Xueqin Tong

Yujian Yang

Wei Dong

Spatio-temporal Variability Analysis of Soil Volumetric Moisture Content on the Field Scale

Keywords:

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

Introduction

Soil was inhomogeneous and continuous in nature, and its characteristics had the spatial structure. One of the challenges facing the adoption of precision agriculture (PA) technology was the identification of productivity-related variability of soil properties accurately and cost-effectively, the all-important work and base of precision agriculture was producing the spatial distribution map of soil property content. The study of soil volumetric moisture content (SVMC), as one of soil properties, was the heated debate of soil-crop system, and was also the key factor connection with crop growth system [START_REF] Yang | The spatial continuity study of NDVI based on Kriging and BPNN algorithm[END_REF] .Time domain reflectometry (TDR) was widely applied to measure soil electrical conductivity to infer SVMC. With a minimum of soil disturbance, TDR enabled to simultaneously estimate SVMC as well as electrical conductivity and was frequently applied as well in studies of solute transport in porous media [START_REF] Bechtel | On the relation between NDVI, fractional vegetation cover, and leaf area index[END_REF] . Although SVMC was an important variable under soil-crop system framework,the spatio-temporal variability of SVMC had the less study from the correlation literatures, let alone analyze the SVMC variability on the field scale at home and abroad. The spatial variability of SVMC was developed from some reseachers [2], mainly with statistical analysis and geostatistics, which were used in many domains, but the spatio-temporal analysis of SVMC was seldom studied on the field scale in recent years. Temporal stability of SVMC was a reflection of the temporal persistence of spatial SVMC patterns. Grayson et al. Vachaud et al [3]. found that soil texture was responsible for an observed high degree of temporal stability. Kachanoski & de Jong expanded the definition of temporal stability and correlated scale dependency of the temporal stability of SVMC [START_REF] Aparicio | Relationship between growth traits and spectral vegetation indices in durum wheat[END_REF] . From the new perspective, developing the comprehensive influence of SVMC to the spatial distribution of SVMC was the other significant objective. The amount of spatial variability as well as the spatial distribution of SVMC can have a critical impact on the irrigation status and management measure on the field scale. The research also showed that a starting point to characterize the structure and heterogeneity of SVMC fields was an examination of the spatial structure. Therefore, the objective of this study was to evaluate the efficiency of spatial statistical analysis and spatial distribution of SVMC during two-cropping for wheat and maize in one year, moreover, and the study explored the comparative analysis of the structure factors and complicated association factors in temporal scale impact on SVMC [START_REF] Jian | Spatial statistics of agronomy parameters and soil moisture content at the wheat jointing stage[END_REF] .

2

Material and Methodology

TDR measurement principle and approaches

An overview of conceptual dielectric models for TDR application can be found in some literatures. TDR estimated the dielectric constant, in a soil matrix by measuring the propagation time of an electromagnetic wave (EM) sent from an EM pulse generator mounted on top of a coaxial cable, inserted into a soil matrix. EM waves propagate through the coaxial cable to the TDR probe, which was a rod made of stainless steel or brass. Part of the incident electromagnetic waves was reflected at the top of the probe because of the difference in impedance between cable and probe. The remainder of the wave propagated through the probe until it reached the end of the probe, where the wave was reflected. The round-trip time of the wave, from the beginning to the end of the probe was measured with an oscilloscope branched on the cable tester. For a homogeneous soil, volumetric water content is calculated by using a calibration curve. The curve was mathematically formulated in these literatures [START_REF] Yang | Comparison Study on the Spatial Estimation of Ji Wheat 22 Yield on the Precision Scale[END_REF] .

Data Acquisition of Soil Volumetric Moisture Content

SVMC was determined by TDR in the study. A total of 104 soil samplings about soil volumetric moisture content were collected from the surface layer (0-20cm) at the experiment field (117°3'55.74"E, 36°43'8.99"N) with the support of grid-sampling method, covering approximately 3 hm2, located on the north shore of Xiaoqing River in Shandong province, P.R. China. Explanatory variables included environmental spatial datasets such as land use, drainage class were put forward to analyze. On each sampling point, we inserted vertically a triple wire TDR probe to monitor soil moisture, each moisture measurement was geo-referenced using a Differential Global Positioning System (DGPS), five moisture measurements were made within a 1-m diameter circle. The average reading for each point was computed as SVMC datum point. Sample measurements were implemented before wheat harvest on June 3rd, maize harvest on September 24th, in the latter sampling, the more validation points, and "maize 958" of maize cultivars was planted in the study area. The Kriging algorithm combined with GIS was applied on drawing the distributing map of SVMC and it also offered the theoretical foundation for the connection between studying SVMC and enhancing yield [START_REF] Yujian | The trend variability of soil organic matter content in the salinity region of Yucheng city in Shandong Province[J][END_REF] .

3

Results and Analysis

.1 Statistical Characteristics of Soil Volumetric Moisture Content

The study explored the classical statistics analysis and developed the normality test of the variable based on Shapiro-wilk test in the normality of 0.05 incredile level. In general, Shapiro-Wilk normality test referred to the correlated documents. The classification of variance coefficient(C.V.), weak variation(C.V.<0.1), medieval variability (C.V.=0.1-1.0), strong variation(C.V.>1.0). Coefficient of variation of soil nutrients content, respectively. [8]C.V. of SVMC was 0.351, 0.105 in the two samplings, respectively, which belonged to the medieval variation, the mean value of the variable was 4.119, 50.739. In order to understand distribution characteristics of SVMC, we analyzed the frequency characteristics of the variable, the results referred to the Figure 1and Figure 2.

Figure 1 The frequency characteristics of SVMC on June 3rd Figure 2 The frequency characteristics of SVMC on September 24 th

In the article, the study explored the frequency characteristics of SVMC on June 3 rd and September 24 th . The SVMC were divided into equal intervals ranging from low value to high value, the frequency statistics was developed for the variable, generally, sampling data falling on the interval boundary was considered of the high equal interval. As shown in Figure 1 and 2, the SVMC ranged from 2.65 to 4.65 covered approximately percent 67% of the total samplings on June 3 rd , which indicated the important effects for the total statistical distribution. Approximately percent 76% of the total samplings ranged from 45.4 to 55.4 on September 24 th , which responded to the whole statistical level. Normality distribution of the SVMC was the precondition of using interpolation to analyze SVMC feature, the normality was also developed in the study, the statistics characteristics of SVMC was illustrated about sampling data rather than the regional characteristics of the whole study area. Consequently, it is necessary to apply geostatistical methods together with GIS to solve this problem.

Geostatistical Characteristics of Soil Volumetric Moisture Content

Kriging Interpolation was the most common interpolation method in geostatistics. It was the method of optimized and precise estimation of partial variables in unobserved locations with application of original data and configuration of semivariance function. The involved variables of semivariogram and the parameters of estimation accuracy provided the basic description of Kriging prediction, these variables and parameters was introduced in the other documents [8]. Such as nugget, sill, range, ME(Mean standardized error),ASE(Average standard error),RMSE(Root-mean-square error),MSE(Mean standard error) and RMSSE(Rootmean-square standardized error). The degree of spatial autocorrelation was described by the ratio of nugget and sill, the ratio of nugget and sill of soil volumetric moisture content was 0.709, 0.549 in the study, respectively. Results showed there was the medium spatial autocorrelation of SVMC. During the semivariogram model, the anisotropic change of SVMC was considered of 4.8 angel direction on June 3rd, good-fitness model was exponential model for the two times samplings of SVMC. From the Kriging results, for the sampling on June 3rd, ME equalled -0.020, RMSE equalled3.844, ASE equalled 3.812, MSE equalled -0.007, RMSSE equalled1.009. For the sampling on September 24th, ME equalled -0.018, RMSE equalled 1.359, ASE equalled 1.298, MSE equalled-0.028, RMSSE equalled 1.047. The prediction results from the two times sampling responded to the simulation accuracy. The evident change was presented in nugget, sill and range parameter of semivariaogram model of SVMC, the ratio of nugget and sill of SVMC indicated that the random factors had an impact on the spatial variability of SVMC for the two times samplings, 70.9% and 54.9% of the total spatial variability, on June 3rd, the spatial results emphasized the rainfall and irrigation stochastic factors, but the samplings on Sep.24th, the exogenous variable decreased the effects on the spatial variability of SVMC from percent 70.9% to 54.9%, the random factors dominated the spatial variability of SVMC. The Kriging algorithm combined with GIS was applied on drawing the distributing map of SVMC of different crops harvest stages and it also offered the theoretical foundation for the connection between studying SVMC and enhancing the yield. This study provided a systematic approach for estimating changes of SVMC at the regional scale. As shown in Figure 3 and Figure4, we concluded that the Kriging algorithm provided a practical spatial statistical tool for prediction and simulation of categorical soil moisture variable. For the two samplings (wheat harvest on June 3, maize harvest on September 24th, 2009), the comparison analysis results indicated that the spatial pattern of SVMC and also clarified the fundamental complicated factors of the spatial structures and the comprehensive factors from time-scale analysis.

The continued distribution of SVMC responded to the random factors and structure factors. The comparative results showed that the sensitivity of soil moisture holding-capacity which restricted the soil moisture content in the results. The high content regions of spatial surface on June 3rd located in the in the upper right corner, the spatial surface on September 24th measurement regions basically inherited the surface on June 3rd, the dominant factors were verified by the comparative analysis of the two times moisture content spatial distribution. Under the sensitivity, involvement of exogenous variable (such as stochastic rainfall factor, irrigation factor), SVMC in the surface layer can be resulted into change, but from a more stable changes, the distribution of high-value and low-value regions of SVMC were in touch with soil moisture-holding capacity. So the continued distribution of SVMC not only provided the foundation of the dynamic monitoring of the total distribution of soil water status, but also was the important part of soil water monitoring system. With the support of classical statistics, analysis results of 104 topsoil soil samplings indicated that the average content of SVMC, frequency analysis results showed that the SVMC ranged from 2.65 to 4.65 covered approximately percent 67% of the total samplings on June 3rd, approximately percent 76% of the total samplings ranged from 45.4 to 55.4 on September 24th, which responded to the whole statistical level. We concluded that the Kriging algorithm provided a practical spatial statistical tool for prediction and simulation of categorical soil moisture variable. For the twice samplings, the comparison analysis results showed that the spatial pattern of SVMC and also clarified the fundamental complicated factors of the spatial structures and the comprehensive factors from time-scale analysis. Making a better spatial distribution map of SVMC was of significance in promptly adjusting precise agriculture management like fertilization and irrigation, especially, the comparative study of the spatial variability of SVMC, which also offered the theoretical foundation for dynamic monitoring the connection between SVMC and the other variable, for example, crop yield. The spatial distribution map of SVMC by Kriging interpolation was better to help researchers precisely and well understand the spatial distribution of the SVMC located in the farmland of wheat-maize rotation system.

Figure 3 .

 3 Figure 3. the spatial variability of SVMC on June 3 rd Figure 4. the spatial variability of SVMC on Sep. 24 th


Peidong Wang
email: wangpd@nercita.org.cn

Zhijun Meng

Changhai Luo

Hebo Mei

Path Recognition for Agricultural Robot Vision Navigation under Weed Environment

Keywords: vision navigation, agricultural robot, weed noise filtration, path recognition

In this paper, a path recognition method for agricultural robot vision navigation under weed environment is proposed. The vision navigation is based on color images sampled by a high speed camera. First, the crop and weed information is extracted using an appropriate color feature model to separate the green crop from background; then the image is thresholded, and the noise caused by weed is filtered by deleting small-area objects in the image; the navigation path is extracted through Hough transformation. Experiments are carried out in corn seedling field, and results show that the method can recognize navigation path correctly under weed environment.

Introduction

Autonomous agricultural robot has many applications such as environment information collection, seeding, fertilizing, spraying, etc. The goal of autonomous robot navigation is to control the trajectory of the robot and keep it along the driving path [START_REF] Backman | Navigation system for agricultural machines: Nonlinear Model Predictive path tracking[END_REF]. Computer vision based navigation has been widely researched recent years due to its advantages such as wide detection range, rich target information, good cost performance and flexibility [START_REF] Reid | Agricultural automatic guidance research in North America[END_REF][START_REF] Keicher | Automatic guidance for agricultural vehicles in Europe[END_REF][START_REF] Torii | Research in autonomous agriculture vehicles in Japan[END_REF].

The key to vision navigation is identifying crop automatically from the images and recognizing the navigation path [START_REF] Toru | Crop row tracking by an autonomous vehicle using machine vision[END_REF]. Usually, the central line of the crop is extracted as the baseline for agricultural robot vision navigation. But the weed in the field will interfere with the crop information because it often has the same green color as the crop, and causes false navigation path recognition results [START_REF] Xavier | Real-time image processing for crop/weed discrimination in maize fields[END_REF]. Astrand has researched on weeding robot, and used two separate vision system, one is used for navigation and the other is used for weed recognition [START_REF] Astrand | An agricultural mobile robot with vision-based perception for mechanical weed control[END_REF]. Zhao Bo uses neural network algorithm to classify the field environment [START_REF] Zhao | Recognition and classification for vision navigation application environment of agricultural vehicle [J][END_REF]; the algorithm can classify the weed environment, but it's too time-consuming, and how to recognize the navigation path after environment classification is not specified. Xue Jinlin adopts a variable field-of-view method to guide an agricultural robot [START_REF] Xue | Variable field-of-view machine vision based row guidance of an agricultural robot[END_REF], which can improve the guiding performance at the end of a crop row. Dong Fuhong has built a row guidance system for an autonomous robot for white asparagus harvesting [START_REF] Dong | Development of a row guidance system for an autonomous robot for white asparagus harvesting[END_REF], but the system is specific to asparagus, thus lack of generosity.

In this paper, we propose a path recognition method for agricultural robot vision navigation under weed environment. The areas of the connected components in the image are calculated, and the noise caused by weed is filtered by deleting small-area objects in the image. Experimental results verify the correctness and performance of the proposed method.

Path recognition method

To recognize the path for agricultural robot vision navigation under weed environment, first we select an appropriate color feature model to extract the green crop and weed information from the image, and separate them from the background; then the image is thresholded, and the noise caused by weed is filtered by deleting small-area objects in the image; the navigation path is extracted through Hough transformation. The method is described in detail below.

Image segmentation based on color feature model

The images used for vision navigation is captured by a color camera. We need to preprocess this image to get useful information [START_REF] Pinto | Vehicle guidance parameter determination from crop row images using principal component analysis[END_REF]. In farmland environment, the crops which are used as guide targets are green, while the soil background is usually not green. In order to extract the crop information and eliminate background information, we need to select an appropriate color feature to raise the weight of the green channel of the color image. In this paper, the color feature model is chosen as below:

I = 2G -R -B (1
) where R, G and B denotes the red, green and blue channel of the color image respectively. This color feature can raise the weight of the green channel, and enhance the contrast ratio with the non-green background.

After calculating the color feature I, a grayscale image is obtained. We segment the grayscale image using maximum between-class variance method to get a binary image BW for future processing.

Weed interference filtration

Due to the fact that weed has the same green color as the crop, there is still weed information remained on the binary image after image segmentation. The weed information may interfere with the useful crop information, and cause false navigation path recognition result. Therefore, we need to filter the weed interference.

In farmland field, the crops are always cluttered in line, while the weeds are scattered separately between crop lines in the field, and the weeds are usually smaller that crops. In the actual image, the crops are represented as connected regions with big areas, while the weeds are represented as small-area objects. Therefore, we use small-area object deletion method to filter the weed interference. The steps of the method are as allows:

1) Determine the connected components in the binary image BW. The result is represented as C i , where i = 1, 2….N; 2) Compute the area of each component, which is represented as S i .

3) Remove small-area objects. The result is represented as: L = ∪{ C i | S i >=P}. where P is the area threshold. After eliminating the weed interference, the navigation path based on crop center line can be further extracted.

Navigation path extraction

The crop center line is regarded as the navigation path. In field environment, there may be crop line discontinuities due to miss-seeding. The Hough transformation is a global and robust line detection method, and can be used to find and link line segments in an image. Therefore, we adopt Hough transformation to extract the crop center line. The steps of the algorithm are as follows:

1) Calculate the image size.

2) Determine the size of the parameter space for Hough transformation based on image size, discretize the ρ-θparameter space as many grids, where one grid represents an accumulator. Allocate memory for the grids. 3) For each point (x i , y i ) in the image, substitute the discretized θ into

   sin cos i i y x  
to calculate the correspondingρ, and the result falls on a specific grid; Add the accumulator corresponding to that grid by 1. 4) Detect the peak value of the accumulators. Estimate the correctness of the peak value detection through navigation path direction angle statistics. 5) Plot the navigation path in the image according to the detected peak value.

Experiment verification and results

To test and verify the method proposed in this paper, experiments are carried out in corn field with weed. The type of the camera used in the experiments is Monta G-125C from Allied Vision Technology Inc. The resolution of the camera is 1296*964, and the hardware interface of the camera is Gig E. The camera is fixed on Seekur robot made by MobileRobots Inc.

Figure 1(a) is an original color image obtained in corn field with weed. Figure 1(b) is the grayscale image obtained based on color feature model described in Section 2.1, from which we can see that the soil background is successful eliminated from the image after transformation, but the weed information is still remained between crop lines on the image, and this interferes with the useful crop information.

Conclusions

Computer vision based robot navigation has been a major topic of discussion in precision agriculture, and path recognition under noise environment is a key factor to precise navigation. In this paper, we propose a path recognition method for agricultural robot vision navigation under weed environment. The soil background is eliminated by image segmentation based on color component. The weed noise is filtered by deleting small-area objects in the binary image. The crop centerlines and navigation path are extracted through Hough transformation. Experimental results showed that most of the weed noise is eliminated from the field image. The path recognition method in this paper is practical and accurate for vision-based robot navigation.

The method proposed in this paper is aimed at straight-line navigation path, which is of little curvature. Further research will be focused on navigation paths with big curvature.

Fig. 1 .

 1 Fig. 1. Corn field images before and after grey level transformationThe binary image obtained by thresholding the grey scale image is shown in Figure2(a), from which we can see the weed noise more clearly. Figure2(b)shows the image after filtering the weed noise through small-area objects deletion, we can see that most of the weeds are eliminated from the image, only a few weed clutters are remained, but this does not affect the result of Hough transformation.

Fig. 2 .

 2 Fig. 2. Binary images after thresholding and weed noise filtrationFigure3shows the crop center lines extracted through Hough transformation, and the navigation path calculated based on the crop center lines. The magenta lines represent crop center lines, and the red line represents the navigation path. The two crop lines around the image center are selected to calculate the navigation path. We can see that the crop center lines and the navigation path are correctly extracted. Similar experiments are carried out, and all of the results verify the correctness and performance of the proposed method.

Fig. 3 .

 3 Fig. 3. Navigation paths show in different images

Acknowledgment

Funds for this research was provided by the National High Technology Research and Development Programs of China (863 Program), under Contract 2013AA040401 and Contract 2013AA102308.


Jin-Lei Li

Wen-Gang Zheng

Chang-Jun Shen

Ke-Wu Wang

Application of Modbus Protocol based on μC /TCPIP in Water Saving Irrigation in Facility Agricultural

Keywords: Modbus/TCP, u C/OS-II, ARM7, real-time, water saving irrigation

As long as the deepen application of the Intelligent equipment technology in the Water Saving Irrigation, a hot topics in research is coming out, that is how to increase the proportion of the efficiency and output for the high tech facility in manufacture. The Modbus protocol based on u C/TCPIP will play an important role in the Water Saving Irrigation application system, comparing to the others, it has such advantages of the Reliability of communication technology and real-time performance. After analyzing components of Modbus/TCP protocol, we applied it on real-time u C/OS-II kernel and ARM7 software and hardware and hardware environment with Modbus frame embedded in TCP frame. Test results show that Modbus/TCP protocol can achieve industrial standards. The protocol realizes the interconnection of control network and information network and has a broad application prospect in the agricultural water saving irrigation.

Introduction

In recent years, the industrial communication technology has been widely used in the field of agricultural water-saving irrigation. Current direction of development of agric ultural water-saving irrigation is that transmitting irrigation data through the network, scientific and reasonable control of irrigation, and improve the comprehensive utilization of agricultural water resources. The specific application process has the following prolems: The first one is that the ways of irrigation data transmittion mode is single, so standardized task is difficult to realize. The second problem is the real-time and reliability performance of information transmission is not good,unnecessary water waste exist in irrigation areas due to the poor efficiency and low reliability of information transmission [START_REF] Proakis | Communication systems engineering [M].UpperSaddle River[END_REF][START_REF] Cook | Smart environments: technologies ,protocols, and applications[END_REF].

Communication technology plays an important role in agricultural irrigation. In recent years, a few methods of network communication are widely used in agricultural water-saving irrigation applications. For instance, set an external serial server irrigation controller, or simply via Ethernet TCP/IP protocol transmission, etc.

Although these methods have lots of advantages, such as interoperability, anti-interference ability and good real-time, but the defects of these ways can not be ignored. For example, communicate through an external serial server not merely engenders large power consumption, poor stability, low reliability but also generates additional costs. However, transmission through TCP/IP protocol also leads to network insecurity occasionally [START_REF]Texas Modbus-IDA.Modbus Messaging on TCP/IP implamentation Guide V1.0b[END_REF][START_REF] Stevens | TCP/IP Illustrated Chapter 1 : Protocol[END_REF].

Since 1970s, the Modbus protocol has been widely used in different areas, it's host/slave-machine communication way perfectly meet the requirement of deterministic. In our system, we combine the Modbus protocol with the TCP/IP protocol by inserting Modbus frame into the TCP frame. Since Modbus protocol has a large number of node installing, it's very suitable for irrigation controllers that require a lot of control valves. That is one of the reasons why Modbus possesses a high performance and low cost in the field of agricultural water-saving irrigation. Modbus

is an truly open and ideal solution for agricultural water-saving irrigation. The effectiveness can be verified by experiment and its value can be proved in practical applications.

In this paper, aiming at the actual situation shortcomings of the current domestic agricultural water-saving irrigation, applied the industrial Ethernet technology to the agricultural water saving irrigation, and embedded the Modbus protocol into uC/TCPIP, and a communication which is not only low power consumption and low-cost but also high compatibility has been realized. The problems of multiplicity and incompatibly of Field-bus standard, less accuracy and limited real time have been solved.

System structure and function

According to specific application, the system is based on the embedded Modbus/TCPIP system structure. Essentially, the system structure is usually shown as that in Figure 1.

RTOS μC/OS-II

Embedded

Hardware design of Network Interface

The system structure is shown as that in Figure 2 , the Ethernet Controller mainly composed of 5 parts. There are the microprocessor MCU, Ethernet physical layer interface chip DP83848, the RJ45 module used for cable connecting, 25MHz clock source, and the status LED lights which indicates the status of the connection. In our system, we use LPC2387 as the microprocessor, whose RAM memorizer of 64K and nominal operating frequency of 72MHz can provide abundant resources for μC / TCPIP transplantation [START_REF] Yu | Research on Embedded Protocol Modbus/TCPIP Conversion Module[END_REF]. In addition, the LPC2387 is capable of providing The transplant system of μC / OS-II operating system use μC / OS-II as the ROTS. So transplant μC / OS-II to hardware platform is the first thing to go before using μC / TCPIP protocol stack , and generally speaking , it is related to four documents. There are OS_CPU_A.ASM,OS_CPU.H,OS_CPU_C.C which associated with the processor, and application-related OS_CFG.H. In our system, we use V2.84, the latest version of μC / OS-II.

TheμC/ TCPIP transplantation

The uC/TCPIP is to achieve the most important and centralized protocols of TCP/IP. semaphores, as shown in Table 1.

Table 1 The semaphores created to transplant uC/TCPIP serial number semaphore

Fig. 1 .

 1 Fig. 1. The structure of MODBUS /TCP

Fig. 2 .Fig. 3 .4 The Software Design 4 . 1

 2341 Fig. 2. Overall block diagram of system.

  Including IP, ARP, ICMP, UDP, TCP and BSD Socket invocation interface. uC/TCPIP include the following protocol module,NIC, ARP, IP, ICMP, UDP and TCP. At present, uC/TCPIP can only support Interface Controller in ethernet,referred to NIC(Network Interface Controllers).And in our system it is DP83848.uC/TCPIP operate as two tasks of uC/OS-II, Rx Task and Tmr Task. Rx_Task in charge of process the data packet received by NIC,if the data packet is needed by the present application layer, Rx_Task will put the data packet from network layer to application layer through TCP-IP protocol; and Tmr_Task is to manage all of the time delays related to TCP-IP protocol. During the uC/OS-II transplantation process, the user needs to realize the following functions: (1) interrupt service function NetNIC_ISR_Handler(), initialization and increate the interrupt nesting in IRQ.S (2) interrupt handler NetNIC_ISR_Handler(),remove the CPU interrupt flag, send a semaphore and call the related function (3) operating system function: It's achieved in net_os.c, which involves a large number of the μC / OS-II-related operations function. This document provides a interface from TCP-IP protocol to the uC/OS-II operating system. If you want to migrate to a different operating system, the user can change net_os.h / c file. In RTOS Layer, tasks should be completed in three aspects: the establishment of Rx_Task and Tmr_Task , establish time control service functions, the establishment of 10

  10M/100Mbps Ethernet access in both half-duplex and full-duplex mode, with the built-in Ethernet MAC controller. Which can support Reduced Media Independent Interface, RMII and Buffered DMA Interface, BDI. We chosed DP83848C which is made by National Semiconductor as the Ethernet physical layer interface chip. It can easily connect with the MCU(LPC2387) with three kinds of interface mode(MII/RMII/SNI),and we chose RMII.

Driver programming in NIC

(1) Initialization. The whole intialization is consist of processor-related MCU initialization and PHY initialization. And the process includes: means mutiple regsiters has to be written. The maximum data in Data domain is 248 bytes, and the specific format associated with functional code. In practice , the client sends the request data which shows the starting address of the register and the register number, meanwhile, the server respond with the number of operating registers and the status value of the register in a certain number.

The overall process in our system is as follows: The irrigation controller as a server waiting for the PC client to connect.The user can manage the irrigation controller with the software on computer when connected the them with reticle.

Modbus/TCP protocol communication test and results analysis

Test results show that the first frame delay basically maintain within 1.3 ms, the last frame delay basically keep within 1.2 ms. The Result Indicates that 300 frames/second is normal processing speed of Modbus/TCP.

Summarization

Through a wholly analysis to the actual demand of current water-saving irrigation agriculture, then get a research of the Modbus/TCP protocol in facilities construction and Embedded System. The design of NIC and Modbus/TCP here have three advantages as follows: 1.Combining the Modbus/TCP technology and embedded system makes the Ethernet controller realize the real-time efficiency 2. The Modbus/TCP protocol here solve the multiplicity and incompatibly of field-bus standard 3. Successfully remove the uC/OS-II format software, making the system high throughput data transferring, also solving the problems during operations to the complex control system, then low down the cost, more practically.


Ming Xiao

Wenjun Yang

Ze Zhang

Xianglin Tang

Xin Lv

Dezhao Chi

The soil heavy metal information accurate collection and evaluation about Lycium Barbarum cultivation in western China

Keywords: assessment, heavy metal pollution, standard, Lycium barbarum, Qaidam

We selected two small sections of Lycium Barbarum cropland at Nuomuhong Farm in the Qaidam basin, western China: one had been farmed for only one year (original land), and the other had been farmed for many years (farm land). We tested surface soil samples for their content and distribution of six heavy metals (Cd, Zn, Pb, Cu, As, and Cr). All six heavy metals appeared at medium levels in samples from both sections of cropland. We conducted an interpolation analysis and drew a spatial distribution map based on the inverse distance weight (IDW) method. The distribution graph revealed a relatively consistent distribution of the six heavy metals in soil samples, a different gradation in the original land, and areas of higher values in the farm land. These findings suggest that the soil had been polluted. According to the Pollution-Free Food Standard and the Green Food Standard, we calculated the integrate pollution index using the Nemerow index method to check whether the levels met Pollution-Free food Standard and Green Food Standards. The values were 0.5 and 0.7 (defined as "clean") in samples from the original land, but were 0.6 and 0.9 in samples from the farm land, which may be considered excessive.

Introduction

Under heavy metal pollution, crop soils may exhibit latency, chronicity, irreversibility, and have strong toxic properties and abiotic degradation. This is an important challenge for producers of high-quality agricultural products and researchers in the fields such as edaphology, resource environmental protection, and agricultural product quality safety. Investigations of spatial variations in heavy metals, which are important to evaluating soil environmental quality and heavy metal pollution, involve analyzing spatial features including the content of heavy metals, along with any changes, trends, and spatial variation in those metals [START_REF] Youlu | Study on the space variable character of soil salt and its component in Huanghuaihai Plain[END_REF]. Recently, researchers have combined geographic statistics with geographic information system (GIS) data to analyze regional environmental processes. Mingkai Qu, Weidong Li and Chuanrong Zhang has assessed the spatial distribution and uncertainty of the potential ecological risks of heavy metals in soil using sequential Gaussian simulation (SGS) and the Hakanson potential ecological risk index (PERI) in Wuhan, China. Results show that the potential ecological risks of Cr, Cu, Pb, and Zn are relatively low in the study area, but Cd indeed reaches a serious level that deserves much attention and essential treatment [START_REF] Mingkai | Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation[END_REF]. Linsen Zhang, Jun Liang, Chunlin Wu et al. evaluated the contents of As Pb Cr Cd and Cu in an apple-planting region in Shanxi province using a method that incorporated a single pollution index and a comprehensive pollution index according the Green Food Standard, show that the heavy metals were not accumulated gradually in old apple orchards and all meet the standards [START_REF] Zhang Linsen | Evaluation and concentration of soil heavy metals in apple orchards in Shanxi province[J][END_REF]. Gailin Wu, like li, Mingde Hao et al. on the basis of the integrate pollution index of heavy metal research proposed fertilizer application should pay attention to the content of heavy metals in fertilizer, a reasonable choice, in order to avoid the contamination of the soil environment [START_REF] Wu Gailin | Effects of Long-term Fertilization on Heavy-metal Contents of Soil and Environmental Quality Evaluation[END_REF]. Xia Huang, Tingxuan Li and Haiying Yu. used a signal pollution index and the Nemerow composite index to evaluate the heavy metals risk, and the results show that there was Cd contamination in greenhouse topsoils in Shouang Shandong Province [START_REF] Xia | Risk assessments of heavy metals in typical greenhouse soils[END_REF].

The Qaidam basin, located on the Tibetan Plateau, is a closed region and few studies have assessed environmental effects in the area of reclamation. No studies have investigated the spatial variation in heavy metals or their potential pollution risk in soils in the planting region of Lycium barbarum (common names: goqi berry, wolfberry, medlar).

2

Experiments and Methods

Experimental Samples

Study area overview

The Qaidam Basin is located on the edge of the Tibetan Plateau, surrounded by the Kunlun, Qilian, and Altun Mountains. Nuomuhong Farm is located southeast of the basin at altitudes ranging from 2700~3000 m. It is irrigated by snow waters from the Kunlun Mountains, which enables oasis farming. The area is cold and dry with little rainfall, abundant sunlight, and large differences between daytime and nighttime temperatures. Soil in the area has PH values ranging from 7.8~8.2. Because the area is mostly sandy loam and has few plant diseases and insect pests, it is suitable for growing L. barbarum, and the L. barbarum produced in this area is famous for its quality and is quite valuable [START_REF] Guangsheng | The current situation of the development of Ningxia medlar in Qinghai[J]. Ningxia agriculture and forestry science and technology[END_REF].

Soil sample collection, tests, and data processing

Sample collection:Samples were collected based on the following guiding principles:

Select cultivated farmland in flat areas (avoid depressions or mounds).Try to avoid non-representative areas, e.g., those that have been altered by human activity or where soil has greatly eroded. Do not collect samples from areas currently being fertilized.

Try to collect samples 30 days before or after fertilization.

The farm land field was a rectangular area of 660 m × 510 m, and the original land was a rectangular area of 400 m × 330 m. We adopted the grid soil sampling method and selected samples at 100 m × 50 m intervals. A dot in each interval served as a center point; we randomly selected five points, each with a 3 m radius. We then took a 20 cm × 20 cm × 20 cm cube of soil from each point. From these samples, we took 1 kg from each and created a mixed sample by equally mixing the 1 kg samples from all the sites.

The samples were taken to our lab for analysis. We recorded the fixed position sampling point coordinates using GPS. In order to prevent sample contamination, we did not let the samples touch metal containers during sample collection, preservation, and processing.

Sample testing:Stones, grass roots, and other plant residues were removed from the samples, which were left to dry in a ventilated room. Samples were then filtered using a 100 mesh sieve (150 um mesh size). A MILESTONE brand microwave digestion instrument was used, along with an inductively coupled plasma emission spectrometer.

The testing method involved using 7 ml (top grade and pure) concentrated nitric acid, 2 ml (top grade and pure) hydrogen peroxide, and 2 ml (top grade and pure) hydrofluoric acid. Standard substances of these chemicals were purchased from the National Standard Substance Center; for the standard curve, we used three density gradients of 0, 0.5, and 1 and a unit of mg/l.

Data processing

The data were entered into Excel and subjected to the inverse distance weighting (IDW) method. A spatial analysis was conducted and the results were presented using ARCGIS9.3.

Heavy metal soil pollution assessment method

Single pollution index evaluation: For the evaluation, P i =C i /S i , where P i is the i single pollution index of a pollutant, C i is the i actual measurement, and S i is the evaluation standard for the pollutant. The evaluation standard was calculated separately in accordance with the industrial standards of "Pollution-Free Food standard" (NY/T 5249-2004) [START_REF]Ministry of Agriculture of the People"s Republic of China. Pollution-free food wolfberry production technology standard procedures[END_REF] (Table 1), and "Green Food Standard" (NY/T 391-2000) [START_REF]Ministry of Agriculture of the People"s Republic of China. Environmental technical terms for green[END_REF] (Table 2).

Values of P i <1 for As, Hg, Cd, Cr, and Pb indicate that the soil is not polluted, and the sample meets environmental standards. If one or more elements has a value of P i >1, the soil is polluted and does not meet environmental standard. 3), soil quality is determined by changes in the P N value and crops are affected by the degree and accumulation of pollutants.

Spatial distribution features of heavy metals in soil

We used the inverse distance weighting (IDW) method to conduct an interpolation analysis and examine spatial variation in the soil pollutants. As shown in Fig. 1, As content in original soil was greater in the west than in the east, and As content in the southwest region is greater than the natural background value. Few regions reached the level II standard. Cd content in original soil was clearly greater in the southeast and northeast than in other regions. In most areas, Cd content in soil was greater than the natural background value: in some areas values were greater than 0.40 and reached the level II standard. Cr content in original soil was less than the natural background value, with considerable spatial variation in the north and southwest regions. Cu content in original soil was highest in most north and northeast areas and the highest point was 90.12 mg/kg, but far lower than the level II standard and did not exceed the natural background value. Spatial variation in Pb content in original land tended to be greater in the north than in the south, and tended to change gradually. Zn content was less than the natural background value, and spatial variations revealed that content tended to be higher in the west than in the east. Zn content was very high in some northeast areas.

As Cd Cr

Cu Pb Zn Fig. 1 The heavy metals content in soil and spatial variation in original land Fig. 2 shows that As content in farm land was lower than the natural background value; only a few southwest and northwest regions reached the level II standard. Overall Cd content was greater than the natural background value, with most regions reaching the level II standard. The contents of Cr, Cu, and Zn in farm land exhibited a scattered spatial distribution. Generally, the comparisons revealed that in original land, spatial variation was very similar for all heavy metals, gradation levels differed, and the natural status of soil was relative stable. In farm land, heavy metal content exceeded the standard and differed greatly in quantity: Cr, As, Zn, Pb, and Cu contents were all more than 1.2~1.7 times higher than normal, and Cd content was nearly 5 times the normal value. These findings indicate that farm land has been polluted.

As Cd Cr Cu

Pb Zn Fig. 2 The heavy metals content in soil and spatial variation in farm land

Soil heavy metal pollution assessment

Soil heavy metal contents compared with the standards

The As from the original land and farm land are both meet to the Green Food standard, but more less than the Pollution-Free Food standard; the Cr、Cu、Pb and Zn, from the original land and farm land are all far less than the Pollution-Free Food standard, and less than the Green Food standard (there is not Limited value for the Zn in the Green Food standard); the Cd is only 0.30 mg/kg in original land, both less than the two standards, but it rise to 0.43 mg/kg in farm land, increased 43%, over the Green Food standard, show that there is accumulation of Cd in farm land(see Fig. 3).

Unit:mg/kg

The superscalar rate of soil heavy metal according different standards

In original land 4 samples exceed the Pollution-free Food Standard and 18 samples exceed Green Food Standard of As, the rate were 10% and 45% (Table 6). In farm land 31 samples exceed Green Food Standard of Cd, the rate is 62% (Table 7), show that there is accumulation of Cd in farm land. indicating that this area also qualifies for the Green Food Standard (see Table 8).

Composite pollution index evaluation

Composite Index is calculated based on Nemerow index method, and the Pollution-Free Food and the Green Food standards. According to the Pollution-Free Food and the Green Food standards the original land soil index were 0.5, 0.7 , both in the "clean" level; the farm land were 0.6, 0.9, in the "clean" level according the Pollution-Free Food standard , in the "potentially unclean" leave according to the Green Food standard. (see Table 10).

Conclusions

We analyzed data about six heavy metals (Cd, Zn, Pb, Cu, As, and Cr) and found that the Nuomuhong farm original land soil is pure and can comply with the Pollution-Free Food Standard and the Green Food Standard, but the farmland were subject to pollution during many years of farming, which resulted in heavy metal pollution, especially Cd pollution. Further research will be required to clarify the process of pollution.

We analyzed soil from two different areas of Nuomuhong Farm: original land and farm land, in terms of their heavy metal (Cd, Zn, Pb, Cu, As, and Cr) content. We used the Nemerow composite index to analyze and evaluate data. According to the Pollution-Free Food Standards, the soils in Nuomuhong Farm are all near or below excessive levels, and according to the Green Food Standard, original soil is all considered clean, whereas the farm land is at a borderline excessive level.

  2) 1/2 , P N is the Nemerow composite index, Pi is the average pollution index for the soil, and P 0 is the maximum pollution index of the soil. As shown in (Table

Fig. 3

 3 Fig. 3 Comparison of soil heavy metals contents according to different standards

Table 7 . 3

 73 The superscalar rate of soil heavy metal in farm land Single pollution index evaluation With regard to upper limits for elements in the Pollution-Free Food Standard and the Green Food Standard, separate environmental evaluation criteria were used to calculate and analyze factors in the pollution index of heavy metals for soil samples taken from the two study areas. With regard to Pollution-Free Food Standard requirements, the pollution index was less than 1 in all samples from original land. This means that this area meets the Pollution-Free food standard for the six heavy metals investigated. With regard to the Green Food Standard, all pollution indices were also less than 1,

Table 1 .

 1 Pollution-Free Food standard (NY/T 5249-2004). Heavy metals: mg/kg

	Item	Grade level

Table 2 .

 2 Green Food Standard (NY/T 391-2000). Heavy metals: mg/kg

Table 3 .

 3 Nemerow composite index as evaluation standard

	Degree	index	Pollution status	Pollution level
	I	P N ≤0.7	safe	clean
	II	0.7<P N ≤1.0	caution	potentially unclean
	III	1.0<P N ≤2.0	mild contamination	pollution over limit
	IV	2.0<P N ≤3.0	mid-level pollution	soil, crops subject to severe pollution
	V	P N >3.0	heavy pollution	soil, crops subject to heavy pollution

Table 4

 4 lists the heavy metal contents in 22 surface soil samples taken from original cropland. The variable coefficient reflects the average variable degree of sampling points in the total sample. Generally, 10%~100% exhibited medium variation: the variable coefficient for Cu in samples from original land reached a maximum of 61.48%, and the variable coefficient for As reached a minimum of 8.45%. The heavy metals can be arranged from maximum to minimum degree of variation as follows:Cd, Zn, Pb, Cu, As, and Cr.

Table 4 .

 4 Descriptive statistics for heavy metal contents from original land

	Element Min	Max	Mean	SD	CV(%) Skew-	Kurtosis
		(mg/kg)	(mg/kg)	(mg/kg)			ness
	As	0	26.40	15.47	8.45	8.45	-0.318	-0.689
	Cd	0.14	0.42	0.30	0.077	25.49	-0.847	0.095
	Cr	18.24	56.34	48.16	8.19	17.01	-2.507	8.265
	Cu	13.44	90.12	28.16	17.66	61.48	2.447	6.572
	Pb	5.76	28.49	13.38	4.92	36.80	1.380	3.287
	Zn	30.08	61.90	44.84	9.28	20.70	0.471	-0.767

Table 5

 5 lists the heavy metal contents in 50 surface soil samples taken from farm land.The variable coefficient reflects the average variable degree of sampling points in the total sample. The variable coefficient for Cd reached a maximum of 34.74%, and the variable coefficient for Cr reached a minimum of 6.15%. The heavy metals can be arranged from maximum to minimum degree of variation as follows: Cd, Zn, Pb, Cu, As, and Cr. Comparison between the two kinds of farmland reveals that the variable coefficients of Cr, Cu, Pb, and Zn decrease over time, but the variable coefficient of Cd increases significantly after years of farming.

Table 5 .

 5 Descriptive statistics for heavy metal contents from farm land.

	Element	Min	Max	Mean	SD	CV(%)	Skew-	Kurtosis

Table 6 .

 6 The superscalar rate of soil heavy metal in original land

	heavy	samples	Pollution-free Food Standard	Green Food Standard
	meatal		Superscalar	Superscalar	Superscalar	Superscalar
				rate(%)		rate(%)

Table 8 .

 8 Single pollution index evaluation of soil heavy metal in original land

	Heavy metal	Mean (mg/kg)	Pollution-Free food	Green Food Standard
			standards	pollution index
			pollution index
	As	15.47	0.62	0.77
	Cd	0.30	0.50	0.75
	Cr	46.60	0.19	0.39
	Cu	28.16	0.14	0.47
	Pb	13.38	0.04	0.27
	Zn	43.10	0.14	/

Table 9 .

 9 Single pollution index evaluation of soil heavy metal in farm land

	Heavy metal	Mean (mg/kg)	Pollution-Free food standards	Green Food Standard
			pollution index	pollution index
	As	14.50	0.58	0.72
	Cd	0.43	0.71	1.06
	Cr	47.13	0.19	0.39
	Cu	25.51	0.13	0.43
	Pb	22.17	0.06	0.44
	Zn	69.18	0.23	/

Table 10 .

 10 Nemerow index depend on different Standards (mg/kg).

	As	Cd	Cr	Cu	Pb	Zn	Mean Max	Nemerow index

Acknowledgment

This research was funded by the Xinjiang Production and Construction Corps major science and technology program "precision agriculture and information on agricultural technology application and demonstration" ( 2007zx03 ). The authors would like to thank Prof. Dezhao Chi from Qinghai university for his good suggestion regarding article writing, and Dr. Hairong Lin from Shihezi university for her chemical analysis.


Guangsheng Zhang
email: zgs_88@163.com

Research on the Optimization of Agricultural Supply Chain Based on Internet of Things

Keywords: Internet of Things, Agricultural Supply Chain, Status Analysis, Optimization

Technology of IOT which used in agricultural supply chain can help to improve operational efficiency and reduce supply chain costs. This paper analyzes the basic structure of agricultural supply chain, current status of the research, and summarizes major obstacles of the development process. The paper also describes application of IOT principle, as well as agricultural supply chain optimization approach based on internet of things, including agricultural production, processing, transportation and sales process. The results show that: integration and information management through IOT can reduce logistics costs and improve circulation efficiency and effectiveness. The government should develop policies to achieve agricultural internet of things, improve efficiency of agricultural supply chain operations.

Introduction

The development of agriculture in China is backward. Farmers' income is low. The reasons are varied. One important reason is the circulation of agricultural supply chain is poor. Major agricultural supply chain process is from initial production to consumption of agricultural products. Among them, the core business is the main component, related to information flow, capital flow and logistics, etc. (main structure shown in Figure 1). Agricultural supply chain is characterized by many volume, broad area, the strong timing requirements of agricultural spending, more agricultural supply chain departments, more staff. As Wamba(2006) [START_REF] Samuel Fosso | RFID technology and the EPC network as enablers of mobile business: a case study in a retail supply chain[C][END_REF] used radio frequency identification (RFID) technology which be applied to the integrated food supply chain to replace bar code recognition technology , builded management information system. Bottani(2009) [START_REF] Eleonora | The impact of RFID and EPC network on the bullwhip effect in the Italian FMCG supply chain[C][END_REF] used IOT to coordinate logistics and information flow, improve the integrated food supply chain management and reduce its cost. Thiesse(2009) [START_REF] Frederic | RFID data sharing in supply chains: What is the value of the EPC network? [C][END_REF] established a system through IOT, which assess the quality of agricultural products and processed products "from farmers to table". Li Jianwei(2011) [START_REF] Li Jianwei | Resear ch on the Optimizat ion of Agr icultural Supply Chain under the Background of Internet of T hings[J][END_REF] analyzed the specific application of IOT in agricultural production and farming, transportation, inventory, sales, and information management and other aspects. Liu Xuemei(2011) [START_REF] Xuemei | Agricultural Supply Chain Risk[END_REF] analyzed the agricultural supply chain system according to recent incidents of food safety problems, effectively promoted the rapid development of agricultural supply chain. Zhou Hang(2011) [START_REF] Hang | The Research on Integrated Grain Supply Chain Management Based on Internet of Things[J][END_REF] studied optimization of the food supply chain relies on IOT. Based on the above, we find that on agricultural supply chain research domestic and foreign scholars has focused on macro-level, multi-round concept, circulation system and other conditions and policy analysis, failed to put forward efficient optimization method. The rapid development of computer technology provides a solution to improve the efficiency of agricultural supply chain operations process. The development of IOT accelerates the progress of this trend.

Agricultural Management Agencies

Internet of Things

In 1999, MIT professor proposed IOT (Internet of Things). The purpose of IOT is to form a system of production and living smarter. IOT is defined as follows: through two-dimensional code reading equipment, radio frequency identification (RFID)

Optimization of Agricultural Supply Chain Based on IOT

Status Analysis

In recent years, our governments develop agricultural supply chains vigorously. The total amount of agricultural products logistics is growing. In the total social logistics, the importance of IOT is increasing, mainly reflected in the sensitivity to the customer, quality of life and social macro adjustment. From Figure 2 we can see that the total agricultural product logistics increased steadily year by year, as of 2010 reached 2.2355 trillion. However, the growth rate of agricultural logistics is relatively large, indicating that agricultural supply chain is vulnerable to be influenced by economic conditions and other external factors. The stability of the supply chain is poor. Because development of agricultural supply chain is not perfect, its level of development is far behind developed countries. As shown in Table 1.

Applications of IOT in the Agricultural Supply Chain

The impact of IOT is enormous. Application of IOT can optimize agricultural supply chain facilities, inventory, transportation, information and procurement and other sectors, as well as manage agricultural production, transportation, consumption and other sectors, reduce supply chain costs, and achieve highly agile supply and fully integrated.

Sensor Internet Internet of Things

Sensor technology

Electronic Product Code

RF technology

Agricultural production

Processing of products

Distribution of products

The sales cycle Sellers

Fig.3. Logistics Network Application in Agricultural Logistics

(1) Improve the overall construction of IOT: Putting IOT, sensor network, and internet together, using Sensor technology, electronic product code, radio frequency technology, we can accurately understand and grasp from procurement, manufacturing, handling, storage, transportation, distribution, sales to the service supply chain each link, reducing the agricultural supply chain in the "bullwhip effect" exists.

(2) The application in the process of production: IOT can solve problems that accord to the manufacturer's requirements to operate, the operation is not higher transparency in traditional agricultural production. In the procurement of raw materials, IOT can tag encoding, establish database, and monitor products throughout the entire production process, including current temperature, humidity and the corresponding operators, all entered into the database to make it clear process prone to risks.

(3) The application in the process of transportation. IOT improves the efficiency of transportation greatly. First, this can realize visualization of the transport, agricultural transport vehicles are scheduled timely, try to avoid invalid transport. Second, the use of RFID tags can provide temperature monitoring, achieve agricultural vehicle dynamic perception, dynamic monitoring of the quality and safety of agricultural products. Again, IOT can dynamically acquire all cold storage inventory and in-transit traffic conditions in order to make transportation decisions science to achieve an effective cold chain logistics of agricultural products circulation.

(4) The application in the sales process: it reads information of product automatically through embedded tags using RFID, monitors the quantity and inventory quantity of goods, adds goods and deals expired products timely. The use of IOT can monitor the supply of goods and sales progress, determine the amount of purchase according to the proportion of sales.In the customer checkout time, we use radio frequency technology to shorten the time for the items of information literacy, improve the efficiency of the customer's purchase and checkout.

(5) Application of the supply chain information transfer: For any one of the agricultural supply chain node enterprises, from the origin to the pin, using IOT you can understand the production of raw materials, processing conditions, who is responsible for inspection, storage warehouse, the current agricultural position, the current storage environment conditions and so on. IOT through effective information technology achieve a blending between man and agricultural.

Conclusions

This paper analyzes the basic structure of agricultural supply chain, current status of the research, and summarizes major obstacles of the development process. The paper also describes application of IOT principle, as well as agricultural supply chain optimization approach based on internet of things, including agricultural production, processing, transportation and sales process. It can promote the development of agricultural products logistics industry through modern agricultural logistics and networking technologies, change the practical problems of rural logistics problems.

The government should develop policies to achieve agricultural IOT, specify IOT "perception of agriculture" direction, play an active role on the optimization of agricultural supply chain.

Fig. 2 .

 2 Fig.2. Amount and Rate of Growth of Agricultural Product Logistics

  Monitoring

		Production	Reserves		Consumer	Export
		Control	Regulation		Policy	Control
	Farmer Farm	Purchase of Agricultural Enterprises	Agricultural Products Processing Enterprises	Agricultural Marketing Enterprises	Domestic Sales Foreign Sales	Final Consumer

Fig.1. Structure of Agricultural Supply Chain In recent years, agricultural supply chain has been developed rapidly. However, due to starting late, the development faces many difficulties, such as the backwardnesses of rural logistics infrastructure, low level of technology, lag agricultural supply chain standardization system and operation mode, weak administrative details and other issues, led to agricultural supply chain operational efficiency low. At present, optimization research of agricultural supply chain is less.

  devices, infrared sensors, global positioning systems and laser scanners and other information sensing device, according agreed protocol, it connects the Internet for information exchange and communication in order to achieve intelligent identify, locate, track, monitor and manage a network.

The structure of IOT can be divided into three layers: the perception layer, network layer and application layer. Perception layer is constituted by a variety of sensors, including temperature and humidity sensor, two-dimensional code label, RFID tags and readers, camera, GPS and other sensing terminals. Things perception layer is used to identify objects and collect information sources. Network layer is constituted by a variety of networks, including the Internet, wide network, network management systems and cloud computing platforms. It is the hub of things, responsible for transferring and processing information obtained by perception layer. IOT application layer is the interface of IOT and user. It combines with the industry needs to realize the intelligent application of IOT.

Table 1 .

 1 Main Economic Indicators

		Cost	Loss rate	Processing Proportion	Value-added Processing	Proportion of Supermarket Sales
	Developed Country	10%	5%	80%	1:4	80%~95%
		40%(Food)	15%(Food)
	China	60%(Fruits and	25%(Fruits and	10%	1:0.8	Less than 30%
		Vegetables)	Vegetables)
	3.2 Main problems
	In production, agricultural supply chain depends on natural conditions, environmental
	and human factors obviously. The Production is different from traditional industrial
	products and services; it has a universality and dispersion of consumption. Logistics is
	burdensome: Because diversity of market demands, this production led to a regional
	agricultural need in different areas. Perishability of agricultural products increases
	with distance and time, which limits the development of its circulation. Technology of
	agricultural logistics is backward: The rural logistics infrastructure and equipment are
	backward, technical conditions can not meet market demand. Given the low level of


Lin Guo
email: guo_guolin@126.com

Zhiyuan Pei
email: bpeizhiyuan@tom.com

Shangjie Ma
email: cmashangjie@agri.gov.cn

Juanying Sun
email: dsunjuanying_my@126.com

Jiali Shang
email: eshangj@agr.gc.ca

Study of Rice Identification during Early Season using Multi-polarization TerraSAR-X Data

Keywords: rice identification, early season, TerraSAR-X

The study of rice identification during early season aims to maintain cultivated area and distribution status of rice during its early season so as to provide decision support for agricultural management sectors to develop policies and measures, as well as important information for agricultural insurance sectors and agricultural products market. Multi-polarization TerraSAR-X data of Xuwen study site locating in Southern China were programmed during early season of early rice in the year of 2009. By means of comparing analysis, it is discovered that rice has more obvious identification features in jointing period and flowering period. Rice identification experiments are carried out using decision tree and object-oriented classification method. The results show that imagery acquired at the jointing period is more suitable for rice identification during the early season for a nationwide operational rice monitoring system.

Introduction

Rice has the highest production and acreage of all grain crops in China. Most of China's paddy rice production is located in the southern provinces of the country where cloud cover and frequent rain severely limit opportunities for optical satellite acquisitions. Synthetic aperture radar (SAR) sensors are well suited for rice mapping with their ability to acquire data regardless of cloud cover. Scholars have used multitemporal SAR data for rice identification and obtain good results [START_REF] Holecz | An Operational Rice Field Mapping Tool Using Space Borne SAR Data[END_REF][START_REF] Toan | Rice Crop Mapping and Monitoring Using ERS-l Data Based on Experiment and Modeling Results[END_REF][START_REF] Zhang | Studies on rice polarization backscatter signatures and rice mapping methodology[END_REF][START_REF] Guo | Rice identification using TerraSAR-X data[END_REF][START_REF] Pei | Rice identification and change detection using TerraSAR-X data[END_REF] .

Taking advantage of SAR data in transplanting period, heading period and early harvest period, Shao Yun and other researchers [START_REF] Shao | Studies on Rice Backscatter Signatures in Time Domain and Its Applications[END_REF] measured distribution and acreage of rice in Zhaoqing, Guangdong Province, with the accuracy reaching 91%. By means of multi time-phase Radarsat-1 image and neural network method, Chen and McNairn [START_REF] Chen | A Neural Network Integrated Approach for Rice Crop Monitoring[END_REF] obtained the acreage of rice with producer accuracy mounting to 96%. Chen [START_REF] Chen | Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China[END_REF] took advantage of Multi-polarization ASAR data to measure the rice acreage in Southern China with accuracy of 81%. However these satisfactory classifications were obtainable using multi-temporal data and coinciding with the end of the growing season. Early season estimates of acreages are of significant interest for clients such as commodity brokers and marketing boards. In-season crop mapping and acreage estimates offer greater advantage.

Inoue and other researchers [START_REF] Inoue | Season-long Daily Measurements of Multi-frequency (Ka, Ku, X, C, and L) and Fullpolarization Backscatter Signatures over Paddy Rice Field and Their Relationship with Biological Variables[END_REF] found that SAR data of X-band is applicable to monitor early growing season of crop, yet more challenging to achieve. New Germany radar satellite provides possibility in rice identification in early stage with regard to working frequency range, polarization mode (multi-polarization and quadpolarization) and revisit cycle (11 days).

This study aims to determine the best time to acquire SAR data for rice mapping during it's early season, determine the best rice growth stage to map rice, and develop the methods and procedure for rice mapping during early season. Thus in Xuwen, Guangdong study site, a time series of SAR data during early stage of early rice was obtained for this early season rice identification study. By comparing analysis of rice identification methods, the best period of rice identification during the early season was obtained, then the methods and procedures for rice mapping during the early season was developed.

Site Description

The Xuwen study site is located close to Leizhou City, Guangdong Province, China (20.88° N, 110.06° E). Being in central part of Leizhou Peninsula and northern part of tropical monsoon zone, the site is featured with abundant heat and rainfall, along with a large number of thunderstorms in summer. It has a terrain with high northwest part and low southeast part. Most of rice is planted in flat area, while a little portion is planted in the valleys. Apart from rice, there are also upland crops such as sugarcane and vegetables, and commercial crops such as watermelon and pineapple in flat area. There are eucalyptus, orchard and upland crop such as sugarcane in brae area.

Rice in the area is double cropped (two harvests per year). Due to separate management pattern, famer's field management is relatively arbitrary with two main planting patterns -direct planting and transplanting. Meanwhile, the planting time span is quite long, so as to result in more obvious diversity in rice growing status during early age. In general cases, seeding period comes in mid-March, and the early-April is the transplanting stage. Tillering stage comes in the end of April and jointing stage comes in mid-May. June sees heading and flowering stage coming in the beginning of the month while mature period and harvest time in the end. Parcel of paddy field in study site is small with 100m long and 30m wide, and the weed echo on the ridge is quite obvious.

Data acquisition

Four TerraSAR-X acquisitions were programmed during early growth period in earlyrice of 2009. Data were collected in stripmap dual polarization mode with VV-VH polarizations with an incidence angle of 36.8° in an ascending orbit. Nominal spatial resolution of the stripmap product is 6 m. This resolution is well suited to the dimensions of the paddy fields typical of this study site. ①The planting situation is complex in March 27 with great variance. Direct seeding paddy fields were planted and some early planted rice has emerged ; raising rice seedlings in transplanting fields was in operation with some fields in watering period awaiting for transplanting and some fields left unused without watering; some fields occupied by upland crops, such as hot pepper, would be transplanted with rice after harvest in the end of April. ②In April 18, most of the rice in direct planting field emerged and was in seeding establishment period, and would be in tillering period in about a week; most of transplanting field began to be transplanted, and some fields after upland crop harvest were in watering operation, while a little portion of fields with upland crop were still in harvest operation.③In May 10, rice in most of transplanting fields were in jointing period as transplanting operation was finished before the end of April. The general stem length was about 60cm and some rice has 45-50cm stem length due to late transplanting operation, while some rice had only 35cm stem length due to transplanting operation in the end of April after the harvest of upland crop. The growing variance was still obvious. ④In June 1, most of paddy fields were in flowering period with remaining discrepancies with regard to stem length and leaf coverage due to different planting mode and time. However, the growing situation tended to be the same with stem length of 90-110cm.

Field surveys were conducted close to the satellite acquisition dates. The contents of investigation contained: ① 41 ground control points were collected using a differential GPS with sub-meter accuracy. These ground control points were required to support ortho-correction of the imagery. ②Several ground samples were collected with crops types, location information and photos. At last 219 ground samples were selected through visual interpretation of remote sensing image. There were 219 screening samples in total with remote sensing images. These samples were divided into two groups randomly. One was used to train class rules while the other was used to evaluate the effect of rice identification.

Methods

In this study, details of the image processing methods were illustrated as follows. First step is Data acquisition and Pre-processing, include image data acquisition, field data acquisition and data preprocessing. Then by contrast analysis of the different imagery of VH and VV, the imagery with more obvious identification variance between rice and other major land cover types was selected. Next, the method and flow of classification was constructed for experiments of rice identification. At last the identification results of 4 temporally different images were obtained using above method and procedure, the best period of rice identification during early season was selected.

Data Acquisition and Preprocessing

To suppress speckle, the imagery was filtered using a 3 by 3 Lee filter. TerraSAR-X data were then converted to β 0 (dB). Ortho-correction was performed on both images using ground control points, DEM, and satellite ephemeris information. An RMS error under 1 pixel was achieved.

Backscatter is defined as radar cross section on unit area and could be obtained using formula (1), in which d  represents the incident angle,K represents absolute calibration constant and A represents pixel brightness value β 0 . In backscattering images, the value of σ 0 is reflected by tonal brightness. Then different SAR data could be compared with each other.

  d K A   sin 2 0  (1)

Data Analysis

The paper acquired TerraSAR-X SM polarized images with dual polarization modes (VH and VV). The backscatter in dual-polarization mode contains more information for all land covers when comparing with single polarization mode. The interaction between radar wave and depolarization capacity of different polarization by land covers reflects attributive characters of different land cover types, such as dielectric properties, surface roughness, geometry form and direction, etc. Therefore, it is feasible for crop mapping using variation information of land covers in different polarization images. Calculate separately the difference imagery (Dvalue) of VH polarization and VV polarization backscattering coefficient for each TerraSAR-X data.

VV VH Dvalue   (2)
Based on the typical ground object samples, acquired throuth ground surveyed, the mean value and variance value of Dvalue for the dominant land cover types are provide in Table 1 and Figure 1. As shown in Table 1, we can see: ①The variance between rice and other land covers becomes greater as time passed by. On March 27, there was minimum variance between rice mean value and construction land and upland due to the variance of rice planting time and mode. There was still a portion of paddy field without being planted or transplanted. The variance between different surface features in May 10 and June 1 was relatively more obvious (See figure 1a). ②As time passed by, the variance value of rice gradually decreased, showing that the difference of rice growth brought about by planting mode and time gradually decreased, which was reflected in the figure as identification feature became more obvious and even (See figure 1b). The visual inspection of the two date TerraSAR-X data of May 10 and June 1 indicate relatively obvious identification features among land cover types. Even though rice showed multi-identification features, there was obvious separability between rice and other land covers.

Method and Flow of Rice Identification

In order to further analyze the rice identification effect of 4 temporally different images, the paper conducted rice identification experiments and obtained classification results of 4 temporally different images based on methods of objectoriented classification and decision tree. Classification procedure (Figure 3 First, Image segmentation was applied to the three-channel image in order to clearly demarcate parcels (objects) of homogeneity [START_REF] Benz | Multi-resolution Object-oriented Fuzzy Analysis of Remote Sensing Data for GIS-ready Information[END_REF][START_REF] Guo | Application of Method and Process of Objectoriented Land Use-cover Classification Using Remote Sensing Images[END_REF] . Segmentation was completed using eCognition® Definies Imaging [START_REF]Definiens Imaging. eCognition User Guide[END_REF] . Next, knowledge conclusion study and rule acquisition were separately realized using training sample information of every land cover type via See5 software, and relative statistics information of VH and VV's backscattering coefficient and D-value was taken as classification feature to create decision tree (DT). Then DT was defined in eCognition to obtain classification results. The classification accuracy was tested using independent test samples.

Results and Analysis

The entire study site was classified into 6 classes, including paddy rice, water, construction land, upland, woodland and other. Field surveyed sample data was settled according to this classification system (Table 2). We can see that the quantity distribution of every class sample was quite mean, in which construction land and water had little variation and little proportion in study site, while paddy and upland were major study objectives and had great variations as time passed by with more sample points. Sample set was divided into two parts randomly. As the quantities of training samples and validation sample were basically the same, the classification effect could be efficiently estimated. The 4 temporally images were processed and classified using the above method and procedures. Rice identification results were distinguished point by point using ground surveyed validation samples, and error matrix [START_REF] Foody | Status of Land Cover Classification Accuracy Assessment[END_REF] could be created (Table 3). We can see:

(1) The image of June 1 had the best classification result, most of the paddies was in the flowering period; The image of May 10 was the second best, in which most paddies were in the jointing period; March 27 and April 18 produced poorer results.

(2) Although the image of May 10 had lower accuracies than that of June 1, its overall accuracy, rice producer accuracy and user accuracy all achieved 85%; as its acquisition was about 20 days ahead of June 1, it also could achieve effective identification of paddies in the study site in spite of a slightly lower classification accuracy (a decrease of 1.7%).

From the above analysis, the image in June 1 had the best classification effect, in which period most of paddy was in flowering period; the image in May 10 took the second place, in which most paddy was in jointing period; March 27 and April 18 had inefficient effect. Although the image in May 10 had less efficient classification effect than that in June 1, its overall accuracy, rice producer accuracy and user accuracy all achieved 85%; as its acquisition was about 20 days ahead of June 1, it also could achieve effective identification on paddy in the study site in spite of classification accuracy decreasing by 1.7%. Considering the accuracy and timeliness, the image of May 10 is more suitable, and its accuracy estimation matrix is listed (Table 4). (1) Paddy, water and construction land have good identification effect as their producer accuracy and user accuracy both reached 85%.

(2) Upland had less efficient producer accuracy and user accuracy, mixing up with woodland and other surface feature. The possible reason was upland crop in flat area, such as sugarcane, dispersed over many hills besides in flat areas, which might cause confusion with eucalyptus seedlings.

(3) Classification combination (Table 5) was conducted based on image classification. The surface features were combined into "paddy" and "other" with obtainable rice identification accuracy of 97.3%.

Conclusions and Discussion

(1) By using a decision tree and object-oriented classification method, TerraSAR-X data at the jointing period can achieve accurate rice identification in the early season with an accuracy of 97.3%.

(2) The images at the seeding period and tillering period had poorer results. The accuracies were not sufficient to fulfil requirements for rice mapping. Both the images at the jointing period and flowering produced good classification accuracies, the flowering period having the best result but providing maps later in the season.

Considering the accuracy and timeliness, imagery acquired at the jointing period is more suitable for rice identification during the early season for a nationwide operational rice monitoring system.

Fig. 1 . 1 Fig. 2 .

 112 The difference channel (VH-VV) temporal profile of typical ground objects As analysis above, the backscatter on May 10 and June 1 showed more obvious features with regard to land cover separability and identification feature. Although the variance value of rice on May 10 was a little more than June 1, we can see from figure2that the variance still has obvious discrepancy with other land cover types, and the growth discrepancy brought by different rice planting mode was reflected by great variance. (a) Colour composite constructed using data of May-10 (b) Colour composite constructed using data of June-Colour composite constructed using TerraSAR-X data where red represents VH, green represents VV, and blue represents the difference channel (VH-VV).

  ) contains image preprocessing (radiometric calibration, ortho-correction, speckle filtering, backscatter extraction), image segmentation, training/testing sample preparation, supervised decision tree extraction, classification based DT and accuracy assessment.

Fig. 3 .

 3 Fig. 3. Colour composite constructed using TerraSAR-X data where red represents VH, green represents VV, and blue represents the difference channel (VH-VV).

Table 1 .

 1 Mean value of D-value

	Class	March-27	April-18	May-10	June-1
	Construction	-7.069	-7.502	-7.379	-7.043
	land Other	-3.369	-4.440	-6.173	-6.107
	Paddy	-7.513	-5.009	-3.950	-4.078
	Upland	-7.279	-7.342	-6.787	-5.782
	Water	-1.746	-2.480	-2.696	-2.076
	Woodland	-5.992	-5.497	-5.254	-5.153

Table 2 .

 2 Ground survey data composition of different land covers

		Ground control point
	Data Preprocessing	DEM (30m) Ephemeris Info.
	Image segmentation using eCognition
	Samples selection (train and test)	Field Survey Data
	Generate decision tree using See5
	Classification based on DT using eCognition
	Classification Accuracy Assessment

Table 3 .

 3 Ground survey data composition of different land covers

	Class	Paddy	Water	Construction land	Upland Woodland	Other	Overall Accuracy
				User's Accuracy
	6-1	100.0% 93.8%	92.9%	80.0%	81.3%	77.8%	87.4%
	5-10	91.7%	93.3%	93.3%	76.0%	76.5%	87.5%	85.7%
	4-18	81.8% 100.0%	70.6%	65.4%	66.7%	76.5%	75.7%
	3-27	84.2% 100.0%	65.0%	59.3%	60.0%	58.8%	69.6%
				Producer's Accuracy			Kappa
	6-1	95.7% 100.0%	86.7%	83.3%	81.3%	77.8%	0.85
	5-10	95.7%	93.3%	87.5%	79.2%	81.3%	77.8%	0.83
	4-18	78.3%	93.3%	80.0%	73.9%	62.5%	68.4%	0.71
	3-27	69.6%	93.3%	81.3%	66.7%	56.3%	55.6%	0.63

Table 4 .

 4 Xuwen study site classification result on May-10

	Fields Surveyed	Paddy Water	Classification Result Construction land Upland Woodland Other	Sum	Producer's Accuracy
	Paddy	22	1	0	0	0	0	23	95.7%
	Water	1	14	0	0	0	0	15	93.3%
	Construction land	0	0	14	1	1	0	16	87.5%
	Upland	1	0	1	19	2	1	24	79.2%
	Woodland	0	0	0	2	13	1	16	81.3%
	Other	0	0	0	3	1	14	18	77.8%
	Sum	24	15	17	24	16	16	112	---
	User's Accuracy	91.7% 93.3%	93.3%	76.0%	76.5%	87.5%	Overall Accuracy	85.7%

Table 5 .

 5 Rice identification result on May-10

	Fields		Classification Result		Producer's
	Surveyed	Paddy	Other	Sum	Accuracy
	Paddy	22	1	23	95.7%
	Other	2	87	89	97.8%
	Sum	24	88	112	---
	User's Accuracy	91.7%	98.9%	Overall Accuracy	97.3%

Acknowledgment

This study is supported by the project named High Resolution Multi-load Cooperative Agricultural Applied Technology Study which number is GF13/15-311-003. We also thank Infoterra Asia Pacific(EADS-China) for the technical support.