Zhigang Wu

Zehua Zhang

Wenxin Li

Guangjun Wang

Zhihong Li

GPC: an expert system based on multi-branch structure for grass pest control information

Keywords: grass pest, expert system, pest diagnosis, grassland

provinces and regions in China have experienced grass pest disasters. The average annual hazard area comprised more than 3 million mu and the annual economic losses totaled more than 20 billion Yuan. Because grassland encompasses very large areas, grass pest disasters occur suddenly and frequently; thus, they are very difficult to predict and subsequently monitor. In order to provide technical support for grassland plant protection, we need to develop effective early warning systems and effective control measures. Herein, we develop and evaluate GPC (Grass Pest Control information system), a web-based expert system for identification of grass pests, which included more than 50 species of grass pests. It has been developed by China Agricultural University and Institute of Plant Protection, Chinese Academy of Agricultural Sciences. Based on user needs, GPC was developed with ASP.NET, C# and Microsoft SQL server 2008 database. In its development we used 8 databases including a user information database, basic information database, and identification knowledge database. This tool and information database was developed both for grassland plant protection technicians and farmers.

INTRODUCTION

Since 1997, 13 provinces and regions in China have experienced grass pest disasters. The average annual hazard area comprised more than 3 million mu and the annual economic losses totaled more than 20 billion Yuan. Because of the continuous occurrence of grass pest disasters, the grassland environment has been degrading. The local agricultural production has experienced serious losses due to grass pests. Because grassland encompasses very large areas, grass pest disasters occur suddenly and frequently; thus, they are very difficult to predict and subsequently monitor. Meanwhile, as climate and ecological environment change, the types and outbreak areas of grass pests are changing yearly. In order to provide technical support for grassland plant protection, we need to develop effective early warning systems and effective control measures. Herein, we develop and evaluate GPC (Grass Pest Control information system), a web-based expert system for identification of grass pests, which included more than 50 species of grass pests.

Liao surveyed and classified Expert systems (ES) methodologies using 11 categories: rule-based systems, knowledge-based systems, neural networks, fuzzy Expert systems, object-oriented methodology, case-based reasoning (CBR), system architecture development, intelligent agent (IA) systems, modeling, ontology, and database methodology together with their applications for different research and problem domains [START_REF] Liao | Expert system methodologies and applications-a decade reviews from 1995 to 2004[END_REF]. Expert systems have been applied in agriculture from 1980s [START_REF] Coulson | Computer-assisted decision-making as applied to entomology[END_REF][START_REF] Jones | Agricultural applications of expert systems concepts[END_REF][START_REF] Travis | Development, implementation, and adoption of expert systems in plant pathology [J][END_REF][START_REF] Jones | Knowledge-based systems for pest management: an application-based review[END_REF][START_REF] Jones | Knowledge-based systems for crop protection: theory and practice[END_REF][START_REF] Ramon | An expert advisory system for wheat disease management[END_REF][START_REF] Yialouris | An expert system for tomato diseases[END_REF][START_REF] Kramers | an Expert System for Diagnosing Flower bulb Diseases, Pests and Non-parasitic Disorders[END_REF][START_REF] Ellison | An expert system for the management of botrytris cinerea in Australian vineyards[END_REF][START_REF] Clarke | Development of the Harrow Greenhouse Manager: a decision-support system for greenhouse cucumber and tomato[END_REF][START_REF] El-Azhary | Pest control expert system for tomato (PCEST)[END_REF].

Most of them are developed for crop production and pest management, animal husbandry, and aquaculture [START_REF] Mahaman | A diagnostic expert system for honeybee pests[END_REF][START_REF] Mahaman | DIARES-IPM: a diagnostic advisory rule-based expert system for integrated pest management in Solanaceous crop systems[END_REF][START_REF] Andújar | An expert system for seedling weed identification in cereals[END_REF][START_REF] Prasad | AMRAPALIKA: An expert system for the diagnosis of pests, diseases, and disorders in Indian mango[END_REF][START_REF] Mansingh | CPEST: An expert system for the management of pests and diseases in the Jamaican coffee industry[END_REF][START_REF] López-Morales | JAPIEST: An integral intelligent system for the diagnosis and control of tomatoes diseases and pests in hydroponic greenhouses[END_REF][START_REF] Li | Fish-Expert: a web-based expert system for fish disease diagnosis[END_REF][START_REF] Fu | Pig-vet: a web-based expert system for pig disease diagnosis[END_REF][START_REF] Xiao | Design and Implementation of Expert System for Cow Disease Diagnose Based on[END_REF][START_REF] Liu | Design and implementation of expert system for prawn disease diagnose and prevention based on[END_REF][START_REF] Li | The development of Quarantine Pests Information and Identification System in China[END_REF][START_REF] Shao | Study on vegetable pests remote diagnosis expert system (VPRDES) [J][END_REF][START_REF] Han | Qpais: A Web-Based Expert System for Assistedidentification of Quarantine Stored Insect Pests[END_REF]. Others also began to integrate mobile communication systems, GIS technology, etc [START_REF] Yu | Research on Mobile Expert System of Tomato Pest Diagnose Based on Microsoft .Net and Web Service [J][END_REF][START_REF] Wang | Study of Web -Based Expert System for Control of Diseases and Insects in Forest[END_REF][START_REF] Xiao | Remote Diagnosis and Control Expert System for Citrus Agricultural Diseases and Insect Pests Based on BP Neural Network and WebGIS[END_REF]. Some scholars also control by expert attempted to apply Expert systems to grassland resources, plantation management, and disease control [START_REF] Liu | Approach of Grass Resources Classification Expert System (GES) [J][END_REF][START_REF] Shi | National Database and Information Service System for Forage Germplasm Resources[END_REF][START_REF] Wang | Analysis and design of GIS based expert system for pasture growing management[END_REF][START_REF] Ma | Preliminary study on the system management model of forage grass diseases in Ningxia [J][END_REF][START_REF] Jones | Knowledge-based systems for pest management: an application-based review[END_REF].

This paper discusses the development and evaluation of a web-based expert system for identification of grass pests, named as GPC (Grass Pest Control information system). The system included more than 50 species of grass pests. It has been developed by China Agricultural University and Institute of Plant Protection, and Chinese Academy of Agricultural Sciences.

USERS' NEEDS AND KNOWLEDGE ACQUISITION 2.1 Users' needs

Although it is widely accepted among scientists that efficient identification work is vital to formulate viable decisions about pest control measures. It is essential, however to convince technicians and farmers that the correct identification of pests is the first step in implementing control measures. Thus, one must investigate and evaluate technicians' and farmers' attitudes and perceptions relative to any expert system. Essentially, understanding their perceptions and willingness to use the tool is crucial to enhance grassland plant protection.

The system proposed can be accessed anonymously. System users are divided into regional administrators, super administrators and system administrators. They can use and mange the system with different authorities. Regional administrators need to input and maintain local monitoring information of grass pests, and they could answer questions of farmers. Super administrators could input and maintain basic information of grass pests, notice, users' question, law information and user account. Only system administrator have authority of adjust the system parameters and data dictionary.

Knowledge acquisition and information collection

Knowledge acquisition (KA) is the process of transferring knowledge from the sources to the engineers.

The translation of the knowledge possessed by the expert into a knowledge base is the bottleneck in the process of knowledge acquisition [START_REF] Jones | Knowledge-based systems for pest management: an application-based review[END_REF][START_REF] Plant | Knowledge-based Systems in Agriculture[END_REF]. To acquire the required knowledge, we followed the KA procedure discussed in Morpurgo and Wada [START_REF] Morpurgo | I-DSS: an Intelligent Diagnostic Support System[END_REF][START_REF] Wada | Knowledge acquisition from both human expert and data[END_REF]. We acquired textual information (e.g. species' morphological characteristics, biology, geographic distribution, etc.) from literature reference such as extension booklets, primary and secondary literature, , etc. We obtained symptom descriptions and the identification rules from some entomologists by Expert interviews. We discussed in depth to clarify questions and process identification. Through this method, we provided a trustworthy procedure for knowledge acquisition, which helped us build a multi-branch structural key for grass pest remote identification. We also collected a large number of pictures by photographing.

SYSTEM DESIGN AND DEVELOPMENT

Software and Database

GPC was designed to run on the internet. Its development was based on the use of internet techniques and SQL programming languages. It was developed with ASP.NET and C#. The server database played a very important role in developing GPC. It was used to sort all the information and knowledge which was needed to actualize every function of the expert system. In GPC, there are 8 databases including a user information database, basic information database, and identification knowledge database. They were all designed using MS SQL Server 2008 Database.

Inference process

Most of traditional expert systems for assistant diagnosis of green plant pests are based on dichotomous structure, which is rule-based. In order to be more flexible, GPC adapted multi-branch structure. In this structure, knowledge is organized in a decision tree, with nodes at different levels. It is another form of hierarchical structure has more advantages for accurate and rapid diagnosis (Fig. 1).

Fig. 1 The Inference diagram of multi-branch structure of GPC

Users interface

To best meet the different users' need, the design must be user-friendly. With the system, users could browse, access, and query any grass pest information easily. Meanwhile, they also can identify a grass pest by the remote pest diagnosis. Regional and super administrators can publish monitoring data and obtain statistics as needed. User interface is the direct media between users and computers, we should consider all users' knowledge level and ensure the interface complete, compact, and easy (Fig. 2).

DISCUSSION

Because grass pest species are numerous and widely distributed, their prevention and control are very difficult. In order to reduce pest damage, the government needs to increase investment in the control of grass pests and improve levels and conditions of pest monitoring and forecasting; such efforts would reduce large agricultural losses. GPC can be used to remotely diagnose grass pests and provide monitoring information for farmers and technicians. All of these benefits would be enhanced if we strengthen and publicize the system, thereby permittin more people to use the system.

The system was evaluated in two steps: verification and validation [START_REF] Harrison | Validation of agricultural expert systems[END_REF]. We determined the system code error and perfected it with the white-box testing method. The key which is used for pest diagnosis was also further improved by pest experts. In the second step, we had students and teachers test the system with black-box testing method. All the students selected were majors in plant protection, but without experience in grass pest identification. Although, students using the expert system were able to identify most of cases correctly, some pests with similar taxonomic characters were not easy to be identified. Presumably, it would be easier for the more experienced students or technicians.

Index page of GPC

	Level 1		Host 1 Fig. 2		Host 2	……	Host n
			1
	Level 2	Symptom 11	Symptom 21	……	Symptom n1
	Level 3	Symptom 12	Symptom 22	……	Symptom n2
	..…	.	.….	.	.….		.	.….
	Level n	Result 1	Result 2	……	Result n

ACKNOWLEDGEMENTS

The authors thank Prof. Stauffer at Pennsylvania State University for his valuable suggestions and modifying the draft. We are also grateful to other members of Plant Quarantine and Invasion Biology Laboratory of China Agricultural University (CAUPQL). This study received financial support from the earmarked fund for China Agriculture Research System (201003079), Special Fund for Agro-scientific Research in the Public Interest (CARS-35), Innovation Fund for Graduate Student of China Agricultural University (KYCX2010017).


Xingli Ma

Zhihong Li

Wenlong Ni

Weiwei Qu

Jiajiao Wu

Fanghao Wan

Xuenan Hu

The Current and Future Potential Geographical Distribution of The Solanum Fruit Fly, Bactrocera latifrons (Diptera: Tephritidae) in China

Keywords: ArcGIS, Bactrocera latifrons (Hendel), climate change, CLIMEX, potential geographical distribution

The solanum fruit fly, Bactrocera latifrons (Hendel), is a major pest throughout South and South East Asia, including very few parts of southern China, and has invaded Hawaii and recently the continent of Africa (Tanzania and Kenya). With the development of international trade in fruits and vegetables, B. latifrons has become a potential threat to Chinese agriculture. In this study, CLIMEX 3.0 and ArcGIS 9.3 were used to predict the current and future potential geographical distribution of B. latifrons in China. Under current climatic conditions, its projected potential distribution includes most parts of southern China (about 32.2% of all 748 meteorological stations), from 16.544°N to 32.442°N. Optimal climate conditions occur in most areas of Yunnan, Guizhou, Sichuan, Chongqing, Hunan, Hubei, Jiangxi, Zhejiang, Fujian, Guangdong, Guangxi, Hainan, Taiwan, Hong Kong and Macao. The factors limiting the boundary of its suitability range are mainly the cold and dry stress. Climate change scenario for the 2020s indicates that the future potential geographical distribution will be increased by 5% of the total land areas of China, and the northern distribution boundary will move from 32.442°N to 33.408°N. There are 34 non-suitable climate sites change into suitable, mainly in Jiangsu, Anhui, Henan, Shanxi, Gansu, Sichuan and Tibet, because of China is likely to become hotter and wetter in the 2020s. In order to prevent the introduction and spread of B. latifrons, the present plant quarantine and monitor measures should be enhanced more where are projected to be suitable areas under current as well as future climatic conditions. At the same time, we should strengthen education for the public's awareness of plant protection.

Introduction

The solanum fruit fly, Bactrocera latifrons (Hendel) ( Diptera:Tephritidae), native to South and South East Asia [START_REF] White | Fruit flies of economic significance: their identification and bionomics[END_REF], is known as a pest of solanaceae crops, and some cucurbitaceae plants were also reported as host fruits in Hawaii [START_REF] Liquido | Ecology of Bactrocera latifrons (Diptera: Tephritidae) populations: host plants, natural enemies, distribution, and abundance[END_REF]. Now, it is widespread in South and South-East Asia, such as Brunei Darussalam, China (Guangdong, Guangxi, Hainan, Hong Kong, Yunnan, Taiwan), India (Karnataka, Tamil Nadu and West Bengal), Indonesia, Japan (Ryukyu Archipelago), Laos, Malaysia (Peninsular Malaysia and Sabah), Pakistan, Singapore, Sri Lanka, Thailand, Vietnam [3]. It is a very damaging pest wherever it occurs [START_REF] Yuko | Invasion of solanum fruit fly Bactrocera latifrons (Diptera: Tephritidae) to Yonaguni Island, Okinawa Prefecture, Japan[END_REF], for example, 60-80% of red pepper was lost in Malaysia due to B. latifrons [START_REF] Vijaysegaran | Fruit flies in Peninsular Malaysia: their economic importance and control strategies[END_REF]. As well as being a serious pest in South and South-East Asia, B. latifrons has established in a number of other regions. In Hawaii, B. latifrons was first discovered on Oahu in 1983 [START_REF] Vargas | Life history and demographic parameters of Dacus latifrons (Diptera: Tephritidae)[END_REF] and became sequentially distributed throughout the major islands [START_REF] Liquido | Ecology of Bactrocera latifrons (Diptera: Tephritidae) populations: host plants, natural enemies, distribution, and abundance[END_REF]. Recently, it has invaded Africa, being detected in Tanzania in 2006 [START_REF] Mwatawala | Detection of the solanum fruit fly, Bactrocera latifrons (Hendel) in Tanzania (Dipt., Tephritidae)[END_REF] and in Kenya in 2007. Surveys were conducted in different parts of Tanzania during 2006~2007, the species was widespread throughout the country but most abundant in the north-eastern region close to the border with Kenya [START_REF] Mziray | Spatial and temporal abundance of the solanum fruit fly, Bactrocera latifrons (Hendel), in Morogoro, Tanzania[END_REF]. They also indicated that the population of B. latifrons based on infestation rates and incidence in host fruits, seems to be relatively high during the wet seasons, probably because of availability of many hosts; the surveys further indicated that B. latifrons is more abundant in low to medium altitude areas compared to high altitude areas [START_REF] Mziray | Spatial and temporal abundance of the solanum fruit fly, Bactrocera latifrons (Hendel), in Morogoro, Tanzania[END_REF].

B. latifrons has been intercepted in China from 1994 [START_REF] Liang | Bactrocera latifrons (Hendel) was intersepted in the imported chili from Thailand[END_REF], and the frequency of interception of B. latifrons in China in recent years increased annually. For example, from 2003 to 2008, according to the intercepted fruit flies information provided by Chinese Academy of Inspection and Quarantine, China had intercepted B. latifrons a total 2156 times, most of which came from the fruit carried by incoming travelers. Ministry of Agriculture of the People's Republic of China (PRC) had listed B. latifrons and all other Bactrocera spp. on the entry plant quarantine pest list of the PRC in 2007. Predictably, with the increase of Chinese-foreign trade in agricultural products and the further opening of China's agricultural market, the risk of introduction of these fruit flies from abroad as well as further spread in our country will continue to increase. In addition, pest ranges are likely to shift in response to changes in temperature, soil moisture and humidity patterns. Therefore, the effects of climate change should also be taken into account when we are assessing the likely climatic suitability of B. latifrons.

CLIMEX is professional biological software that is widely accepted worldwide and used to estimate the potential geographical distribution and seasonal abundance of a species in relation to climate. It was first described by Sutherst and Maywald [START_REF] Sutherst | A computerised system for matching climates in ecology[END_REF], and has been used successfully to describe the potential distribution of other Tephritid fruit fly species, such as Ceratitis capitata (Wiedemann) [START_REF] Worner | Ecoclimatic assessment of potential establishment of exotic pests[END_REF][START_REF] Vera | Potential geographical distribution of the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), with emphasis on Argentina and Australia[END_REF], B. tryoni (Froggatt) [START_REF] Yonow | The geographical distribution of the Queensland fruit fly, Bactrocera (Dacus) tryoni, in relation to climate[END_REF][START_REF] Sutherst | The vulnerability of Australian horticulture to the Queensland fruit fly, Bactrocera (Dacus) tryoni, under climate change[END_REF], Carpomya vesuviana Costa [START_REF] Lv | Potential geographic distribution of Ber fruit fly, Carpomya vesuviana Costa, in China[END_REF], B. tsuneonis (Miyake) [START_REF] Wang | The potential geographic distribution of Bactrocera tsuneonis ( Diptera: Tephritidae)[END_REF]. This model also has previously been used to predict the effects of climate change on species' potential distributions using both global climate model (GCM) [START_REF] Kriticos | The role of modelling in weed management[END_REF] and synthetic climates [START_REF] Kriticos | Climate change and biotic invasions: a case history of a tropical woody vine[END_REF][START_REF] Kriticos | Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia[END_REF]. Examples of studied subject species are B. dorsalis (Hendel) [START_REF] Stephens | The current and future potential geographical distribution of the oriental fruit fly, Bactrocera dorsalis (Diptera:Tephritidae)[END_REF], Melaleuca quinquenervia [START_REF] Watt | The current and future potential distribution of Melaleuca quinquenervia[END_REF] and Nassella neesiana [START_REF] Bourdôt | The potential global distribution of the invasive weed Nassella neesiana under current and future climates[END_REF]. In this study, the CLIMEX 3.0 model was used to infer the response of B. latifrons to climate and to predict its potential distribution in China under current climate and future climate scenario out to the 2020s.

The Geographic Information System (GIS) is a space information system, a way of collecting, storing, managing, analyzing, describing the data of the Earth surface and geographical distribution. From the 60's of 20 th century up to now, the development of the world GIS experienced four stages: Expanding period consolidating period, development period and the consumers' period. It is widely used in almost all areas nowadays. In this article, the CLIMEX outputs were processed and visualized by using ArcGIS 9.3.

Materials and Methods

Overview of The CLIMEX Model and ArcGIS

CLIMEX is a dynamic model [START_REF] Sutherst | CLIMEX version 3 user's guide[END_REF] that integrates the weekly responses of a population to climate using a series of annual indices, which is based on the assumption that if you know where a species lives, you can infer what climatic conditions it can tolerate [START_REF] Sutherst | CLIMEX v2 CD and User's Guide[END_REF]. The model combines the growth index (GI) and the stress indices (SI) into an overall ecoclimatic index (EI) which is scaled from 0 for locations where the species is unable to persist to 100 for environments that provide perfect habitat all year round. In this study, EI is classified into four classes of climatic suitability for B. latifrons: unsuitable (EI = 0), marginal (0<EI≤10), suitable (10<EI≤20) and optimal (EI>20).

ArcGIS provides the integrated environment of mapping, display, edit and output maps, has powerful graphics editing features. Preparation of maps using ArcGIS, we must first get the map data in digital form. Then, symbolic the data and place map annotation. Finally, follow the application needs to produce a complete map, which includes the paper size settings, mapping scope and scale identification, map name, legend, grid reference, a compass and a series of elements placed. We made the maps of special subject display that the current and future potential geographical distribution of B. latifrons in China, and carried out the space distribution and analysis that comparing the different suitable degrees of areas projected under current and future climatic conditions by processing the CLIMEX outputs using ArcGIS 9.3.

Meteorological databases and Climate change

Two climate databases were used in this modeling exercise. Firstly, the CLIMEX standard meteorological dataset was used to create an initial fit. This dataset that comes with CLIMEX 3.0 consists of 30-year averages from 1961 to 1990 for an irregularly spaced set of 2500 climate stations. Subsequently, the relevant climate data for 748 weather stations from China as used in previous studies [START_REF] Lv | Potential geographic distribution of Ber fruit fly, Carpomya vesuviana Costa, in China[END_REF][START_REF] Wang | The potential geographic distribution of Bactrocera tsuneonis ( Diptera: Tephritidae)[END_REF][START_REF] Geng | Analysis of the suitability of Mexican fruit fly, Anastrepha ludens in China[END_REF][START_REF] Kong | A Predication of potential geographic distribution of melon fruit fly based on CLIMEX and DIVA-GIS[END_REF][START_REF] Rao | A predication of potential geographic distribution analysis of Queensland fruit fly based on DYMEX and DIVA-GIS[END_REF] were appended to the CLIMEX 3.0, which were acquired from the National Weather Bureau of China and include minimum temperature, maximum temperature, relative humidity, and rainfall for each month for each station (average values of 30 years from 1971 to 2000).

Due to the impact of human activities, the significant warming in China might continue. Comparing with the 30 years mean over 1961 to 1990, according to the projections [START_REF] Qin | Climate and Environmental Changes in China[END_REF], the nationwide annual mean temperature might increase by a range of 1.3~2.1℃ and the annual mean precipitation might increase by 2%~3% in 2020.

The electronic map of national boundary, province boundary and the distribution of national forest land used in this study were downloaded from http://nfgis.nsdi.gov.cn/ with the scale 1:4,000,000.

Fitting CLIMEX parameters

To fit the CLIMEX model of B. latifrons, the parameters were manually and iteratively adjusted until the simulated geographical distribution as estimated by the EI values coincided with the species known native distribution and the reported description of its range. The parameters were then validated using data from regions where B. latifrons has invaded or established. Parameters used in the CLIMEX model are presented in Table 1.

Degree-days per generation (PDD)

PDD is degree days required for a generation. The Parameter PDD for B. latifrons was reported 415.397 degree days [START_REF] Huang | Morphological identification, artificial diet, suitability analysis and Prediction, qualitative and quantitative risk analysis of important Bactrocera species[END_REF]; we adjusted it to 415.4 degree days used for the CLIMEX model.

Temperature index

The lowest overwintering temperature, the highest over summering temperature and lower threshold temperature of B. latifrons were reported as -3.7, 36 and 15.68℃, respectively [START_REF] Huang | Morphological identification, artificial diet, suitability analysis and Prediction, qualitative and quantitative risk analysis of important Bactrocera species[END_REF]. At 16°C, reproduction was completely suppressed and the survival rate of larvae was only 2.6% in B. latifrons [START_REF] Vargas | Survival and development of immature stages of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures[END_REF][START_REF] Vargas | Demography of four Hawaiian fruit flies (Diptera: Tephritidae) reared at five constant temperatures[END_REF]. So the minimum temperature and upper threshold temperature for development (DV0 and DV3) was set at 15.7 and 36℃, respectively. B. latifrons (Hendel) is native to South and South-East Asia, so the lower and upper temperature optima (DV1 and DV2) were set at 18 and 33℃, respectively.

Moisture index

The moisture requirements of B. latifrons are mediated through their host plants. The lower soil moisture limit for development (SM0) was set to 0.1 to indicate the permanent wilting point, which is normally about 10% of soil moisture. The lower and upper limits for optimal growth (SM1 and SM2) were set to biologically reasonable levels for many host plants; and B. latifrons like moist environment, so SM1 and SM2 were adjusted to be slightly higher, but also ensures distribution of B. latifrons in North Asia to Pakistan border. The selected value for SM3 was a compromise determined from fitting the threshold soil moisture wet stress threshold (SMWS). A lower value of SMWS would have made it more difficult to achieve a satisfactory fit to the known distribution in southern Asia.

Cold stress

TTCS was set to 2.0℃. The northern Asian boundary of B. latifrons is indistinct. Pakistan are suitable; in China, B. latifrons was detected in Tianlin and Baise in Guangxi [START_REF] Deng | Investigation and monitoring of fruit flies in Guangxi province[END_REF], Wanting and Ruili in Yunnan province [START_REF] Liang | The note of fruit flies in south parts of China, and two newly recorded species[END_REF]. Accordingly, parameters were adjusted to allow suitable in some areas of Pakistan, persistence in Yunnan and Guangxi provinces of China and at higher altitudes in Hawaii.

Heat stress

According to the highest over summering temperature B. latifrons is 36℃ [START_REF] Huang | Morphological identification, artificial diet, suitability analysis and Prediction, qualitative and quantitative risk analysis of important Bactrocera species[END_REF] and the upper tolerable temperatures for egg development and hatching of B. latifrons was 46 to 48℃ [START_REF] Armstrong | Thermal Death Kinetics of Mediterranean, Malaysian, Melon, and Oriental Fruit Fly (Diptera: Tephritidae) Eggs and Third Instars[END_REF], TTHS was set to 36℃ to account for the averaging effect of climate, whereby several days where temperatures exceed 40℃ may be expected in a long-term climate record where the monthly average of daily maximum temperature is 36℃.

Dry stress

To be consistent with SM0, SMDS was set to 0.1 to indicate the permanent wilting point, which is normally about 10% of soil moisture.

Wet stress

Wet stress was adjusted to limit the southerly distribution in Asia. According to CABI [3] records, in the south, the critical fitting considerations were the reported presence of the fly in Malaysia (Peninsular Malaysia and Sabah), Singapore and Indonesia. SMWS and HWS were adjusted to ensure that the distribution has been reported in Asia in the southern border.

Results

Potential geographical distribution under current climate

Under current climate, considering the parameters in In order to visually see the difference in the potential geographical distribution for B. latifrons under current and future climatic conditions, the suitable areas of different degrees projected under current and future climate scenarios were calculated by ArcGIS 9.3 software, respectively. Then, these suitable areas' different proportions of the total land areas of China were displayed in the line graph (Fig. 3). To the 2020s, it was shown that unsuitable areas' proportion of the total land areas of China will fall 5% from 76% to 71%, marginal areas' proportion will increase 2% from 5% to 7%, suitable areas' proportion will maintain at 2% and optimal areas' proportion will rise 3% from 17% to 20%. Therefore, all suitable areas will be increased by 5% when it comes to 2020s.

Discussion

Analysis of limiting factors for distribution of pests

Ecoclimatic factors, such as temperature, humidity and light, are the most direct factors in limiting the potential geographical distribution of pest, in addition, other factors, especially soil types, geographical features, natural barriers, vegetation types should also be considered. Due to the CLIMEX model only taking these ecoclimatic factors into account, there is no doubt that the predicted results have practical restrictions. Therefore, in order to get more scientific predictions, we should take all of the above factors into consideration.

The most important thing is that the relationships between species and human activities will greatly affect the actual distribution of pest in their potential geographical distributions. For example, the development of international trade in fruits and vegetables and the increasing incoming travelers carried fruit will remarkably raise the risk of introduction of pest into a new area. Consequently, these are the starting point and foothold, and then, all of the above factors should be considered when we are framing prevention and control measures.

Development of prevention and control measures

The significant increases in the potential distribution of B. latifrons is projected under the climate change scenario in the 2020s suggest that the biosecurity authorities should consider the effects of climate change when undertaking pest risk analysis. Of course, establishing invasive alien species risk assessment system, improving early warning and rapid response mechanisms, strengthen international exchanges and cooperation, and the establishment of data sharing are very important in preventing the invasion of alien species is important.

In order to prevent the introduction and further spread of B. latifrons, the airline passengers must be strictly prohibited from bringing fruits or vegetables into China; Quarantine licensing system for imported fruits should be strictly implemented; we must step up port inspection and surveillance work. Last but not least, strengthen the public education is the most basic and most effective measures in limiting the introduction and spread of this fruit fly.

Fig. 1 .

 1 Fig.1. The climate suitability (EI) for the solanum fruit fly under the current climate (1961-1990 averages) projected using CLIMEX3.0 (□, unsuitable (EI=0); ■, marginal (0<EI≤10); ■, suitable (10<EI≤20); ■, optimal (EI>20))

Fig. 2 .

 2 Fig.2. The climate suitability (EI) for the solanum fruit fly in the 2020s projected using CLIMEX3.0 (□, unsuitable (EI=0); ■, marginal (0<EI≤10); ■, suitable (10<EI≤20); ■, optimal (EI>20))

Fig. 3 .

 3 Fig.3. The proportions of the suitable areas of different degrees of the total land areas of China under the current and future climate conditions

Table 1 .

 1 Parameters used in the CLIMEX model for the solanum fruit fly, B. latifrons.

	Parameter	Mnemonic	Value
	Lower threshold temperature	DV0	15.70
	Lower optimum temperature	DV1	18.00
	Upper optimum temperature	DV2	33.00
	Upper threshold temperature	DV3	36.00
	Degree-days to complete one generation	PDD	415.40
	Lower threshold of soil moisture	SM0	0.10
	Lower limit of optimum soil moisture	SM1	0.50
	Upper limit of optimum soil moisture	SM2	1.00
	Upper threshold of soil moisture	SM3	1.80
	Cold stress temperature threshold	TTCS	2.00
	Cold stress accumulation rate	THCS	-0.10
	Heat stress temperature threshold	TTHS	36.00
	Heat stress accumulation rate	THHS	0.005
	Dry stress soilmoisture threshold	SMDS	0.10
	Dry stress accumulation rate	HDS	-0.005
	Wet stress soil moisture threshold	SMWS	1.80
	Wet stress accumulation rate	HWS	0.002

Table 1 ,

 1 the potential geographical distribution of B. latifrons in China was predicted and mapped. The predictive distribution map (Fig1) suggested that, from 16.544°N to 32.442°N, about 241 meteorological stations (about 32.2% of all stations) in most parts of southern China are projected to be suitable for B. latifrons. Optimal climate conditions occur in most areas of Yunnan, Guizhou, Sichuan, Chongqing, Hunan, Hubei, Jiangxi, Zhejiang, Fujian, Guangdong, Guangxi, Hainan, Taiwan, Hong Kong and Macao. Climatic conditions are projected to be marginal in parts of Jiangsu, Anhui, Henan, Shaanxi, Gansu, Sichuan and Tibet, where the principle range-limiting factor is likely to be cold stress and reduction in rainfall. The remaining 507 meteorological stations in northwest, northeast and central China were unsuitable for the establishment of B. latifrons.

  ). It will be increased by 5% of the total land areas of China compared with the current potential geographical distribution, and the northern distribution boundary will move from 32.442°N to 33.408°N. High suitable areas for B. latifrons will increase from 232 meteorological stations to 265, and the expansion is mostly in Guizhou, Sichuan, Hubei, Anhui, Jiangsu. There are 34 non-suitable climate sites change into suitable, mainly in Jiangsu, Anhui, Henan, Shanxi, Gansu, Sichuan and Tibet.

Acknowledgement

The authors would like to thank Mr. Ke Chen for his kindly guidance of information collection. We also acknowledge Jing Yu and other members of Plant Quarantine Laboratory of China Agricultural University for their help. This study received financial supports from the 973 project (2009CB119200), the 11th Five-Year Support Project of China (2006BAD08A15) , the public service sectors (agriculture) special research project (200903034) and the International Science & Technology Cooperation Program of China (2011DFB30040).


Juan Feng

Shengwei Wang

Gang Liu

Lihua Zeng

A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information 

Keywords: Separating method, Chain code difference, Concave point, Match

Fruit location is an important parameter for apple harvesting robot to conduct picking task. However, it is difficult to obtain coordinates of each apple under natural conditions. One of the major challenges is detecting adjacent fruits accurately. Previous studies for adjacent detection have shortcomings such as vast computation, difficulty in implementation and over-segmentation.

In this paper, we propose a novel and effective separating method for adjacent apples recognition based on chain code information and obtain the centroid coordinates of each fruit. Firstly, those valid regions of fruit are extracted by pre-processing the initial image. Secondly, chain code information is obtained by following the contour of extracted regions. Thirdly, through observing the changing law of chain code difference and adopting local optimum principle, concave points are found. Finally, the best point pairs are determined with different matching principles, and those adjacent apples are separated exactly.

The experimental results show that the average rate of successful separation is greater than 91.2% with the proposed method, which can meet the requirements of applications in harvesting robots.

Introduction

Apple harvesting robot is one of important research and application fields in agriculture, the accurate and automatic recognition of fruit is part of its primary task [START_REF] Bulanon | A Color Model for Recognition of Apples by a Robotic Harvesting System[END_REF] [2] [START_REF] Zhao | On-tree Fruit Recognition Using Texture Properties and Color Data[END_REF]. In the process, multi-fruit adjacent recognition is more difficult than the others, because the adjacent region is apt to be considered as a whole, and centroid coordinates acquired are not the ones of single fruit. In this case, Not only is recognition rate affected, but also fruit may be damaged by hard manipulator and its quality can be affected. How to recognize centroid coordinates of each apple is a problem not satisfactorily resolved yet, due to the complexity of environment.

In the research of adjacent objects separation, many scholars have put forward different methods, Such as mathematics morphological operation [START_REF] Zhou | Comparison of Two Algorithms Based on Mathematical Morphology for Segmentation of Touching Strawberry Fruits[END_REF] [5] [START_REF] Chinchuluun | Citrus Yield Mapping System in Natural Outdoor Scenes Using the Watershed Transform[END_REF], improved watershed algorithm [START_REF] Lee | Recognition of Partially Occluded Plant Leaves Using a Modified Watershed Algorithm[END_REF], and active contour model tracking algorithm [START_REF] Wang | Automatic Segmentation of Touching Rice Kernels with an Active Contour model[END_REF], etc. However, there are some obvious shortcomings in these algorithms, including a need to track the process of calculation every time, computation is vast, the demand of computer software and hardware is higher, and results often appear over-segmentation.

Other scholars have studied unique brightness information within adjacent region to separate the fruit [START_REF] Zhang | Separating Adjoined Apples Based on Machine Vision and Information Fusion[END_REF], but the brightness difference on the surface of fruit is an indispensable factor. Edge concave points split touching objects are widely used in the field of cell segmentation [START_REF] Liu | Automatic Segmentation of Overlapping Powder Particle Based on Searching Concavity Points[END_REF]. Its main advantage is in a faster processing speed.

Extracting and matching those concave points are two important aspects. Using the method for separating adjacent fruit has never been reported before. Under natural conditions, noises, branches and leaves make fruit boundary uneven, and the adjacent fruit recognition in real time also need to be taken into account, in this way, conventional method is not applicable. This paper presents a new separating method of adjacent apples based on machine vision and chain code information. It consists of six main steps: extraction of valid region, acquirement of chain code information, computation of chain code difference between discrete points, selection of concave points referring to given thresholds, match of those points according to different principles, determination of the separating point pairs and realization of fruit separation. The experimental results show that method is simple, effective, accurate and ideal for separating adjacent apples.

2

Materials and Methods

Materials

In this study, the cultivar of apple was Fuji. A digital camera (DH-HV3130UC, CMOS color camera) was selected and shooting distance was 1.5m. A laptop was used to program and drive the camera to acquire the RGB images. Image frames were 512×512 pixels in the BMP format. The software for image processing was MATLAB7.0.

Valid Region Extraction

Observing those apple pictures taken from natural environment, the color of ripe fruit is red while most of background is green. Both ends of a component of Lab color space are just the two colors. Therefore, the model was used for segmentation. The component can be matched by a non-linear combination of tricolor (Red, Green and Blue) [START_REF] Annerel | Methods to Quantify the Colour Development of Concrete Exposed to Fire[END_REF]. The transformational relationship is known as Eq. (1). Due to the influence of natural luminance, some objective colors aren't red, which results in losing some pixels after image segmentation. In order to reduce the effect, a

Gaussian lowpass filter was used to blur the initial image [START_REF] Lak | Apple Fruits Recognition Under Natural Luminance Using Machine Vision[END_REF]. An example is displayed in Fig. 1.Then a component was used to separate fruits from background.

OTSU is regarded as an optimal means for auto threshold selection, it was used here to convert filtered image to binary form [START_REF] Otsu | A Threshold Selection Method from Gray-level Histograms[END_REF]. Medium value filter and area threshold were employed to remove noises.

The size of each ripe fruit is approximate and its shape is close to circle. When shooting distance is fixed, the size of fruits located the same distance on the image is similar. Calculating the sum of pix in many single-fruit regions, average area of fruit is obtained; Rotating single fruit region to find maximal width of all external rectangles and treating the width as average diameter of apple. By means of these methods, S threshold and D threshold were calculated, which are both a vital requirement of region extraction and constrain separating. It has been validated using data, finding that best value was obtained when S threshold was set at 5800 and D threshold was set at 97.

Every area in the binary image was calculated. There was a multi-fruit adjacent region if its area value was greater than S threshold and the region was kept. On the contrary, there was a single apple and the region should be deleted by way of turning its gray value into zero [START_REF] Bulanon | A Fruit Detection System and an End Effector for Robotic for Robotic Harvesting of Fuji Apples[END_REF].

From the above process, adjacent region on the apple image was extracted, which was called valid region. Speeds of separation and centroid coordinates determination would be accelerated. This paper exploits an idea based on boundary curvature to detect, because curvature usually indicates the curving degree of border well. However, discrete curve is discontinuous and non-differentiable, it is difficult to calculate the curvature directly.

Separating Method

Using the slope-difference of adjacent boundary line to describe curvature of line segment intersection proves to be reasonably effective [START_REF] Gonzalez | Digital Image Processing Using MATLAB[END_REF]. This Freeman chain code represents a chain as a sequence of direction codes from one pixel to the adjacent one. This code is highly effective for compression of line images. According to the contour information of valid region from the boundary trace, chain code is obtained [START_REF] Liu | Cotton Recognition Based on Randomized Hough Transform[END_REF]. The common eight-direction chain code for the representation of line-drawing data has low precision , because one difference equals to 45° [START_REF] Freeman | Computer Processing of Line-drawing Date[END_REF]. The concept of average chain code is introduced, which describes the direction of line approximately, and can be replaced by chain code sum if the number of points involved is fixed. Sum(i) denotes chain code sum and is given as Eq.2.

Sum(i)=A(i)+A(i-1)+…A(i-n+1) (i=1,2,…n) (2)
Where A(i) denotes absolute chain code of current point, specific procedure can be found in [START_REF] Zhu | Concave Spots Localization and Region Segmentation in Fibrous Material Image Based on Chain Codes[END_REF]. A local boundary is similar to straight line, so its tangent direction(slope) can be figured by the absolute chain code sum of n sequential points.

Selecting n=3 means the whole circle being divided into 24, one difference equals to 15°, therefore, the accuracy of direction judgment improves greatly.Diff(i) denotes the difference of chain codes sum between the n sequential points ingoing and outgong, and is shown as Eq.( 3). The value is proportional to curvature, which indicates the direction of boundary exactly and its positive and negative describes the convexo-concave of concerns. If the boundary is clockwise, those positive chain code difference shows concave points, while those negative shows convex points. The graph of Sum(i) and Diff(i) is shown as Fig. 3.

Diff(i)= Sum(i+n)-Sum(i) (i=1,2,…n) (3)
In order to calculate an arc length of boundary, a concept of accumulated length is introduced, which indicates a length between starting and current point. Therefore, arc length between any two points is equal to difference of each accumulated length. For briefness, a step of level and vertical direction was set at 1,while other steps were set According to different matching principles, point pairs matched could be located.

Then, these point pairs were connected to form a separating line, which could realize the separation of adjacent apple. It is shown as Fig. 2c.

If there is a region of two adjacent fruit, namely, expression is exhibited as S threshold <S l ≤2S threshold , where S l is the area of No.l valid region, only is a pair of concave points obtained at last , so there is no need to match, but to connect points directly.

If there is a region of many adjacent fruits, several pairs of concave points are obtained, which is shown in Fig. 2b. It is obvious that there is a problem of concave pointe's combination and match. On the basis of test and analysis, the matching procedure includes two following steps:

(1) Start from the first element in the two-dimensional array of concave points, and calculate the chord length between current and other point. The Equations can be expressed as Eq.( 4):

C(i,j)= ((x i -x j ) 2 +(y i -y j ) 2 ) 1/2 (i,j=1,2,…n) (4)
Where C(i,j) is chord length between i point and j point. In order to advoid the over-segmenting, those point pairs satisfy the predefined distance limits C(i,j)<D threshold , Which are saved as matching point candidate.

(2) Let AC=Arc(i,j)/C(i,j) denotes the rate of arc and chord. Objective boundary satisfied the major arc limitation, AC>/2 can form arc.As far as optimum separating position is concerned, not only is its curvature relatively big but AC is local maximum.

By comparision AC of point pairs obtained from step 1 , the maximum and mark the corresponding points position can be found. As soon as a pair of matching points is determined, the gray value of separating line is changed into 0, then the region is divided into two parts.To judge from S threshold , whether and which part has still adjacent fruits, and repeat the above process until all adjacent regions are separated completely. The separating flow is described as Fig.

Centroid Coordinates Computation

After the separation of adjacent fruit region, each centroid coordinates calculated was regarded as approximate central position corresponding to a apple. Result is shown in Fig. 2d. Their values are often not an integer. Considering a fruit region's size is far greater than a pixel'one, and the rounding coordinates will not cause an obvious effect on accuracy for post process. weighs the disparity, the expression is followed as Eq.( 5):

d mc = ((x m -x s ) 2 +(y m -y s ) 2 ) 1/2 (5)
Where (x m ,y m ) denotes centroid coordinates acquired by artificial method, (x s ,y s ) denotes centroid coordinates acquired by the separating method. Where N S is the number of apple meeting the expression, d ms <10, N T is the total number of adjacent apples. Through testing with 100 apple images, we draw a conclusion that the average rate of successful separation is greater than 91.2%, which meets the requirements of application in harvesting robots. Furthermore, variable light has not obvious impact on the separation.

Conclusion

In this study, a novel and effective method for separating adjacent apples is developed in spite of various natural lighting conditions. Characteristics are as follows:

(1) In order to reduce the effect of natural luminance, a Gaussian lowpass filter is used to blur the inital image. Considering the feature of apple image itself, the a component of Lab color space is employed to separate fruits from background, then medium value filter and area threshold are made use of removing image noises.

Following the above steps, ideal binary images of apple are acquired for post processing.

(2) Valid regions of adjacent apples are extracted by area threshold method, which can improve the speed of total image processing. Following the contour of extracted regions, chain code information is obtained. Observing the changing law of chain code difference and adopting local optimum, concave points are found. Through the different matching principle, best point pairs are determined and adjacent apple can be separated accurately. The rate of arc and chord and the length of segment chord are key parameters of matching principle. Experimental results shows that average rate of successful separation is greater than 91.2% by this method, and meet the practical need of harvesting robot.

(3) However, there are some limitations for this method: Need of obvious concave-convex change of adjacent boundary; chain code information can be taken full advantage for separating fruits; Fruits are occluded by leaves and branches and its boundary is brought about some marked concave points, which will influence extraction and match of concave points.

a=500[ 1 . 1 )Fig. 1 .

 111 Fig. 1. Filtering the apple image (a) Original image, (b) Blurring image

Fig. 2 .

 2 Fig. 2. Procedure of separation (a) Valid region image (b) Concave points image (c) Separated image (d) Centroid coordinates image

at 1 . 5 .

 15 The corresponding rate of arc and chord was obtained by calculating distance and accumulated length between two points.The boundary of the digital image is not absolutely smooth, and corners are mainly points ,whose chain code differences do not belong to the interval [-1,2]. For the sake of finding concave points of adjacent fruit boundary, those points corresponding to big curvature are extracted, where Diff(i)>M(i=1,2,…n) is a requirement. After repeated test, M=2.5 was proved to be the best threshold. Chain code information of points extracted was stored into a two-dimensional array.

Fig. 3 .

 3 Fig. 3. Graph of chain code sum and chain code difference

Fig. 4 .

 4 Fig. 4.The flow chart of separation

Fig. 5 .

 5 Fig. 5. Images for experiment (a)The back lighted apple images including a two adjacent apples region (b)The back lighted apple images including a multi-apple adjacent region (c)The front lighted apple images including three adjacent regions

Table .

 .

	defined as Eq.(6).
	R S =(N S /N T )×100%	(6)
		1 exhibits the
	comparison.
	When d mc <10, it means successful in separation, Table.1 showed the most of results
	meeting the above requirement, only two was largely different.No.5 fruit was
	overlapped by front fruits on the Fig.5b, most parts occluded were separated during
	the image processing, which resulted in notable coordinates migration; No.7 fruit was
	occluded by the leaves and branches, which made its area smaller after the separation
	of a component and its coordinates migrate. The rate of successful separation is

Table 1 .

 1 Comparison of experimental results between artificial calculation and separating

			methods
			Centric coordinate	Centric coordinate
	Figure	Apple	of artificial	of separating	centroid distance
	Number	number	calculation	method	d ms
			(x m ,y m )	(x s ,y s )
		1	(161,253)	(162,253)	1.00
	a
		2	(198,214)	(201,213)	3.16
		3	(334,170)	(337,172)	3.61
		4	(395,202)	(395.198)	4.00
	b
		5	(440,172)	(426,165)	15.65
		6	(456,197)	(460,196)	4.12
		7	(226,172)	(232,181)	10.81
		8	(279,210)	(282,213)	4.24
		9	(315,280)	(314,277)	3.16
	c	10	(200,377)	(198,376)	2.24
		11	(272,375)	(273,382)	7.07
		12	(358,458)	(354,453)	6.40
		13	(412,418)	(412,417)	1.00

* The research was financially supported by National Natural Science Foundation(31071333)


Zhimin Liu

Weidong Xiong

Xuewei Cao

Design of Precision Fertilization Management Information System on GPS and GIS Technologies 1

Keywords: Precision Fertilization, PFMIS, GPS, GIS, CORS

Aimed on the efficiency, energy-saving, yield and pro-environment, the precision agriculture is needed to develop. Presently, the field information quick-acquisition technology has become the important topics in the international precision agriculture research fields, because that still far behind the development of other precision agriculture technologies. The technological innovations focus on the new surveying technology, such as the fast real-time operation, which are helpful for improving sampling densities and accuracy. The author firstly summarized the three aspects development of data acquisition, data processing and management information system based on GPS and GIS of Precision Fertilization Management Information System (PFMIS), then we designed the framework of PFMIS which included the data-acquisition technique on the continuously operating reference stations (CORS), the dataprocessing methods on intelligence algorithms and Management Information System (MIS) on ArcGIS Engine, in order to satisfy the needs of network publication and query to precision fertilization.

Introduction

In the information society, precision agriculture has become the focus on the frontier forces to how to use rationally the agricultural resources, enhance the quality and quantity of the agricultural production, reduce the production costs, and improve the ecological environment and sustainable agricultural development in the international developed country in the 21st century [START_REF] Yael Edan | Automation in Agriculture[END_REF]. Based on the information and knowledge to the agricultural production management systems, the practice to this new concept would have the revolutionary significance, that promote the transformation of the information technology application, and enhance the civil traditional agriculture level and agricultural equipment technology, develop the research and application of agricultural production-oriented of information technology, and achieve sustainable agricultural development [START_REF] Wang | Development of precise agriculture and innovation of engineering technology[END_REF][START_REF] Wang | Thinking Through the Experiment, Demonstration and Development Research on Precision Agriculture[END_REF]. Food shortage and fertilizer price-rising is a global issue. And with the multifaceted problems on national economy of the impact on food security, environmental pollution, lack of resources, increased costs, the precision fertilizer management information systems (PFMIS) would be established, that included the spatial data collection, soil nutrient data test, data standardization, management information system platform, the expert systems of the fertilizer recipes and fertilization model, and network queries, in order to achieve "increased food production and fertilizer efficiency". Therein the soil nutrient tester [4] had been put into the full production. Moreover the foundation on the expert systems would not research in this paper including the fertilizer recipes and fertilization model with the different crops, geographical location and climate varies [START_REF] Chen | Design and implementation of precision rubber fertilization information system based on Web GIS[END_REF][START_REF] Chen | Tobacco Precision Fertilization Formula Information System Based on GIS[END_REF][START_REF] Sun | Design and realization of vegetable precise fertilization expert system[END_REF][START_REF] Wang | The Research on the Method of Precision Fertilization Based on GIS and GPS in China[END_REF][START_REF] Xu | The Pre-Investigation of Precision Fertilization and Expert Consultation System[END_REF].

Presently, the research of field information rapid acquisition technology is still far behind the other technologies in support of precision agriculture, and it had become an important issue on the international related research field. The keys on technological innovation are the fast real-time survey method in order to increase the sampling density, and find new sensor technology to meet the actual production requirements or even further improvements [START_REF] Wang | Development of precise agriculture and innovation of engineering technology[END_REF]. Author firstly overviewed three areas development of PFMIS on data collection, data processing and information management systems based on GPS (Global Positioning System) and GIS (Geographic Information System) technologies, secondly designed the PFMIS framework with the new technical methods, thirdly its advantages and feasibility were analyzed.

The development of precision fertilization based on GPS and GIS

Precision fertilization agriculture technologies are generally the agriculture management information system [START_REF] Blackmore | Precision Agriculture: an introduction[END_REF], which can capture quickly and efficiently and describe the spatial information of the variable environment impacted on crop growth, and are important to carry out the precision agriculture practice. The spatial coordinates and their varieties attribute information of soil resources were obtained after collecting data. Then data-processing technology and information management systems of attribute information based on spatial coordinates are keys.

2.1

The spatial data surveying technique of PFMIS RS (Remote Sensing) and GPS single point positioning were the popular surveying modes on the spatial data acquisition [START_REF] Lu | Application of GPS in Precision Agriculture[END_REF][START_REF] Zhang | The Study of the Collection and Process of Field Information of Precision Agriculture Based on the GPS and GIS[END_REF]. RS technology was one of main field-data sources to precision agriculture, which collect the ground spectral reflectance features used relative high-resolution-rate sensors, to monitor comprehensively in different crop growing period, in order to provide a large number of the space-time change information in farming field. Then the analyses of spatial characterization and location were finished based on the spectral information [START_REF] Wang | The earth observation technology and precision agriculture[END_REF]. The time-series images, obtained by RS, were widely applied on the large-scale agricultural yield estimation. Because of some shortages such as the low-resolution rate spatial information, data post-processing, related support needs and lack of infrastructure, and so on, now GPS was used to make up the soil samples collected and fertilizer operations in positioning and navigation. GPS single point positioning can collect spatial information on kinematic real-time, all-weather, and convenient mode. Only one GPS receiver was used to arrive to the positioning accuracy in the 10m level. But the accuracy level can not meet the requirements of the precise agriculture, while time, some shortages of high cost, low efficiency and poor real-time on the updating database; Otherwise DGPS (differential GPS) requires less than 2 receivers, one for the base-station, others for the mobile-stations, which must keep distance to base-station within 20km, and the code observations can be achieved [START_REF] Zhou | Theory and application of GPS satellite surveying(Revision)[END_REF][START_REF] Hofmann-Wellenhof | GPS Theory and practice[END_REF][START_REF] Xu | GPS Theory, Algorithms and Applications[END_REF], which is a large farm operations is limited.

With the development of CORS established more and more in the region, and in different fields, the RTK positioning and navigation realized conveniently in the centimeter-level precision used the network RTK technology on at least one receiver [START_REF] Hofmann-Wellenhof | GPS Theory and practice[END_REF][START_REF] Xu | GPS Theory, Algorithms and Applications[END_REF][START_REF] Dang | Principles and applications of global navigation satellite system[END_REF][START_REF] Huang | Theory and Practice of Global Positioning System[END_REF][START_REF] Tang | Ambiguity resolution of single epoch single frequency data with baseline length constraint using LAMBDA algorithm[END_REF]. This paper designed the spatial data -soil resources information and data acquisition subsystem by GPS CORS technology, and improved the positioning accuracy and stability by accordingly data processing and software development.

Data processing method of the soil resource information

After data collection, a variety of attribute information on spatial coordinates data were accessed: The physical and chemical properties of soil include soil types, soil texture, organic matter and soil pH, and other data. Soil nutrient data include soil nitrogen, phosphorus, potassium and other large number of nutrients and available nutrient data, calcium, magnesium, sulfur, chlorine, zinc, manganese, copper, iron, molybdenum and boron and other micronutrients data are included [START_REF] Chen | Tobacco Precision Fertilization Formula Information System Based on GIS[END_REF]. The key issue is how to process the data transformation from point-to-surface of the soil resource attribute data. The spatial interpolation methods were adopted on data-processing, mainly the Kriging geo-statistical interpolation and inversely proportional to distance interpolation method. On the condition of the same sampling density, in descending order of interpolation precision is plains, post area, hills, mountains, that is, with the complexity increased of the terrain interpolated, the accuracy was reduced. While time the sampling density increased more on the hills, mountains, etc., and the accuracy of the results were improved more [START_REF] Lei | Comparison of Kriging interpolation precisions in different topographical units and number of samples:a case study of spatial distribution of soil total nitrogen in the Shucheng County[END_REF]. Compare with the neural network methods (NN), as the sampling density is high, the same accuracy of the results of Kriging and inversely proportional to distance interpolation methods was obtained; but the case of low sampling density, the results of neural networks were the better accuracy.

Mueller T.G. and so on [START_REF] Mueller | Soil fertility map quality: case studies in Kentucky[END_REF] experimented on the impact of soil fertility distribution to the different grid sampling density and interpolation methods. They selected the three fields, the different sampling plan were made according to the different sampling instance points and the different interpolation of Kriging and inversely proportional to distance methods. They drew the different attribute the spatial distribution of the soil available P, K and PH value, and calculated the standard deviation values of the above interpolation results. The results showed that Kriging interpolation accuracy is different for the soil available P, K and PH in the different plots of the same kind of soil properties. And in the same plot, the available P of different sampling interval of Kriging results indicated that the higher sampling density, the higher the interpolation accuracy.

In summary, the spatial variability laws of the agricultural soil information have their own characteristics. And the qualitative and quantitative relationship between crop production and soil properties on the spatial distribution was reported rarely. So the spatial heterogeneity of the soil information under certain conditions still needs further study. The artificial neural network (ANN) is a self-adaptive method of mapping, and do not make any assumptions in data processing. ANN is reasonable in theory, and be able to avoid the unknown factors, to reduce the model error. So ANN's results are high accuracy. The spatial interpolation accuracy based ANN are higher and more stable than the usual variety of data interpolation methods, and the fewer number of sample points are required, the results can be directly obtained, the loss of accuracy are reduced in the middle link. Therefore, the rational design of NN can be efficient to solve the fertilization test zone on the condition of the less known points.

Management information platform on GIS

GIS are computer software platforms used for input, storage, retrieval, analysis, processing and presentation of geospatial data. And it associated with each other coordinates on the same location values based the geo-spatial database of the geographic characteristics. GIS is the brain of precision agriculture. And GIS firstly deposited the advance decision-making system, such as expert systems, and the data processing module, then received the collected information from the real-time sensor on variable rate fertilizer and the monitoring systems (GPS, RS, etc.), thirdly showed and mapped electronic pictures on unified coordinate system for these data through organization, processing, statistical analysis, finally made intelligent decisions through the expert system and achieve adjust the amount of inputs or operations used automatic controller-executer equipment [START_REF] Wang | The earth observation technology and precision agriculture[END_REF]. Domain is the computer jargon of the functional domain covered with group systems on the similar needs or software applications. Domain engineering is the procedure that certain a system of domain application was analyzed, the common and variability of demands was refined, the domain models were made, and the architectures were designed, then the reusable components of domain were developed and organized. Domain engineering includes several parts of domain analysis, design, implementation and spread out. Domain engineering provides a strong support to software reuse, which helps to produce components with high reusability, and its product is the domain analysis model and domain-specific software architecture [START_REF] Ren | Research on the Integration of Geography Information Systemand Model in Multiple Tier System[END_REF][START_REF] Wang | Realization of geological section map model based GIS template[END_REF]. In this part, authors make a guide use the knowledge and theory of the domain engineering, software components, design patterns and others, and utilize the ESRI's ArcGIS Engine as the platform for the needs of GIS application functional modules were met in of GIS application development component, and the developers only focused on the specific business development of the module, to avoid duplication of functions of development on based GIS applications, so as to improve the development efficiency, reduce the development costs. After the function of operational modules has been refined and standardized, they can be added to the component library used component management tools, for use the other similar GIS application system development in future [START_REF] Liu | Design and Realization of ArcGIS Engine Component[END_REF][START_REF] Xiong | Design and Realization of the Cartography Template Management System[END_REF][START_REF] Zhang | Transport planning information system based on Arc GIS[END_REF].

In the practice of precision agriculture, GIS is mainly used for the establishment of agricultural land management, the development trend of soil data, natural conditions, production conditions, the seedlings crop growth and insect pest, and the geographic statistical processing, graphics conversion and expression of crop yields and other spatial information, in order to offer prescription scheme of the analysis of spatial differences and implementation of control [START_REF] Runquist | Development a field-level geographic information system[END_REF][START_REF] Zhang | Applications of a field-level geographic information system(FIS) in precession agriculture[END_REF]. The multi-layer maps of farmland yield and spatial information were firstly generated on GIS. Then the decision-makers analyzed the causes, make a diagnosis, and provide scientific prescription based on yield-spatial differences with crop production management decision support systems and expert intelligent management systems. Thirdly the field crop management prescription maps were produced on GIS, and zoning regulation implemented to guide the scientific operations. When the agricultural geography spatial information map were produced, how to define the sampling density, sampling costs and data-processing methods, satisfied to more accurately reflect the spatial distribution of parameters, is still next-step study [START_REF] Wang | Development of precise agriculture and innovation of engineering technology[END_REF].

Design of PFMIS based on CORS and GIS

As known, two and above GPS receivers are used on DGPS to collect the spatial information on fields. However, with the regional CORS (Continuously Operational Reference System) widely development in the domestic, only one GPS receiver can be finished the spatial data of large-scale, all-weather, high-precision, kinematic real-time observation on CORS, which make up for the shortages of RS, such as low-precision of spatial information, time-delay of collection. The accuracy, reliability and real-time characters of spatial data were improved on CORS technology.

3-dimensional spatial coordinate information on GPS and soil resources attribute information on soil nutrient testing device were obtained to found the fields database, and the thematic maps of crop growth environment and yield spatial distribution were generated in real time or near real-time, in order to update the achievement on current trend. Then these maps overlay with other soil elements maps based spatialconsistency to analysis. Finally the corresponding mathematical models were founded to provide the basis of the soil fine fertilization.

Based GIS platform, the spatial data grid maps of soil resource were re-developed on ArcGIS Engine, including of some management information functions, such as analysis, queries, additions and deletions, updates, and contrast of the collected soil resource data. Then the basis on decision making was provided to variable-rate fertilization with the expert systems. WebGIS system were built with ArcGIS Server to complete the web publishing or query information of precision fertilization, and the efficient fertilization programs and crop fertilizer criteria were recommended for users in order to achieve the modern agricultural production patterns and the technology system of the higher yield, quality and level, and arrive to high efficiency, savingenergy, high production, environmental protection.

3.1

Technique of PFMIS on CORS and GIS

According to the Shandong Province's actual situation of agriculture, soil and climate, the research of application of technology of data collection, data processing and management information system queries were carried out on the soil resource information of PFMIS. And PFMIS were designed aimed to reducing the investment on crop early growth, low waste in the growth-medium, increasing the profits the later, and reducing the environmental pollution in the long term. When PFMIS were established based on GPS and GIS technology. The farmland spatial information were collected using GPS, and the accuracy, and real-time of the spatial data were improved with CORS technology. The keys on data standardization technology were researched, and the spatial grid distribution maps of soil nutrients and crop yield were produced by GIS re-development on soil topics, some functions of web publishing and query were realized to provide the basis on decision making, above as shown in Fig. 1,including: (1) To argue the scheme design of CORS of the geographic information acquisition subsystem of PFMIS;

(2) To model on the from-point-to-surface data processing of soil resources information and contour of soil resources property information, such as geostatistical Kriging, surface fitting, ANN and each other algorithms comparison;

(3) To realize the standardization of data processing methods on the soil resource information, including the undulating terrain classification, sampling interval grading, sample units install, etc., and achieve the layover and analysis on multi-characterlayers of a variety of space-based information.

(4) To determine the communication and query tool on the Web-platform of PFMIS, to interactive services, including synchronous communication: IRC, ICQ, MSN, Skype; and asynchronous communication: E-mail, Mailing list and so on.

Design of PFMIS on CORS and GIS

Research procedures and data-process flow shown in Fig. 2, including:

(1) Design the collection subsystem of PFMIS on CORS technology. As a large areas of farmland, a long time measurement, mobile frequently, basically covered by CORS of Shandong Province, to prove the scheme of network distribution and the feasibility of cm-level accuracy. A GPS receiver was used to observe and receive realtime differential signal, and cm-level positioning was obtained by data processing. The agricultural nutrient tester, temperature-hygrometer and others were used to collect the soil resources information samplings. To ensure the accuracy of those, the sorts data were input GIS platform to manage, the kinematic real-time acquisition subsystem of the spatial and soil attribute information of PFMIS was completed. And this subsystem provided these real-time or near real-time various soil-attribute layer to analysis on the follow-up GIS applications.

(2) Re-development of the scheme GIS based on ArcGIS Engine. To research the key technologies of data standardization, mainly attribute data standardization and a variety of data analysis and processing, including geostatistics, neural networks, genetic algorithms, to generate DEM, soil resources, nutrients (N, P, K, Ca, etc.), temperature, humidity and yield, etc. Attribute distribution maps, then various types of thematic maps were produced by the spatial analysis and mapping capabilities of GIS, through spatial overlay analysis of land resources. Next-step the variable fertilizer distribution maps were generated together with expert knowledge. It is keys to develop GIS analysis component library on this stage of system design.

(3) To renew collecting data and update the variable fertilizer maps with the growth of crops in different time periods. The network applications of PFMIS were achieved by Web GIS, including the dissemination of information, interactive inquiry service, and so on.

Conclusions

Precision agriculture is an inevitable trend on the agriculture development from the coarse type to the fine. The research on the high-precise spatial data real-time collection, data processing and management information system technology is presently one of focuses on precision agriculture. On the topics the key technologies include: firstly the achievements of real-time data collection and processing were obtained used CORS technology, and whose accuracy levels would be better than 10cm; secondly the models of data processing and analysis of soil attribute and crop yield on spatial data were founded based on multi-intelligence computing algorithms; thirdly the re-developments of the soil-thematic GIS on ArcGIS Engine were run. While, how to define the sampling density, sampling costs and data-processing methods, to more accurately reflect the spatial distribution of parameters, is worth of deeply study.

Fig. 1 .

 1 figure

Sponsored by the National Natural Science Foundation of China (40704001,40876051) Email: liuzhimin010@163.com

Acknowledgments. This work is supported by National natural science foundation of China (No. 40704001,40876051).

CORS network system construction GPS