Yeping Zhu

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

Shengping Liu

Design of Agent-based Agricultural Product Quality Control System

Keywords: Agent, Agricultural product, Quality control

Aiming at the problems existed in agricultural product quality control, management and traceability such as the considerable influence of human factors, weak ability of handling emergencies and lacking of support of intelligent key technologies, this study explores the application of agent theory, method and technique to solve the problems of process control, traceability, intelligent information watching and information technology application to emergency reaction conditioned on the general characteristics from production to circulation. In this study agent application and development method will be put forward. By means of investigating its communication and cooperative mechanism, we design the agent-based universal system framework for quality control of agricultural product. According to production and processing characteristics of different agricultural products, corresponding controlling unit and condition are increased to adapt to the special aim.

Introduction

As the important problem which concerns the masses most, food safety affects people's health and life, involves the economic healthy development and social stabilization. How to control and manage food safety effectively has been becoming a research focus in recent years. The watching and effective management to product flow of farm produce can not only settle the problems of quality control and information delivery existed in every link such as production, processing, transportation, storage and sales, but also protect native agricultural product market and food safety. Europe began quality monitor and control in stockbreeding long ago. After the study and evolvement year by year, now relative perfect system has been come into being [START_REF] Sheng-Dou | The development and enlightenment of the quality traceability system of livestock in France and Netherlands[END_REF][START_REF] Hong-Hua , Tian Zhi-Hong | Comparative Study on Traceability system of agricultural products at home and abroad[END_REF][START_REF] Regattieri | Traceability of food products: General framework and experimental evidence[END_REF]. Since 21st century china gradually strengthened the study on food safety control method and system. A series of relative standards and guides have been established. Zheng Fengtian and Zhang Yongjian et al proposed that china must set up food safety system [START_REF] Feng-Tian | The Adjustment from Food Security System to Food Safety System--The Strategic Transfer Faced by Chinese Food Production Systems[END_REF][START_REF] Yong-Jian | Study on Building and Promoting the System of China's Food Security[END_REF]. Ye Yongmao considered the compellent food safety standard system should be constituted by means of reforming food safety management and operational mechanism and enhancing food safety legislation [START_REF] Yong-Mao | Thinking about the Establishment of China Food Safety Quality Control System[END_REF].

The author ever studied bee production quality traceability and developed software system [7, 8 9]. In the process of agricultural product quality control and traceability, man-made factors play an important role. Being lack of intelligent key technologies, although some technologies such as sensor, radio frequency identification and image recognition had been adopted in information collecting [START_REF] Delicious | Analysis of RFID technology in meat and food safety in the application of retrospective[END_REF][START_REF] Xiang-Yang | The Situation and Development of Food Traceability[END_REF][START_REF] Jian-Ping | China's agricultural products quality and safety of the status quo and recommendations[END_REF][START_REF] Liu Jun-Hua | Application of Automatic Identification Technology in Quality Traceability[END_REF][START_REF] Zheng-Zheng | Application of RFID in Securing Food Supply Chain[END_REF][START_REF] Da-Yu | Implementation of Tracing and Tracking Safety Mechanism of Agricultural Product Packaging Based on RFID[END_REF], database management, query and analysis still are the main method in agricultural product quality control. So there are no enough technologies involved in quality control to reply emergency. The difficulties existed in information collection, tracking and control for the small agricultural product, which need to be blended such as foodstuff and bee production, demand the corresponding information technologies as support to settle the key issues and reduce the effect of man-made factors. In this paper a method to control agricultural product quality with the characteristics of perceptivity, intelligence and cooperation will be studied. It provides technology support and universal resolution for control and traceability of such agricultural products as grain, bee product, vegetable and fruit etc., which are small, distinctly characteristic of dispersive production and need to be combined processed.

Status and Analysis of the Study

The concept of agent was promoted in the end of 1970s, and the study of its methods and implementations has been developed in an active period. As one research domain of the distributed artificial intelligence, agent theory and technology have aroused a great deal of attention because multi-agent system (MAS) plays an important role in modern computer science and its application. In a multi-agent system any agent needs to communicate and cooperate with the other agents. Its behavior and decision vary with the other agents and conversation rules. The universal language-behavior theory formal language is used to make the agents in communication understand their respective inner state and purpose [START_REF] Giordano | An intelligent agent for the groundwater pollution risk evaluation[END_REF][START_REF] Lima | Distributed production planning and control agent-based system[END_REF][START_REF] Patt | Agent Based Modeling and Adaptation to Climate Change[END_REF][START_REF] Roche | The Potential of Multi-Agent Systems in Virtual Manufacturing Enterprises[END_REF][START_REF] Guan-Wei | Research of Multi-Agent System[END_REF][START_REF] Ying | Studies of Agent-based Project Management Information System[END_REF][START_REF] Ni-Hong | Application Research on Mobile Agent in Expert System of Forest Diseases and Pests[END_REF][START_REF] Lin-Du | Execution and Evaluation Strategies of Multi-agent Diagnostic Task[END_REF][START_REF] Yong-Chuan | The Summary of Agent Technology Researching[END_REF][START_REF] Li-Na | Agent Technology Application on Water Quality Decision Support System[END_REF].

The application of MAS technology in agriculture is less than that in other fields. Liu Huimin at Capital Normal University studied multiple collaborative approaches based on the analysis of MAS technologies and theories, promoted an implementation plan for the MAS coordination mechanism based on Web Services technology to provide effective method and implementation technology [START_REF] Liu | Study about MAS-based Agricultural Expert System Platform[END_REF]. Through the analysis on agricultural expert system and its characteristics, Yang Yan put forward the design project of web-based agricultural expert system. Xue Ling at Peking University and Chinese Academy of Agricultural Sciences applied agent to the study on modern agricultural economic management and decision-making system, provided the structure and components of Agriculture Economy Intelligent Decision Support System (AEIDSS), analyzed the implementation method of classifier-based agent, and discussed the work principle of dynamic analysis, evaluation, forecasting and optimization under multi-agent communication and cooperative environment. The author also designed the Agent-based Cooperative Analysis and Decision Support System for Regional Agricultural Economic Information [START_REF] Zhu | Agent-based cooperative analysis and decision support system for regional agricultural economic information[END_REF][START_REF] Xue | Rearch on agriculture economy intelligent decision support system based on multi-agent system[END_REF][START_REF] Zhu | Agent-based cooperative analysis and assistant decision-making method for regional agricultural economic information[END_REF][START_REF] Liu | Establishment of wheat simulation model based on agent technology[END_REF].

With the issue of "Food Safety Law" in 2009, china governments and departments all levels increased the construction of agricultural standard system and the control power of agricultural product market, and a multitude of production bases for high quality agricultural products have come into service [START_REF] Xiao-Feng | Information traceability and food safety management of agricultural products[END_REF][START_REF] Hong-Jiao | Design of Traceability Architecture of Food Supply Chain[END_REF][START_REF] Wei-Bin | Mechanism analysis on food safety based on management of food supply chain[END_REF][START_REF] Yang | Research on China's food safety tracing system[END_REF][START_REF] Li-Fang | Design and implementation of a novel swine tag and traceability system for swine and meat products[END_REF][START_REF]National Economic and Social Development Statistical Communique[END_REF][START_REF] Di | Food quality and safety traceability system research progress and application prospect in the tea industry[END_REF]. China, as a large country for production area and yield, not only is a production country for agricultural products, but also is a consumption country. So how to make the best of existing agricultural information network and comparative advanced technologies to provide information service of agricultural products quality, safety, standard and trademark etc. in order to boost the information construction and control method study of agricultural products quality safety is important and impendent.

Building agricultural products traceability system is an inevitable trend for world agriculture, and traceability system has been becoming an important development direction.

No study directly concerns the agent application on agricultural products quality safety and control except only a few reports about key technology and quality inspection method. Dmytro Tykhonov et al described a kind of multi-agent imitation model for trust tracking game. Trust tracking game simulated the game player, was a research measure to collect the human behavior data in the food supply chain with the characteristics of asymmetry food quality and safety information [START_REF] Tykhonov | Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks[END_REF]. Dr Eleni Mangina and Ioannis Giavasis provided a multi-agent system to supervise gellan gum production, which included online data collection, forecasting the future benefit by capturing historical data automaticly [START_REF] Dr | The theoretical framework of agent based monitoring for use in the production of gellan gum in a microbial fermentation system[END_REF]. Moises Resende-Filho mentioned a sort of commission surrogacy model to encourage food safety tracking system. His study indicated the more reliable tracking system make the dealer attach more importance to food safety; the inapposite tracking system couldn't inspirit the dealer to use the safe material in food industry [START_REF] Resende-Filho | A Principal-Agent Model for Investigating Traceability Systems Incentives on Food Safety[END_REF]. Zhu Li and Wang Haiyan used the theory and principle of HACCP to discuss how to build food safety quality control system in food supply chain of Chain Supermarket [START_REF] Li | Research on Quality Control System of Food Supply Based on Chain Supermarket[END_REF]. Deng Ning et al tried to apply the core concept of agent on supply train risk control system, and proposed the framework mode to effective manage supply chain risk [START_REF] Ning | Agent-based Research on Supply Chain Risk Management[END_REF];From the angle of quality guarantee Xiao Yuan and Liang Gongqian analyzed the conflicts among enterprises in supply chain, put forward the quality supervision mechanism. Multi-agent technology was applied to modeling for quality supervision system, and the system structure was provided [START_REF] Yuan | Quality supervise mechanism of supply chain model base on Multi-Agent system[END_REF]. Li Feng studied how to collect economically the real-time information of transporting goods and send to the back-end server (logistics information system). Radio Frequency Identification (RFID) was used by mobile front end subsystem to retrieve information of goods in automatic fashion, and the corresponding agents fulfilled the data processing and delivery. Back-end server also was built on mobile agent. This can not only ensure the customization of information collection, but also increase the system opening [START_REF] Feng | Goods Tracing System Based on RFID and Agent Technology[END_REF].

The increased demand on innovating food safety and quality control method offers a chance for agent technology with characteristics of sociality, autonomy, intelligence and mobility. MAS technology provides a method and measure for collaboration integration to guarantee quality, offers a feasible scheme for running quality supervision system successfully. Appling agent technology to study agricultural product quality safety and control method has far-reaching meaning on food safety and citizen health, at the same time will provide a new research method.

Design of Agent-based Agricultural Product Quality Control System

First of all, let's analyze the traditional flow of production, processing, sales, quality tracking and traceability for agricultural product (Fig. 1). In quality traceability database is the kernel technology.

Figure 2 shows agent-based agricultural product quality control flow which core is the agent cooperation and intelligent control.

The information delivery, task assignment and cooperation in whole system are on the control of information management agent and task management agent. Agent-based agricultural product quality traceability ensures the quality control from the source of supply chain. Production information agent,purchase information agent , processing information agent , sales information agent , circulation information agent guarantee the communication with each node on supply chain and the responsiveness and veracity of information collected. System will feed back the corresponding behavior to task management agent according to traceability demand. Task management agent communicates with each agent concerned and delivers the results to consumer. Agent-based agricultural product quality traceability and control is bestowed with unique advantages. It can realize the real-time information collection and intelligent processing, make purchase, dealer and enterprise obtain the product and industry information so as to adjust their management, offer related traceability information to the consumer.

System Architecture

The multi-agent system to control agricultural product quality through the whole course includes several agents such as information management agent, task management agent, tracing and traceability agent, market analysis and forecast agent, logistics management agent, instant event monitoring agent, emergency treatment agent and data mining agent and so on.

System architecture is based on multi-agent management platform. Agent-oriented thinking mode and cooperative evolvement theory replace the traditional structural design and object oriented design methods. System is composed of five parts i. e. user, communication and task sales, task resolving, information management and basic information (Fig. 3). Each part includes many agents which do their own work.

Function Design of System

User management agent. Interface agent accepts the task requests from users and delivers to task management agent. The results from multi-agent system are sent back to interface agent through task management agent. User interface agent creates different interface including drawing, table, text and figure depending on the different tasks.

Information management agent. Information management agent is responsible for collecting and managing the information from production to sales for example information modification, storage and delivery.

Task management agent. After receiving the requests from users, task management agent assigns distinct tasks to different agents for instance information analysis, production information collection task , tracing and traceability, logistics management, market analysis, instant event monitoring, emergency treatment. These agents fulfill the information collection, analysis and time arrangement in agricultural production. For example, when emergency takes place, task management agent will call emergency treatment agent which gives reaction and information prompt and makes assistant decision on emergency.

Task resolving agent. Task resolving agent consists of many agents. When user or other agent sends out task or cooperation requirement, task resolving agent searches and call the relevant agents, then submits task request. If the agent receiving task is idle, it will accept the task and begin to address the task. The results will be returned to task resolving agent by communication and task management agents.

These collaboration agents include: Information analysis agent. Which makes out statistical analysis and creates various graphs and report forms.

Agricultural product tracing agent. It is responsible for the information collection, filtration, treatment, storage, earmark and bar code creation of the whole supply chain from production to processing to table.

Agricultural product traceability agent. Which searches for each tache of the whole supply chain aiming at the different demands of consumer, enterprise and government, for instance whether each tache accords with respective standards or not.

Market analysis and forecast agent. It analyses market information and comes into being market forecast with various methods. If there is a great discrepancy between the two forecast results, one result or several results will be provided to predict market dynamic by means of consultation, competition and ratiocination.

Logistics management agent. It administers the logistics information and dispatch.

Instant event monitoring agent. On the one hand, it deals with the information from interface; on the other hand, it supervises the abnormity with each sector on supply chain, analyzes whether an emergency occurs or not. If system thinks an abnormal event or emergency has taken place, the early-warning will be sent out and communication starts.

Emergency treatment agen., It classifies the emergency for example quality safety, quantity safety, abnormal fluctuation of market and so on. Then the corresponding agent will be called to analyze and search the response plan. If there is not response plan, system gives an alarm to ask for human intervention. Otherwise the response plan will be called to deal with the emergency. At the same time, system will give the assistant decision.

Data mining agent. It makes data mining and learning on the basis of collecting a large of information. It promotes the intelligence of agent, needs human intervention and certification.

Communication Language and Standard Between Agents

In order to guarantee the veracious cooperation between agents we must standardize their communication manner. Communication of multi-agent has been built on the Agent Communication Language (ACL), and follows ACL standard of the Foundation for Intelligent Physical Agents.

In cooperation process matching conditions include communication action, message parameter and parameter expression and so on. Every agent has special matching format and combination. Only the message instruction suiting the format completely can be recognized by target agent and implemented.

Cooperation Among Multi-agent System

Cooperation embodies the sociality of agent, and is a main predominance of multiagent system. In agent-based agricultural product quality control system, we take certain unqualified product as example. After receiving task requirement, interface agent calls communication agent to deliver the task to task management agent. Task resolving agent analyses the massage from task management agent, then sends out news to data management agent in order. Data management agent looks up the data concerned and sends out to emergency treatment agent. Emergency treatment agent runs the relative models to create a suit of assistant decision scheme, and then calls the other agents to consult. Finally interface agent will receive the plan and data used, which maybe is an assistant decision scheme or a warn-earning message. Each agent can be activated at any moment. Many agents can realize consultation and competition.

System Development and application

We chose Java language to develop system on Windows operation platform in order to guarantee the universality and transferability of multi-agent system. Agents with various function were constructed by means of exploiting principal part and interating softwares. For the function modules such as collection, monitoring and analysis of bee product information, we made best use of former programme to encapsulate, transfer and build the corresponding function agent. The reuse and share of the research materials on our hand can save workload effectively. The functions such as decision-making and emergency response need the cooperation, consultation, communication and competition among agents to reach the practical results. Communication of multi-agent had been built on the TCP/IP protocol. The most popular agent communication language--KQML (Knowledge Query Manipulation Language) had been used to exchange messages among agents so the cooperation and collaboration decision-making was realized.

Conclusion

This study on agent-based agricultural product quality control method will provide elementary way and information technology tool for building quality control, supervision and traceability system as well as information platform, will reduce the amount of development work greatly, will increase the ability to answer for the quality safety problems, can offer a tool of information supervision, control and coopration for whole supply chain, will help to realize the digitilization and intelligent management for agricultural products.

Fig. 1 .

 1 Fig. 1. Traditional flow from production to sales of agricultural product and quality traceability.

Fig. 2 .

 2 Fig. 2. Agent-based agricultural product quality control flow.

Fig. 3 .

 3 Fig. 3. Architecture of agent-based agricultural product quality control system.

Acknowledgments

. This study was supported by the National Natural Science Foundation of China (Grant No. 60972154) and the National Science & Technology Pillar Program (Grant No. 2009BADA9B02).


Yan Xue

Yeping Zhu

Shijuan Li

Analysis of Income Difference among Rural Residents in China

Keywords: Income Difference, Rural Residents, Analysis

This paper studies and analyzes the income difference among Chinese rural residents from 1997 to 2008 through absolute difference indices and relative difference indices. It comes to the conclusion that the absolute income difference among rural residents in China has been increasing year by year, while the relative difference remains around the average level and tends to increase in fluctuations in recent years. The paper also discusses the results and proposes corresponding countermeasures.

Introduction

Since the reform and opening up, China's economy has maintained a momentum of rapid development. But the income of residents, especially those in rural areas, has not increased along with the economy, causing a big gap between productivity and consumption level, which inevitably hampers the sustainable economic development. Therefore, to keep residents' income growth in line with economic development has both social and economic significance. As China is a large agricultural country, rural residents' income growth is especially important, which is also the starting point of this study.

Measurement of Income Difference

The selection of method and index system for measuring residents' income difference is directly related to the accuracy and rationality of the results. Currently, there are two kinds of basic indices for studying income difference: one is absolute difference measurement, e.g. standard deviation, weighted standard deviation and average deviation; the other is relative difference measurement, e.g. extreme value deviation, variation coefficient, Gini coefficient, Lorenz curve and Theil index. Generally these indices can reflect the overall difference and changes of residents' income. The paper analyzes the income difference among Chinese rural residents in recent years through standard deviation, weighted standard deviation, average deviation, Gini coefficient and Theil index respectively.

Standard Deviation (S)

Standard deviation reflects the deviation between the regional index and the corresponding arithmetic mean. The greater standard deviation, the greater absolute difference in residents' per capita income across regions. The formula is:

2 ( ) n y y j j S N    (1)
Where S is standard deviation; y j is per capita income of rural residents in j region; y is average per capita income of rural residents in different regions; and n is the number of regions. Standard deviation is an intuitive, simple and clear index that is relatively easy to calculate. But as an arithmetic mean based on regional index deviation, it can not fully reflect the scale difference across different regions.

Weighted Standard Deviation (Sw)

Weighted standard deviation is also an easy and effective measurement tool for analyzing regional income difference.

Where y j is the income of rural residents in j region; y is the average per capita income of rural resident in all regions; P j is the population of j region; P is the population of all regions; and n is the number of regions. The greater Sw value, the greater absolute difference. Compared with standard deviation, weighted standard deviation is obviously much more resistant to the disturbance of region-division method and more stable when it comes to multi-angle analysis of the regional difference.

Deviation (D)

Average deviation is based on the relationship between income distribution and equivalence distribution. It is equal to the expected deviation between the overall income level and the average income level. The greater average deviation, the greater difference between income distribution and equivalence distribution, and vise versa. The formula is:

n y y j j D n    (3)
Where D is average deviation; y j is per capita income of rural residents in j region; y is average per capita income of rural residents in different regions; and n is the number of regions.

Gini Coefficient (G)

Gini coefficient is commonly used to measure inequality of income, consumption or any other things. It calculates the inequality index based on the Lorenz Curve. A practical way is to use a formula deducted by triangle method, i.e. rank income in increasing order and divide the total population into n income groups (no need to divide into equal parts by proportion). Assume the population of the ith group account for Pi of total population, and the income account for Ii (i=1, 2, …, n) of total income, then the cumulative proportion of population from 1st to ith group Mi=P1+… +Pi, and the cumulative proportion of income Qi=I1+…+Ii. The formula of Gini Coefficient is:

1 1 1 ( ) n i i i i i i G M Q M Q       (4)
If the Gini coefficient is smaller than 0.2, the society is quite equal in income distribution. Values between 0.2 and 0.3 indicate a high equality; between 0.3 and 0.4, moderate inequality; between 0.4 and 0.5, a high inequality; and greater than 0.6, an absolute inequality.

Theil Index (T)

An index to measure income difference between individuals or regions. The smaller value, the less disequilibrium. A major advantage to measure inequality by the Theil index is to measure the contributions of intra-group and inter-group difference to the total difference. But note that the calculation is complicated, and the income difference represented by Theil index is largely impacted by the sample size. These shall be taken into consideration in calculating income difference with the Theil index. The total level of income difference of regions represented by the Theil index is the weighted total of the logarithm of income share against population share of each region, the weight being the income share. The formula is:

log 1 n Y i T Y i P i i    (5)
Where T is the Theil index, n is the number of regions, Yi is the income share of the ith region, and Pi is the population share of the ith region.

Empirical Analysis

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Data Source

The data in this paper are sourced from China Statistical Yearbook 1997-2008, and basic data including population and rural residents' per capita net income are selected according to the measurement indices. Naturally, from a statistical view, the calculation of standard deviation and other indices are impacted by changes of mean value, i.e. the increase of Chinese rural residents' per capita net income can be summarized in part as the improvement of overall income per capita. Figure 2 shows that the increase of standard deviation, weighted standard deviation and average deviation of Chinese rural residents' annual per capita net income is consistent with the rising trend of overall net income per capita. It confirms that the rise of net income per capita contributes to the increase of absolute indices of rural residents' annual net income per capita. Accordingly, there must be certain error if standard deviation and other indices are used to study the income difference. Therefore, to eliminate the influence of this factor, relative difference indices shall be combined with absolute differences for further analysis.

Analysis of Absolute Difference

3.3

Analysis of Relative Difference From Table 1, Figure 3 and4, we can see that from 1997 to 2004, the relative difference of Chinese rural residents' per capita net income evolved as follows:

First, Gini coefficient changed insignificantly from 0.401 in 1997 to 0.4097 in 2008, while obvious fluctuation appeared from 1998 to 2002 as decrease after increase. During the 12 years from 1997 to 2008, Gini coefficient was always around 0.41, which shows that the income gap is relatively reasonable or a little large, and the gap tended to widen especially from 2006 to 2008.

Second, the Theil index slightly slipped from 0.3477 in 1997 to 0.3339 in 2008. It is not difficult to find out that the Theil index also fluctuates during the 12 years. In general, however, the disequilibrium of rural residents' per capita net income appears to be at average level or slightly decreasing.

Discussion and Proposal

According to the above analysis, the absolute difference indices of Chinese rural residents' income over the 12 years from 1997 to 2008 are enlarging year by year, with obvious phase characteristics; the relative difference indices appear to be at average level in general, but the Gini coefficient tends to increase in fluctuations in recent years. Therefore, the rural residents' income gap is widening in China. Appropriate income gap can stimulate competition and break the absolute equalitarianism to promote economic development. However, as the gap is significantly widening, if we don't take corresponding measures, the trend will continue for a long term to cause excessive disparity in income, which will easily influence the social stability and hamper the economic development. Therefore, we should fully understand how rural residents' income gap may influence social economy to correctly handle the relations between efficiency and impartiality. Instead of placing our hope on the natural evolution of economy, we shall consciously and actively adjust and improve development policies for different regions to control the current rural residents' income gap in a certain range, and allow the existing gap to promote instead of hampering the economic development. To this end, we put forward the following proposals: First, establish corresponding policies and measures to support agricultural development, increase funds to support agriculture, invest more in basic education, support rural infrastructure construction and social undertaking development, identify scientific rural industrial development strategy, and promote industrialization and urbanization in rural areas.

Second, increase rural residents' income, while narrowing their education gap and improving their quality. For low-income areas in particular, we shall encourage intellectual work and investment through macro control while actively improving farmers' income level so as to increase the income of professional technicians and managers, prevent loss of talents and resources, and ensure the development potential of low-income areas. Meanwhile, we shall offer preferential policies to attract the transfer of labor, technology and fund to the backward areas.

Third, regulate distribution principle, crack down illegal earnings and adjust tax system. We shall adjust and improve income distribution system, protect legitimate earnings and adopt differentiated support policies for different areas to reduce farmers' burden.

Fourth, another way to increase rural residents' income is to ensure equal opportunity to earn income. The government should ensure social members have a basic and equal start when they enter the society, i.e. each individual in the society should have equal fundamental rights including the equal right to existence, employment, education and relocation, etc.

Fig. 2 .

 2 Fig. 2. Comparison of standard deviation, weighted standard deviation,average deviation with per capita net income of rural residents.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Changes in Gini coefficient of Chinese rural residents' per capita net income from 1997 to 2008.

Table 1 .

 1 Standard deviation, weighted standard deviation and average deviation of Chinese rural residents'per capita net income from 1997 to 2008.

	Years	Example Standard Deviation (S)	Weighted Standard Deviation (Sw)	Average Deviation (D)

Table 2 .

 2 Gini coefficient and Theil index of Chinese rural residents' per capita net income from 1997 to 2008.

	Years	Gini coefficient (G)	Theil index (T)
	1997	0.4010	0.3477
	1998	0.4026	0.3511
	1999	0.4061	0.3513
	2000	0.4109	0.3441
	2001	0.4072	0.3564
	2002	0.3995	0.3629
	2003	0.3924	0.3668
	2004	0.3966	0.3538
	2005	0.3954	0.3564
	2006	0.4005	0.3582
	2007	0.4055	0.3449
	2008	0.4097	0.3339
		Gini coefficient (G)
	0.415
	0.41
	0.405
	0.4
	0.395
	0.39
	0.385
	0.38
	1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008


Bei He

Gang Liu

Ying Ji

Yongsheng Si

Rui Gao

Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision

Keywords: Navigation path, Machine vision, Orchard environment, Image segmentation, Least square-fitting

An algorithm of generating navigation path in orchard for harvesting robot based on machine vision was presented. According to the features of orchard images, a horizontal projection method was adopted to dynamically recognize the main trunks area. Border crossing points between the tree and the earth were detected by scanning the trunks areas, and these points were divided into two clusters on both sides. Resorting to least-square fitting, two border lines were extracted. The central clusters were gained by the two lines and this straight line was regarded as the navigation path.Matlab simulation result shows that the algorithm could effectively extract navigation path in complex orchard environment, and correct recognition rate was 91.7%. The method is proved to be stable and reliable, and with the deviation rate of simulation navigation angle compared with the artificial recognition angle is around 2%.

Introduction

As a type of agricultural robot, fruit-picking robot has great potential application prospect. Picking robot technology mainly includes three aspects: recognition, picking and movement [START_REF] Kitamura | Recognition and Cutting System of Sweet Pepper for Picking Robot in Greenhouse Horticulture[END_REF] . Recognition contains recognition of ripe fruits, and acquiring the location of fruits; picking mainly includes the design of the mechanical arms and motion control; movement mainly refers to robot navigation. At present, the recognition has been studied and researched by many research institutions and has become a relatively mature method to many varieties of fruits and vegetables like apples, oranges, cucumbers etc [START_REF] Tarrio | A Harvesting Robot for small Fruit in Bunches Based on 3-D Stereoscopic Vision[END_REF][START_REF] Kondo | A Machine Vision for Tomato Cluster Harvesting Robot[END_REF][START_REF] Fangming | Applying Joint Transform Correlator in Tomato Recognition[END_REF][START_REF] Hannan | A Real-time Machine Vision Algorithm for Robotic Citrus Harvesting[END_REF][START_REF] Bulanon | A Segmentation Algorithm for the Automatic Recognition of Fuji Apples at Harvest[END_REF] . However, the research about robot navigation based on open environment of orchard is rare on report [START_REF] Wilson | Guidance of Agricultural vehicles-a historical perspective: Computers and Electronics in Agriculture[END_REF] .

As the development of automation, generally used navigation sensors include global positioning system (GPS), vision sensor, ultrasonic wave sensor, laser scanner and geomagnetic direction sensor at present [START_REF] Kondo | Agri-Robot(I)-Fundamentals and Theory[END_REF][START_REF] Keicher | Automatic guidance for agricultural vehicles in Europe[END_REF] . Current research mainly focuses on two promising methods, machine vision and GPS navigation. Most of these studies of automatic guidance systems dealt with spatial positioning-sensing systems and steering control systems for following a predetermined path [START_REF] Noguchi | Development of an agricultural mobile robot using a geomagnetic direction sensor and image sensors[END_REF] . And there are only a few researches on the field of the orchard navigation robots. Among them, applying machine vision to orchard navigation has lots of advantages, and can effectively solve the problems in autonomous navigation of agricultural robot as explained below. First of all, it does not require a specific navigation aid. Secondly, machine vision can adapt complex environment, including complex terrain, unknown and variable environment parameter, etc. Lastly, more flexible visual field, integrate information and high reliability and accuracy will be used. The robot can move autonomously more effectively with vision technique applied to the navigation of harvesting robot.

The research of the paper is mainly about traveling device, vision system, Arm & gripper of the apple picking robot. Machine vision system is to recognize and locate fruits and the navigation system is to provide the moving route shown as the Figure 1. This paper presents a method which adopts machine vision to acquire orchard images which are studied to obtain the navigation route to achieve robot visual navigation. Materials and methods

Characteristics of the orchard environment image

Orchard navigation, like farmland Navigation, can also use the ridge boundary line detection that makes the aerial view of the whole orchard as target to obtain the navigation route through its visual system so as to complete autonomous navigation operation [START_REF] Jiayi | Algorithm of Path Navigation Line for Robot in Forestry Environment Based on Machine Vision[END_REF] . Actually, the orchard environment is complex with its non-structural characteristics and diverse background. Besides, the orchard is affected by natural sunshine, temperature and other natural aspects. Different size of fruit trees and varied growth patterns make the orchard more uncertain, which raises the real-time requirement of orchard navigation. The plants of early crops in the farmland such as wheat, soybeans, etc. are relatively short and are cultivated neatly by row. Each row is parallel to another [START_REF] Gang | Research Advance in Machine Vision Guidance of Agricultural Vehicles[END_REF] . Meanwhile, the crops are usually green, the crop rows are consecutive and in line shape or small curvature and the navigation characteristics detected do not mutate within a short time [START_REF] Qian | Vision Navigation Based on Agricultural Non-structural Characteristic[END_REF] .However, fruit plants have different height, complex levels and random spatial arrangements, the vision system can hardly detect the obvious and consecutive navigation characteristics and cannot use the algorithm of current farmland visual navigation system directly.

The plants of standard fruit trees are really tall with a trunk height of 70-80cm as usual, which makes it more obvious to make a distinction between trunk and background in visual. Based on these characteristics of standard fruit trees, the intersection points of trunks and the ground can be found after highlighting the main trunks. And these points can be used to generate the navigation path.

Image Acquisition and Laboratory Equipment

The Images used in the experiments are collected from the apple orchard in Nanlang village, Qinshui County, Shanxi province and taolin village, Changping District Beijing. The resolution of the collected images is 640 × 480. The image processing computer is configured to be 2.2GHz frequency, 1.25G memory. The simulation platform is Matlab R2009a

Identification of the Main Area

At present, most apple trees are planted in dense manner, but different standard trees have different growth patterns. The apple trees in Nanlang village, Shanxi province are the mixture of both Gala and Fushi, which have similar characteristic with Qiaohua tree whose trunk is about 70-80cm high. Based on this characteristic, the main area of apple tree can be made more visible through the color characteristics of image segmentation. Through the analysis of profile control line graph shown as follows, a suitable partition factor can be found to study the color characteristics of trunks and the background area. As shown in the Figure 2.

Fig.2 Result of line profile map

As to the study of line L, R and B pixels are the components of their gray value respectively. The yellow curve represents R-B value. It is easy to find that there is little difference between Red R and Blue B of the trunk area, while R and B of the soil differ a lot and green component of the leaves is a little bit more obvious. R-B can separate the trunk area and the orchard background effectively. It can be seen from the yellow curve that the R-B components of the trunk are in a peak region, while the R-B components of the background area are in the much falter region. The little noise produced in the process of orchard pruning trimming where the branches fell on the ground will be eliminated in later algorithm.

Having the original image been transformed into gray level by using the R-B color factors, the optimal segmentation threshold value can be obtained by two-dimensional OTSU algorithm [START_REF] Xiaojun | Sun Jingao: Image segmentation based on 2D maximum betweencluster variance[END_REF] . And then get the gray image diarized, so as to acquire the requested information in the trunk area. The algorithm not only uses the intensity distribution information of the points but also consider the relevant pixel space information among the points, which makes it better than one-dimensional OTSU segmentation algorithm.

Binary image of the orchard

There will be a small amount of dry branches, weeds, etc., on the ground, which can be regarded as noise. The small branches are also easy to produce noise. Before accessing to the trunk region, morphological image processing is needed to avoid effects which noise has on the extraction of trunk's characteristics. The above images are corroded and dilated by 3*1 respectively. The corrosiveness is to remove the effects of small and dry branches, the dilation in the growth direction of trunk is to eliminate empty. After this process, there will still be some noise left, which should be removed to avoid the misunderstanding of the extraction of future characteristics. First of all, give the morphology image area mark, and calculate the area of each region, then remove the area of land that is less than 1/15 of the largest area, finally we can get the binary image shown as Figure 3 From the binary image in Figure 4(a), it can be seen that the intersection of the main trunk and the ground are concentrated in the lower part of the image, the further the main trunk area is, the smaller the image is. The upper part of the image is small branches, sky, and so on. In order to highlight the main trunk area, the local characteristics of fruit trees can be neglected, and the horizontal projection method is used to extract the main trunk area. The steps are as follows:

Set image resolution to N M × , ( , ) I i j as the image gray value point ) , ( j i , then scan the binary image progressively, and calculate the horizontal projection value of each line ) (i s ,

∑ = = M j j i I i s 1 ) , ( ) ( ) , , 2 , 1 ( N i L =
which is to select the appropriate threshold, if it is appropriate, it is the main trunk area. Otherwise, it is not.

From Figure 4(b), it is easy to see that the horizontal line value in the central area is really low. That is the line where the trunk, small branches and the sky separate from each other. In actual practice, first set threshold 200 = T , record the row number and keep the pixel value below the line when the horizontal line number is smaller than the threshold value to extract the trunk area. In the image processing, extract main parts of the image area if the horizontal projection value is smaller than the threshold, the image resolution in the follow-up process is h M × .

Main feature point extraction

According to the features of standard tree that the trunk are upright and obviously easy to distinguish, the intersection of the trunk and the ground can be regarded as feature points to represent fruit trees, to reduce calculation. As the impact of small branches, a very small amount of noise is still existed in the picture after trunk extraction. By using the area threshold method, an area of less than the maximum area of 1/80 of the region is removed to get a binary image whose trunk area is clear as Figure 5(a) shows. Feature point extraction algorithm is described as follows:

(1) Set an empty matrix P, the size is the same as the size of the trunk extraction regional image, marked as h M × .

(2) Mark regionally the images whose noise has been removed. Scan each region which represents each fruit tree that has trunk feature. Suppose that there are n regions exist in this image.

( values are both 0, the tested point is suit to the features that the intersection of the trunk and the ground.(Referred as the candidate point).

(4) Put the coordinates of the suitable candidate points in region k into the empty matrix P. Test again. Search the point has the largest abscissa value, which means finding the point that is closest to the ground to represent the fruit trees. Set the remaining points as background points.

(5) If the detecting area n k = , stop searching. Otherwise, return (3). After scanning the feature points by the above steps, each region will have a unique feature point to represent the fruit tree. By comparing 60 apple pictures which were taken in similar position, different time, different parts, fruit trees--the intersection of the trunk and the ground distribute on both sides of the image according to the probability. Therefore, when the feature points are classified, firstly the vertical midline of the image (half of the total number of columns the image) is taken as the base line of the feature points to classify. When the feature points in the image are on the left, the corresponding coordinates will be saved into array Q1; otherwise, when the feature point in the image are on the right, the corresponding coordinate values will be saved into array Q2.

Navigation path line detection

Similar to crops, fruit trees are naturally formed in a straight line. Similarly, the path of mobile robot in a short time can be approximately seen as a straight line. So the straight-line path model can be used on the study [START_REF] Qian | Vision Navigation Based on Agricultural Non-structural Characteristic[END_REF] .The most generally used line detection methods are the least square method and the Hough transform and some methods based on these forms. This paper takes the intersection points of fruit trees and the ground as the feature points. Feature points in the vision field are limited, so the least square method which has high speed and accuracy, are adopted to test the two junction lines. Finally the robot's navigation path is generated by extracting the center points of the junction line. 3

Results and analysis

The angle between geometric central line which serves as navigation line and horizontal line is an important factor of navigation. It decides the angle that the robot needs to adjust. It means the robot is walking along the best direction of safe moving in visual field, when the angle is close to 90 ° [11] . 60 images were used to test the algorithm. 20 images are taken in orchards in Nanlang Village, Qinshui County, Shanxi province, and the rest are taken in orchards in Taolin Village, Changping District, Beijing. Correct recognition rate was 91.7%.

Table 1 shows the segmentation, extracted feature points and simulation angles generated by this algorithm under different orchard environment and the automatic extraction of navigation lines in the two different orchard backgrounds. It can be seen from the table, the navigation line could be auto generated in various orchards environment. The angles of the simulation navigation lines are nearly 90°and can fulfill the path extracting request of autonomous navigation in complex orchard environment. Five images which are failure to extract the navigation line are all taken in Taolin Village, Changping District of Beijing. Firstly the main failure reason is the complexity of background. And there are the iron rods next to fruit trees, which can cause the adjacent segmentation of trunk regions, reduce the feature points, result in detection errors. Secondly, because of the light effect, there are many shaded area in trees, resulting in the similar color of dry twigs and leaves, causing false segmentation.

Table.1 Comparison of navigation lines in different orchards

To verify the reliability of the algorithm, the simulation result was compared with manual recognition. Four images were taken from each orchard. Calculate the horizontal level of artificial fitting navigation under Matlab to get the deviation between the artificial recognition angle and simulation navigation angle. (shown as Table 2), and the deviation turns out to be around 2%.

Conclusions

(1) The color difference R-B and two-dimensional OTSU algorithm was employed to segment the trunk from the background. Dead leaves and soil background did not affect the segmentation of the trunk region. But the algorithm is not effective when processing green weeds on the ground. Morphological method was adopted to eliminate the noises such as tiny branches and fading leaves, horizontal projection method was adopted to dynamically recognize the tree trunks, and the region segmentation was used to eliminate the influence of tiny branches for a second time. This algorithm can extract the main trunk area effectively.

(2) By scanning the trunks areas, border crossing points of the bottom of the tree and ground were detected, and these points were divided into two clusters on both sides based on neighboring relationship. Resorting to least-square fitting, two border lines were extracted. The central line was gained by the two lines. It is robust and effective in many orchard environments. The recognition rate is 91.6%.

(3) The simulation result is compared with artificial recognition in two orchard environment. The result shows that the generated navigation path is reliable, safe and can satisfy the moving request of harvesting robot.

(4) This algorithm is suitable for the orchard where the ground had less weed and the main area of standard trees were more visible. For the stunted trees, if there are more weeds and the background is extremely complex in an orchard, it is better to improve the algorithm or use another method.

Fig. 1

 1 Fig.1 Components of the robot

  Fig.3 Binary image of the orchard

Fig. 4

 4 Fig.4 The main trunk area detection by horizontal projection

Fig. 5

 5 Fig.5 Guidance path line of an apple orchard (Qinshui, Shanxi)

Fig. 6 Fig. 7

 67 Figure number

Table . 2

 . Comparison between simulation and artificial recognition

			Simulation	Artificial	Deviati
	The orchard					Deviation
		Number	navigation	recognition	on
	Environment					rate /%
			angle /(°)	angle /(°)	/(°)
		1	113.2	110.6	2.6	2.35
	Taolin, Beijing	2	106.9	103.6	3.3	3.19
	(Fig.6 etc)	3	94.1	97.2	3.1	3.19
		4	80.7	83.8	3.1	3.70
		5	82.5	83.9	0.6	0.72
	Qinshui, Shanxi	6	86.1	85.7	0.3	0.35
	(Fig.7 etc)	7	93.8	92..6	1.2	1.30
		8	87.5	86.8	0.7	0.81

Acknowledgments.

This research is sponsored by the project 2006AA10Z255 and National Natural Science Foundation of China (Grant No.30900869). All of the mentioned support is gratefully acknowledged.


Haokun Zhang

Heru Xue

reenhouse reenhouse reenhouse E E E nvironmental nvironmental nvironmental Parameters Parameters Parameters P P P rediction rediction rediction System System System

Keywords: Greenhouse environmental prediction, Prediction model, MVC framework, Intelligent management system

This paper designs an environmental parameters prediction system based on web for greenhouse. The system is designed using the MVC framework, and includes monitoring module and prediction module. The system can obtain the main environmental parameters from sensors, such as light, temperature, humidity, CO 2 and so on. Based on mass and energy balance principle, the prediction module of the system can predict the parameters of the greenhouse environment each day. The system displays the measured real-time data and the predicted data for the users to manage greenhouse easily. This paper provides a specific method to realize an intelligent management system for greenhouse.

In In In troduction troduction troduction

Solar greenhouse is a unique greenhouse structure in China, with low cost, low running cost, good insulation and high efficiency advantages. But the current level of greenhouse environmental control is lower, and the greenhouse environmental control is still a manual control-oriented. It is difficult to adjust to the best environment for crop growth. This paper designs an environmental parameters prediction system realizing a function of remote monitoring and early warning. The system provides reliable and accurate greenhouse environmental parameters for users to manage the greenhouse.

With the development of the Internet and WWW technology, Web has become the interactive interface for most software users. WWW is considered the most successful information system. In particular the development of dynamic Web technologies having come a long way, WWW is becoming the mainstream of various types of information system development platform. Dynamic Web system structure is a threetier client/server model. In the three-tier system architecture, Web browser occupies client layer, database server and other external service account the service layer, and occupy the middle layer is the Web server and server extensions. Three-tier structure makes the dynamic Web browser users can access the existing database resources, and enhances the system interactivity.

This paper designs a web-based system, which implements the B/S design pattern and three-tier structure to shield underlying network and provides the users a friendly and consistent interface.

System System System Designing Designing Designing

The environmental parameters prediction system is based on B/S design pattern of the dynamic three-tier architecture of Web systems [START_REF] Shenglang | Study on remote control system for the greenhouse based on B/S model[END_REF]. The users request to the server by submitting a form in a browser. The server calls the data in the database after receiving the requests, and the results are returned to the users.

Using software engineering, the system is divided into different functional modules, according to the setting of the types of the greenhouse environmental parameters and the processes and characteristics of greenhouse environmental parameters monitoring and prediction. The system implements functions of monitoring and predicting the greenhouse environmental parameters and provides an interactive platform for users. The structure of system is shown as fig. 1.

. System flow chart

The system includes the following functions: Function of login and registration. Before using this system, users need to register and login. After users submit the registration information successfully, the system returns the registration information to the users. This function is designed to manage the system for users.

Function of environment monitoring. This function implements transmission and display of the environmental parameters and stores the parameters in the database. The system sends the parameter data to the users' browsers using the web server. The function can show the environmental parameter data in a table and also can draw a line chart of the data to users.

Function of environment prediction. The implement of this function is base on solar greenhouse environment model, which uses mass and energy balance equations to describe the climate in the greenhouse. The function can predict the greenhouse environmental parameters everyday. Input parameters need by this function submitted, the prediction is calculated based on environmental prediction model, and the prediction results is displayed in a table or a line chart to users.

S S S ystem ystem ystem I I I mplementation mplementation mplementation

In the development of the system, JSP technology and DAO technology are used. JSP technology is based on Java, and can create dynamic Web pages supporting crossplatform and cross-server. Following the object-oriented design, JSP programming is easy and independent of web browsers [START_REF] Ning | Design and Implementation of Intelligent Business Expanding Expert System Based on JSP Technology[END_REF][START_REF] Sigrimis | Computer integrated management and intelligent control of greenhouse[END_REF]. In developing web information systems, JSP technology is widely used.

Access Access Access t t t o o o D D D atabase atabase atabase

This system is designed to use Access desktop database. All operations on access to database are packaged in a separated Java class named by DB.java, in which all member functions are defined as static functions, such as Connection getConn(), getStatement(Connection conn), getResultSet(Statementstmt, String sql) and so on.

The following statement can implement the access to database: Class.forName("sun.jdbc.odbc.JdbcOdbcDriver"); conn = DriverManager.getConnection("jdbc:odbc: driver={Microsoft Access Driver (*.mdb)};DBQ=path"); The "path" in the above sentence is the variable of the physical path of the data file. Implementation of the function depends on the deployment of sensors. After sensors working successfully, greenhouse environmental parameters stored in the Access database are obtained by this module. The system uses AUTO-22 greenhouse environmental data collector. According to the need of the greenhouse environmental model, twenty-one sensors are used. The attributes of the table created in the database is shown as table 1. Table 1 1 1

Table Table

. . . Meaning of the database table attributes

The system uses a JavaBean named Condition to set and get data from the database. DAO of the system obtains data from the table in database and stores it in a list consisted of objects of Condition. JSP pages read the list to display the data in browsers.

Query statement of access: sql=select top "+pageSize+" * from temp_humi_0 where id not in (select top "+(size)+" id from temp_humi_0 order by id asc

The "pageSize" in above sentence is a variable to define the number of data item in a page. The "size" variable presents "(pageNo-1)*pageSize".

To display the data in a line chart, this system needs JFreeChart, which is an open chart drawing library on Java platform [START_REF]JFreeChart API Documentation[END_REF]. JFreeChart is programmed completely by Java Language, and designed for the use of applications, applets, servlets and JSP. The system needs the JFreeChart package to draw line chart. Add jfreechart-1.0.6.jar, gnujaxp.jar and jcommon-1.0.10.jar in lib directory.

Procedure of generating chart in this system: 1). Create a dataset to include the data displayed in a line chart, which is stored in database.

2). Create an object of JFreeChart to present the chart to be shown.

3). Output the chart. Environmental Environmental Environmental prediction. prediction. prediction. Calculating of each prediction module is developed by Matlab. With the component of Matlab Builder for Java, package the function of prediction module calculated in Matlab into a Java component. This Java component can be called in JSP web system. Taking the solar prediction module an example, package the file named shortwaveradiation.m into a file named shortwaveradiation.jar. Including this new file in the project, the system can call this prediction function.

Attributes Attributes Attributes

Matlab Builder for Java (known as Java Builder) is an extension of Matlab Compiler. Java Builder packages Matlab functions into one or more Java classes. Matlab functions are packaged into Java classes, and can be called by Java applications.

Implementation of environmental prediction function is based on greenhouse environmental model [START_REF] Wei | A Theoretical Model of Thermal Environment in Solar Plastic Greenhouses with One-Slope[END_REF]

C C C onclusion onclusion onclusion

The system designed by this paper can provide high-precision data of changes of greenhouse environmental parameters to greenhouse managers. The system realizes remote monitoring and prediction via Web and provides an actual method to realize precision agriculture. The system is an application system for greenhouse environmental parameters prediction.

Fig

  Fig. Fig. Fig. 1 1 1 . . . System flow chart

  M M M onitoring onitoring onitoringThis function deals with the data get from sensors, and displays the data in web pages.

  . The model integrates solar model, air temperature model, air humidity model and CO2 Concentration model to build an overall prediction model for greenhouse environment. The model needs local weather forecast information. With the forecast information, indoor solar illuminance, air temperature, air humidity, CO2 concentration, soil temperature and soil moisture can be realized. By inputting values of cloud and local time, solar illuminance prediction function calculates the total flux of solar radiation which has reached surface of the o I greenhouse, and the total flux of solar radiation which has reached the crop I 冠层 canopy. The function also calculates solar radiation energy absorbed and reflected by crop canopy , solar radiation energy absorbed by surface of soil , solar Q rd-c Q rd-s radiation energy absorbed by inner and outer surface of back slope and , Q rd-ri Q rd-ro and solar radiation energy absorbed by inner and outer surface of back wall and . Q rd-bi Q rd-bo Variables calculated in solar illuminance prediction function are needed in air temperature prediction model. The model is based on thermal balance equations such as indoor air thermal balance equation, indoor soil thermal balance equation, back slope thermal balance equation, back wall thermal balance equation and so on. Take the indoor air thermal balance equation as an example, the equation is Greenhouse temperature prediction model can predict indoor air temperature, crop canopy temperature, surface of soil temperature and so on. Greenhouse solar illuminence prediction model can predict crop canopy flux of solar radiation. They are the known conditions for prediction of greenhouse CO2 dynamic prediction model. Mean values in hours per day of crop canopy flux of solar radiation, crop canopy temperature, surface of soil temperature, air temperature, concentration of CO2, and inner surface temperature of translucent membrane obtained by sensors, and measured values each hour per day of outdoor temperature, outdoor humidity are the known (1 1 1 ) conditions for greenhouse air humidity prediction model. The function of prediction is shown as fig. 2.

Fig.

  Fig. Fig. Fig. 2 2 2 . . . Greenhouse environmental prediction model calculating flow chart

4

 4

Acknowledgements Acknowledgements Acknowledgements . . . This study has been funded by Inner Mongolia Natural Science Foundation Projects (Contract Number: 20080404).


Deyong Yang
email: yangdy@163.com

Jianping Hu

Zuqing Xie

Detection Technology for Precision Metering Performance of Magnetic-type Seeder Based on Machine Vision

Keywords: precision metering, performance detection, magnetic-type seeder, machine vision

Magnetic-type seeder is a precision metering device, particularly suitable for small seeds. A visual inspection system for precision performance of magnetic-type seeder was established based on machine vision and image processing technology. By using pre-treatment techniques including image binarization and image filtering, image quality was enhanced effectively. As the gray-scale value of the seeds coated with magnetic powder is very close to the electromagnet, the method of seed feature extraction based on morphological image processing is proposed and performance testing model of precision metering seed is put forward. The results of comparison between machine vision and manual detection showed that the relative error of preciseness was less than 3% and coefficient of variation and standard deviation were less than 5%, which indicated the system is of high accuracy when used in real-time detection.

Introduction

Metering device is the core component of seed planters, which performance is important for planter design and manufacture. The performance improvement of precision metering device depends on accurate and efficient detection technology. There were manual detection, opto-electronic scanning [START_REF] Lan | Opto-electronics Sensor System for Laboratory Measurement of Planter Seed Spacing with Small Seeds[END_REF], piezoelectric pulse and high-speed photography [START_REF] Qingxi | Application of the High Speed Photography Checking the Precision Metering Performances[END_REF] and other methods. In the recent years, the research of non-contact detection of precision planting quality using machine vision technology conducted gradually [START_REF] Yushun | Performance detection and analysis of a machine vision based metering mechanism of drill[END_REF], [START_REF] Shaoxing | Detecting Technology of the Properties of Seed Metering Based on Computer Vision[END_REF], [START_REF] Xiaohua | Graindistance real-time checking and measuring system based oncomputer vision[END_REF], [START_REF] Xiaohua | Grain Distance Real-time Checking and Measuring System Based on Computer Vision[END_REF]. Magnet roller-type precision seeder was developed according to magnetic seed-metering principle, which solve precision seeding of small seed like vegetable seeds and flower seeds [START_REF] Jianping | Analytical and experimental study on principle of precision seed2meter by magnetic force[END_REF].In this research, vision detecting of precision performance of magnetic-type seed metering device was undertaken, and visual detection accuracy was discussed.

Deyong Yang1,2, Jianping Hu2, Zuqing Xie2

Materials and methods

Seed

Rape seed of 2.412 g /1000 seeds was used for this study. Seeds must be coated with magnetic powder.

The vision detection system

A test stand with camera system was used to detect performance of precision metering device, as shown in Fig. 1 and Fig. 2. Magnetic-type seed metering device has 4 rows magnetic head per revolution. Sufficient oil was added to the top surface of the seedbed belt to capture the seed as it was released from metering device. The speeds of the metering roller were set at 15, 20 and 30 rpm while the speed of seed-bed belt was 0.5 m/s.

The camera is a high-resolution, black-and-white, low-light, manual gain adjustment MTV-1881EX equipped with AVENIR Seiko CCTV lens, manual iris of F1.3, focal length of 8mm. Lighting system is made up of the lighting box, 12V DC lamps and blocking mask, and 6 lamps are fixed below the box, and the camera and lighting are arranged hierarchically to obtain stable, no shadow and uniform illumination image to meet the needs of image processing.

1 screen filter and fuel tank 2 driving roller 3 seed-bed motor 4 seed-bed belt 5 camera system 6 metering motor 7 magnetic-type seeder 8 fuel injection device 9 driven roller seeds go through the camera and the camera records images and collect data, then transfer into the computer for processing.

2.3.image processing

Because the images in the collection and transmission process were interfered by all sorts of disturbance and contain random noise in addition to images useful signal, the images must be treated by a series of methods, such as median filtering and image splicing, to remove the noise and make seed target and background separate completely.

2.3.1.Binary image processing

In seed image acquisition process, relatively stable reservoir thickness, viscosity, seeds, colour and lustre etc are likely to make gray-scale values of image pixel except seeds are 1. Therefore, seeds are separated with background by using binary processing.

The background in this research is simple and few changed, so a preprocessing fixed threshold method was tried, namely, by setting a certain threshold, make gray scale image into two gray value of black-and-white images, which would target and background separated. Its function expression is as follows:

0 ( ) 1 x T f x x T       (1)
Where ( ) f x is the image matrix, x the gray value and T the selected threshold value of binary image processing.

Threshold value was set 90 in this research after repeated tests and the image after binary processing is shown in Fig. 3.

2.3.2.Image filtering

Sequence of images contain useful signals and also contains a variety of random noise because of internal and external interference. In order to reduce the noise and improve image quality, it is necessary to smooth image after binary treatment.

Image smoothing processing adopt 3 x 3 neighborhood median filtering method, that is, the value of a point in the digital image is the median value of values of all points in a field instead.

If   2 , ( , ) ij x i j I 
is gray value of digital images, two-dimensional median filter with filtering template A can be defined as:

2 , { } { } ,( , ) ,( , ) ij A ij i r j s y Med Med x x r s A i j I       (2)
The image after filtering is shown in Fig. 4. Labeling algorithm combining with sequential scanning and parallel dissemination is used for regional mark, as shown in Fig. 5. The image is scanned by TV raster, and 1-pixel with no label is detected. The unused label is assigned to it, and 1-pixels located in 8-neighborhood of it are given the same label. The label begins to spread from first 1-pixel one by one until no 1-pixel in 8-neighborhood of labeled pixel exists and the operation ends. All connected 1-pixel have the same label. The image is scanned again, and if unlabeled 1-pixel is found, the same treatment is conducted. Otherwise, the processing is completed.

2.4.2.Seed number feature extraction

While marking regions of seeds, the pixel that is not belong to seed area is marked to 0, the pixel that is belong to the same seed area is given the same label which is not equal to zero. The label of pixel is different from regions of the seeds. Therefore, the number of seed is obtained by calculating number of these nonzero label.

Detection Technology for Precision Metering Performance of Magnetic-type Seeder Based on

Machine Vision 5

Seed position feature extraction

After the region of seeds was marked, pixels with the same label are regarded as a seed. As long as the centroid of seed region was obtained, it is regarded as the center of the seed [START_REF] Cvibovic | An approach to the design of distributed real-time systems[END_REF].

If a binary image

    , ; , 0,1, , 1 f x y x y N   
is given, the pq-order image moment is defined as follows [START_REF] Xiaohua | Graindistance real-time checking and measuring system based oncomputer vision[END_REF]:

( , ) , 0,1,2 p q pq M f xyxy pq     (3)
Different values of p, q can obtain different moments pq M . As the performance was concerned with center of mass, the center coordinates of the seed is defined as follows:

10 01 00 00

( , ) ( , ) M M x y M M  (4)
Coordinate with the top-left corner of a image, for the X axis is to the right direction, for the Y axis is downward, as shown in Fig. 6. As seed spacing was measured along longitudinal coordinate, y difference between two adjacent seed was just seed spacing. Through testing the seeds and coordinates the seed spacing between adjacent seeds, and analysis of metering device of qualified rate, leakage, replay broadcast rate main performance indexes.

Results and discussion

According to the relevant test standards, single seed precision sowing experiment was done continuously measured 250 seed spacing as a statistical sample. The speeds of the metering roller were set at 15, 20 and 30 rpm respectively while the speed of seedbed belt was 0.5 m/s. Deyong Yang1,2, Jianping Hu2, Zuqing Xie2

metering precision detection

Main indexes of precision performace of magnetic-type metering device are preciseness, multiple index and miss index. Detection of precision metering were done by using machine vision and manual methods respectively, comparative analysis for different metering roller speed is listed in Table 123 1,2,3 show preciseness decreases and multiple index increases with metering roller speed increasing. Relative error of preciseness between machine vision detection and artificial detection is less than 3%, relative error of multiple index less than 6%,relative error of miss index less than 5%. The results show that testing accuracy of precision metering by machine vision is required.

Sowing uniformity detection

Sowing uniformity factors include mean seed spacing, standard deviation and coefficient of variation of seed spacing. Detection of precision metering were done by using machine vision and manual methods respectively, comparative analysis for different metering roller speed is listed in show coefficient of variation of seed spacing increases with increased metering roller speed. Relative error of standard deviation between machine vision detection and artificial detection is less than 5%, relative error of coefficient of variation less than 5%. The results show that testing accuracy of precision metering by machine vision is required.

The main reasons of differences between the manual measurement and system measurement based machine vision were error of manual measurement and error of image processing because location of center of mass of seeds changed and tiny seeds were neglected.

Conclusions

This paper constructed the performance detection system of precision magnetic-type metering device. After a series of image processing ,such as image binary, image filtering, the region of seeds are seperated with background. By regional marking, seed properties are easily determined. Such number of seeds was obtained efficiently and center of mass coordinates were achieved by using moment characteristics, which provided a satisfactory basis of the performance detection. Deyong Yang1,2, Jianping Hu2, Zuqing Xie2

The parameter data of precision performance from vision detection system and manual detection are not significantly different. Therefore ,vision detection system can be used instead of manual detection.

Fig. 1 .Fig. 2 .

 12 Fig. 1.Structural diagram of precision seeder test-bed

Fig. 3 .

 3 Fig. 3. image of Binary processing

Fig. 4 .

 4 Fig. 4. image of filtering

Fig. 5 .

 5 Fig. 5. Mark regions of seeds

Fig. 6 .

 6 Fig. 6. Coordinate system of image

Table 1 .

 1 . Precision results at metering roller speed of 15 rpm

		Preciseness (%)	Multiple index (%)	Miss index (%)
	Manual method	84.28	15.72	0
	Machine vision	83.33	16.67	0
	Relative error	0.94	5.69	0

Table 2 .

 2 Precision results at metering roller speed of 20 rpm

		Preciseness (%)	Multiple index (%)	Miss index (%)
	Manual method	31.13	39.63	29.24
	Machine vision	30.77	38.46	30.77
	Relative error	1.17	3.04	4.97

Table 3 .

 3 Precision results at metering roller speed of 30 rpm

		Preciseness (%)	Multiple index (%)	Miss index (%)
	Manual method	26.99	48.51	25.50
	Machine vision	26.32	47.37	26.31
	Relative error	2.54	2.41	3.08
	Table

Table 4 - 6 .

 46 Detection Technology for Precision Metering Performance of Magnetic-type Seeder Based onMachine Vision 7

Table 4 .

 4 Sowing uniformity results at metering roller speed of 15 r/min

		Mean seed spacing	standard deviation	Coefficient of variation
		(m)	(%)	(%)
	Manual method	1.175	0.23	19.58
	Machine vision	1.200	0.24	20.00
	Relative error	2.13	4.17	4.2

Table 5 .

 5 Sowing uniformity results at metering roller speed of 20 r/min

		Mean seed spacing	standard deviation	Coefficient of variation
		(m)	(%)	(%)
	Manual method	0.771	0.18	23.61
	Machine vision	0.793	0.19	23.96
	Relative error	2.85	4.21	1.46

Table 6 .

 6 Sowing uniformity results at metering roller speed of 30 r/min

		Mean seed spacing	standard deviation	Coefficient of variation
		(m)	(%)	(%)
	Manual method	0.483	0.187	38.69
	Machine vision	0.498	0.192	38.57
	Relative error	3.11	2.6	3.11

Table 4 ,

 4

[START_REF] Xiaohua | Graindistance real-time checking and measuring system based oncomputer vision[END_REF][START_REF] Jianping | Analytical and experimental study on principle of precision seed2meter by magnetic force[END_REF]

Acknowledgments

. This work was financially supported by the open fund of Jiangsu Provincial Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University (NZ200604), by Eleventh Five-Year-Plan National Scientific & Technological Supporting Project(2006BAD11A10).


Buyu Wang
email: bywang05@163.com

Changyou Li

Xueliang Fu

Meian Li

Dongqing Wang

Huibin Du

Yajuan Xing

Design of ETL Process on Spatio-Temporal Data and Study of Quality Control 1

Keywords: Sensor data, Frequency increment rule engine, Inverse distance weighted method

In order to use the space-time data mining technology to conduct operation research in WuLiangSuHai Eutrophication, the water quality sensor parameters of heterogeneous data which reflect the characteristics should set up a spatial data warehouse through ETL process, and water quality sensors for quality control of spatial and temporal data plays a vital role in building an effective analytical environment. The paper designs the ETL process from the data and water quality sensors artificial duty and other heterogeneous data sources spatial data, and proposes data quality control strategy based on the incremental frequency rule engine and the space the inverse distance weighting on the Combination. Experiments show that the incremental frequency rule engine could more effectively find the missing sensor data and abnormal, Space inverse distance weighting method can find the missing data and outliers in the errors within the allowed interpolation processing, ETL procedure is effective and feasible.

Introduction

At present, the integrated analysis and process of water quality services have been investigated using the spatial data mining techniques [START_REF] Chen | Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake[END_REF], [START_REF] Lenat | Water Quality Assessment of Streams Using a Qualitative Collection Method for Benthic Macroinvertebrates[END_REF], [START_REF] Neal | A summary of river water quality data collected within the Land-Ocean Interaction Study: Core data for eastern UK rivers draining to the North Sea[END_REF]. In particular, some work has studied multi-eutrophication services of water environments based on the heterogeneous data sources [START_REF] Codd | Cyanobacterial toxins, the perception of water quality, and the prioritisation of eutrophication control[END_REF]. The pre-requisite for the investigation of eutrophication related services is to establish a data warehouse including the waterquality data characterized by a variety of eutrophication attributes. The design of the ETL (Extract, Transform, Load) procedure plays a key role to establish such an effective analysis environment.

ETL process design, many scholars from the conceptual model, conceptual model to logical model of the transformation of both done a lot of research work [START_REF] Vassiliadis | Conceptual modeling for ETL processes[END_REF], [START_REF] Simitsis | Mapping conceptual to logical models for ETL processes[END_REF]. Abnormal data, some scholars have proposed the use of rules engine to detect, and

The Fourth International Conference on Computer and Computing Technologies in Agriculture made the related theoretical research work, but did not give a specific implementation strategy [START_REF] Loshin | Rule-based data quality[END_REF]. Deal with the problem of missing data values, some academics have suggested the use of inverse interpolation method, the weight-related research and in the field of meteorology has been successfully applied [START_REF] Sun | Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China[END_REF].

In this paper, three aspects of the work done. Designed with the characteristics of lake water quality data ETL process. A comprehensive consideration of water quality parameters of water eutrophication factor weighting inverse method interpolation. Designed based on the Drools rule engine kernel dynamic incremental realized abnormal test data volume.

Design of the ETL Process with Heterogeneous Data Sources

Heterogeneous Data Sources

In order to study the issues related to the water eutrophication, data source, on which the analytic environment is established, should consist of two kinds of data: the data of water quality eutrophication and the data representing the key elements of eutrophication. In this paper, the data source mainly includes the following two parts: the water-environment monitoring data and the data manually collected in the Wuliangsuhai Lake. The water-environment monitoring data is collected by the online water-quality sensors in the Wuliangsuhai wireless sensor network, and the time granularity of the data sampling is once per 15 minutes, which is conducted in each monitoring station in the Wuliangsuhai Lake. The manual collected data comes from the researchers who collect the related data on the spot in summer, and the associated time granularity is once per day, the sampling space varies each day. Obviously, the data is inconsistent with the manually collected data in terms of time granularity and space granularity.

Design of the ETL Process

The ETL process provides data for data warehouse. Therefore, the quality of the ETL process relates to the success or failure of the establishment of data warehouse. The ETL processes adopt different strategies and implementation methods for dealing with different data sources. This paper proposed the design of the ETL process which can unify the heterogeneous data sources varying with time granularity and space granularity. The proposed ETL process is as follows.

Firstly, the cleaning of the heterogeneous data is performed with the strategy of data quality controlling introduced next. Secondly, the data uniformity of space granularity is achieved as follows: extract the monitoring data of the sensors located in the sampling stations which are near to the man-made sampling points in terms of latitude and longitude. Following this, the data uniformity of time granularity is achieved as follows: superimposing and fitting of 96 sensor groups of data of 96 sampling times over 24 hours. Thereby, the transformation of data is completed. Finally, the consistent data is loaded in the data warehouse.

Data Quality Control

The Missing or the Abnormal of Water Quality Data

The issues related to the data quality of water-quality sensors mainly are manifested in the two following aspects. One aspect is that WSN is affected by the quality of the communication signal in the data acquisition and transmission. As a result, the sampling data may be missing in some time intervals. The other is that the electrical signal noise and man-made factors may lead to the sampling data abnormal during the process of sensor monitoring. This paper investigates how to deal with the data missing and the data abnormal, which is essential for the control on data quality in the ETL process.

IWQPW Interpolation

For k points of sensor data in some sampling period employed in the procedure of the data uniformity, the missing and abnormal data may result not only from the parameters of water-quality sensors, but also from the signal strength which may varies with the sampling frequency over one hour. This paper took into account the signal strength and the factors of time period and sampling frequency, and proposed the IWQPW (Inverse Water Quality Parameter Weighting) interpolation method to cope with the issues mentioned above.

Definition1. Assume that the starting instant is t 0 , and a group of data is manually collected data over 24 hour period. Accordingly, 96 groups of water-quality sensor data have also been collected, which are indexed by a sequence of integer numbers. Each index corresponds to the related sampling instant.

Definition2. Assume that the data record is a group of manually collected data, the 96 corresponding sequential groups of data is

1 2 9 6 { , , , } D R d d d = … . For any (1 96) i d i ≤ ≤ and ) 96 1 ( j i and j d j ≠ ≤ ≤
in R D , if their indices are x and y respectively, the sequential distance between two sampling point is calculated as x y -.

Definition3. Let M (x, y, z, t) be an arbitrary interpolation point in the sample space, N (x i , y i , z i , t i ) be an arbitrary point in 96 sequential sampling values. The sequential weight of the sampling point N, W i, t is defined as:

1 (1 96) it k w k l l = ≤ ≤ - (1)
The Fourth International Conference on Computer and Computing Technologies in Agriculture Where l indicates the sequential position of the interpolation point, l k indicates the sequential position of the sample point.

Definition4. IWQPW (Inverse Water Quality Parameter Weighting) is a spatial temporal sequence interpolation method with the comprehensive consideration of the weight of water quality parameters and the sequential weight. IWQPW takes the distance as weight between the interpolation point and the midpoint of the sample space for weighted average calculation. The sample point is assigned a larger weight if it is nearer to the interpolation point and with a short sequential distance. Assume that a monitoring station is a basis. There are n samples in the 96 sequential sensor sampling space. Let z i be the value collected of water quality, z be the value of water quality to be estimated as follows:

1 1 ( )/ n n i i i i i z z w w = = = × ∑ ∑ (2)

Dynamic Incremental Rule Engine

Dynamic Incremental Rule Engine Overview

The rule-based engine technology originates from the rule-based expert system. It has been becoming a popular research topic recently that the application of this technology to the ETL process for the control of data quality. Due to the hidden of the abnormal data collected by water quality sensors, it is not easy for non-professionals to tell the abnormal data. This paper proposed to exploit the rule-based engine technology to deal with the issue that how to make the outlier detection rules dynamic increment with the accumulation of expert experiences and the development of related disciplines. However, the following two issues appeared in the application of this technology to the detection of outlier in the sensor spatial data.

• Existing data-cleaning methods based on the rule engine can only process a data record. However, the sensor parameter data is closely related to the data from time and space. Thus, one record data is not sufficient to identify the outliers. Therefore, it is necessary to consider all these related data together. • It is not flexible of existing cleaning methods based on the rule engine to process the rule file. The historical versions of the domain-expert rule files cannot be made full use. Especially, it is impossible for the existing rules to self-learn and update continuously. This paper investigated the method for the detection of outliers in the sensor parameter data based on the rule engine technology in order to cope with the above two issues, and proposed a new engine technology of dynamic incremental rules.

The Architecture of the Dynamic Incremental Rule Engine

The designed dynamic incremental rule engine is Drools rules engine as the rules of the nuclear, through plug-in components, to achieve the continuous dynamic growth rule and incremental updates. The architecture of the dynamic incremental rule engine includes: Rule generation interface. Graphical user interface (GUI) is exploited to edit the user rules and the plan of the rule conversion. In GUI, the rule is represented using custom XML files.

Rule-related job dispatcher. The dispatcher is used to dynamically adjust the priority of the rules and the sequence of the rule conversion.

Rule converter. The rule converter takes the responsibility of the conversion of the custom XML rules to the rules supported by Drools. That is, the converter transfers the object-oriented rules to the Drools-supported rules.

Rule-version controller. With the rule-version controller, the user rules are managed in a centralized manner. The rules with old versions are also regulated. The system maintains a record list regarding the rule usage of each individual user with specific role(s). The personalized interface thus is provided to different users.

Runtime database. The database is used for data persistent storage, which is shared by the other components.

Code generator. The generator produces the code according to the rules, and returns the data anomalies stamp. There are two kinds of return data. One is the PL/SQL code; the other is Java code. The generated code, as input, sends to the module of data layer.

Data layer modules. Data layer modules receive the code, and determine which operation should be performed according to the abnormal stamp. If it is necessary, the interpolation module is called. Then, the persistence operation is performed.

A Sample of the Rule File

This dynamic incremental rule engine designed to use a custom XML file to represent the objects of the rules, call the Java API code embedded in the rules file, rules through five steps to achieve the dynamic and incremental update capability, in detail Process described below.

• Import the classes used in the code, nested the classes in <java:import> tag; • Describe data set schema. The original data set is divided into the current data set and the other data set, differentiated by the attribute tag, i.e., type. The sample fragment is as follows for the definition of the data set.

<java:receive name='sensorDataset'> <java:ds name='currentdataset' type='current'> <parameter name='sensor'> <class>sensorDataset</class> </parameter> <parameter name='struct_sensor' type='srcstruct'> <! The structure of data set descriptions > <col name='col1'> <matched column value='time'></matchedcolumn> <datatype value='Date'></datatype> <datalength value=7><datalength> </col> <col name='col2'> … </col> </parameter> </java:ds> </java:receive>

• Define the function of object operators. The operations are defined between the objects in the set of abstract source data and the objects in the set of training data.

The specific function definition is included in the <java:functions> tag. The sample fragment is as follows:

<java:functions> public int positonInOtherDataset(currentdataset, otherdataset, itemtocompare) { //Return the position of the specified field of the //current data set in the other data set ordered by //the related values } </java: functions> • Define the specific rules. The Boolean expressions are used to describe the rule conditions, which consist of the class, the data sets and the operator functions defined in the above, the sample fragments is as follows:

<rule-set> <rule name="rule1" salience="10"> <java:condition name1='cond1' cleanitem='phx'> isMaxValue (currentdataset, otherdataset, "phx") equals the value of a return code </java:condition> </rule> </rule-set> • Define an operation. The described operations are performed when the Boolean expression of the rule condition is true.The sample fragment is as follows:

<java:consequence> <!Mark the outliers> markExceptionData (data sets,outlier row,outlier column); </java:consequence>

Quality Control Strategy

The process of data cleaning is the most critical part of quality control of waterquality-sensor data, which includes two parts: the interpolation processing of the missing data in water quality monitoring and the detection processing of the outlier. First of all, the missing values of sensor water-quality data are interpolated using IWQPW method. Then, the outlier detection is run using the dynamic incremental rule engine with the input of the processed data set and the rules edited by water experts. Following this, the space interpolation is performed on the outliers using IWQPW method again. Finally, the achieve data set get into the follow-up processing.

Experimental Design and Analysis

The purpose of the experiment is to verify the accuracy and reliability of IWQPW interpolation, as well as recall ratio and precision ratio of DIRE rule engine. Recall ratio is calculated as the ratio of the number of detected outliers over the number of statistical outlier. Precision ratio is defined as the ratio of the correct number of detected outliers over the number of detected outliers.

The test set A consists of the four sampling spaces indexed by 1, 2, 3 and 4 respectively. Each sampling space is constructed based on the test sampling space of the real data records of PH, ORP and oxygen content collected in April in the Wuliangsuhai Lake. Then, the test set B is obtained through elimination of the uncompleted record and outliers in the four sampling spaces in the set A under the guidance of related experts.

Experiment 1. Under the guidance of related experts, the statistical number of outliers is achieved manually for each sampling space in the test set A. The number of outliers is also obtained from the DIRE rule engine with the input of each sampling space. Thereby, recall ratio and precision ratio can be calculated. Experimental results are shown in Table 1. Experiment 2. The purpose of this experiment is to compare the ratio of interpolation accuracy using three following methods. From the test set B, we remove normal data 4 times with the quantities of 105, 150, 245 and 400, respectively. Then, three interpolation methods, i.e., IDW, Kriging and IWQPW are used individually for data interpolation within the allowable range of the error (0.62). The experiment results are shown in Table 2. The experiment results show to some extent that our proposed method is useful in practice and effective. The results also show that our methods strongly rely on the rules.

The Fourth International Conference on Computer and Computing Technologies in Agriculture

Conclusion

The objective of this paper is to deal with the issues related to the quality control of water-quality sensor data. The ETL process is first proposed in order to establish data warehouse. In addition, the quality control strategy is proposed which combines IWQPW method with the dynamic incremental rule engine. Thereby, it can be clean up that the missing values and outliers. The future work is to investigate further universal interpolation methods and the self-learning capability of the rule engine.

Table 1 .

 1 Detection of outliers in sensor data with the rule engine No. Number outliers Number detected Correct number Recall ratio Precision ratio

	1	364	345	337	94.7%	97.7%
	2	253	237	229	93.7%	96.6%
	3	377	344	319	91.2%	92.7%
	4	423	389	377	92.0%	96.9%
	5	340	309	298	90.9%	96.4%

Table 2 .

 2 Accuracy of different interpolation methods for sensor data

	Interpolation of the average accuracy Measuring the number of missing values IDW Kriging IWQPW
	105	79.2%	77.7%	99.6%
	150	77.3%	78.2%	96.2%
	245	66.0%	66.9%	95.4%
	400	70.3%	70.5%	90.0%

The research is supported by Chinese Natural Science Foundations (50969005,40901262) and by Specialized Research fund of High Education for Inner Mongolia (Njzy08046).