 . 1 )

 91 Fig.9. Interface of virtual wheat growth system

* This work is partially supported by China National 863 Plans Projects #2008AA10Z220 and key scientific and technological project of Henan Province #082102140004.


Mingli Li

Daoliang Li

Qisheng Ding

Ya Chen

Chengfei Ge

A Multi-parameter Integrated Water Quality Sensors System

Keywords: water quality monitor, multi-parameter, aquaculture, low consumption

Because of the rapid development of aquaculture in China at present, it is more and more urgent to apply hi-tech devices in aquaculture field to guarantee its efficiency. There are already many devices used for monitoring water quality have been developed. However, many of them are only in a step of academic research or too expensive to apply to practices. In this paper, a multi-parameter integrated water quality sensors system which has a practical value based on self-contained design is introduced and the hardware and software of this system are researched and discussed. A test conducted at Taihu Lake in Jiangsu province, China shows that this system can perform well. And this system has many features such as low cost, low consumption, multi-parameter and real-time data upload. The features and good performance above suggest the practical and potential application of the system in water monitoring. And this system still has much space to improve. It may become lighter, more integrated, and more portable in the future.

Introduction

China has a flourishing aquaculture and the production of it ranks first in the world for many years which occupies above 70% of the world. And output of aquaculture in China is still increasing increasingly [1 -2]. Thus aquaculture has made much contribution to development of Chinese agriculture. However, compared to other developed country, aquaculture in China has many weak points such as low efficiency and high consumption due to the low level of technology and management [START_REF]Research group of the Ministry of Agriculture Bureau of fisheries aquaculture. Research of main aquaculture in China[END_REF]. We still monitor water quality through experience and visual observation. Even though we also sample water for experimental analysis, lacking real-time monitoring and adjustment cause its low accuracy. In addition, experimental test costs much, has a long circle and collects limited data [START_REF] Pengxiang | On-line water quality monitoring system integration technology in aquaculture[END_REF]. Water quality is a vital factor in the aquaculture. Short of monitoring of water quality parameters such as pH, Dissolved Oxygen (DO) and temperature can cause the low quality of water. What's more, the problems above may lead to waste of forage, residue of medicine and bacterial reproduction which have big terrible impact to aquaculture of our country. For example, the EU restricted the import of shrimps from China due to the medical residue a few years ago [START_REF] Zhijie | Aquatic disease and pollution-free cultivation[END_REF]. Therefore the high technology and smart management are important to raise production output and quality, improve productive efficiency, guarantee production safety and achieve sustainable development of aquaculture [START_REF] Daoliang | Research of integrated digital aquaculture system[END_REF].

There are some researches in this field in China. A plan about a real-time multi-parameter test system is put forward by Zhang Libao from Qingdao University which can monitor four parameters of water quality as pH, temperature, DO(dissolved oxygen), and conductivity continuously in real time [START_REF] Libao | Online multi-parameter water quality monitoring system[END_REF]. A real-time smart water quality monitoring system is researched by Ma, congguo from Jiangsu University, achieving smart controlling and information sharing in aquaculture. There are also some researches abroad [START_REF] Congguo | Intelligent Monitoring and control for aquiculture process based on field bus[END_REF]. A Multi-Sensor System is developed by O. Postolache from Portugal which can test turbidity, pH and temperature of water quality [START_REF] Postolache | An internet and microcontroller-based remote operation multi-sensor system for water quality monitoring[END_REF].

Losordo, Piedrahita and Ebeling from California researched an automated water quality data acquisition system based on self-contained microprocessor. This system can monitor and record weather data and pond environmental data [START_REF] Losordo | Automated water quality data acquisition system for use in aquaculture ponds[END_REF]. However, researches and plans inland are often far from perfect and stay in a developing step.

They cannot be applied in practice. On the other hand, productions abroad are often too expensive to afford by common people. Price of a set of foreign monitoring system can be higher than¥100,000, thus they are not economical in large scale aquaculture.

Aiming at problems above, this paper develops a multi-parameter integrated water quality sensor system which can achieve multiple parameters collection, data storage and upload. 6 parameters of water quality are considered: pH, dissolved oxygen(DO), temperature , conductivity , NH3+ and water level which play important roles in water quality. For example, pH can influence the solubility and biological availability of water. Short of DO can generate toxic substances in water.

Moreover, suitable temperature and other parameters also contribute to the good growth of aquatic life.

Each parameter of water quality can be monitored through this system of aquaculture to make it convenient for people to observe the states of water and adjust water quality in time to fit the requirements of aquaculture. And this system can achieve self-identification, self-correction and self-complement. It also has characteristics as low cost, low consumption, anti-interruption, multi-channel collection and easy operation.

Principle and Structure of System

Principle of System

Principle of multi-parameter integrated water quality sensors system is shown in Application layer receives data from process layer. And then it will store these data in the flash and upload them to PC. In the application layer, users can configure and read parameters of sensors and read real-time data of every channel through software Unilog.

Structure of System

Multi-parameter integrated water quality sensors system is shown in Fig. 2. The left part shows the appearance of the system and the right part shows the section of system. From these figures we can see that this system is a cylinder, with a hook on its top. PCB is placed at the middle of system. Batteries are put near the top of the system. Sensors and water pump connected with PCB are fixed at the lower place.

Signals are collected through sensors from the bottom of the system.

Fig.6 communication module

Moreover, there is also communication between CPU and storage module. The I² C bus protocol is used in communication between FM24CL64 and CPU. This is a serial extended bus, using two-wire system. Every node is linked to clock line SCL and data line SDA and every device has a unique address and independent electrical characteristic which can simplify the structure of circuit. It can realize the modularization and standardization design of circuit system [START_REF] Chuhang | Control method of I 2 C bus memory[END_REF]. SPI bus protocol is used in communication between chip M23PE80 and CPU. SPI is serial peripheral interface which is a four-wire system and full duplex. SPI provides programmable clock and have write conflict protection and bus contention protection [START_REF] Anguo | Design of FRAM based on SPI bus interface[END_REF][START_REF] Bing | IP design and achievement of general SPI bus[END_REF]. The step Data adjustment shown in the flow above is used to revise data to more accurate ones through a mathematical calculation. And the results are given by:

Design of System Software

() y A x B  (1)
Where A and B are coefficients. The default values of A and B are 1.0 and 0.0 respectively. And the real values of A and B vary according to specific applications.

Communication Module

Software of this module can code command frame according to target device address and purpose of instructions. And CRC is used as check code [START_REF] Yan | Frame error correction in WLAN system using CRC[END_REF]. Later these commands will be sent to PC or sensors by serial ports.

When serial port receives command frames, software can calculate the checksum to judge whether the frame is correct. And then software will analyze command frame to extract address and purpose of the command frame. If the address is not the CPU itself, command frame will be sent to the sensor which has the corresponding address.

Finally, command will be implemented in the correct device.

Dormancy Module

In order to save energy, multi-parameter integrated water quality sensors system is in dormancy mode when there is no active task. After a circle of operation, CPU mode is changed into LPM. The consumption of CPU in LPM is a thousandth of normal mode. When timer is timed out, CPU will quit LPM and a new circle of operation starts.

Results and Discussion

Configuration of System Parameters

A multi-parameter integrated water quality sensors system was installed in Yixing, China to test the water quality of Taihu Lake. Sensors connected to main part of system have been put in the water to start to acquire data of 6 water quality parameters. First of all, in order to ensure that data can be collected correctly, every module of software has many parameters to configure. Each device should have a unique address which guarantees that the communication information can be transmitted to right places. Second, time parameters should be configured to ensure that every operation should last proper minutes. Among all the parameters, important ones have been set as Tab.1 below before the beginning of monitoring. They are set by the software Unilog.

Tab.1 shows the basic time relative parameters in each module.

Conclusion

This paper introduced a procedure of development for multi-parameter integrated water quality sensors system. This system can monitor water quality to meet the demand of large scale aquaculture and it can be used in practical applications. Because this system can make the monitor operations easier, more effective, and more accurate, it saves much time and money of human in a long term. This method may replace the traditional monitor way which depends on human's observation and takes people much more time for monitoring water quality and cannot obtain precise data. As shown above,

this system is able to obtain states of 6 parameters for water quality continuously in humans' absence. And the data of water quality is available on PC in real time. We can get much useful information for estimating water quality based on these responses of sensors. And the result proves that the system works well in practice.

Fig. 1 .Fig. 1

 11 Fig.1. The whole system can be divided into three layers: Application layer,process layer and perception layer. Perception layer face the monitoring objects. Application layer faces users. And they are connected by process layer.

Fig. 2 Fig. 3

 23 Fig.2 Multi-parameter integrated water quality sensors system

Fig. 4 Fig. 5

 45 Fig.4 Wash pump control module

4. 1 Fig. 7 Fig. 8

 178 Fig.7 Main flow of software

Fig. 9

 9 Fig.9 Flow of communication module

2 .

 2 Responses of Water Quality SensorsAfter a one-week monitor, the multi-parameter integrated water quality sensors system has collected lots of information of water quality. Data is collected every 10 minutes, so there are 1008 records have been acquired for each water quality index in a week. Rely on these records, charts which show trends of 4 indexes have been plotted. And they are shown in Fig.10. From curves in these charts, we can analyze the states of water quality parameters as DO, pH, conductivity and NH3-H. Parameter as pH is more stable than the rest. And others as DO and conductivity are changing obviously based on the circle of the day. We can notice that DO concentration is lower than the normal standard from the curve of DO of the fifth day. Thus on these days we should take steps to enhance the content of DO. For example turning on the oxygen enhance machine.

Fig. 10

 10 Fig.10 Curves of water quality parameters over time

Acknowledgements

This work was supported by 948 Project of China Agriculture Ministry (2010-Z13).

And the programs "Development and Applications of sensor network applied to monitor bloom of blue-green algae in Taihu Lake" (2010ZX03006-006)


Jiangang Liu

Yongchang Wu

Tingting Tao

Qingquan Chu

Research and Development of Decision Support System for Regional Agricultural Development Programming

Keywords: region, agricultural development, evaluation on resources, programming, decision support system

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

Introduction

The agriculture and rural economics of China have achieved outstanding success since the reform and opening up. The agriculture of China has entered upon a new historical stage. Its are main the feature emphasizes expression to be in the following respects [1] .firstly, Amid a lot of new changes taking place in agricultures, many new problems and contradictions arise, which are mainly reflected in the following aspects [START_REF] Wang | Problem and Solutions of Soil and Fertilizers in Agricultural Sustainable Development[END_REF] . The alternating buyers' market and sellers' market that the primary products have been experiencing are gradually transitioning into the buyers' market. Also, the structural and regional oversupply of a few products occurs.

Secondly, the sustainable development of agriculture in the new stage requires adjusting the agricultural structure. The utilization of agricultural resources is unreasonable. Some areas suffer severe shortage of agricultural resources while some other areas' resources are idle or wasted. Farmers' income does not increase along with the increase of grain yields, resulting in the low-speed rural economic development. The gap between the east and the west district is expanding, so that the imbalance in regional economy is aggravated [3] .Thirdly, the similarity in industrial structure has led to the situation of the blind competition for some regions. Such situation weakens the organic connections and the complementary advantages among those regions. In some regions,the phenomenon of "blind follower" and "herd mentality" occurs during the adjustment of agricultural structure. 1 Due to the disparity on natural and social conditions of different regions, There was significant distinct variation in the agriculture of China. Therefore, bases on the analysis of agricultural resources and regional variations, the studies manage to find out the advantages and disadvantages of the regions and to fully explore the regional characteristics, at last , a specialized and high-efficient regional layout should be put forward. Since the agricultural production system is a complex system based on the regional natural resources, the decision-making and administration on agricultural problems needs to take many factors into account [START_REF] Devries | Systems Approaches For Agricultural Development[END_REF] . By applying information technology for managing and analyzing information, decision support system can be helpful for administrators in different levels to make the right administrative decisions [START_REF] Daniel | Decision Support Systems[END_REF] . However, research and development in the agricultural information technology and the decision-making system in china is relatively late compared with the developed country. Because of lacking information and agricultural expert system support, mistakes in agricultural decision-making can hardly be avoided, which leads to a great loss of agriculture. Therefore, it is very urgent to build up all sorts of agricultural decision support systems. Based on the system theory, optimal theory Founded by National main basic research and development plan(2009CB118608);

Author: Jiangang liu. E-mail:ljgwr0619@sina.com; and decision-making support system theory [START_REF] Van Ittersuma | Integrated assessment of agricultural systems -A component-based framework for the European Union (SEAMLESS)[END_REF][START_REF] Fischer | Risk-adjusted approaches for planning sustainable agricultural development[END_REF][START_REF] Roetter | Integration of Systems Network (SysNet) tools for regional land use scenario analysis in Asia[END_REF][START_REF] Chen | Decision Support System and Development[END_REF] , by utilizing the information technology, the regional agricultural development decision support system (hereinafter referred as RADDSS) was researched and developed in this study.

The main functions of RADDSS

RADDSS is an intelligent decision support system [10] designed for agricultural administrative and companies at medium or small size, by providing users information and data required for decision-making, predicting related projects, offering a variety of alternative proposals, and making a selection and evaluation. By using RADDSS, users can evaluate resources, predict market and make a plan for production in a region. Users could also compare different plan, and make a judgment over and again through the human -computer interface, so as to support and assist decision makers to solve decision-making problems or prepare plans for regional agricultural development. Specifically, RADDSS encompasses evaluation on resources and modernization level, and assessment in agricultural sustainable development, multi-cropping design, planting structural optimization and analysis in agricultural product market.

Evaluation on regional resources

The agricultural production potential of a region or a production unit depends on the natural resources, while the fulfillment degree of potential is partly determined by social and economic conditions. Therefore, the evaluation on resources is vital for decision-making. By using RADDSS, users can make a comprehensive evaluation and analysis on the natural resources, the social and economic conditions and the agricultural sustainable development level in a region.

Evaluation on natural agricultural resources

By using RADDSS, users could analyze and evaluate natural resources of land, water, climate; base on the fundamental data on regional agricultural population, per capita farmland area, per capita forest area, per capita grassland area, per capita water area, per capita water resources; and calculate the index of regional natural resource in a region. RADDSS can reflect the trend of regional natural resources through dynamic analysis on changes among different regions as well.

The evaluation and analysis on agricultural modernization level

There are some big differences between different regions on agriculture develops level in China. Then evaluating a region's agricultural development level correctly is vital. The evaluation on a region's agricultural modernization level is an important prerequisite for inspecting the development conditions of the agriculture and rural areas [12] . The established model for evaluating and analyzing the agricultural modernization level in RADDSS is used to measure a region's agricultural modernization level. The weight analysis method is largely adopted by RADDSS evaluation. In the process, indicator system, which consists of 1 overall index, 9

subject indices and 26 group indices, is divide into three levels, i.e. the overall indicator, the subject indicator and the group indicator. The analytic method is to evaluate the weight of subject index and each group index, while the weighted quadrate method and weighted summation method are adopted to calculate the comprehensive index of agricultural modernization..

The analysis on agricultural sustainable development potential

The sustainable development of agriculture refers to the sustainable development of a compound system, which consists of such interacting sub-systems of nature, society and economy [START_REF] Zhu | Sustainable Development of Agriculture and Rural Modernization[END_REF]. Therefore, a plenty of indices, such as population, resources, environment, economy and society in a certain region will be involved in the analysis of agricultural sustainable development. The indicator system for sustainable development could be divided into descriptive indices and evaluative indices.

Descriptive indices refer to those are difficult to be defined in quality and quantity and reflect the compound relation between society and nature as well as economy.

While the evaluative indices refer to those are quantitative. In the model, the overall index is decomposed into three subject indices, i.e. the agricultural production sustainability, the ecological environmental sustainability and the agricultural economic sustainability. According to the accounts of the group indices and the subject indices determined by the expert review and analytic method, the weighted summation method could be adopted to figure out the overall index, i.e. the agricultural sustainable development potential index which could evaluate regional sustainable development level. In addition, the model can make a historic analysis on regional sustainable development level and a comparison analysis among different regions.

Comprehensive evaluation on resources

In this module, RADDSS build up a combination-barrel model to evaluate the resources of nature, society and economy in a region comprehensively.

Prediction and decision

Agricultural production system is a complex system based on the regional natural resources and economic characteristics, it consists of such subsystems of grain, economic crop, vegetable production and horticulture, fisheries, animal husbandry and other subsystems. Furthermore, it not only embodies biological and economic characteristics, but also involves a multitude of certainty and uncertainty factors that are either linear or nonlinear with interwoven effects and influences. Because of the complexity of influencing factors and the low accuracy in predication, the predicted result for macro agricultural production is imprecise. In this study, artificial neural network based on error back propagation was used, which can enhance the reliability of predicting effects. RADDSS attempts to predict the agricultural macro data [START_REF] Zhang | Studies on agriculture production using artificial neural networks based on crops culture complete collected information complex concert control theory[END_REF] . BP (Back Propagation) neural network model is mainly adopted in the model.

BP algorithm is widely populated and important for training neural network forward, which is a kind of teaching network performing a very powerful self-organizing and self-adaptive ability. Once the model is finished, we can input parameters, and select input and output factors, and predict and make decision after model training, and then user can obtain an effective result. In return, the result of prediction and decision-making can be used as an instruction for agricultural production and agricultural development programming.

Farming system optimization and design

Farming systems of crop production in China are quite complicated. The traditional multi-cropping design is a manual process based on the agricultural natural resources and economic conditions in a region, which will be then validated in production. In this study a new method of optimization and design was brought out based on crop ecological adaptability. The model can analyze regional climate and economic conditions, then find out the mainly suitable crops for combinations, and list the anniversary of the multi-cropping patterns and various parameters, so as to offer choices for decision makers. Besides, the model can analyzes and calculates the production potential of each crop, for example, photosynthetic production potential, light temperature productivity potential, climate production potential and comprehensive production potential.

Moreover, RADDSS can provide a comprehensive programming for the planting production in a region [START_REF] Li | Structural Model for Optimizing in Agricultural Production System and Application[END_REF] . A multi-objective method is mainly adopted for programming and design. However, in the previous study, the linear programming method was applied. However, the practical production is multiple objectives, in which not only the production efficiency is considered, but also the ecological, economic and social benefits. As a matter of fact, the optimization results of a single objective functional linear programming tend to fail the requirements of practical problems. In this view, RADDSS uses the multiple objectives programming to solve regional agricultural programming problems which not only provides optimal solutions, but also offers alternative schemes for decision makers to choose.

The analysis on agricultural product market

Agricultural product market and price is always changeable because it is constrained by the law of supply and demand and the government policies. Besides, as one of the main factors that affect decision makers, agricultural product market and price affect agricultural products in the coming year. This study uses GM (1, n) method and time series model to predict the market and price of main agricultural products, the predicted results are presented in graphics or other visual styles, which directly reflect the fluctuation of price in agricultural products and provide evidence for programming and decision making.

The structure of RADDSS

RADDSS focuses on supporting or assisting users to make a decision for half-structural and nonstructural problems rather than replacing users to realize the automatic decision-making. For this purpose, the basic functions of RADDSS are as follows: the first function is to collect, store and provide internal and external data related to agricultural management decision, the second function is to offer common system models which are equipped with the internal and external data for prediction, decision, evaluation and Analysis, the third function is to provide expert advice for the macro management, the fourth is to provide user-friendly interface that is easy for users to operate and master. To support the operation of the above functional modules, the organizational structure of RADDSS consists of man-machine interface, database, model bank and method bank. In the process of system development and design, RADDSS adopts the most popular programming tool (Visual C++) that is object-oriented. Moreover, the entire RADDSS system is running on Windows95/98/2000/XP.

Man-machine interface

Being a link between the system and users, the man-machine interface exchanges and communicates information between RADDSS and users. It acts as a component of RADDSS logic structure and weaves together with other components in the program. The powerful interface function and data processing function of Visual C++ are given full play in the man-machine interface. Windows for explanation and description are set to make a brief instruction about its preselected functions. The input and output information is presented in such directly visual forms of windows, charts, words and others, and all inputs with are designed with powerful checking function and fault tolerance.

Database system

Database system is made of database and its management system. The database constructed by two main databases, several auxiliary databases and database management modules, that stores data and data management operations, includes a RADDSS system which involves basic data, an important intermediate data and the results. While the external information base is to stock the market price of crop seeds, chemical fertilizers and pesticides and main crops, agricultural meteorological database, as well as the data related to the future decision-making regions and its subordinate administrative region, such as population, labor force, land resources, basic statistics in national economy, areas and scales of agricultural production industries, costs, material consumptions and other data about agricultural management and decision-making. The internal one deposits labor index, farmland area, average yield per area, capital and other data related to internal users. Apart from the basic functions of data input, modification, deletion, searching, database management system also can extract data from main database to generate new database, realizing the data transferring between varieties of languages through the interface program and providing basic data for other subsystems according to the information demands of decision makers.

The system of model library and method library

The system of model library and method library is an important part of RADDSS system and a crucial tool to support decision-making. All models are included in model library, such as the models for analysis, evaluation, prediction and optimization.

The main function of model library management system is to provide model advice, generate models, modify models and solve problems using models. While method base management system is made up by method base and its management module.

The traditional DSS method base is actually a model that refers to a solution seeking to a model. This study defines the method bank as one for decision-making and analysis, which breaks through the traditional concept about DSS method base. The system base is a kind of algorithm that collects goal programming, GM (1, n), multiple linear regression, and principal component for analysis and so forth [START_REF] Pei | Multivariate Statistical Analysis and Application[END_REF] .

The models and the methods used in the system are as follows: a programming model; f) the model for resources evaluation, including weight analysis method, weighted integration method, and weighted sum method; g) the model for factors analysis, including regression model and model for difference analysis.

Knowledge-base system

Knowledge-base system is consisted by knowledge-base, inference engine, self-learning machine and knowledge-base management module. The knowledge-base is to store some relevant knowledge, such as knowledge about professional agriculture and decision-making, expert's experience in making decision, scientific data. Actuality, the expression of agricultural knowledge is the key component.This study combines frame method and production rule as well as the relational database together, i.e. using frame method and production rule to form rule database, while using relational database to store knowledge elements and establish logic relations between them.

The analysis of agricultural development in Xuchang using RADDSS

In this paper, we analyze, evaluate and plan the agricultural production in Xuchang, Henan province with the established RADDSS.

Xuchang city is located in the central part of Henan. There is three counties, two cities, and a district with a total area of 4061.49 square kilometers, 304,600 hectares of cultivated land, and 0.084 hectares of arable land per capita area. In 2000, total income in agricultural production hits 3,737,360,000 yuan with 575,200 hectares sowing areas in whole year. Xuchang has a long history with an earlier agricultural development. The climate are suitable for agricultural production, include adequate light, a long frost-free period and four distinct seasons. Moreover, there are 314 farming days with daily average temperature above 0 ℃, 217 frost-free period days and 631.1-736.0mm rainfall. Therefore, the favorable climate and the suitable conditions for crops make Henan become a major producing region for rice and economic crops in China. For example, it's rich in wheat, tobacco, cotton, corn, soybean and sweet potato.

Making an analysis for Xuchang with RADDSS. Firstly, agricultural resources in Xuchang were evaluated with barrel model-analysis model for resources combination, the graphic of "barrel model" is as follows:

According to the graphic of "barrel model" of Xuchang(Figure 1), we can see that the gap among "missing" height is small, which indicates the matching for agricultural production factors of Xuchang is reasonable and suitable for agricultural development. The values of elements of equivalent are shown in the table 1.With investment of only 2952 yuan/hm 2 that is below the average level of Henan province, capital is the first limiting factor in Xuchang. Since agricultural investment is the primary factor that restricts yields, we must raise agricultural investment to increase crop yields. Finally, the multi-cropping methods [16] were applied to design farming system in Xuchang, 14 kinds of suitable planting modes were screened out and their potential were analyzed respectively. Meanwhile, after optimizing the crops layout in Xuchang with the multi-objective method, two proposals based on analysis and calculation are put forward,which include grain-oriented project and benefits-oriented project. The grain-oriented project increases grain by 174,009.2 tons while the benefits-oriented project increases income by 439,762,000 yuan. The comparison analysis for the two projects obviously shows that, the grain-oriented project makes a weaker economic performance than benefits-oriented project as a result of ensuring grain output in Xuchang. Therefore, the low efficiency of agriculture in Xuchang cannot be solved by the planting sector alone, and it is unrealistic to increase farmer's income and accelerate the development of rural economy in Xuchang by relying on the nation to mark up the price of national grains. Seeking high-efficient sectors rather than planting sector is an imperative way out.

Conclusion

The body structure of RADDSS was build, and different analysis methods, models and theories were integrated into the system. The developed system can provide the users with all kinds of data information, model and analytical method. By providing a macroscopic insight on the local resources, society and economic development, as well as the stability and development direction of regional agricultural system, RADDSS offers proposals for the decision-making of administrators, operators, managers and production departments, and offers decision-making support for the administration and programming of agricultural administration, which improing the administrative and decision-making level on agricultural production, optimizing the allocation of resources, reducing the failures of agricultural macroscopic decision, promoting the agricultural development and raise the farmers' income.

  combination-barrel model is based the quantitative functional relationship on the amount of resources and crop productivity, By using the "barrel model" user can find restricted factors for agricultural development of a region. The factors combination barrel model can be used to evaluate the natural resources and crop demand quantitatively, to assess the restriction of limiting factors quantitatively and to determine the first or the second limiting factor, then making man-made investment targeting limiting factors, making full use of all kinds of natural resources and avoiding blind investment.

  ) the model for agricultural factors evaluation, including barrel model, model for principal components analysis; b)the model for agricultural sustainable development evaluation; c) the model for agricultural modernization level analysis; d) the model for prediction, including neural network model, grey model, model for time sequences analysis ; e) the model for agricultural planning, including linear planning model and objective

Figure 1 .

 1 Figure 1. Resource Evaluation in Xuchang-Cask model diagram

  Figure 2.Comparison of the actual value , the value predicted by ANN and the value predicted by grey system

Table 1 .

 1 the values of elements of equivalent in Xuchang Secondly, the agricultural modernization level was analyzed in Xuchang. This study analyzes the data in 1980, 1985, 1990 and 2009 in Xuchang, and figures out the agricultural modernization comprehensive index of the 4 historical stages. The results are in the following table. This shows that in order to catch up with the developed district, Xuchang needs to accelerate the development of agriculture and rural economy while paying attention to the protection of both resources and environmen.Fourthly, the agricultural production in Xuchang was predicted by using artificial neural network model. In this study,we uses gross agricultural income (Y) as output neurons, and planting areas of economic crops and food crops, rural electricity, chemical fertilizer dosages and several other factors as input neurons for network learning and prediction from 1990 to 2009 in Xuchang. The application of the system has been validated, which performed well in predicting gross agricultural income.

	Eleme nts	Radiat ion	Frost free period	Growi ng season	Nitrog er en fertiliz	Phosp er hate fertiliz	Accumu ture lated tempera	Water	Machi nery	Fun ds
	Value -s (KG)	2285. 71	2500	2000	1453. 33	2125	1800	1900	1013. 64	925
	The Agricultural modernization comprehensive index from 1980 to 2009 was
	calculated by the system, we know that the comprehensive index of agricultural
	modernization, which is still in its initial stage, is 45.9 % until 2009 in Xuchang.
	However, when we analyze the development process, the index has increased 29.6%
	in the past 29 years from 1980 to 2009, and the fastest development happened from
	1990 to 1998 and reaches 13.2%.
	To further analyze the 25 group indices, we can find that the progress in
	agricultural modernization is rapid in the 1990s in Xuchang, Henan province, which
	attributes to two reasons,one reason is the bigger rise in agricultural production level
	and a more reasonable structure in agricultural production. For instance, the
	production proportion of farming land is 1.7 times, agricultural labor productivity
	4.14 times, meat of pigs and sheep 2.8 times in 1998 than in 1990, which changes the
	traditional agricultural mode. The second reason is the increase of farmers' income
	and the enhancement of consumption ability. From 1990 to 1998, the net income of
	farmers raised 4.18 times which promoted the growth of consumption and the

Specifically, the learning rate is defined as 0.85, ε= 0.0001 and the data from 1990 to 2009 is taken as a set of learning samples. We predict the gross agricultural income with the trained neural network in 2013, 2014 and 2015,and the results indicate that the gross agricultural income will increase year by year.

* Corresponding author: Qingquan Chu. E-mail address: cauchu@cau.edu.cn


Fantao Kong

Liyuan Xin

Wen Yu

Jianzhai Wu

Yongen Zhang

Master; Xin Li-Yuan

PhD Y U Wen

; Wu

Designation of R & D on Pig Production Intelligent Monitoring and Early Warning 1

Keywords: live pig prosperity, price fluctuation, forecasting, price movement 1

This paper analyzed live pig production, consumption and price, and proposed the monitor system and the early warning mechanism. It is indicated that the study has a theoretical and practical significance in this area in China.

In the view of the unusual fluctuation in the current live pig and its product prices, we carried out more works in live pig risk factors, fluctuation, detection methods, model building, as well as R & D relevant circumstances. In order to facilitate future studies in this field and to provide a useful exploration, we additionally proposed the implementation of the program of research and design at the last part of the paper.

compared with 2000 and 1990, and was 3.36 times that of 1980. Pork production increased to 5.07 million tones, 27.87% and 122.31% up from 2000 and 1990 respectively, and 4.47 times that of the year 1980. Though the livestock produce structure had been adjusted, and the rate of pork production in the meat decreased step by step, it still accounts for 63.98%. The pork possession per capita is 37.82 kg in 2010, which is 3.29 times that of the year 1980, and 20.86% and 89.56% more than that in 2000 and 1990 respectively. However, from January to November 2000, pork price in China tended to fluctuate continuously and significantly (Figure 1). The price was stable in the period of January 2000 to May 2003. But after then, the price went higher and fluctuated largely, especially after June 2006, the pork price increased from 10.58 yuan/kg to 26.08 yuan/kg in February 2008. During the 20 months, the price fluctuation range got 146.50%. After that, it quickly declined by 39.88% to 15.68 yuan/kg, and followed by 16.04 yuan/kg. In September 2011 the price reached 30.35 yuan/kg, continuously went up 86.61% in 15 months, The fluctuated pork price seemed like a "roller coaster", which made "live pig production stable development" difficult to be realized. So, it is in urgent need to make a live pig price control plan. Its applications also expand to more related fields, i.e., the international economic prosperity, the regional economic prosperity, industry prosperity, economy and market prosperity. Abdollahzadeh adopted the synthetic index and proposed agricultural development index. In recent years, China has made a good progress in prosperity monitoring research and application. The study was launched in a social risk, the SME economy, business prosperity monitoring, the macroeconomic climate, regional economic prosperity. Up to now, the prosperity monitoring has been widely used and accepted. Around the prosperity research goals, we have engaged in a large number of studies on the Composite Index, the AC models, data base, neural network-based monitoring, and early warning, and etc.

Even though the important progress has been made on the research of pig production monitoring and early warning, many problems still remain to be resolved in this field. Firstly, integrative study is limited in pig production, pork prices and pig prosperity rules, but the laws of pig prosperity have not been developed. Secondly, the pig prosperity risk factors are not formed. Thirdly, the research on price fluctuation is so less that the monitoring data have seldom been used and the monitoring methods have not been established. Actually the monitor research has not been carried out in China. Fourthly, early warning is mentioned more, while the research on establishing early warning indicators, dividing into early-warning warning-limit, building the model and its mechanism are limited. The early warning mechanism of the theoretical framework is not yet paid special attention.

Main Content of R&D

Fig 1

 1 Fig 1 Trend of Pork Price (2003-2012) y uan//kg

Focusing on exploration and

  analysis of the pig prosperity fluctuation rules, the optimization of the monitoring pig prosperity and the solution of the model of pig prosperity warning three key issues referred above, the goals of the R & D are designed as follows: (ⅰ) verify pigs risk and its risk factors; (ⅱ) propose the rule of the pig prosperity fluctuations; (ⅲ) construct the pig prosperity monitoring methodology system; (ⅳ) build the pig prosperity warning model; (ⅴ) form a theoretical framework of risk early warning mechanism for the pig prosperity monitoring. To achieve these goals, the main five aspects of R & D were studied by us as follows.Pig Risk and Its FactorsWe carried out the research on pig natural risks and economic risks, i.e., the basic properties, types, the degree of risk influencing, the probability of risk occurrence, risk loss, and conducted the pig prosperity risk policy scenario simulation. The pig risk factors were analyzed by using the approach of risk management. Based on a number of risk factors, the risk factors known as the "candidate risk factors", which may affect the consumption of pig production, were initially selected. Comparing with the pig fluctuation, assimilating to historical data of candidate risk factor, and scenario simulating to the impact of the candidate risk factor, analyzing the sensitivity and relevance of the simulation results, and we finally confirmed the risk factors of the live pig.Pig prosperity Fluctuating RulesAccording to the pig industry development situation, combining the sectors of pig production, circulation and consumption, we built the pig sentiment indicator system using quantitative and qualitative analysis, and integrating other relevant indicators. By comparing and selecting of quantitative and qualitative methods, such as Analytic Hierarchy Process (AHP), theory of Gray Model GM (1, 1), Support Vector Machines (SVM), BP artificial neural network, Delphi Method, the link between the characteristics of pig production and consumption in different periods and backgrounds was formed. In addition, in order to determine the indicators in the basic period; we therefore put forward the scientific "pig prosperity index algorithm". In the future, we are going to analyze the historical data of live pig production to get live pig prosperity index data; and to apply live pig prosperity index algorithm to work out the live pig prosperity index in the past 30 years. Sequentially, we will explore the character, circle, account of increasing and summarize the law of live pig prosperity fluctuation.Pig Prosperity Monitoring Method By applying monitoring methods in actualwarning and emergency, and the risk information on pig prosperity, the research was carried out on pig prosperity monitoring methods involving the aspects of survey range, place, quantity, methods and data transformation.

1 This paper is supported by (1) the 12th 5-year plan National Key Technology R&D Program " Agricultural Production and Market Circulation Matching Management and Research and Demonstration of Information Service Key Technology"(2012BAH20B04); and (2) Basic R&D Research Fund for National Non-profit Research Institutes "Construction of Agricultural Information Monitoring and Early-warning Technology and System"(2012ZL017) 2 KONG Fan-tao, born in 1968, Male, PhD,

Pig Prosperity Warning Model

The safety interval and the risk interval of the pig industry were obtained using Value at Risk (VaR) to measure and assess the risk of the pig industry. Then by comparing the Pig prosperity index with the safety interval and the risk interval of pig risk, we determined the degree of pig prosperity. The warning results showed with the lights. The releasing mechanism was first established in China. The mechanism consists of the main body, subject, publishing content, distribution channels, means and frequency of publication.

Pigs Prosperity Monitoring and Risk Warning Mechanism Theory

We formed the A • D • Hall three-dimensional structure for pig prosperity monitoring and early warning with the methodology of systems engineering. Studying on the pig risks and risk factors, the pig prosperity fluctuation, the pig prosperity monitoring method, and the pig prosperity warning model, we applied the basic conceptions of the system science, risk management and early warning analysis, and finally formed the theoretical framework of the pigs prosperity monitoring and risk early warning mechanism.

The Program Implementation of R&D Designation

To collect and analyze the historical data of live pig industry in the past 30 year, we made the literature analysis and spot investigation. Especially, for the missing key data, in order to get the exact information, we also took the spot investigation. Our works supported the project strongly in data.

The Value method (Value at Risk of VaR) was adopted to calculate the risk in risk management. The method was based on extreme value theory and the quartile theory of VaR calculation. Under a certain confidence level (c) and within the time interval, the means of maximum loss expected in a normal market environment was defined as:

In order to test the VaR model, Kupiec statistical tests were applied to verify its validity. Through mathematical statistics and econometric methods, Analytic Hierarchy Process (AHP), Theory of gray system Model---GM (1, 1), Support vector machine (SVM), BP artificial neural network, Delphi Method and other quantitative and qualitative methods were compared to determine the base period index, and therefore the scientific pig prosperity index algorithm was finally proposed. The prosperity indicators, the warning status were denoted with Liket's five-point scale method. The safety interval and risk interval of the swine industry are corresponded to no warning, and warning in the early warning system. As the warning system included light warning, middle warning, heavy warning and great warning, we adjusted and calibrated the related parameters to optimize early warning model.

Theoretical analysis methods were also used in our study, i.e., the methodology of system engineering was applied to establish A • D • Hall three-dimensional structure for pig prosperity monitoring and early warning. We proposed the comprehensive risk analysis and assessment of system to pigs, pig prosperity fluctuation, monitoring method, early warning techniques and methods, pig risk prevention and control, and the theory framework and risk early warning mechanism. The specific works involved were as follows. Firstly, we confirmed the issues to be researched. Based on China pig production and consumption practices, we carried out literature analysis, more spot investigations, theory analysis, and expert consult. Secondly, we collected and analyzed data and information of live pig in the past 30 years using risk management so as to find live pig risk and its risk factors. Thirdly, we built the pig prosperity index system to propose the pig prosperity index algorithm, and explore the law of live pig prosperity fluctuation. Fourthly, we established pig prosperity monitoring system including the contents of flowing such as the monitoring area, sites, indictors, methods, information transmission and data processing, and we also founded seven pig bases to monitor pig prosperity. In the end, we utilized the monitor data and calculated the live pig prosperity. With live pig risk consults, we constructed the live pig prosperity early-warning system as well as the pig warning releasing mechanisms and risk warning based on the three-dimensional structure of AD Hall. We put forward a theoretical framework for the pig prosperity monitoring. The project adopted the technology roadmap shown in Figure 2.


Nengfu Xie
email: nfxie@caas.net.cn

Research on the Inconsistency Checking in Agricultural Knowledge Base

Keywords: Agricultural ontology, Inconsistency, Agricultural knowledge

The paper will propose a method to check the knowledge inconsistency in the agricultural knowledge Base, which is one the main measures to evaluate the agricultural knowledge base. This paper will pay attention to analyzing main types of knowledge inconsistencies and factors arousing it, and then discuss possible solving strategies which reduce agricultural knowledge inconsistency, so that agricultural knowledge is utilized correctly. In our practical application, it is very effective to find the inconsistency.

Introduction

People have realized that ontology is an important for knowledge reuse, knowledge share and modeling. In philosophy, ontology is a systematic explanation of existence, and is about the essence of description [START_REF] Gill | PLANET: a shareable and reusable ontology for representing plans[END_REF]. In cyberspace, lowercase letter "o" represents ontology, which means an entity, the consequence of analyzing and modeling by ontology. That is, to abstract a group of concepts and relationship between concepts from one field in the objective world [START_REF] Valentina | A knowledge model to support inconsistency management when reasoning with shared knowledge[END_REF]. Nowadays ontology is widely used in information systems, natural language understanding and knowledge systems. In agriculture, knowledge will be made use of effectively if we use ontology to organize agricultural knowledge. Agricultural knowledge is a production factor of high quality, which can improve the labor force and capital production in agriculture and accelerate agricultural informatization. What's more, agricultural knowledge is a kind of special knowledge, which lays the foundation of agricultural information application. Systems in agriculture education, language processing and expert systems all depends on agricultural knowledge.

On the other hand, in the process of organizing agricultural ontology based knowledge, differences will arise on concepts when people cooperate to construct knowledge base. And in the process of agricultural knowledge formalization, individuality error and editing error exist. Because of the large quantity of agricultural knowledge, text knowledge itself has inaccuracy, which leads to knowledge inconsistency. In philosophy, as time goes on, correctness of knowledge may be not right. It is easy to know the consequences of knowledge inconsistency. In military, knowledge inconsistency may cause wrong missiles launch and disability of aircrafts. In agriculture, farmers cannot grasp the right time of fertilization and reliable market information.

The paper will discuss the problems in the process of agricultural knowledge construction, and concludes the corresponding strategies of checking.

Definition of Inconsistency

In general, the knowledge consistency means some judgment accords with both history's judgments and the current facts. On the other hand the inconsistency means the contradiction between history's judgments and the current fact. From the aspects of ontology, the consistency means logic relations of terminology are consistent, while inconsistency means conflicts existing between some parts of ontology. For example, we define grain crops and cash crops as disjoint classes that have not the same instances. If the class wheat belongs to both grain crops and cash crops, the inconsistency will occur.

In the paper, agricultural ontology consistency includes the consistency of the definition of ontology and knowledge based on ontology, which means we can not get the conflict knowledge from knowledge base. Generally, whether knowledge base exists conflict knowledge depends on the following conditions:

1) The consistency of concept defining. That is to say, the formal definition contains the same means with informal one. Take the concept dogs as an example. If the formal definition of dogs goes with that of the concept cat, it brings inconsistency.

2) The consistency of concept extension. In terms of formal or non-formal concept definition, it can bring out conflict knowledge by concept explanation (include reasoning). For example, cats can catch mice, but we cannot say that mice can catch cats.

3) The consistency of axiom. The axiom system will not reason the conflict knowledge.

In the view of knowledge application, knowledge base can guide users to make the right decisions and ensure no confusion conclusion arising. In brief, the consistency is an important criterion to evaluate an ontology-based knowledge base. Knowledge inconsistency will lead to unreliable service, which threatens the knowledge correctness [START_REF] Xiong | Supporting automatic model inconsistency fixing[END_REF]. The paper will propose a method of ontology consistency checking.

Definition 1: Given knowledge base K, knowledge inconsistency problem is a 3 triple KI=(K,Y,Q), which satisfies that  Y={y 1 ,y 2 ,…, y n } is Knowledge operation set.  Q is a given knowledge query. Definition 2: knowledge inconsistency problem KI=(K,Y,Q). If there exist knowledge conflict in K, it satisfies the following conditions:

 ∃ k , k 11 , k 22 , … , k 1j  K , y 11 , y 12 , … , y 1j  Y , k k y j l l   | ) ( 1 1 1
 kQ. the Symbol |= indicates "reason out" and

 represent " can satisfy" .  ∃ k , k 11 , k 22 , … , k 1m  K , y 21 , y 22 , … , y 2j  Y , k k y j l l    | ) ( 1 2 2
 kQ 。 And then we judge out that the knowledge base has inconsistent knowledge. From the above definition, the knowledge base has inconsistency if there are two pieces of contradictory knowledge. It is very import to find a mechanism or method to checking the inconsistency of knowledge base.

Agricultural knowledge Inconsistency Problem Analysis

To explain inconsistency problems, we assume that agricultural knowledge adopts frame-based representation and organized by agricultural ontology. Firstly, some definitions is written as the following: ). We can get the fact that instance-of(i,C

Definition 3: individual-of(i,C) means that i is a unit of category C. instance- of (i,C) means i
2 )instance-of(i,C 1 ) and for each i individual-of(i,C

2 )  individual-of(i, C 1 ) is true for each i。 Definition 9: IsHParent(A,B)means that A is the sub category of category B, or B is the sub category of category A.

Definition 10:IsHasBaseParent(A,B) means that A and B is the sub category of a category, and IsHParent(A,B) is true.

Category Error

In agricultural ontology, the most important and basic semantic relationship is inheritance. Ontology constructed by this relationship can be taken as a description of a hierarchical model. It can also be taken as an information category system. Therefore, category error checking is the most basic requirement.

Loop Error

When classifying a concept, we usually take sub class as a partition of the concept. Though this method satisfies the integrity of concept definition, sub category error arises. We call it ownership contradiction of categories, shown as Fig. 1. We assume A=∑P i represents category B has sets as IsRParen(P i ,B). To any P k 、P j ∈A, if IsHasBaseParent(P k ,P j ) is true, ownership contradiction of categories arises. For example, to IsRParent(grain crops,rear crops) and IsParent(cash crops,rear crops), if IsHasBaseParent(grain crops,cash crops) is true, contradiction arises.

Fig. 1. The Class ownership contradiction

The checking algorithm is described that for each class B, finding A=∑P i with IsRParen(P i ,B). If there exists P k ,P j ∈A, IsHasBaseParent(P k ,P j ) will be true. The method will find out conflict knowledge and mark it so that knowledge developers can delete error relations.

Classifying Category

When classifying a concept, we usually take sub class as a partition of the concept. Though this method satisfies the integrity of concept definition, sub category error arises. We call it ownership contradiction of categories, shown as Fig. 2. We assume A =∑P i represents category B has sets as IsRParen(P i ,B). To any P k 、P j ∈A, if IsHasBaseParent(P k ,P j ) is true, ownership contradiction of categories arises. For example, to IsRParent(grain crops,rear crops) and IsParent(cash crops,rear crops), if IsHasBaseParent(grain crops,cash crops) is true, contradiction arises.

Fig. 2. The Class ownership contradiction

The checking algorithm is described that for each class B, finding A=∑P i with IsRParen(P i ,B). If there exists P k ,P j ∈A, IsHasBaseParent(P k ,P j ) will be true. The method will find out conflict knowledge and mark it so that knowledge developers can delete error relations.

3.2

Class Definition Error

Class definition of ontology should be correct and have clear semantic. Naturally, inconsistency of class definition may cause ambiguity and knowledge inconsistency of instances. Therefore before acquiring knowledge, we must ensure the consistency and integrity of class definition. Particularly, under cooperation of several engineers, different agricultural terminology of the same concept may arise, which causes redundancy of category definition. So we must define clearly to ensure one concept with one terminology in ontology, and defining synonyms is allowed. In another situation, the same terminology may represent several concepts. It is also caused by unclear class definition, and it is reflected in the relation, attribute and facet definition.

Axiom Inconsistency

Axiom is used to limit contacts between categories, attributes and relations, to ensure consistency of frame-based knowledge. Besides, axiom is used in reasoning, to supply the system with intellectualized judges. Axiom Inconsistency means axiom stands reasoning in different situations, while inconsistency is the opposite. Any axiom system should satisfy the consistency, otherwise geometric systems built by it will be conflict and such system is Worthless. Formally, we can take axiom system as a logic system with first-order logic. In that sense, axiom inconsistency means the axiom "yes=no" arises in the logic system. For example, such axiom P and Q are conflict, if Axiom P: X ∈ plantation crops) , GreaterThan (MaturityDate(X), SeedingDate(X))). Axiom Q: X ∈ plantation crops), LessThan (MaturityDate (X), SeedingDate(X))). Formally, we can take the axiom defined by ontology as first-order logic. Then axiom consistency checking can be translated to first-order logic operations.

3.4

Definition&Description Error

Contradiction of Case Ownership

If there exists an instance i and category A and B, which satisfies instance-of(i, A)∧ instance-of(i,B) →IsHparent(A,B), then instance inconsistency arises, shown as Fig. 3. For example, for IsParent(grain crops, rear crops) and IsParent(cash crops, rear crops), if instance-of(Ararat wheat, grain crops) and instance-of(Ararat wheat, cash crops) are true at the same time, then contradiction arises. Because IsHparent(grain crops, cash crops) is false.

Fig. 3. The instance ownership contradiction

Checking Operations: for each case i, if there exists category A ≠B, which satisfies instance-of(i,A) ∧ instance-of(i,B), then check IsHparent(A,B). Assume IsHparent(A,B) is false, and knowledge developers should delete relation error. Besides, if there is IsParent(A,B), delete instance-of(i,B) too.

Instance level Contradiction

We can use a tree to represent ontology category structure. If there is IsRParent(A,B), node B serves as son node of A. Thus all leaf nodes consist the biggest partition of the tree root. We call the leaf set as the ideal category of the tree root, and nodes as the leaf category, shown in Fig. 4. Atypical cases arise when it is not the instance of ideal category, but of non-leaf nodes. For example, divide horticultural crops into three categories as fruit trees, vegetables and flowers. If we define lilies as an instance of the concept crop, then the relation as IsInstance(lily, P i ) in the categories.( P i is a leaf category) Fig. 4. The concept tree.

The checking algorithm is described that this operation is relatively easy. For each case i, find category A which satisfies instance-of(i,A), and judge whether A belongs to the non-leaf category. If it is yes, then we mark the error.

Description Contradiction of Instances

Instance description should be clear. Xiong et al. [START_REF] Xiong | Supporting automatic model inconsistency fixing[END_REF] define a language in which it is possible to specify the inconsistency rule and the possibilities to resolve the inconsistencies. Egyed et al. [START_REF] Egyed | Generating and evaluating choices for fixing inconsistencies in UML design models[END_REF] define such an approach for resolving inconsistencies in UML models. They take into account the syntactical constraints of the modeling language and they only consider the impact of one consistency rule at a time. Xie et al. [START_REF] Xie | Rule-Based Agricultural Knowledge Fusion in Web Information Integration[END_REF] design a fused method to solve the inconsistency. In the fact, But in the process of formalization, sometimes different names represent the same instance. Therefore

we need to describe and analyze synonyms to judge whether they are the same instances. This method reduces redundancy and error caused by one name with different instances. Besides, in the process of realizing cases, we need to check attribute values and relation constraint error. For example, the instance wheat describe that it has main producing countries: China and Russia and the number of its main producing countries is less than 2". We can use axiom system to check such error.

Conclusions

From the point of inconsistency, this paper discusses the possible situations and causes of the agricultural knowledge inconsistency in the process of building agricultural knowledge. It also discusses the corresponding processing strategies to reduce knowledge inconsistency as much as possible. It is very important to check the knowledge inconsistency for knowledge base-based application and make full use of agricultural knowledge. In the future, more studies should be focused on the effective and quick algorithms to check inconsistency of agricultural knowledge bases and the method should be put into the practical application for find new method to solve the inconsistency in the knowledge base.

Acknowledgment

The work is supported by the special fund project for basic science research business Fee, AIIS "Tibet agricultural information personalized service system and demonstration" (No.2012-J-08) and The CAAS scientific and technological fund project "Research on 3G information terminal-based rural multimedia information service" (No.201219).


Jianjun Zhou

, Xiuwang

Rui Zhang

Qingchun Feng

Wei Ma

doctor, Xiu Wang

Automatic Navigation based on navigation map of Agricultural machine 1

Keywords: automatic navigation, path tracking, genetic algorithm; self-adaptive fuzzy control

This article mainly studies automatic navigation control methods for agricultural machinery. The navigation system takes RTK-DGPS and inertial measurement unit as positioning sensors. The preview control mode was researched for curve path tracking. The preview control model is to determine the aiming point and calculate the lateral deviation. Automatic turning was realized by fuzzy control method based on genetic algorithm. This control method can online adjust the parameters of fuzzy control using the genetic algorithm, in order to realize adaptive control. Thus the navigation control precision can be improved by the methods studied. The navigation control methods can meet the demand of agricultural machinery operating.

Introduction

Automatic navigation of farm machinery is an essential technique to realize variable rate applications and collecting field information [START_REF] Han | A dynamic performance evaluation method for DGPS receivers under linear parallel-tracking applications[END_REF]. The potential benefits of automated agricultural tractors include increased productivity, increased application accuracy, and enhanced operation safety [START_REF] Xiwen | Research and development of intelligent flexible chassis for precision farming[END_REF][START_REF] Zhou Jian-Jun | Automatic navigation for agricultural vehicle based on fuzzy control[END_REF].

Automatic guidance of agricultural tractors has been studied over the past several decades. Various guidance technologies, including mechanical guidance, machinevision guidance, radio navigation, and ultrasonic guidance, have been investigated [START_REF] Reid | Agricultural automatic guidance research in North America[END_REF]. In recent years, high-accuracy Global Positioning System (GPS) receivers are widely used as guidance sensors [START_REF] Yukumoto | Robotization of agricultural vehicles (part 2)description of the tilling robot[END_REF]. Using an absolute positioning system, GPS-based guidance technology has the potential to achieve completely autonomous navigation [START_REF] Qing | Automatic guidance system for tractor inter-row operations based on multiple sensors[END_REF][START_REF] Zhang | A Dynamic Path Search Algorithm for Tractor Automatic Navigation[END_REF]. Numerous fuzzy control applications on vehicles have been reported. Todo et al. developed a fuzzy controller that utilized the offset and the orientation errors to control the steering of a mobile robot and resulted in satisfactory trajectory tracking performance [START_REF] Reid | Agricultural automatic guidance research in North America[END_REF][START_REF] Toda | Navigation method for a mobile robot via sonar-based crop row mapping and fuzzy logic control[END_REF].

The basic idea of this article conveyed was researching fuzzy control method for automatic steering and methods for curve path tracking. In order to validate these methods, an agricultural robot for precision farming was developed, which are refitted from a tractor. Navigation control system was developed based on the robot using RTK-DGPS, digital compass and other sensors.

Materials and Methods

Field operation maps for autonomous navigation tractor are often created off-line using a GIS, and then be loaded into the navigation computer before the operation starting. Navigation map is a shape format point map. An autonomous field operation of a tractor was a sequence of instructions that both guide the tractor movement in the field and control synchronously the concurrent tractor operations. Each instruction contains a location in the field (x i , y i ) and desirable control values, (z1 i, z2 i , … , zk i ), at that location. Control values include steering wheel angle, action of machine, and so on.

Preview Point Searching Arithmetic

An important content in the path tracking is searching dynamically the preview point in field navigation map. Fig. 1 shows the work flow of the preview point searching.

Firstly the preview points are searched within the circle of radius equal to 1.8 meters, with the current navigation position as center on the navigation map. If the point's amount of searching result is zero, then the searching extent is extended. The preview points are searched one time again. If the search result is still zero, then the preview point is the one which id is equal to ID1 plus two. If the searching results points are equal to or greater than three, the preview point is the biggest of ID number within the searching range (ID number of point is increased in navigation map).

Computing Cross-tracking Error Sign

In curve tracking the relationship of the current position of vehicle and path is complicated, computing the current vehicle lateral deviation error is difficult. This paper provides a vector multiplication method to determine the lateral error. As shown in Fig. 2, the relationship between point p' and line P 0 P can be denoted by the location vector P 0 P' and vector P 0 P. The relationship between vector P 0 P' and vector P 0 P can be described by these two vector cross multiplication. Vector cross product accords with the right hand regulation. Formula (1) and (2) showed the two vector cross multiplication process, the position relationship between points P 'and line P 0 P can be easily obtained from the result of formula [START_REF] Xiwen | Research and development of intelligent flexible chassis for precision farming[END_REF].

N N<=1 N<=2 N<=3 N>3
R=2 meters

Last point ID ID1

The point which id is id+2

ID1

The

0 0 0 0 0 0 0 0 ' ' 0 0 ' ' y y x x y y x x k y y x x y y x x k j i u           2.3

Dynamic Path Search Algorithm

In Fig. 3, predefined route is {…,p n-2 ,p n-1 ,p n ,p n+1 ,p n+2 ,…}, and the current tractor position is point P c . The predefined circle route points are searched within the circle. The circle's center is Pc. as center, radius is R. P n-1 ,P n , P n+1 are the points in the search circle. P n is the nearest point in predefined path from P c recent points. EF direction is the direction of the tractor body, which can be got through the electronic compass acquisition; NM is the front wheel turning direction that can be obtained by angle sensor. The current position of tractor is P (x 3 , y 3 ). The distance between point p and the AB line is:

2 2 3 3 | | B A C By Ax d    
, d is the cross track error. ∠EP c P n+1 is the heading error.

Thus XTE and heading error can be as input for expected steering angle decision algorithm, according to the fuzzy control output can control the tractor automatic tracking curve path.

Self-adaptive Fuzzy Controller Based on Genetic Algorithm

The steering control of a vehicle is realized by using a step motor. Steering control is based on the error between the desired and the actual wheel angles. In this system turning control is realized by fuzzy control method. The design of the fuzzy steering controller was based on typical vehicle steering responses. Two inputs of fuzzy control are XTE and heading error. The output from the fuzzy controller was a realvalued steering rate command to drive the step motor. System adopts fuzzy control arithmetic to control the turning of the front wheel. The cross track error E and heading error H was the input of fuzzy controller. The output was the expected angle U. The cross track error basic domain is [-24cm, 24cm]. Quantification factors are 0.5. The heading error basic domain was [-15, 15]. Quantification factor of this variable is also 0.5.

) 1 , 0 ( , ) 1 (           H E U (3)
By adjusting the correction factor  , the weighted degree of the lateral deviation error E and heading error can be changed, thus the fuzzy rule could be changed. The correct factor of the fuzzy control was online adjusted by the genetic algorithm, in order to reach the adaptive control. Control algorithms principle was as Fig. 4.

Genetic algorithm is robust, parallel search and group optimization. It is a kind of effective parameters optimization method. Its basic operation mainly includes encoding, decoding, selection, crossover and mutation, the design of genetic optimization algorithm is as follows.

Encoding and Decoding. Coding method using binary code which search ability is strong. Chromosome is length for 8 binary strings, α accounted for eight. The corresponding decoding way is as follows. a=binrep(a) [START_REF] Reid | Agricultural automatic guidance research in North America[END_REF] binrep(a)----integer expressed by a eight bit string of binary Structure of the Fitness Function. The selection of fitness function directly influenced the convergence rate of the GA and where to find the optimal solution. Because of the system designed is small overshoot according to ITAE index department. In the agricultural vehicles to be automatic navigation, lateral deviations are the primary indicators to weight control effect. So the reciprocal of ITAE index of the lateral error are taken as the fitness function.

Genetic Operator. Genetic operator mainly includes two crossover and mutation genetic operator. Crossover operator adopts single point crossover modes. Mutation operator is logical opposition to every bit of chromosomes according to mutation probability. Float chart of genetic algorithms are as follows. The type of the digital compass is LP3300. There are three orthogonal magnetic field sensors and two axle inclination angle sensors in this digital compass. LP3300 outputs data through the RS-232 format and the configuration of RS232 port is "9600, n, 8, 1". Each frame outputs 20 bytes hexadecimal number.

Vehicle Navigation Control Methods System

In this research, RTK-GPS, digital compass and angle sensor are used for automatic navigation. GPS can provide the absolute position of the vehicle. At first, the path of tractor is preformed through GIS. Tractor can compare the appointed path with the current position to control the steering. Thus automatic navigation can be realized. The type of RTK-GPS is Trimble 4700. The GPS data is received from serial port. The serial port can be easily set using MSComm active X control. After setting the parameter of serial port and opening the serial port, the GPS data can be received. According to the data format of NMEA 0183, useful GPS data can be extracted. The system uses GGA format.

Experiment

The experiment location is in the Xiaotangshan precision agricultural demonstration station, and the experiment was preceded in March 2012.

Firstly artificial driving tractors walked on a curve path. The tractor's path could be recorded through the data acquisition program, and was saved as a text file. File data included GPS data of tractor x, y, heading and id numbers. This text files could be input by Arcview software and generated a GIS format layer shape. This layer was the predefined curve path for tractor tracking.

It can be analyzed that is XTE is less than 12cm, the biggest error is 0.12cm, and the average error is 5cm .The lateral error is shown in Fig. 5.

Conclusion

The main goal of this study was to develop a dynamic path search algorithm and fuzzy control method based on genetic algorithm capable of automatic navigation along the curved path on a field. The dynamic path search algorithm of this article is to determine the aiming point and calculate the lateral deviation. Self-adaptive fuzzy controller based on genetic algorithm could .The cross track error was less than 12cm at a travelling speed of 1m/s. The developed dynamic path search algorithm and Selfadaptive fuzzy controller based on genetic algorithm could achieve the design goal of guiding an autonomous agricultural tractor to track curve path.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Work flow of the preview point searching

Fig. 3 .

 3 Fig. 3. Curve path tracking mapThe start point coordinates of P n P n+1 line is (x 1 , y 1 ). The end point coordinates of P n P n+1 line is (x 2 , y 2 ). The equation of AB line is:

Fig. 4 .

 4 Fig. 4. Genetic algorithms flow chart

Fig. 5 .

 5 Fig. 5. The lateral deviation of curve path tracking