Zhongqiang Liu

Kaiyi Wang
email: wangky@nercita.org.cn

Shufeng Wang
email: wangsf@nercita.org.cn

Feng Yang
email: yangf@nercita.org.cn

Xiandi Zhang
email: zhangxd@nercita.org.cn

A SaaS-based Logistics Informatization Model for Specialized Farmers Cooperatives in China

Keywords: Informatization, SaaS, M odel, Specialized Farmers Cooperatives, Logistics

The logistics informatization of Specialized Farmers Cooperatives (SFC) plays an important role in the development of agriculture, and Softwareas-a-Service (SaaS) is the development trend of information technology (IT). Firstly, the state-of-the-art of IT on SFC and the enlightenment SaaS to the IT application in logistic are introduced. Then, analyzing of the characteristics of logistics management system for SFC based on SaaS, we explore a SaaS-based logistics informatization model for SFC in China, and some key technologies to model application are also discussed, such as software architecture, system security and data modeling. Finally, some suggestions are put forward for a large-scale promotion of the model.

Introduction

Along with the continuous development of SFC and agriculture, as the intermediary organizations that connect the markets and farmers, cooperatives play an increasingly significance affect in agricultural logistics [START_REF] Qian | M anagement Patterns of Farmer Professional Associations Based on Information Technologies[END_REF]. The application of information technology has improved the logistics management leve l of SFC .It also has brought about the healthy development of the cooperatives . However, info rmation talent shortage, information infrastructure imperfect and high information costs become the bottleneck of information technology for SFC at this stage.

SaaS is a new kind service mode of Software that provides a new strength for informat ization construction for SFC [START_REF] Choudhary | Comparison of Software Quality under Perpetual Licensing and Software as a Service[END_REF]. The products based on SaaS have some prominent advantages and characteristic, such as good expansibility, easy extensibility, strong flexib ility and low using threshold [START_REF] David | Software as a Service[END_REF]. To solve the existing problems in the process of IT construction, we will exp lore the service model of logistic information suitable for SFC based on SaaS. The feasibility and implementation plan of realizing agricultural logistics informat ion on SaaS mode is studied in this paper according to the agricultural logistics management system for SFC.

Status of Logistics Informatization for SFC

As the main implementing subject of agricultural informat ion, SFC is also the most important link in the agricultural informat ization chain [START_REF] Liu | Informationization Solution for Farmers' Cooperatives[END_REF]. By the end of 2009,there are 246.4 thousand farmers' cooperatives with 21 million households (accounting for 8.2% of total households ) registered in the industrial and co mmercial management department accordance with law. The services expand gradually fro m the production area to the circulation and processing integration business . Information technology builds an important platform for improving the logistics management level, the service level and the marketing ability of SFC to meet the unceasingly scale expansion and functional improve ment and service quality improve ment of SFC. The IT in logistics can provide real-t ime logistics informat ion for SFC. It can help cooperatives make a scientific decision to reduce blindness in logistics and improve the efficiency and economic benefit, etc. Anyhow, the construction of IT for logistic can accelerate the development of SFC.

Agricultural products logistics informat ion is an important content of the SFC of informat ional management. It can pro mote the farmers' cooperatives , as well as can be good for the mode of informatization and scientific management of cooperatives [START_REF] Liu | Informationization Solution for Farmers' Cooperatives[END_REF]. Introducing a modern management mode of enterprise logistics to SFC can make the logistics management of cooperatives more standard and scientific. Agricultural products logistics management system for SFC can not only apply the information technology into cooperatives but also be extended to the informat ion fro m production to marketing to realize in formation management.

Currently, the study on the IT for SFC logistics has carried out with a lot of achievements. However, in the process of IT construction there are a lot of questions as follows. Firstly, we are lack of information knowledge and talen t even we have a stronger informat ion consciousness. Secondly, information infrastructure is equipped perfectly, but access to information and information interaction are imperfectly. Thirdly, lack of funds became an obstacle to improve the level of logist ics informat ion. Lastly, stronger logistics information service demand but backward informat ion service means, etc. The reasons are the underlying causes that restrict the IT construction for logistics has not been thought about deeply and the service model for logistic informat ion hasn't been exp lored. So, we have not gain the maximu m efficiency of IT and the service model for logistic info rmation hasn't been explored.

The Enlightenme nt SaaS to Logistic Informatization of SFC

SaaS

SaaS is a unique and innovative hosting type application service. It also is a software application mode as the development of the internet technology and software. The main features of SaaS service mode is SaaS service providers purchase IT infrastructure and deploy IT environ ment, and enterprise purchase software services .

Service prov iders deploy the software in h is uniform. Then, customers can order the software services via the Internet according to their actual needs and get the service fro m the Internet, and pay fees via the Internet according to the service they ordered.

Service providers can ensure every enterprise data security and confidentiality through effective technical measures [START_REF] Campbell-Kelly | Garcia-Swartz: From Products to Services: the Software Industry in the Internet Era[END_REF].

The Superi ority of SaaS Applied in Logistics Informatizati on of SFC

Co mpared with the traditional software, SaaS has four prominent advantages such as low construction cost, low maintenance, low threshold and low investment risk applications. That is why it can cover a logistics information market. SaaS service mode applied in the construction of IT for logistics of SFC has the following advantages.

Low Level Demands

The demand of SFC to logistics informat ization is lo w level, which accords with the concept of SaaS, as configurable, customizable and extensible concept [START_REF] Wu | Analysis and Design of Dining M anagement System Based on SaaS[END_REF]. Log istics informat ization of SFC is main ly to the underlying basis of informat ion. That needs the information products can immed iate access to data, respond quickly to market, improve decision making and seize the opportunities before us . Because of the small business coverage, SFC doesn't need the large and co mprehensive information products. SaaS makes SFC purchase and expand its modules online, and enjoys informat ion good results with mut i-function and low cost to min imize the "chicken ribs" effect that traditional software produces [START_REF] Dan M A | The Pricing Strategy Analysis for the Software-as-a-service Business M odel[END_REF].

3.2.2

Regional and Di versificati on Needs Logistics of SFC serves farmers and members. It needs automation and active service in order to ascend cooperatives' logistics service ability and reduce cost [START_REF] Li | China's Rural Information Development Report[END_REF]. Th is is a primary problems that the construction of IT fo r logistic of SFC to solve. But the traditional software system cannot adapt to rapid market changes and maintain advancement of the present situation of information technology . This is determined by the traditional software model. The system framework of SaaS fo llows the configurable, extensible, customizable features of open architecture. According to the market and the demand on logistics services , cooperatives may order the corresponding software services to reconfigure system fro m the service provider to serve market and farmers better. It also can customize indiv idualized function according to the logistics business to support a rapid development of logistics business.

3.2.3

Comprehensi ve Services SaaS mode will make it possible that different stages of the supply chain and different types of SFC provide services collaboratively. Currently, the SFC has a s maller coverage, mostly just involving a small portion of content in agricultural industrialization chain. As a single type of service providers, cooperative itself is unable to finish all the service requirements throughout the supply chain . Many services require other cooperatives for collaborative operation. While traditional software system flo w is hard to breakthrough the border among cooperatives. There would be a lot of the repeated work because of the in formation in a large share on the supply chain link. The logistics efficiency and service capacity of cooperative cannot have a fundamental improve ment. Therefore, SaaS has a good applicability to the cooperative with logistics service function.

Presently logistics business of SFC main ly embodies the characteristics of low concentration in the cooperation, the low degree of logistics information and the low output of IT construction for a shortage of funds and so on. But all of these are very conducive to the development of the SaaS in cooperatives. SaaS mode makes cooperatives in logistics informat ion process can enjoy lower investment returns, and reduce the high difficulty in the logistics informat ion to help cooperatives build an informat ion era in a short time . It will bring into a greater profit , and play the logistics service function fully.

SaaS-Based Logistics Informatization Model for SFC

Analyzing of the characteristics of logistics management system for SFC on SaaS, we explore a SaaS-based logistics informatization model for SFC. The model fu ll considers the features of logistics management system, such as individuation, integration, differentiation, etc. The key technologies for model applicat ion, as software architecture, system security and data modeling have also been e xplored.

The structure of the model is shown in figure 1.

Database

Model Analysis

The emergence of SaaS to SFC opened a road leading to the informat ion. But there are some difference between the characteristics of information product in SaaS mode and traditional mode. The model firstly extracts logistics business process,and then screens and integrates services through the controller and regulator, finally generates logistics information product. The model takes agricultural logistics business of SFC as a prototype with the follo wing features.

Various Complexi ty and Indi vi dualized Multi-User Interface

The main business of farmers' cooperatives involves agriculture, forestry, animal husbandry and fishery, covering many fields of farm p roducts production, storage, processing, marketing, etc. Different types of agricu ltural logistics management system have more than one difference in logistics links, logistics subject, logistics packaging and logistics information mark, etc. Fu lly considering logistics demand in different main business model of cooperatives, the model provides a mult i-user interface to cooperatives with personalized and free. Cooperatives can distribute characteristics of agricultural logistics management and logistics information subject , according to different kinds of agricultural logistics . For so me cooperatives with more complex business in agricultural varieties, the model offers a variety of d ifferent complexity system interface. The agricultural log istics management system based on the model can satisfy the concern and emphasis of different types of cooperative.

4.1.2

Expanding B usiness Logic With the cooperation of a chain logistics link to the full extension, narrow supply is becoming to general logistics. So, the whole process and scientific management of agricultural products supply chain will become the most important content of the operation mode and cooperative informat ion. The model should first meet the need of the application of IT for logistics, and then, should have the business expansion logic function, allowing the system based on the model can be extended to the information along industrial production management and market ing .

On the basis of the supply chain, production and sales of agricultural products , the model hold on the basic informat ion of the system fro m the production links to the sales link. The integrated logistics supply construction management system based on the model to realize the fu ll informat ion management, supply and the industrialized operation of agriculture, production, circulation, processing and sales lin k o rganically, effectively solve the cooperatives in industry production and market ing problem.

The range of log istics management system on SaaS can be extended to the production before, during and after the process, and have online transactions and realtime management etc. The system also can monitor each loop fro m manufacturing to market sales, and will largely enhance the ability of driv ing and market ing.

4.1.3

Flexi ble Data Structure

The most challenge that different cooperatives use the same software s ystem installation examp les in SaaS is how to create mu lti-user data model, and to support the different data structure. Cooperatives order their logistics management software according to their management way and the system execute the business absolutely with a b igger difference way. Different business process requires different data structure. For the characteristics of Cooperatives for d ispersive location, main business involving mu ltiple fields, no unified standard features and so on, the model achieve several sets of data structures support through the corresponding relationship of real data structure and logical data structure. A logistics management system wh ich constructed for farmers' cooperatives based on this model is very flexib le software.

Model Applicati on

Considering the main and expansion of business that the model applicat ion may involve, the applied mode and realizat ion technology of the model are d iscussed. The structure of the model application is shown in figure 2. The point a logistics management system in SaaS to apply successful is the system arch itecture, service oriented architecture and data safety. One isolate design according to the key problems is given below by the model.

Cooperatives

Service Oriented to B uil d Software

The system architecture of SaaS is based on service-oriented architecture. The business process is decomposed into service components. And then it builds various service modules by the specific imp lementation technology [START_REF] Laplante | What's in a name? Distinguishing between SaaS and SOA[END_REF]. First, the design of business-driven can more quickly meet logistics management requirements of different main business of cooperatives. Second, it can imp rove system reusability according to the logistics demand for change quickly and low price update service. Third, it can integrate the service of the logistics management system for SFC and other services in the supply chain, and finally realize the integration of brands .

4.2.2

Multilevel Security Archi tecture Design The responsibility shift of data management on SaaS mode makes safety priority higher in the software architecture. In data security, secure socket models use secure socket layer (SSL) [START_REF] Zhu | Online Application of Circulation on SaaS M ode[END_REF] to protect data transmission in order to ensure the safety, reliability and safety, under the premise of minimizing the safety factor of in fluence on system performance. On safety design of authentication and authorization, in order to improve the system integration and user authentication efficiency, the model uses lightweight directory access protocol (LDAP) to unified manage user authentication informat ion. On data security, the model makes use of Java cryptographic key data extensions (JCE) to implement encryption and decryption.

4.2.3

Adapti ve Data Isolation Strategy Procedures and databases are requested by SaaS mode to build in mult i-user models and ma ke independent between cooperatives [START_REF] Dan | The Business M odel of Software as a Service[END_REF]. Middleware provides a new concept for SaaS mode [START_REF] Hu | Analysis and Discussion of Logistics Information Platform Based on SaaS mode[END_REF]. It can effectively shield in d ifferent database system access method and the user interface, also provide users with a public interface of distributed database access of heterogeneous platform. The model design which based on middleware according to the characteristics of a business, can make the logistics business of SFC logic layer to handle database data with unified way, and make the data more flexible. Cooperatives that have a low safety requirements and a small amount of data will use a shared database. And the exclusive one will be used for safety requirements.

Conclusion and Suggestion

The informatization for SFC is of great significance for the rapid development of cooperatives and the promoting of IT in agriculture. The paper analyzes the characteristic of agricultural logistics management system for SFC and exp lores the construction of IT technical scheme for SFC. But because of the later of starting, narrow range of logistics business scope, low level of IT for SFC, and the application status of SaaS in agricu ltural logistics field, the model of large-scale applications needs to consider long-term strategic objectives. It should be used in a certain types of cooperatives, such as greenstuffs. Along with the continuous development of logistics business of SFC and the gradually co mplete of SaaS mode, the logis tics information products for SFC based on SaaS will have broad development prospects.

Fig. 1 .

 1 Fig. 1. SaaS-based logistics informatization model for SFC.

Fig. 2 .

 2 Fig. 2. The structure of the model application

Acknowledg ments. This work is supported by National 11th Five-year Plan for Science & Technology of China under Grant no. 2009BA DB6B02.


Ligen Yu

Guanghui Teng

Zhizhong Li

Xuming Liu

Study on Acoustic Features of Laying Hens' Vocalization

Keywords: vocalizations, spectrums, laying hens, time structure

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

Introduction

Vocalization is a common response to the emotional state of an animal, which could be used to attract the opposite sex, search companions, look for food, convey danger signal, play and communicate with each other. Study on the vocalizations of animals is not just to identify wild animal species, but also the physiological state of livestock and poultry can be monitored [START_REF] He | The Observation and Analysis of Acoustic Characteristics of Pika Woo Calls[END_REF][START_REF] Jiang | Study on Development of Multi-functional Analyzing & Processing System of Bioacoustics Information[END_REF]. The characteristics of animals' sound were concentrated, especially the time-domain features and frequency-domain properties [START_REF] Sanvito | Vocal Signaling of Male Southern Elephant Seals is Honest but Imprecise[END_REF]. Which could provide a theoretical basis for the establishment of voicebased animal welfare assessment system and health warning system by deep study of animals' vocalization.

Vocalizations of laying hens are quite different when under distinct patterns. Laying hens were thought to express an expectation of a rewarding event through a specific vocalization, which could serve as an indicator of laying hens' welfare [START_REF] Zimmerman | The Effect of Frustrative Nonreward on Vocalizations and Behavior in the Laying Hen, Gallus Gallous Domestics[END_REF][START_REF] Manteuffel | Vocalizations of Farm Animals as a Measure of Welfare[END_REF]. Vocal expressions of 50 chicks were analyzed during step by step social isolation. Results showed that vocalizations of laying hens were strongly dependent on social contacts and could be changed when under different degrees of social deficits [START_REF] Marx | Vocalization in Chicks (Gallus gallous domestics) During Stepwise Social Isolation[END_REF]. Pecking behavior in individual and social condition was compared in two lines of laying hens at ten different ages. Vocalizations were recorded as well to measure the response to isolation in the test [START_REF] Rodenburg | Comparison of Individual and Social Feather Pecking Tests in Two Lines of Laying Hens at Ten Different Ages[END_REF]. This study was to analyze the acoustic features of vocalizations of Korean native cows and to examine the differences between estrus and feed anticipating vocalizations [START_REF] Seong | Acoustic Features of Vocalizations of Korean Native Cows (Bos Taurus Coreanea) in Two Different Conditions[END_REF]. The fear reaction of three different genetic strains of laying hens to a simulated hawk attack in a free-range system was tested. It was concluded that they were similar in the behavior or vocalizations [START_REF] Christina | Breed-Independent Susceptibility to Acute Sound Exposure in Young Chickens[END_REF][START_REF] Zeltner | A Note on Fear Reaction of Three Different Genetic Strains of Laying Hens to a Simulated Hawk Attack in the Hen Run of a Free-range System[END_REF].

At present, research to the relationships between vocalizations of laying hens and their living behavior were studied more. But the characteristics of laying hens (Hyline Brown) were not focused so much. Here was illustrated a simulation of flat breeding model of laying hens in laboratory. An experiment designed was to obtain the acoustic features of vocalizations of laying hens (Hyline Brown) to find the changes with different ages.

Materials and methods

Animals and data collection

The amount of animals used in this experiment was 30 laying hens. The breed was Hyline Brown (U.S. standard). Ten were 35days-old, 95days-old were ten and the others were 125days-old. They were housed in a 1.5×1.0×1.0m (length× width× height) breeding pen respectively. The temperature inside the pen ranged from 20 to 30℃ at a relative humidity between 35 and 45%. Laying hens were placed under 8h light/16h dark (lights on 09.00) using a 25W incandescent electric bulb in each pen [START_REF] Christina | Breed-Independent Susceptibility to Acute Sound Exposure in Young Chickens[END_REF][START_REF] Seong | Acoustic Features of Vocalizations of Korean Native Cows (Bos Taurus Coreanea) in Two Different Conditions[END_REF]. They were free to drink and eat. Vocalizations of laying hens were recorded from November to December in 2007. Every record lasted 0.5h with half-hour interval from 09.00 to 17.00 each day. All vocalizations were collected using a microphone (MPA201, Bswa Technology Ltd, P.R. China) set at the top of the pen.

Sound measurement

Sound was sampled by the PXI test platform (PXI-1050, National Instruments Ltd, USA) at a rate of 44.1 kHz using a 24 bit sound acquisition card (PXI-4472B, National Instruments Ltd, USA). The sound files were analyzed using Cooledit Pro 2.1 (Syntrillium software, 2003) and Praat4.6 (Boersma P & Weenink D, University of Amsterdam, The Netherlands). Cooledit was used to edit the sound files which served for the analysis of vocalizations by Praat [START_REF] Zimmerman | The Effect of Frustrative Nonreward on Vocalizations and Behavior in the Laying Hen, Gallus Gallous Domestics[END_REF][START_REF] Marx | Vocalization in Chicks (Gallus gallous domestics) During Stepwise Social Isolation[END_REF]. Main algorithm in the acoustic field developed by Praat which can be used to obtain time-domain features and frequency-domain properties of vocalizations of animals.

Statistical analysis

All statistical analysis was performed using SPSS 16.0 for Windows (SPSS Inc., 2005).

Results and discussion

Vocalizations of animals are similar to human language, which have complex changes and different meanings [START_REF] Chen | Simulation and Experiments for Hybrid Noise Control System[END_REF]. But animals can't speak out by themselves like human. So it is very important for people to understand what meaning of the vocalization of animals. Voice processing technology can also be used for animal research.

Generally, sound of laying hens is mainly composed of monosyllabic tweet. The syllable frequency of the sound of laying hens will be changing when under different stage ages. Digitalizing the vocalizations of laying hens need to treat the monosyllabic tweet as an identification unit [START_REF] Wang | Speech Recognition[END_REF][START_REF] Mair | Development of Multi Parametric Sound Analysis Parameters as Welfare Indicator in Chicken Flocks[END_REF]. In data processing, vocalizations of laying hens with different ages were randomly selected to execute syllable segmentation. Four-hundred and fifty calls were selected in order to get the acoustic characteristics of laying hens.

Time structure

Wave and frequency of laying hens on 35days-old, 95days-old and 125days-old were shown below. Spectral envelope curve of laying hens' vocalizations were typical. The pitch frequency of laying hens was not less than 5000Hz.

Spectrums

The following chart revealed the spectrums of laying hens with different ages. From the illustration we can inform that the spectrums of laying hens were relatively simple. The composition of each syllable slightly differed. But there were significant differences among the energy center on sound of laying hens under different days. The frequency of energy center of laying hens were more than 2000Hz, on 125th day was the highest, 35days-old was lower, and the last one was 95days-old. It is possible to judge which growing period of laying hens through analyzing the energy center frequency of their vocalizations.

Conclusion

In this paper, vocalizations of laying hens (Hyline Brown) with different ages were analyzed and compared. From this, we could grasp the variation law of the basic characteristic parameters of laying hens' vocalizations. The results showed that the duration time and energy center on sound of laying hens existed with different ages under normal feeding; even the trend of variation was not the same. Therefore, we could understand the growing period of laying hens from the analysis of their vocalizations. But if we want to achieve automatic identification and classification, the representative acoustic feature parameters must be extracted. There provides as a theoretical basis for the establishment of voice-based animal welfare assessment system and body health warning system to improve animal welfare.

Fig. 1 .Fig. 2 .Fig. 3 .

 123 Fig. 1. Wave and frequency of laying hens on 35 th day

Fig. 4 .

 4 Fig. 4. Duration time of laying hens under different days

Fig. 5 .

 5 Fig. 5. Spectrums of laying hens on 35 th day Fig. 6. Spectrums of laying hens on 95 th day

Acknowledgements

We thank Beijing DQY Agricultural Ltd for help with experimental site and materials. We also thank Professor Zhengxiang Shi and Jian Deng for experiment instruction. This work was supported by National 863 Plan (No: 2006AA10Z238, The Ministry of Science and Technology of P.R. China) and Special Project for Graduate Innovation (No: kycx09110, China Agricultural University). We thank all laboratory members for the help of this study.


Xiaodong Yang
email: yangxd@nercita.org.cn

Wenjiang Huang
email: huangwj@nercita.org.cn

Cunjun Li

Xingang Xu

Hao Yang
email: yangh@nercita.org.cn

Winter Wheat Quality Inspection and Regionalization based on NIR Network and Remote Sensing

Keywords: Winter Wheat, Quality Inspection, Quality Regionalization, NIR Network, Remote Sensing

In the crown of the year, inspection of wheat quality fast and accurate is very important for all of grain enterprises, farmers and governments. Governments would like to construct a fair and equitable market for grain transaction with explicit grain quality standard. Farmers would like to sell their high-quality grain at a high unit price for they have paid more attention and investment. Enterprises also would like to purchase high-quality grain with higher unit price for it can bring more profits. At the same time, generating regionalization map of wheat quality accuracy in time is very important on the grain enterprises' purchase strategy formulating and purchase region choosing. The authors collected 1200 NIR samples in 240 points (in other words, 5 samples per point ) in 12 counties in the main wheat producing areas in China (Hebei, Henan, Jiangsu and Shandong), then analysis these samples by both GIS spatial interpolation method and RS inverse method. In contrast, RS inverse method can simulate the quality parameter more accuracy than GIS spatial interpolation method. In conclusion, RS inverse method is preferable to generate quality regionalization map with NIR network samples.

Introduction

Near infrared (NIR) technology has been widely used in quality inspection in China and now only a single NIR instrument is usually used to inspect the sample which can evidently improve the efficiency of inspection [START_REF] Yanlu | Elements and application of Near-Infrared Spectra Analysis[END_REF]. However, a single NIR instrument lacks the universality and uniformity because its calibration model is the default model of producers or the model being generated according local samples. In this situation, all the instruments not in network only can inspect samples in the local Lab with higher reliability and higher accuracy; while the inspection results between different instruments may be have large difference (known as difference between instruments) [START_REF] Wang | [END_REF].

Grain Quality Inspection NIR Network can make up the gap to achieve the consistency and credibility of results between instruments by transferring calibration model to Client-Instrument from Host-Instrument through the server in Network Management Center. The stable calibration model provided by the unified analysis network can ensure all devices within the network producing the inspection results having the same accuracy and stability and make all devices not being influenced by the difference between the sample sources and the test environment. It's the important technology base of Grain Quality Analysis in large scale region to implement the inspection results of any instrument in the NIR network having the same accuracy and reliability with integrating with GIS or RS [START_REF] Lu Wan-Zhen | Modern Near Infrared Spectroscopy Analytical Technology (Second Edition)[END_REF].

Remote sensing technology is widely used in crop growth monitoring, pests monitoring and quality monitoring since it has a natural advantage in the analysis of distribution and change of land cover on earth [START_REF] Humburg | Spectral properties of sugar beets related to sugar content and quality[END_REF]. But we can't get the full crop growth information from RS data directly since RS data is comprehensive, namely the pixel value in the image is a comprehensive reflection of the features located in the certain region and the information in the image cannot completely be interpreted to the corps growth information [START_REF] Wang | Estimation of leaf biochemical components and grain quality indicators of winter w heat from spectral reflectance [ J ][END_REF].

From above can be concluded that NIR Network technology can inspect grain quality accurately, rapidly and reliably while RS monitoring technology can expand the accurate information of the earth's surface from point to plane by a reverse model. So we can generate the quality regionalization map for crop enterprise and government by combining these two technologies. The enterprise can purchase the appropriate and necessary crop with higher price according this map.

Material and methods

The Structure of NIR Network

NIR network technology has been widely used in Europe and America. Now there more than 30 worldwide NIR grain quality analysis networks which are composed by nearly ten thousand NIR instrument running in the United States, Canada, Germany, France and Australia, etc [START_REF] Wei | Corn Quality and Food Processing-Wheat Grain Quality and Food Processing[END_REF]. More and more quality analysis of crop trading in Europe and the United States were inspected by NIR network. NIR Analysis technology has been certificated by a number of international organizations and national official, including the United States, Canada, Germany, France, Australia, etc [START_REF] Zhang | Estimating wheat nitrogen concentration with high spectral resolution image[END_REF].

Winter Wheat Quality Inspection and Regionalization based on NIR Network and Remote

Sensing 3

The first NIR network for crop quality analysis in china was established by NERCITA consisting of a number of universities, research institutes and crop enterprises in Heilongjiang, Jilin, Hebei, Shandong, Jiangsu and Hunan [START_REF] Wang | The vertical distribution characteristic and pectral response of canopy nitrogen in different layer of winter wheat[END_REF].

NIR grain quality monitoring network is composed by Network Steering Committee, Calibration Center, Network Management Center and Client Users (As shown in Fig. 1). The functions of every component are described as below:

Network Steering Committee's task is to determine to use the new calibration or adjust the existed calibration based on the statistics from the calibration center.

Calibration Center's task is to develop the application model for all instruments in network.

Network Management Center's task is to manage and maintain the network.

Client Instrument is the end-user of the network.

Calibration Transferring and instruction standardization

Calibration transferring and instruction standardization can eliminate the model difference generated by the host and client instructions manufacturing tolerances [START_REF] Zubillaga | Assessment of nitrogen status in wheat using aerial photography[END_REF].

The common method is to analyze a same group samples by host and client instruments, then to adjust the slope and intercept of calibration model of client instruments. The authors adjusted 7 instruments in the NIR network for Proton, moisture and wet gluten of winter wheat, the results are shown in Table 1 ~ Table 3, the instruments in the network are accuracy and consistent.

Samples collecting

The samples were collected from 12 counties in Henan, Hebei, Shandong and Jiangsu, the main winter wheat belt of China. For every sample point, the authors recorded detail information including GPS location, wheat variety name, sample sizes and sampling time. On every site, the authors collected 5 samples-2 for the NIR network detection, 2 for chemical contrast and 1 for backup. The factors of detection included protein content, moisture content and wet gluten content. Firstly, the Infratec Data Logger -a special soft for data collecting developed by FOSS -collected the inspection data from instrument to PC which connected with the Instrument by intranet. Then these data were uploaded to the server in network management center by a FTP synchronization software tools.

methods

GIS interpolation

In our research, an optimal space interpolation method-Kriging method-was used to analysis the moisture, protein and wet gluten in wheat for getting the spatial distribution map of the wheat quality class interpolation. The map can reflect the quality class of wheat.

The fundamental principles of Kriging method is similar to least squares method and its essence is to consider not only the general trend changes ( statistical features) , but also the related changes and the random variations on the sample surface [START_REF] Wang | Prediction of grain protein content in winter wheat (Tritium aestivum L.) using plant pigment ratio (PPR)[END_REF]. The general trend changes, the related changes and the random variations are respectively named the structured item, the relevant item and the random noise (as shown in Fig. 2) [START_REF] Wu | Principles and Algorithms of GIS[END_REF]. The usual fitting function equation is as follows:

C y x f y x f y x f + + = ) , ( ) , ( ) , ( 2 1
(1)

Where

is the structured item, is the relevant item and C is the random noise.

RS inverse model

The basis method of RS inverse is to set up a correlation model between the RS information and the real system on earth [START_REF] Li | Study on monitoring starch content in winter wheat grain using Land-Sat TM image[END_REF]. Firstly the linear or non-linear relationship between the crop growth state index and the single phase or multitemporal spectral vegetation index (VI) is analyzed; then based on the relevant relations the statistic model, including the single variable and the multivariate statistical models, is set up. In addition, there is the RS inverse method based on the priori knowledge, which mainly uses the Bayesian statistical method. Our research adopts the single variable statistical models and the formula of correlation coefficient between the crop growth state index and the vegetation index is

1 2 2 1 1 ( )( ) ( ) ( ) n i i i xy n n i i i i x x y y R x x y = = = - - = - • - ∑ ∑ ∑ y ( 2
)
Where Rxy is correlation coefficient value, Xi is the observed value of the crop growth state index of No.i sample point, Yi is the value of spectral vegetation index of No.i sample point, is the average value of the observed value of the crop growth state index of all sample points and is the average value of spectral vegetation index of all sample points.

Winter Wheat Quality Inspection and Regionalization based on NIR Network and Remote Sensing 7

3 Results and discussion

Accuracy analysis of NIR network

The authors selected 240 sample points in 12 counties in the main wheat producing areas in China (Hebei, Henan, Jiangsu and Shandong, as shown in Figure 2.) in 2009.

In every point, the authors got 5 samples, 2 for the near-infrared detection, 2 for chemical assay, the test results averaged. Comparison of detection of one protein results shown in Fig. 3:

The contrast for NIR method and chemical method in Protein Content

Winter wheat quality regionalization by integration with GIS interpolation

GIS technology is used in spatial analysis field widely. The GIS spatial interpolation map of samples points as shown in Fig. 4. Because the collection of samples are limited by regions, labors, date and others, it is difficult to collect samples at large-scale and on certain percentage. The thematic map generate by GIS technology is always not very accuracy and reliable for the lack of enough sample points.

Winter wheat quality regionalization by integration with RS inverse model

RS image is taken in a moment, so the information represented by its pixel value is steady and sequential. Some vegetation indexes (VI) generated by RS image can reflect the quality information of covered crop so the inverse model having a high correlation can derive the regional quality thematic map reliable.

The authors chose randomly 115 of the 240 sample points to build inverse model and the other 125 points to verify the model. The details are shown in Table 4. The inverse thematic map is shown in Fig. 5.

Fig. 1 .

 1 Fig. 1. The structure of the NIR network in NERCITA

Fig. 2 .

 2 Fig. 2. the principle of Kriging interpolation method

Fig. 3 .

 3 Fig. 3. The accuracy of NIR network contrast to Chemical method

Fig 4 .

 4 Fig 4. The winter wheat quality thematic map generate by GIS spatial interpolation

Winter 9 Fig. 5 .

 95 Fig. 5. The winter wheat quality thematic map generate by RS inverse model

Table 1 .

 1 The accuracy of calibration transfer and model adjustment for Protein

	Instrument	RMSEP	Bias	Slope	Intercept Correlation
	Hebei-1	0.05	-0.03	0.98	0.40	1.00
	Henan-1	0.07	0.00	0.97	0.42	1.00
	Henan-2	0.10	-0.08	0.98	0.45	1.00
	Jiangsu-1	0.03	-0.01	0.97	0.42	1.00
	Shandong-1	0.11	-0.11	1.00	0.04	1.00
	Shandong-2	0.07	0.01	0.97	0.37	1.00
	Jiangsu-2	0.04	-0.02	0.96	0.56	1.00

Table 2 .

 2 The accuracy of calibration transfer and model adjustment for Moisture

	Instrument	RMSEP	Bias	Slope	Intercept	Correlatio n
	Hebei-1	0.03	-0.02	0.97	0.36	1.00
	Henan-1	0.14	-0.14	0.92	1.04	0.99
	Henan-2	0.18	-0.18	0.93	0.97	0.99
	Jiangsu-1	0.04	-0.02	0.92	0.98	0.99
	Shandong-1	0.05	0.04	0.94	0.63	0.99
	Shandong-2	0.02	-0.02	0.99	0.10	1.00
	Jiangsu-2	0.04	-0.03	0.93	0.81	1.00

Table 3 .

 3 The accuracy of calibration transfer and model adjustment for Wet Gluten

	Instrument	RMSEP	Bias	Slope	Intercept	Correlatio n
	Hebei-1	0.24	0.06	1.16	-5.43	1.00
	Henan-1	0.93	0.53	0.69	9.73	0.98
	Henan-2	0.49	0.40	0.96	0.77	0.99
	Jiangsu-1	0.59	0.32	0.78	6.94	0.99
	Shandong-1	0.41	-0.07	1.29	-9.42	0.99

Table 4 .

 4 Detail info of sample points assign and accuracy contrast between GIS method and RS methodBy above comparison, the Quality Regionalization map generated by RS inverse model is more accuracy and reliable than by GIS spatial interpolation map. So we can conclude that Integration RS monitoring technology with NIR network to generate Quality Regionalization map is valuable for crop enterprise on making purchase policy and bringing interests to enterprises, farmers and governments at the same time.

		Hebei	Henan	Jiangsu Shandong
	Points for build Model	40	40	35	0
	Points for verification	35	30	20	40
	standard deviation of GIS	0.134	0.117	0.122	0.178
	standard deviation of RS	0.048	0.055	0.054	0.102
	4 Conclusion

Xiaodong YangTF(FT, WenJiang Huang, Cunjun Li, Xingang Xu, Hao Yang

This work was supported by Non-profit Agriculture Research Special Fund support by Ministry of Agriculture of the People's Republic of China (200903010 & 201003039).


Baihua Zhang
email: zbaihua@126.com

Prediction Prediction Prediction Prediction of of of of freight freight freight freight ability ability ability ability in in in in country country country country base base base base on on on on GRNN GRNN GRNN GRNN

Keywords: Prediction, Freight ability in Country, GRNN

Developing the country's economic is a hot topic in Chinese society, and then the freight ability is a value that show the development of the local economic. The prediction of freight ability in country is benefit to the planning of country's transport. This article choose suited input first, and then make up the GRNN model, at last ,predict the ability of the freight.

Research Research Research Research background background background background

Perfect transportation is key guarantee for country to solute "Three Rural Issues". Accelerating the development can efficaciously help to deal with problem about circulation of agricultural products, strengthen the agricultural integrated produce ability and improve the adjustment of industry structure in country. Besides, it can also widen developing idea, improve the investment environment and achieve the goals of agricultural development and income rising; what's more, smooth transport could resolve those predicaments about sale and buy, stimulate the growth of rural consumption and shrink the distance among regions.

Perfect transportation plays a supporting role in development of urban and rural, construction of harmonious society. Convenient communication of labor and goods will effectively change the binary pattern contacted with employment, income, education, medical resource and so on. Rural transport is the important component and the end of integrated transportation network. Just like the capillary in body, rural road network builds bridge between aorta of the whole nation and great channel of special regions, and is the symbol show the width and depth of transport. We must take measures to speed up the development of transport network in rural regions, so as to arouse its skeleton effect, and keep the whole network development coordinately and proportionally.

Woeful condition of transport infrastructure is one of the key reasons leading to poverty of rural regions, which play objectively inciting role in development of country economy. The success of rural transport plan partly lies on the prediction of freight ability in country, which prepensely and preparedly carries on program for rural transport for related workers when they face interrelated works, to meet the need of the rural economy development [START_REF] Wang | Research on Agriculture Logistics System Evaluation and Optimization[END_REF] .

GRNN GRNN GRNN GRNN

The common freight volume prediction contains time series, regression analysis, grey prediction and composed prediction integrating kinds of methods. These methods almost concentrate in the analysis on Regression Model and Time Series Model toward its causality, these models cannot predict the inner structure and complicated character of these predicted dynamic data comprehensively and constitutionally, as a result, many the amount of information are missed. As a kind of parallel calculation model, Artificial Neural Networks holds many advantages surpassing traditional modeling methods, and is more effective for nonlinear mapping. ANN don't need too much experienced knowledge about modeled objects, such as the structure, parameter and dynamic character, only demanded is working out objects' output data and input data, and then input/output mapping will be easily reached through network' own learning function. Prediction of freight volume can use BP net and RBF net, which, however, owns some weak point such as low convergence speed and much too tiny parts. So, unfortunately, the result is not absolutely desirable when the sample is not enough and unwanted sound is crowded. Compared with BP net and RBF net, GRNN have a strong advantage in approximation capability, category capacity, learning speed. Net will finally converge to optimal regression surface which contains more samples, get better predict result even when the sample data is in shortage and in unsteadiness. Hence, I will attempt to build prediction model to predict rural freight volume by the help of GRNN [START_REF] Yang | The use of GRNN in the area of transport prediction[END_REF] . (1) (1) (1) (1)

2.1 .1 .1 .1 GRNN GRNN GRNN GRNN introduction introduction introduction introduction

Structure of GRNN is shown as

2 ( ) exp 2 i i x c R x σ ⎛ ⎞ - = - ⎜ ⎟ ⎝ ⎠
In addition, decides the shape of basis function in the first hidden layer, the i σ larger it is, the smoother the basis function is, so is also called smoothing factors. obtain every element. And the result will be offered for transfer functions [START_REF] Ge | The theory of ANN and the realization by matlab R[END_REF] :

2 n (2) (2) (2) (2) 2 2
( ) purelin n α = Study modification of GRNN connection weights also use BP Algorithm. Basis function in network hidden layer nodes chooses Gaussian function. As that above can show, this kind of network has capacity of local approximation, which just is the reason leading to quicker study programs. In addition, there are little personal adjusting parameter, only one threshold, and all the network study depend on data sample, which bring up the ability preventing prediction result from being affected by those personal and subjective presumption to the utmost extent. Compared with BPNN, GRNN demands less parameter need to be adjusted and has one smoothing factor, so can find proper forecast network quickly, and holds more advantage in calculation.

2....Construction Construction Construction Construction and and and and training training training training of of of of network network network network

(1)Choose impact factor The analysis on impact factor of rural freight volume contains human, wealth, objects. Adopted in this paper have some impact factors, which is regarded as network output as follow.

①Transport instrument. For water transport in the whole freight is extremely small, the main transport instrument is motor vehicle and railway. Adopt rural freight cars number to indicate(x1) and the number of freight train(x2).

②Mileage of transport route. water transport take up bitterly little. So adopt mileage of rural highway(x3) and Railway(x4).

③Number of practitioners on transport. The amount of those people own driver's license and driving permit in rural region(x5).

④Level of local economy development. Local GDP (x6)and agriculture, animal husbandry and fishery value(x7) will be chose.

⑤Tax on rural transport. Total tax of local transport can reflect the ability of rural transport macroscopically.

(2)Choose output factors. Make the freight mileage in the country(x10)、railway freight traffic (x10) and the highway freight traffic(x11) as the output factors, which is called the network output amount.

Building GRNN after choose impact factor and output factors. Because smoothing factor may affect the capability of network, so it is necessary to try again for the best result.

According to the output factor and input factor being determined above, make one rural region ' 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Picture 3 The error of forecasting of the net Combine the two figures above and examine the result, we can find that smoothing factors closely decide the approximation performance of network sample, and express to be positive correlation. When smoothing factors is 0.1, the deviation will be small for not only approximation performance and forecast performance. As the addition of smoothing factor, the deviation will accordingly increase.

When smoothing factors is 0.1, the test output is: yc= 0.7702 0.7802 0.7370 0.7570 0.7599 0.7799 Deal with these data by Inverse normalization, and then inverte the Matrix, the result are as follows:

Table 2 The data between the true value and predicted value Consequently, prediction error is more obvious, for the reason that the increasing degree between 2008 and 2009 is bigger, and these dates are far away from those training data,it is hard to reason. Besides, the number of the training sample is less, so the forecasting precision is not well. Taking into account these, the result of forecasting can be accepted.

Year

x(9) x( 10

Conclusion Conclusion Conclusion Conclusion and and and and analysis analysis analysis analysis

Basis on the analysis of influencing factor and predict character of rural freight volume, this paper predict the rural freight volume through GRNN. The testing and analysis of prediction consequent prove that the GRNN is valid for predicting rural freight volume. The guiding ideology and calculate method also need to be perfected, though this paper provide a reference for the prediction of rural freight volume in a certain sense. The result consequentially plays instructing role in rural transport and rural economy.

Figure 1 : 1 IW

 11 Picture 1 The picture of GRNN net's conformation The first layer is hidden layer of Radial basis, the amount of neurons equals that of training samples. In this layer, weight function is Euclidean distance function, whose function is calculating the distance between data in computing network and the first weights , b 1 stands for threshold in this hidden layer. Mark "g" expresses 1,the relation between data from and b 1 . Transfer function in this layer is dist RBF, and the Gaussian function is regarded as transfer function:

σ 1 α

 1 The second layer is linear output layer, whose weights function is standardization dot product weight function, been used to calculate the vector , multiply 2 n each line element in , then divided by the sum of every factor of , we can 2,1 LW 1 α

Table 2

 2 s historical statistics from 2001~2007 as training sample of network, and regard historical statistics between 2008 and 2009 as extrapolation of test samples. Input sample and target sample is as follow table.The data between in a country area after normalization After data processing, I will establish a GRNN and carry on training and test. For great effect of smoothing factor on network function, it is necessary to try again for best value. Use matlab and its ANN toolbox newgrnn to program and calculate.

		Table1 The sample data between 2001 to 2009 in a country area(input
				sample)
	Year	x(1)	x(2)	x(3)	x(4)	x(5)	x(6)	x(7)	x(8)
	2001	21.30	1349.41	0.97	0.32	23.1	707.58	109.73	80.5
	2002	20.93	1484.05	1.10	0.32	28.6	821.40	123.68	94.5
	2003	21.88	1616.93	1.27	0.35	30.8	900.99	148.18	87.5
	2004	22.85	1767.14	1.51	0.36	31.9	947.97	163.63	91
	2005	24.63	1910.76	1.79	0.36	34.1	993.01	183.19	91
	2006	26.07	2062.04	2.05	0.37	36.3	1081.78 195.27	98
	2007	27.85	2191.44	2.34	0.37	39.6	1160.79 214.17	98
	2008	29.56	2353.32	2.49	0.39	42.9	1267.96 231.90	105
	2009	31.85	2504.06	2.74	0.39	45.1	1412.00 250.62	105
	Table1 The sample data between 2001 to 2009 in a country area(goal sample)
		Year		x(9)		x(10)		x(11)
		2001		1241.08		633.62	559.64
		2002		1507.50		735.93	680.56
		2003		1800.38		837.96	815.08
		2004		1967.07		966.26	945.80
		2005		2257.76		1109.06	1095.63
		2006		2490.93		1205.58	1194.97
		2007		2741.84		1329.33	1241.02
		2008		2972.20		1458.85	1346.21
		2009		3189.50		1577.57	1456.31
	First, take normalization with those data, after it the data are as follows:


Yang Yang

Yang Zhang

Tian He

Application analysis of machine vision technology in the agricultural inspection

Keywords: machine visual technology, Inspection of agricultural products, application

. The current status of machine visual technology applied in inspection of agricultural products will be analyzed in this text. Parts of problems in practical application will be summarized and its future develop direction will be discussed here.

Introduction:

China is a large agricultural country. Agriculture is paid more attention since ancient. Our ancestor has invented a lot of new ways to create the splendid agricultural civilization. With the development of agriculture technology, agricultural modernization raises new requirement for agriculture development, for which the progress of industry offer new technical support. Newly-typed agriculture has changed former whole man-made working style and promoted agriculture to develop toward automation and intelligence [START_REF] Hao | Machine vision based quality inspection of potato Potato Research Single[END_REF]. A workman must sharpen his tools first if he is to do his work well. To make the land a large output growth and high quality of crops, we must grasp detection. However, former traditional detection methods have not satisfied the requirement of modern agriculture, which promotes new detection technology applied. Among them machine vision technology can provide efficient and reliable products detection approach. It has prospect in many of the testing technology application in agricultural products.

Machine visual technology characteristics

system structure and work principle

Machine Vision actually replaces the human eye to do the measuring and judging. Machine vision system is to be up taken by the visual sensor object, which is converted into an image signal, then passed to the image processing system, according to the pixel signal distribution and collection brightness, color and other information, image signal will be converted into digital signals; system will operate the signal features and compared with the data stored in the database, then estimate and determine the relevant features of the results. Characterized increases production flexibility and automation extent, and releases people from some danger, monotony, any error in the liberation of the working environment, and easy to integrate information, easy to implement the system intelligent [START_REF] Rao | Rice seeds based on machine vision Quality Inspection Machine[END_REF].

A typical industrial machine vision systems including: lighting (Halogen light, LED light source, high frequency fluorescent source, flash source, other special light source), optical lens (CCT camera, microscope head), cameras, image processors, image video capture card, image processing software, monitors, communication / input and output units and so on. The working requirements of machine visual are as follow. One hand, lighting is the key factor to effect the input of machine visual system, which influences the quality of imaging machines and application effect. How to make the light stable in a certain extent is the problem to need to resolve in practical process. On the other hand, ambient light might affect the quality of the images, so adding protective screen is used to reduce the effect of the environmental impact of light. And the requirements of industrial lens: (1) the focal length (2) Target height (3) Image height (4) amplification factor (5) the distance to the target image [START_REF] Liu | An Improved Color Image Segmentation Algorithm Based on Wavelet Denoising and Regional Growth[END_REF] center/node [START_REF] Cai | Research and Application of Relative Algorithms in Real-time Automatic Vision Inspection System[END_REF] distortion; According to the different application condition, different resolution cameras are chosen, photographic equipment of different camera. A video capture card is only one component of completed machine vision system, which should be selected according to the interface requirement of camera. Visual processor is the combination of capture card and processor. The processing ability of computer was weak in the past time. It usually needs visual processor to accelerate visual processing tasks. Video capture card now images can be quickly transmission to memory, and computer image processing capacity has greatly improved, so now generally use general-purpose processors and processing software can be realized, the working principle of machine vision as shown in figure 1.

Figure 1.Current analysis of agriculture products inspection

The advantages of machine vision

(1) High precision: such measurements do not need to touch, so there is no dangerous and wear on the fragile parts.

(2) continuity: visual system make people suffer from fatigue. Multiple systems can be set to run separately.

(3) cost-effective : With the sharp decline in the price of the computer processor, the visual system operating and maintenance costs are much low.

(4) Flexibility: the visual system can carry out a variety of measurement. When the application changes, the softwares change or simply up grade to meet the new demands .

Machine vision systems have better adaptability than optical sensors or machines. They enable automatic machines to have the diversity, flexibility and reconfigurability. When you need to change the production process, on machine vision for the "tool to replace" just change the software, not to replace expensive hardware. When the reorganization of the production line , visual system often can be reused.

3.Currently the main agricultural products inspection includes safety, quality and quantity inspection.

The main agricultural products safety inspection is mainly on pesticide residue (organophosphorus pesticide and carbamate, chrysanthemum ester) and veterinary drug residue (malachite green and nitrofuran, sulfa, chloramphenicol), prohibited additives (lenobuterol hydrochloride, shrek dopamine), the harmful elements (arsenic, the cadmium, lead, mercury), to ensure its safe to eat. The main products quality inspection aims at crop appearance, YanZe, brix, acidity, soft pulp and external damage, the internal defects, grotesque, nutrition, etc chemical index physical parameters testing. The detection rate of agricultural products mainly guarantee for high quality and high value-added products, good technical level is the main high-tech agricultural, have very good market prospect. Agricultural production testing mainly tells from agricultural economic benefits on macroscopic.

As an important test of agricultural products, a link to the quality inspection is to guarantee the international competitiveness of agricultural products in the market at home and abroad, the important research institutions and equipment manufacturers all focus on the research of this field. In the early time, applying machine visual technology on the research of agriculture products quality has started abroad. Good effect has got in fruits, vegetables and quality detection, which achieve intelligent degree. Detected in the fruit, In 2001, DM Bulanon invented apple picking machine such as hand-machine vision navigation system, analyzed the brightness of the apple and red color difference histograms, guessed the ultimate classification of image segmentation threshold Apple value using the best way. In 2002, Zhao Yan, such as virtual instruments such as basis, pears were detected using machine vision technology pilot study of external quality. Detected in vegetables, in 2004, K. Ninomiya and so developed a three machine vision system consisting of eggplant automatic classification system. In 2004, Zhang Yane machine vision technology for applications such as diagnostic method of nutritional status of greenhouse crops [START_REF] Han | Computer image processing technology in the Inspection and Grading of agricultural products[END_REF]. In 2005, Wang Shuwen such comprehensive use of computer vision technology, BP algorithm, the image of the characteristics of tomato, by eight characteristic parameters of BP algorithm using the new multi-layer feed forward neural network to classify the damage on the tomato.

4.Machine Vision in Agricultural Products of the specific applications

Investigation on grading of Machine vision to the apples' non-destructive testing

In 1986, U.S. scientists used surface crushing machine vision inspection of apples, while Apple's standards based on their classification, was developed using machine vision to defect detection and classification of apple processing equipment, and verify that the machine vision technology used for the feasibility of damage detection of fruit grading. Current nondestructive testing machine vision fruit grading methods are mainly the following aspects [START_REF] Huang | Computer Vision in agricultural products detection[END_REF]. People using computer vision technology to detect bruising of fruit, according to fruit shape and characteristics of bruising bruising area of measurement established a mathematical model in order to achieve detection of fruit bruising; people use machine vision to the image shadow correction and image segmentation, for no apparent edge of the scratch using near-infrared image recognition; people first use of computer graphics technology for automatic detection of decayed fruit and fruit surface color distribution on the fractal dimension of research.

Color Machine Vision Inspection and Grading applications for agricultural products

The color of agricultural products is one of the important signs of quality. It can tell the fruit ripeness, growth, colour and lustre, etc accurately by machine visual technology. Color test application of general use outdoor natural classification under the condition of the images filmed crops and establish a brightness color information using color information of color image recognition of crop model; It can also use indoor machine vision system for agricultural products, fruit color images in the analysis of the characteristics of agricultural products based on color, with appropriate hue under shaded area percentage of total value of classification method of color.

Applications in the agricultural shape size detection grading

Agricultural appearance not only can affect the size of its appearance and size of the agricultural growth of judgment and knocked wounded and defects, is now in the detection of popular agricultural research direction. Testing analysis of agricultural system mainly includes machine vision hardware platform and software [START_REF] Liu | An Improved Color Image Segmentation Algorithm Based on Wavelet Denoising and Regional Growth[END_REF]. The processing software flowchart analysis mainly include the following: image input, image preprocessing, feature extraction and recognition, output four varieties. As figure2 shows, test process is an important link of us to the original image acquisition process, including the grey-scale conversion and threshold image analysis is specified approach is analyzed, the contour of key products. Size shape, The work is mainly based on the gray image processing and database are calculated or image data were compared, and the shape and size of agricultural products to analyze the results. In the field of strong LabVIEW test data acquisition, also it for most function of support, good camera with its Vision of visual development module, a method can realize many functions. Therefore we used with mathematical tool calculates LabVIEW Matlab joint development of realizing detection image analysis. Another part of using VC + + series image processing software development, the industrial production manufacturers are given camera VC setups. There are many open-source library's support, such as OpenCV OpenGL, etc., and its weakness is ShouMan, programming, slightly complex maintenance etc [START_REF] Rao | Rice seeds based on machine vision Quality Inspection Machine[END_REF].

Existing problems and the development trend

(1) The agricultural research is static or slower. The accuracy of detection is low generally. To extract useful information from rapid movement of agricultural products, to strengthen the multi image processing method and the hardware structure of identifying algorithm is the main direction of future research in order to solve the problem of low detection and accuracy in existing research [START_REF] Cai | Research and Application of Relative Algorithms in Real-time Automatic Vision Inspection System[END_REF].

(2) The testing technology combined with nearly infrared technology, technology and image processing GLS, which is used to form a composite products quality inspection and grading and agricultural growth analysis related aspects of the automation management [START_REF] Cai | Research and Application of Relative Algorithms in Real-time Automatic Vision Inspection System[END_REF].

(3) Computer image processing technology is applied in agricultural grading test research in domestic later than in overseas. There is a large disparity between domestic and overseas whether on hardware or on software. We need references based on the international newest research, to explore the new theory and method, suitable for China's national conditions of agricultural products in automatic detection system, improve the level of agricultural modernization.

Figure 2

 2 Figure 2 detection flowing chart of agricultural products'dimensions


Lairong Chen

Qingchun Wang

Ronghua Ji

Research and Application of Modern Information Technology in the Forest Plant Protection Machinery

Keywords: Forest, Plant Protection Machinery, Pests and diseases, Information Technology

Forest pests and diseases is very important to forest because it not only restricts the development of forest, but also causes huge economic. Chemical pesticides control the pests and diseases effectively and pollute environment seriously. How to reduce the amount of pesticide is the research hotspot in the field of plant protection. Forest plant protection machinery is the most important way to spray chemical pesticides. With the development of various modern information technologies, forest plant protection machinery has entered the electronic age in developed country. There are many modern information technologies which are applied in forest plant protection machinery, such as database, 3S technology, and sensor technology and so on. In this paper, a large number of relevant scientific literatures have been analyzed and compared in order to summarize application of modern information technology in the forest plant protection machinery.

As the wide forest range, forest plant protection machinery should base mainly on large-scale spraying. 3S technologies, database technology, computer control technology and a variety of detection techniques should be effectively combined together and applied in forest plant protection machinery in order to meet environmental safety requirements and improve the efficiency.

Introduction

Forest is essential resource for human being. Forest resources protection is comprehensive measure which includes the reasonable usage of soil, water and other renewable resources, improving the ecology environment, supporting social and economic sustainable development and so on. Precision forestry is purposed to meet the strategy of developing forestry production science, and realize the change forestry production from extensive to intensive. The basement of precision forestry is to collect all kinds of forestry information quickly and accurately, and detect and manage forest objects effectively. However, forest object detection and management are bottleneck for precision forestry because forest objects are various and forest is a random variable environment. The forest pests and diseases become more and more serious for global warming, severe weather occurring frequently and inter-regional trade. Forest pests and diseases cause significant economic losses. Chemical pesticides are the important measure to control the forest pests and diseases. However, chemical pesticides also pollute environment seriously. To improve chemical pesticides utilization and reduce environmental pollution, forest plant protection machinery became one of important application directions of precision forestry.

Modern society is changed from industrialization to information. The global wave of information technology is droved by modern information technologies which are included computer technology, communication technology, network technology, multimedia technology, 3S technologies, and sensor technology and so on. Meanwhile, modern information technologies are widely used in modern forestry, especially for forest plant protection machinery.

In this paper, a large number of relevant scientific literatures have been analyzed and compared in order to summarize application of modern information technology in the forest plant protection machinery. The prospects of application and development of modern information technology in plant protection are discussed in this paper.

Modern information technology refers to access, process, transmit and utilize the information by computer and modern communications technology. Modern information technology is interdisciplinary science that includes computer technology, communications technology, multimedia technology, network technology and so on. The modern information technology develops rapidly and brings great economic and social benefits.

In order to find the development of modern information technology, the published academic papers are searched from Engineering Index Database and Science Citation Index Database with the key words of modern information technology (such as databases, remote sensing, image processing and so on) for nearly 20 years (1990~2010). The results show that the research and application of all kinds of modern technology has become more popular since 1998, especially since 2004, the modern information technologies developed rapidly. Plant protection is taken as the joint keyword to search again. The research and applications of a variety of modern information technology in the plant protection began to increase from 1999. The trends are consistent with the development of modern information technology. It can be concluded that, with the rapid development of information technology, its application in plant protection has also been widespread concern. For example the number of published academic papers which are related with remote sensing is shown in Fig . 1. The pests and diseases are controlled effectively by chemical pesticides. The plant protection machinery is the machine to use chemical pesticides effectively. Plant protection machinery also protects the security of agriculture and forestry production. At the same time, it is a strong guarantee of protecting the ecological environment and natural resources. Intelligence, automation and green plant protection machineries become trends with the rapid development of modern information technology.

How to reduce the amount of pesticide was the research hotspot in the field of plant protection machinery. A solid technical foundation is provided by the variety of modern information technology (such as 3S, control technology, image processing, pattern recognition, decision support systems and databases, etc.) for the realization of plant protection machinery.

Research Overview

There are many modern information technologies which are applied in the forest plant protection machinery, such as database, 3S technology, and sensor technology and so on. The application of main modern information technologies are discussed as following.

Database technology

Database technology is an important component of the modern information technology and the core of the data processing and information management system. Database technology can organize and store massive data efficiently for the information processing. Database technology can reduce the storage redundancy, safe sharing, and efficient processing. Database technology is widely used in pest management, especially in combination with expert system.

Data are the study foundation of the plant protection machinery. There are a variety of data which include observational data (such as meteorological data), historical data (such as beginning period, the disease index of forest pests and diseases in recent years and so on), empirical data (such as various of plant protection machinery application, operating conditions, etc.), experimental data (such as plant protection machinery control efficacy data, etc.) and a variety statistics data for pests and diseases and so on. It is important to effectively analysis and manage these data for plant protection machinery. The management information system of ornamental plants diseases and pests was established [START_REF] Guilin | The Establishment of the Management Information System of Ornamental Plants Diseases and Pests in Qingdao(in Chinese)[END_REF] .The system was included the ornamental diseases and pests database. Ornamental plants diseases and pests could be searched and distinguished in this system quickly and accurately. Forest disease remote consultation system was established [START_REF] Nihong | Design and Realization of forest disease and insects Remote Consultation System based on natural language understanding(in Chinese)[END_REF] . The system was based on natural language understanding technology, database and network technology. The forest disease and insects could be remote diagnosed in this system. The inspection and forecast system of the forest plant diseases was build [START_REF] Hong-Min | The Design of the Inspection and Forecast System for the Forest Plant Diseases[END_REF] . The developer tool and the structure design of database were introduced.

All relevant data could be effectively organized by database technology and formed independent data set for the application program. The strategic reference forest pest control could be provided by the independent data set. Pesticides could be sprayed accurately and effectively with the plant protection machinery.

A database set for intelligent plant protection machinery was build [START_REF] Zhang Huichun | Specialized database technology for intelligent plant protection machinery[END_REF] which included the meteorological database, forest pests and diseases management database, plant protection machinery database, chemical application decision making dataset and plant protection machinery decision making dataset. The database set provided a case study showing how to prevent and control forestry pests and diseases effectively by using proper climate conditions, proper sciences and technologies.

Database technology is mainly applied in pest management especially the expert system of plant protection. With the rapid development of plant protection and integration, database technology must be an important component of plant protection machinery.

3S technology

There are three parts of 3S technologies, which include Geographic Information Systems (GIS), Global Positioning System (GPS) and Remote Sensing (RS). Global Positioning System is global, all-day, continuous and real-time navigation, positioning system. Spatial location information is obtained in forest plant protection machinery in order to provide the spatial location of various types of sensors (such as CCD camera) and the delivery platform (such as vehicles, aircraft, etc.) rapidly. The forest plant protection machinery works according to the prescription map. The data, which is relative with geographical distribution of Earth's surface, is stored, managed, analyzed and described in Geographic Information System. The geographical location and attributes are associated together organically in GIS. At the same time, the spatial and attribute information are output accurately as a map according to user need. The spatial and attribute information are from a variety of sources in the forest plant protection system. The information is stored, managed, analyzed effectively, and mapped. GIS is a platform for forest plant protection system. Remote Sensing is the Earth observation technology with multi-platform, multi-band and high-resolution. The spectral information of natural target on Earth surface are obtained and processed by remote sensing in order to understand the natural world. Remote sensing is used to access the information of the environment and conditions of tree and update the GIS data timely in plant protection of forest.

Research was initiated on map-driven variable rate sprayers [START_REF] Rockwell | Variable rate sprayer development and evaluation[END_REF] using GIS or remote sensing information. A real time GPS-guided vehicle was the mapping of the spatial distribution and density of weeds combined with herbicide application with variable rate technology [START_REF] Nielsen | Control of an autonomous vehicle for registration of weed and crop in precision agriculture[END_REF] . Through a learning community approach led by the Upper Midwest Aerospace Consortium [START_REF] Seelan | Remote sensing applications for precision agriculture: A learning community approach[END_REF] , information was shared among scientists, agricultural producers, and data providers. SHI [START_REF] Shi Wanping | Variable Rate Spraying Technology on the Basis of GPS and GIS(in Chinese)[END_REF] discussed the controlling theory of the three kinds of variable rate spraying system and their advantages and disadvantages. The variable rate spraying system was based on GPS and GIS. Development of the SDSS for Medfly control in Israel, also known as MedCila, was described [START_REF] Cohen | Spatial decision support system for Medfly control in citrus[END_REF] .The MedCila was integrated into a GIS environment.

Working with the MedCila would enable the coordinator to make decisions for only around half of the plots. Cotton insect pests can be controlled early season using spatially variable insecticide applications [START_REF] Mckinion | Spatially variable insecticide applications for early season control of cotton insect pests[END_REF] . Technology was developed for using GIS-based map. A spatially sensitive map could be developed that could drive a spatially variable insecticide application for the control of the insect pest. Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing, Geographic Information Systems, and Global Positioning Systems may provide technologies needed for farmers to maximize the economic and environmental benefits of precision farming. However, most farmers do not have the skills to utilize these technologies effectively. The positioning accuracy measurement of GPS in the forest was discussed [START_REF] Palmer | Calculation of distance traveled by fishing vessels using GPS positional data: a theoretical evaluation of the sources of error[END_REF] . It was put forward that the more concentrated positioning point, the higher positioning accuracy, otherwise the lower positioning accuracy.

One of the objectives of precision forest is to minimize the volume of herbicides that are applied to the plants through the use of site-specific plant management systems. RS, GIS and GPS may provide the technologies needed to maximize the economic and environmental benefits of precision forest. It is the inevitable trend that the plant protection machinery integrated with 3S technologies for the complex forest object.

Sensor Technology

It is an importance problem for the development of intelligent plant protection machinery to detect the object effectively. A great variety of plant protection objects are changeable and complicate. It is an important bottleneck for the development of plant protection machinery to detect the plant protection object effectively. With the rapid development of various sensor technologies, the various sensors were widely used in various fields. The sensors, which are widely used in plant protection machinery, are optical sensor, ultrasonic sensors, and infrared sensors and so on.

Optical sensors can be used for recognition of plants and discrimination between plant species by utilizing image analysis methods and/or multi-spectra information. These types of sensors and processing methods have been developed intensively during recent years. Because the optical sensors have many advantages, it becomes the main sensor to apply in the intelligent plant protection machinery. The optical sensor-based plant detection systems [START_REF] Shearer | Selective application of post-emergence herbicides using photo electrics[END_REF] can detect all the green plants (weed and crop plants) and spray only on the plants. A machine vision guided precision band sprayer for small-plant foliar spraying [START_REF] Slaughter | Precision band sprayer with machine-vision guidance and adjustable yaw nozzles[END_REF] demonstrated a target deposition efficiency of 2.6-3.6 times that of a conventional sprayer, and the non-target deposition was reduced by 72-99%. A precision spraying system called micro-spray was developed [START_REF] Slaughter | Robotic weed control system for tomatoes[END_REF] . Micro-spray was designed for intra-row weed control.

Herbicide is applied to the weed plants, only, and not to the crop plant and the soil. For high-value crops, high-accuracy machine vision and control systems have been studied for outdoor field applications in California [START_REF] Tian | Development of a precision sprayer for site-specific weed management[END_REF] . The smart sprayer, a local-vision-sensor-based precision chemical application system, was developed and tested [START_REF] Tian | Development of a sensor-based precision herbicide application system[END_REF] . An automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops was described [START_REF] Tellaeche | A vision-based method for weeds identification through the Bayesian decision theory[END_REF] . A weed recognition method based on Support Vector Machine was presented [START_REF] Zhu | The application of support vector machine in weed classification[END_REF] .

Intelligent Automatic Targeted tree sprayer with ultrasonic sensors can identify the distribution of trees (with tree or no tree) to do real time monitoring and make a decision whether to spray and how much to spray. Some work to verify the suitability of an ultrasonic sensor as a plants detector for a Mobile Robot was described [START_REF] Harper | Recognition of plants with CTFM ultrasonic range data using a neural network[END_REF] . An ultrasonic recognition system which was installed in the orchard crop spraying aircraft was developed by Taiwan Chung Hsing University. The Results showed that the proportion of pesticide savings of 57% compared with traditional spraying methods. Ultrasonic sensors to continuously detect the presence or absence of target trees; in regions where a sparse target is detected, spray output is modulated; when no target is detected, spraying is ceased.

The research challenge is to process the image timely and accurately for the application optical sensor in the plant protection machinery. The research hotspot is the image processing algorithm.

Conclusion

Forest plant protection is to protect and expand the forest resources. Forest plant protection machinery is an effective means to control forest diseases and insect pests. There are two major issues of plant protection machinery: how to improve efficiency of pesticides utilization and reduce the impact of pesticides to non-target organisms and the environment. With the development of various modern information technologies, forest plant protection machinery has entered the electronic age in developed country. The most important international plant protection machinery productions are Europe and the United States. Its products cover the world's major markets while its technology and equipment represent the highest level in today's world. As the wide forest range, forest plant protection machinery should base mainly on large-scale spraying. 3S technologies, database technology, computer control technology and a variety of detection techniques should be effectively combined together and applied in forest plant protection machinery in order to meet environmental safety requirements and improve the efficiency.

Fig 1 .

 1 Fig 1. The number of published academic papers related with remote sensing

Acknowledgements

This program was supported by Beijing Forestry University Excellent Teacher Program (BLYX200907) and Beijing Excellent Talents Program (No. 20081D0500600147).