Manman Li

Junming Liu
email: liujunming2000@163.com

Reconstructing Vegetation Temperature Condition Index Based on the Savitzky-Golay Filter

Keywords: Savitzky-Golay filter, VTCI, drought monitoring

Vegetation temperature condition index (VTCI) is a near-real-time drought monitoring approach which is derived from normalized difference vegetation index (NDVI) changes in a given region to land surface temperature (LST) changes of pixels with a given NDVI value.It can be physically explained as the ratio of temperature differences among the pixels which have the same NDVI values.Due to the noise in the NDVI and LST, results of VTCI have much deviation. The Savitzky-Golay filter, a weighted moving average filter as a polynomial of a certain degree, is applied to smooth out noise in NDVI and LST time-series. VTCI were taken into Guanzhong Plain of Shaanxi Province of each 10-days from March to May in 2007 and 2008 as the study data. In order to reconstruct VTCI space-time (temporal and spatial )series data, the Savitzky-Golay filter was used to reconstruct the VTCI time-series of each pixel in remote sensing images. Then, the results were extended to the surface from the point. The results show that the Savitzky-Golay filter could improve the quality of VTCI and could get a better drought monitoring result.

Introduction

Drought is a long-term, gradual change natural disaster and occurs slowly and complexly. Drought is always induced by water shortage. Drought causes the most yearly economic loss among all natural disasters in China. Thus, drought monitoring plays a very important role in understanding the level and distribution range of drought, which could help in putting forward the measurements against drought in time.

Remote sensing technology could monitor a wild range surface using information of spectroscopy, time, space and direction in objects' surface. On the basis of the multi-spectral characteristics of image, the high spatial resolution and the periodicity of remote sensing information, the research of vegetation and ground coverage become more extensive and more intensive as compared with before. Currently the drought monitoring models are established by vegetation index.

Based on the regional distribution of the scatter diagram of remotely retrieved normalized difference vegetation index (NDVI) and land surface temperature (LST) present like triangular, vegetation temperature condition index (VTCI) is developed for monitoring drought occurrence at a regional scale by Wang Pengxin [START_REF] Pengxin | Vegetation-Temperature Condition Index and Its Application for Drought Monitoring[END_REF]. VTCI is applied in the drought monitoring of Guanzhong Plain of Shaanxi Province and the excellent result has been obtained.

However, residual noise in the NOAA-AVHRR remote sensing image will be remained into the computation of VTCI, though strict pre-processing is applied. The further analysis will impeded and un-correct results might be generated. In this model, the 10-days Maximum Value Composite (MVC) products is used, however, there is much noise in the data sets used in computing VTCI, which may affect the further analysis. Before taking the data to the research, the noise in the data needs to be smoothed out. Lu Linlin used Savitzky-Golay filter to reconstruct NDVI time-series in analyzing the extraction method of winter wheat phonology [START_REF] Linlin | Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[END_REF], and it smoothed out noise in the data and loosed less information in the original data.

The phonology information, gotten from NDVI fitted by Savitzky-Golay filter, was more similar with the realistic phonology. The 10-days maximum value composite (MVC) SPOT-VEGETATION dataset was used as main remotely sensed data from 1999 to 2005 in Gu's research [START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF]. The Savizky-Golay filter, which could improve the representation of the growth curve of winter wheat growth by NDVI time-series curve, was used to eliminate the noise. Gong Pan used the Savizky-Golay filter to smooth the 10-days compositing data and the results indicate that the timeseries data has better correlational relationship with the vegetation growth [START_REF] Pan | Land Cover Classification based on MODIS NDVI & LST Time Series Data in Northeast China[END_REF].

This study was intended to introduce the Savizky-Golay filter into VTCI data. The objectives were: To smooth out noise before computing VTCI and to reconstruct NDVI and LST time-series with Savitzky-Golay filter. Thus, more accurate results of drought monitoring could be obtained.

Methodology

Study Area and Data

For this study, proper research region is needed. The region with large range of vegetation coverage and soil surface water content from wilting water content to field capacity are needed for drought monitoring with VTCI. Thus, Guanzhong Plain is selected for the study (Fig. 1). Guanzhong Plain is located in the center of Shaanxi province and between Qinling Mountains and Shanbei Plateau. Guanzhong Plain is a graben structural plain, flat and fertile, and is one of the most important wheat and cotton growth areas.

Vegetation Temperature Condition Index

Wang Pengxin proposed the concept of vegetation temperature condition index (VTCI) for monitoring drought [START_REF] Pengxin | Researches and Applications on Drought Monitoring Approach of Vegetation Temperature Condition Index[END_REF]. The index can be used to monitor drought occurrence at a regional level for a special period (e.g. 10 days) of a year, and to capture spatial variatious of drought occurrence within the region. The new drought monitoring index is not only related to NDVI changes in a region, but also give emphasis on land surface temperature changes of pixels which have the same NDVI value. The index can be defined as [START_REF] Pengxin | Vegetation-Temperature Condition Index and Its Application for Drought Monitoring[END_REF]:

min max max ⋅ ⋅ ⋅ - - = i i i i NDVI NDVI NDVI NDVI LST LST LST LST VTCI (1)
There into, The value of VTCI is from 0 to 1.The lower value of VTCI,the heavier the drought occurrence is.The index is site-specific and time-specific.

2.3

The Savitzky-Golay Filter

The Savitzky-Golay filter, called SG filter for short, is a simplified least-squares-fit convolution for smoothing and computing derivatives of a set of consecutive values, proposed by Savitzky and Golay in 1964 [START_REF] Savitzky | Smoothing and differentiation of data by simplified least squares procedures[END_REF].It can be understood as a weighted moving average filter with weighting given as a polynomial of a certain degree. This polynomial is designed to preserve higher moments within the data and to reduce the bias introduced by the filter. This filter can be applied to any consecutive data when the points of the data are at a fixed and uniform interval along the chosen abscissa, specially the time-series data. The general equation of the simplified least-squares convolution for time-series data smoothing can be given as follows [START_REF] William | Numerical Recipes in C -The Art of Scientific Computing[END_REF]:

∑ = - = + = m j m j i j j i Y C Y * (3)
where Y is the original time-series data value,Y* is the resultant time-series data value,C i is the coefficient for the ith time-series data value of the filter (smoothing window).The index i is the running index of the original ordinate data table.The smoothing array (filter size) consists of 2m+1 points,where m is the half-width of the smoothing window.

There are two parameters that must be determined according to the time-series data observations, when the filter is applied. The first parameter is m,the half-width of the smoothing window.Usually,a larger value of m produces a smoother result at the expense of flattening sharp peaks.The second parameter is d,the degree of the smoothing polynomial. A smaller value of d will produce a smoother result but may introduce bias.And a higher value of d will reduce the filter bias,but may over fit the data and give a noisier result.

Experimental Results

Reconstruct time-series

Select a pixel in Xi'an, whose geographic coordinate is (108 56′E,34 18′N). The parameters are set to (2, 3) and [START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF][START_REF] Pan | Land Cover Classification based on MODIS NDVI & LST Time Series Data in Northeast China[END_REF]. Fig. 2 shows the change of the NDVI and LST time-series fitted by the Savitzky-Golay filter with different parameters in contrast. Obviously, the flitted time-series could pass through the original time-series and remove the low value in the original time-series. A certain extent, the Savitzky-Golay filter plays a role in eliminating noise.

T i m e 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 0 -d a y s N D V I
the original NDVI time-series the NDVI time-series fitted by SG fliter with [START_REF] Linlin | Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[END_REF][START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF] the NDVI time-series fitted by SG fliter with (3,4)

T i m e 2 6 0 2 7 0 2 8 0 2 9 0 3 0 0 3 1 0 3 2 0 3 3 0 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 0 -d a y s L S T ( K )
the original LST time-series the LST time-series fitted by SG fliter with [START_REF] Linlin | Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[END_REF][START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF] he LST time-series fitted by SG fliter with [START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF][START_REF] Pan | Land Cover Classification based on MODIS NDVI & LST Time Series Data in Northeast China[END_REF] a. Comparison of time-series before and after fitting by the Savitzky-Golay filter with different parameters from March to May in 2007

T i m e 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 0 -d a y s N D V I
the original NDVI timeseries the NDVI time-series fitted by SG fliter with [START_REF] Linlin | Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[END_REF][START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF] the NDVI time-series fitted by SG fliter with (3,4) From the comparison, it shows that the results of two different parameters are very close, the difference is not significant. The result got from parameters (2, 3) is little larger than the result got from parameters [START_REF] Gu Xiaohe | Monitoring of growth changes of winter wheat based on change vector analysis[END_REF][START_REF] Pan | Land Cover Classification based on MODIS NDVI & LST Time Series Data in Northeast China[END_REF]. However, noise usually depress timeseries value. The top value may keep more realistic information. Sun Wei et al. monitored drought of Guanzhong Plain in each early ten days of May in recent five years and the result show that VTCI is close to the amount of precipitation in recent 20 days [START_REF] Wei | Further improvement of the approach to monitoring drought using vegetation and temperature condition indexes from multi-years' remotely sensed data[END_REF], indicating that the VTCI is a near-real time drought monitoring approach. When there is little difference in the results of two parameters, when m is set as 2, more data could be gotten. Considering that, when fit the NDVI and LST time-series by the Savitzky-Golay filter, the parameters set as (2, 3) in the study.

T i m e 2 8 5 2 9 0 2 9 5 3 0 0 3 0 5 3 1 0 3 1 5 3 2 0 3 2 5 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 10-days L S T ( K )

VTCI image

Based on formula (1), when VTCI is computed with NDVI and LST is fitted by the Savitzky-Golay filter, images were generated. Comparative results are in fig. 3 By the data from the agricultural meteorological station of Shaanxi Meteorology Bureau, from the last ten days of March to the middle ten days of May in 2007, most areas of Shaanxi province showed higher temperatures. Compared with the average year, the amount of precipitation decreased about 20% to 70%. The entropy in soil loosed fast and the development of drought is continuous. The drought area of Guanzhong Plain extended from east to west gradually. The study area had precipitation in the early ten days of April, though it was not enough for releasing drought, it helped in increasing the water content of the soil surface. Fig. 3 shows that VTCI images fitted by the Savitzky-Golay filter could get a better monitoring result. From March to May in 2008, the average temperature of Guanzhong Plain was a little higher than average year. The total rainfall amount of the midwest of Guanzhong Plain was 50--100mm, less than average year [START_REF] Xinling | Evaluation of Climate Influence in Shaanxi Provine in 2008[END_REF]. Guanzhong Plain has irrigation conditions that could help in releasing drought. VTCI images fitted by the Savitzky-Golay filter could get a better monitoring result (fig. 4). There was a larger process of precipitation in the middle of April, which increased the water content of the land. So, the value of VTCI of monitoring results increased.

Guanzhong Pain has higher probability of spring drought and summer drought. And the drought in the east is worse than in the west. Based on this trend, the VTCI images fitted by Savitzky-Golay filter could reflect this better.

The noise usually depresses the value. The Savitzky-Golay filter could smooth out the noise more or less and remove the low value.The VTCI images fitted by the Savitzky-Golay filter are more close to the truth.

Conclusion

The Savitzky-Golay filter is a simplified least-squares-fit convolution and a weighted moving average filter. The time-series data fitted by the Savitzky-Golay filter can smooth out noise in time-series, specifically that caused primarily by cloud contamination and atmospheric variability. The experiment with NOAA images from March to May in 2007 and 2008, indicate that the VTCI calculated with the NDVI and the LST time-series fitted by the Savitzky-Golay filter, can improve the quality of VTCI and get a better drought monitoring result.

Selection of parameters is extremely important when use the Savitzky-Golay filter to reconstruct NDVI time-series. That affects the accuracy of the results directly. Selection of parameters in this study was determined by the study data and taken into the actual situation on the ground. Subject to the limitations of the data, we just use the Savitzky-Golay filter simply in the study. If there are the whole year VTCI data, higher accuracy VTCI data could be gotten in the fitting iteration process with the Savitzky-Golay filter.

VTCI is a real-time approach, and has a certain correlation with the soil water content. When there is a precipitation, even it's not enough to release the drought completely, but it increases the soil water content. The results obtained through remote sensing monitoring show no drought happen. That affects the accuracy of the results. It's worth to study how to reduce the influence. At the same time, it's a research direction that fills the missing data within the region through the Savitzky-Golay filter.

Fig. 1 .

 1 Fig. 1. The study area NOAA-AVHRR images day transited from March to May in 2007 and 2008 were choosed. Firstly, geometric correction was done in two steps: (1) revising the distortion of pixels; (2) using a traditional geometric correction method -polynomial correction method. The size of the pixel of AVHRR image after resembling with bilinear interpolation is 1.1 km×1.1 km. The projection method is Lambert equal area projection. By using AVHRR data through atmospheric correction, radiometric correction and calibration, we can transform data of band 1 and band 2 into the surface reflectivity and data of band 3, 4 and 5 into the surface radiance. Then we can calculate the brightness temperature of band 3, 4 and 5 by center wavelength technology. Lastly, NDVI and LST can be calculated severally.

  minimum of land surface temperature when NDVI i is equal to a particular value; LST NDVIi is land surface temperature of a pixel when the value of NDVI is NDVI i ;a 、 b 、 a' 、 b' are undetermined coefficients.In this research,we can get the coefficients approximately in formula[START_REF] Linlin | Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[END_REF] in the scatter diagram of NDVI and LST of the study area.

Fig. 2 .

 2 Fig. 2. The change of time-series before and after fitting by the Savitzky-Golay filter with different parameters

Fig. 3 .

 3 Fig. 3. Comparison of VTCI images in 2007 on the base of the agricultural weather data of Shaanxi Meteorology

Fig. 4 .

 4 Fig. 4. Comparison of VTCI images in 2008

Acknowledgments

. This research was supported by the National Natural Science Foundation of China (Grant No. 40871159 and 40571111), China National High Technology Research and Development Program (Grant No. 2007AA12Z139), and the European Commission (Call FP7-ENV-2007-1 212921) as part of the CEO-AEGIS project.


Wengang Zheng
email: zhengwg@nercita.org.cn

Xin Zhang
email: zhangx@nercita.org.cn

Xiaojun Qiao
email: qiaoxj@nercita.org.cn

Hua Yan
email: yanh@nercita.org.cn

Wenbiao Wu

The Design of Smart Wireless Carbon Dioxide Measuring Instrument Used in Greenhouse

Keywords: greenhouse, carbon dioxide sensor, measuring instruction, Zigbee; low power consumption

Different crops, seasons, greenhouse's structures and planting area need different concentration of carbon dioxide which fertilized in greenhouse. Measuring the concentration of carbon dioxide accurately in greenhouse is very important to fertilize carbon economically and reasonably Concentration of carbon dioxide measuring equipments were often used in chemical industry and environment fields, these equipments are not suitable for greenhouse's environment and had many disadvantages such as high price, high power consumption and low anti-interference ability. Based on the research of carbon dioxide measuring equipment, this design took part of Non Dispersive Infrared (NDIR) carbon dioxide sensor produced by GSS, embedded Zigbee module provided by Jennic, smart calibration program and solar panel charge system. The results showed that the accuracy was±30ppm, average power consumption was 2mW, high consistency, response fast. This design can change the present of carbon dioxide measuring instrument hard to popularize, and make carbon dioxide fertilization accurately.

Introduction

Adding carbon dioxide to fertilize can advance the crop's ratio of photosynthesis and resistance. This technology can also accelerate the growth and improve quality. So it is one of important technologies used in greenhouse production [START_REF] Chen | The carbon dioxide fertilization technology in facilities vegetables[END_REF] [START_REF] Shan | The carbon dioxide fertilization in greenhouse[END_REF]. This technology has popularized for twenty years. Many methods have been used for achieving carbon dioxide such as nature ferment, solid carbon dioxide granule, ventilation, chemical reaction etc [START_REF] Wang | The application and countermeasure of greenhouse CO 2 fertilization technology[END_REF]. The amount of gas mainly calculates by manpower. CO 2 detect devices are used rarely for measuring current CO 2 concentration. This situation makes fertilize shortage and cannot achieve the desired effect. Once manual calculation or operation errors, higher CO 2 concentration can destroy the green plant photosynthetic system. The plant will appear leaf curl and even cause lack of nutrient, high temperature risk of crops [START_REF] Liu | The essential technology of CO 2 fertilization in greenhouse[END_REF]. Even more worse, it will do harm to the life of the operator and production human. When the CO 2 detecting devices are added for monitoring carbon dioxide concentration in real time , it will provided technique supplement for traditional fertilize method and device. And it makes fertilizing carbon dioxide more economical and reasonable.

Parts of agriculture gardens and research institutes can assemble CO 2 measuring equipments which are chosen from chemical industry or environment field. U.S.'s ACI, GREYSTONE have explosion-proof encapsulated and high concentration measuring devices which have transform, display, alarm and control functions. Because of the high price, these productions can not widely used in agriculture. Portable CO 2 measuring instruments such as Telaire(America)[5], Sense Air(Sweden) [START_REF] Senseair | Model Esense Carbon Dioxide sensor and transmitter[END_REF], Vaisala (Finland) etc. are not suitable for longtime measuring in greenhouse because of the activeness of operator and high power consumption. Domestic low cost CO 2 transmitters use semiconductor, solid electrolyte, electrochemistry sensors to measure the CO 2 concentration. All these sensor probes have low precision, large drift, high repair ratio and cannot suitable for greenhouse's high temperature and humidity. The domestic research focuses on sensor measurement theory and structure [START_REF] Chao Yuan | Research advance in detection of carbon dioxide[END_REF]. The fertilize voice generator which promoted crops to absorbing CO 2 by audio [START_REF] Hou | The application of plant audio controlling technology in facility vegetable production[END_REF]. But there are few studies of CO 2 measuring instruments used in facilities environment. This paper designed a smart wireless carbon dioxide measuring instrument used in greenhouse by using NDIR CO 2 sensor and embedded Zigbee module. It had advantages of cost controllable, solar panel powered, low power consumption, high precision self-calibration etc. This instrument could solve the problems, such as hard to be popularized, lack of specific products, low attention.

The Brief Introduction of Carbon Dioxide Measuring Technology

The mainly used methods for measuring CO 2 are titration, chromatography of gases, infra-red spectrum, electrochemistry, semiconductor ceramics, solid electrolyte etc. [START_REF] He | Design and experiment research of carbon dioxide detection based on infrared spectrum absorption theory[END_REF]. The titration method device is complex, low selectivity and sensitivity. Gas chromatography has advantages in sensitivity, accuracy and speed. Greenhouse needs simple measurement instrument with low price, high accuracy and rapid response CO 2 . But the device based on Gas chromatography method is not suitable for greenhouse's demand for the high price and huge size. So the infra-red, electrochemistry, semiconductor and solid electrolyte sensors are widely used. Electrochemistry and semiconductor sensors have small size, but are not good at accuracy, stability and gas choice. The working life of the electrochemistry sensor is also very short. Table 1 shows the parameters comparison of common CO 2 sensors. The solid electrolyte sensor has low price, it is good for measuring on line for a long time. FIGARO(Japan) CDM4160 integrates the sensor of TGS4160 [START_REF] Figaro | Product information of CDM4146-Pre-calibrated module for carbon dioxide[END_REF], which has alarm controlling functions of four concentration points. But its' long warm-up time and high power consumption features are not suitable for accurate measuring in greenhouse. The NDIR sensor has advantages in accuracy and stability. GE/Telaire (America) 6004 which already been stopped producing has large market share before. Module 6113 is replaced. Korea's ELT H500 has low cost and small size. It is

The Design of Smart Wireless Carbon Dioxide Measuring Instrument Used in

Greenhouse 3 suitable for the testing production which does not sensitive with the power. Although domestic CO 2 sensor MG811's price is very low, it is not good for greenhouse application environment because its' accuracy interfered by humidity. By comprehensive comparison, U.K. GSS Company takes solid state light module and reference diode to make sensor lower price, faster, lower power, higher accuracy and long time stability. Low power module C20LP/C100LP with 10mW consumption will be brought at recent time. It is fit for combining with wireless sensor networks. This design uses this module to build greenhouse smart wireless carbon dioxide measuring instrument. This design takes wireless sensor networks technology for measuring CO 2 concentration by functional module method. The sensor is installed in radiation shield. This instrument can gather, transform, display, storage the data. It also can communicate with PC or data collector by wireless of Zigbee. All the functions of this design could add or delete to control the price. And this can optimize with the performance, price and power consumption. Multi-points or single point monitor in one greenhouse, star network and point to point network are used by PC,Co-ordinator and end device.(Figure 1). Chain network was used for multi-points monitor in greenhouse. (Figure 2) This paper introduces the design of CO2 measuring instrument.

The Hardware Design of the Equipment

The design has to consider the whole functions. But it can reduce the function module to lower the cost base on the real application. 3V voltage is used in this system. All chips' power supply is managed. 2A lithium battery and solar panel were power for system's longtime working. This instrument's hardware structure is showed in Figure 3. The Jennic company's third of generation Zigbee module JN5148 supports Zigbee Pro protocol It has low power consumption (low power module receive and transmit current is 17.5/15mA, high power module receive and transmit current is 23/110 mA), abundance resource on chip (32 bit MCU, 128Kb RAM、128kB ROM、12bit ADC/DAC、two UARTs etc.). The functions of this device can come true by embedding application routine.

The Design of Smart Wireless Carbon Dioxide Measuring Instrument Used in

Greenhouse 5 GSS's CO2 sensor is linked with the JN5148 by UART port. It can transfer the data gathered, calibrate and setting instruction. CO 2 module's calibrate and zeroing ports were linked with the switched and MCU's I/O port which could make calibration and zeroing to come true by manual operation and automation .The chip of 74HC04 achieves the electrical level transform because CO 2 module needs 5V voltage supply.

The segment LCD display module is used to display the gathered data. FM31256 integrates real time clock, watch dog timer and FRAM in a chip [START_REF] Ramtron | The datasheet of FM31256[END_REF]. It provides accuracy real time clock and 256Kbit storage space. This IC communicated with I2C port. This system provides dry contact by relay module. This dry contact could link with the motor and alarm devices to achieve controlling and alarm functions. RS232/RS485 module occupies one of the URAT port. The module is used for network communication and program processing. This design provides switches for setting number, communication channel, baud rate etc.

1-2W solar panel is used for charging 2Ah lithium battery. The charger IC CN3082 could finish this function. CN3082 can adjust charge current base on solar panel's current output ability [START_REF] Consonance | The data sheet of CN3082[END_REF]. The part of power management uses NCP500 to provide 3.3V voltage for system. Power switch IC TPS2044 controls the power supply of the lcd, communication IC. This power management method achieves minimum power consumption.

The Software Design of the Equipment

The software design base on Zigbee chip JN5148 uses the ZPS Configuration Editor and JenOS Configuration Editor which are integrated in IDE Eclipse for head file. Added with application program and linked to finish the development of software [START_REF][END_REF].

Overall Thinking of Software Design

The software is made up of message event which comes from ZPS protocol, Task and time trigged application program. All these message, task and software timer application are driven by the JenOS, and communicates with the ZPS protocol. Basic end device net parameters are set by ZPS Configuration. As showed in Figure 4, system has six message events. The App_msgRelaycontrol, App_msgTransmitter, App_msgPowermanage, App_msgCalibrate are used for making the controlling, transmit, low power and calibrate functions to come true. App_msgSensorevent is used to storage, display, communication etc. App_msgZpsevent was used for management App_taskSensornode. Through setting the FIFO, interrupt of UART and recall function which used as application routine to achieve the software calibration, fast response, filter, LED's signal intension, temperature of PCB etc. functions.

Taking instruction: M6\r\n Output: Z00057z00060 for example, it means that fast output is 570ppm, and filter output is 600ppm. Other function achieved will not be listed here one by one.

The Calibration Function

Although the CO 2 module has been rigorously calibrated before delivery, measuring data will not accurate like before for long time using. So it is necessary to calibrate the sensor. Module can use the nitrogen gas or other gas which does not include CO 2 to calibrate the sensor by pulling down the pin2 of sensor. The surrounding environment is also used for calibration by pulling down the ninth pin of sensor.

For compensating the problem of easy to interference when using the air calibration method. Over sample algorithm is used in the software design [START_REF] Telaire | series module CO 2 sensor UART and SPI communications protocols[END_REF]. This algorithm takes 12 hours as acquisition cycle in outdoor no interference air. The data measured each 15 minutes are compared with the reference data(380-400ppm) to get average calibrate factor. Then the factor is used for compensating the CO 2 concentration. Because the implementation is simple, this paper will not list the specific algorithm grogram.

Experiment and Testing

This wireless CO 2 measuring instrument has already tested and calibrated in laboratory and application site. Wireless communication, communication distance, real time and bit error rate etc. Jennic has the test reports of module and antenna. Its' features meet the demands of the greenhouse -site wireless network applications.

Different standard concentration CO 2 gas which provided by National Standard Material Research Center. The instrument's small size and wireless communication ways are suitable for calibrating in the sample gas bar. The data are showed in Table 2.

Table1.

  Parameters comparison of common CO 2 sensors or module Sensor picture Model U.S.A.GE/ 6004/6113 Korea ELT H550[10] U.Kmeasuring instrument in greenhouse has some functions bellow.(1)Transmitter function that could provide 4-20Ma, 0-5V, or frequency output, it is convenience to link with the collection and controlling devices. (2) Function of record and display. (3) Long distance transmitting function. Multi-point measurement and long distance transmission by wireless or wired to form network. (4)Controlling and alarm function, It could control the actuator and alarm devices in greenhouse by comparing data gathered with setting alarm points or critical point. All these functions were used to achieve the precise the concentration of CO 2 fertilization [12].

Fig. 1 .Fig. 2 .

 12 Fig. 1. Multi-points or single point measurement in single greenhouse

Fig. 3 .

 3 Fig. 3. Hardware design structure Transmitter module transfers digital data to 4-20mA output by IC AD694. This chip links with the module's 12bit DAC converter, and transfers 0-2V which DAC output into standard current signal.This system provides dry contact by relay module. This dry contact could link with the motor and alarm devices to achieve controlling and alarm functions. RS232/RS485 module occupies one of the URAT port. The module is used for network communication and program processing. This design provides switches for setting number, communication channel, baud rate etc.1-2W solar panel is used for charging 2Ah lithium battery. The charger IC CN3082 could finish this function. CN3082 can adjust charge current base on solar panel's current output ability[START_REF] Consonance | The data sheet of CN3082[END_REF]. The part of power management uses NCP500 to provide 3.3V voltage for system. Power switch IC TPS2044 controls the power supply of the lcd, communication IC. This power management method achieves minimum power consumption.

Fig. 4 .

 4 Fig. 4. Software structure

The Design of Smart Wireless Carbon Dioxide Measuring Instrument

						Used in
					Greenhouse 7
	5.2 Communication with CO 2 Module
	JN548 communication with CO 2 module by setting and calling UART1 API which
	included in Integrated Peripherals API. Related communication settings function is
	shown as follows:
	void vAHI_UartEnable( E_AHI_UART_1); // Enable UART1
	void	vAHI_UartSetBaudRate(	E_AHI_UART_1,	E_AHI_
	UART_RATE _9600); //Boad rate9600
	void	vAHI_UartSetControl	(	E_AHI_UART_1,	,
	E_AHI_UART_PARITY_DIS	ABLE	,	E_AHI_UART_WORD_LEN_8,
	E_AHI_UART_ 1_STOP_BIT, FALSE);//data 8bit , no parity,
	one end bit,no hardware flow control

Wengang Zheng1,*, Xin Zhang1,2 , Xiaojun Qiao1, Hua Yan2 , Wenbiao Wu2

Wengang Zheng1,*, Xin Zhang1,2 , Xiaojun Qiao1, Hua Yan2 , Wenbiao Wu2 Device 1* is the instrument used in greenhouse for ten months. Device 1 was calibrated by the over sample algorithm based on Device 1. CO 2 sensor should calibrate when used over one year. As Table 2 shows the accuracy is lower than 30ppm. From the data of device 2 and 3, we can see that the sensor's accuracy could meet the demands of greenhouse production's acquirement.

Conclusions

This paper aims at CO 2 fertilization which lacks of suitable facilities CO 2 detection device and depends on imports and industrial equipment to integrate the Zigbee embedded technology, low power CO 2 sensor 、 over sampling calibrate program (accuracy is <=±30ppm ) and efficient solar power system for designing a kind of smart wireless carbon dioxide measuring instrument. This instrument has a lot of advantages, such as low power consumption (average power is 2mW), wireless communication, controlling and alarm and cost controllable (basic function is 500RMB) etc. Wireless Zigbee Pro protocol is used in wireless sensor network which provides a new model of CO 2 measurement. This design provides equipment to greenhouse CO 2 fertilization, and achieves the precise control the concentration of CO


Yang Yang

Tian He

Yang Zhang

Mobile phones of 3G era in small and medium-sized of agricultural production and application prospect

Keywords:

The arrival of the era of third-generation (3G) mobile phone will cause earth-shaking changes, as a kind of new communication technologies will play in the fields of estimation. The rapid development of electronic technology and information technology, which had brought about a revolution of control and management of agricultural, mobile 3G era will give the agricultural revolution fueled, and bring a new era of agricultural productivity liberation. Mobile phones of 3G can achieve the real-time monitoring and control of agricultural through automatic control and electronic computer technology and communication technology, but also can get rid of the remote control and regional restrictions. Mobile phones of 3G realize the agricultural field of remote real-time automatic control through video image processing and fast speed of technology.

INTRODUCTION:

China is a large agricultural country, but the level of agricultural production is still in the hand-cultivation, not to achieve a large area of mechanized mass production situation. It is required a considerable period of time that achieve large-scale mechanized production because of China's economic system and the geographical features. Therefore based on China's national conditions, the development of smallscale automated production should be present a fresh development trend of agricultural production which difficult to implement joint large-scale operation situation because of Contracting by an individual. Our agricultural production may be contracted to individuals which results in a region the land under the area of fragmented, together with mountainous and hilly region, which brings large-scale machinery to some trouble. Combined with China's national conditions and characteristics of China's agricultural production patterns, it is necessary to achieve the implementation of small-scale automated production of Chinese in agricultural machinery automation.

Along with fast development and compact combination of both electronics technology and communication technology ,a person may be have more than a cell phone in average .With entered into an age of 3G, using a mobile phone deal with information is the exception ordinary daily life. Mobile communication terminal as a microprocessor controlled intelligent sensor automatic production mode 3G, full name of the third Generation. It enhanced voice and data in transmission speed based on previous data. It can be better in the global seamless roaming, and process images, music, video streaming and other forms of media, provided various services including web browsing, conference calls, e-commerce and other information services. Wireless network can support different data transfer rate, for example at least were 2Mbps (megabits / second), 384kbps (kilobits / second) and 144kbps transmission speed indoor, outdoor, and driving environment. With high-speed 3G network to download opens up a new era of mobile multimedia, mobile phone is bound to be another PC.

As rapid development of electronic information and communication technology, agriculture has been basically achieve modernization and automation .using of 3G mobile phone and electronic information and communications technology to simplify and automatic agricultural control, agricultural production must be brought to the rapid leap. 3G mobile phone technology as the core of the control will adapt to the scale of agricultural production, promote small and medium agricultural production in China, show the extraordinary in all aspects of the function.

AUTOMATED IRRIGATION CONTROL

60 years of New China, the Chinese effective irrigation area expanded from 2.4 million hectares to 8.77 million mu, accounting for the world's total 1 / 5, ranking first in the world. China to account for 48% of the irrigated land area, produces 75% of the country's total output of grain and 90% cotton, vegetables and other cash crops. At present the existing embankments, small pumping stations, motor-pumped wells, ponds, cellars and other small-scale water conservancy projects and the independent operation of more than two million, medium-sized irrigation channels to the end of class, small irrigation channels of nearly 3 million kilometers fixed, fixed at about 1.8 million kilometers of irrigation channels , the corresponding supporting structures close to 7 million, countless field projects cover nearly all of irrigated farmland.

Can see from the above data, our small-scale irrigation systems in a dominant position, to achieve small-scale irrigation which automatic irrigate and monitor is essential. As the motor-pumped wells and ponds occupy a vast cellar rate in the smallscale irrigation, Therefore, the establishment of 3G mobile phone system as the core of the automation of irrigation means which is a automation project of easy to be afforded and implemented. It is composed of the core of 3G mobile phone , Sensor data in real-time measurement of the field and the SCM system to accept delivery system consisting of wireless, The system limit the use of fuzzy criteria to achieve the simplification of small and medium irrigation , which meet the educational level of farmers limited. Using of the project to control pump motor starts and stops, irrigation canal gate opening and closing to achieve automatic irrigation of farmland, The function of System are real-time control, real-time data acquisition and preservation, on-site real-time video surveillance capabilities, unmanned operation of the field, which achieve real-time monitoring of the situation but no field operations. That is the advantage of 3G mobile phones has a strong transmission capacity, to ensure realtime agriculture and automation.

LIVESTOCK BREEDING AUTOMATION

In order to increase rural incomes, farmers increase the breeding livestock during agricultural production. The problems is Automatic feeding and automatic watering for farmers while agricultural production.

As the farmers economic conditions, small line of automation systems to be a star which build of sensor technology and 3G mobile communications technology, In recent years, with the prices of live pigs and breeding conditions of randomness, greenhouse pig become a new means of building up a fortune. Using 3G mobile phone integrated automation system, monitoring of pigs could be the case anywhere, automatic feeding, automatic watering according to real-time detection of ambient temperature and humidity, to adjust necessary conditions for pig. Automated system for 3G mobile phone adapt to individual small-scale farming in rural China, emancipate the productive forces ,which accelerate the income of farmers, meet the basic requirements of the State.

GREENHOUSE AUTOMATION

Greenhouse provides a new modern production environment to agriculture , and achieved good economic and social benefits. It can raise the income of countrymen, improve the industrial level and the ability to withstand natural disasters to extend the time for crop growth, increase crop yields, and so on. With the continuous development of the economy develop simultaneously on the greenhouse temperature, humidity, carbon dioxide and other automated monitoring and control has also been a corresponding development, thus improving income. using of electronic and information technology monitoring and control of greenhouse climate change data, such as temperature, humidity, illumination time and intensity, wind direction and wind speed and other factors, then back to the 3G mobile phone, to judge by artificial simple, once again sent through mobile phones order to plant growth requirements under the automatic adjustment. For example the temperature and humidity control. If the temperature is too low or too high, can be furnace heated up or can exhaust or otherwise deal with. If the humidity is too low, you can start the sprinkler system, when the humidity is too high can exhaust fan out of the air humidity is too high. At the same time can be CO2, light, irrigation and other automatic control. According to different crops, different times, 3G mobile phones to set different commands, using different control targets and control modes.

CONCLUSION:

3G mobile phone communications technology combined with sensor technology to realize the automation of small and medium agricultural production, reduce labor input to achieve the implementation of the monitoring of real-time control and remote control can be achieved, without geographical restrictions, thereby bringing agricultural labor simplify the automation, improved economic efficiency, reducing labor.

REFERENCES:

  【1】Industrial Computer Application in agriculture 【2】Computer control and management of greenhouse technology development overview and prospects in China, Agriculture Computer Miscellaneous 【 3 】 The Chinese Academy of Agricultural Irrigation Water Science and Technology Development Center, China Rural Water Network, 2010.1


Xuan Wang
email: wangxuan@swu.edu.cn

Jiake Lv

Wei Chaofu

Deti Xie

Modeling Spatial Pattern of Precipitation with GIS and Multivariate Geostatistical Methods in Chongqing Tobacco Planting Region, China

Keywords: Modeling spatial prediction, GIS, Cokriging, Small grid simulation, Regression Kriging

Precipitation is important factor affecting vegetation and controlling key ecological processes. In order to quantify spatial patterns of precipitation in Chongqing tobacco planting region, China, under ArcGIS platform, three multivariate geostatistical methods including cokriging, small grid and regression kriging, coupled with auxiliary topographic factors extracted from a 1:100000 DEM were applied to predict spatial distribution of precipitation for January (the least month), June (the richest month) and the whole year. The results showed that cokriging was the best for prediction precipitation of January, which could explain 58% of the total variation. Small grid simulation with IDW interpolation exhibited higher accuracy for both June precipitation and annual precipitation, which explained 72% and 61% of the total variation respectively. Generally, multivariate geostatistics accounted for most of the spatial variability in mean precipitation and especially could exhibit great improvement for estimating precipitation in areas where topography has a major influence on the precipitation.

Introduction

Precipitation is a very important climatic characteristic used in determining site suitability for agricultural and forest crops [START_REF] Marquinez | Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis[END_REF][START_REF] Sanchez-Palomares | Parametric Autoecology of beech tree forest in Castilla y Leon[END_REF], and it is also important in parameterizing the habitat of plant species and determining the patterns of vegetation zonatio [START_REF] Lapen | Spatial analysis of seasonal and annual temperature and precipitations normals in Southern Ontario, Canada[END_REF].However, accurate precipitation data only exist for point locations, the meteorological stations, as a result of which values at any other point in the terrain must be inferred from neighboring stations or from relationships with other variables [START_REF] Michaelides | Precipitation: Measurement, remote sensing, climatology and modeling[END_REF]. Therefore, spatial modeling of precipitation is of importance for understanding and supporting agricultural sustainability development.

Over the past decades, a number of methods have been proposed for modeling the spatial distribution of precipitation. The simplest approach is Thiessen polygon method which amounts at drawing around each gage a polygon of influence with the boundaries at a distance halfway between gage pairs [START_REF] Thiessen | Precipitation averages for large areas[END_REF]. Although the Thiessen polygon method is essentially used for estimation of areal rainfall, it has been applied to the interpolation of point measurements [START_REF] Dirks | High-resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data[END_REF]. Meanwhile inverse distance interpolation that makes unknown rainfall depth as a weighted average of surrounding values and the weights being reciprocal to the square distance from the unsampled location is also widely applied in many regions [START_REF] Dirks | High-resolution studies of rainfall on Norfolk Island Part II: interpolation of rainfall data[END_REF][START_REF] Carrear-Hernandez | Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico[END_REF]. However, for both Thiessen polygon method and inverse distance interpolation weighting method, they do not consider topographic variables such as elevation, altitude or latitude influences on precipitation, which rather is believed to be important factors especially in mountain regions with complex terrain. In order to overcome this deficiency, some researchers develop the relationships between precipitation and a range of topographic variables and used regression analysis and GIS techniques to model rainfall spatial pattern [START_REF] Marquinez | Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis[END_REF][START_REF] Goovaerts | Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall[END_REF][START_REF] Ninyerola | A methodological approach of climatological modeling of air temperature and precipitation thourgh GIS techniques[END_REF][START_REF] Lloyd | Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain[END_REF].But the method heavily depends on the accuracy of regression model and rarely consider spatial relationships among sample points.

Multivariate Geostatistics which is based on the theory of regionalized variables [START_REF] Wackernagel | Multivariate Geostatistics 2nd edition[END_REF], is recently increasingly preferred because it allows one to capitalize on the spatial correlation between neighboring observations to predict attributes values at unsampled locations, and also can be complemented by ancillary attributes [START_REF] Carrear-Hernandez | Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico[END_REF][START_REF] Els | Multivariate geostatistics for the predictive modeling of the surficial sand distribution in shelf seas[END_REF][START_REF] Haberlandt | Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event[END_REF]. Several authors have shown that the multivariate geostatistical prediction provides better estimates of rainfall than conventional methods or single ordinary kriging.For example Goovaerts [START_REF] Goovaerts | Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall[END_REF] used cokriging for incorporating elevation to predict mean monthly rainfall spatial distribution of Algarve, Portugal and compared that with Thiessen polygon, inverse square distance as well as ordinary kriging ,found that cokriging could achieve better estimation results. Lapen [START_REF] Lapen | Spatial analysis of seasonal and annual temperature and precipitations normals in Southern Ontario, Canada[END_REF] used regression kriging to spatially model the total precipitation normals in the Great Lakers. Hussain [START_REF] Hussain | Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan[END_REF] interpolated precipitation during monsoon periods in Pakistan. Ouyang [START_REF] Ouyang | A small grid method for local climate calculation in mountain area[END_REF]incorporated altitude, latitude, elevation, slope, aspect components and used small grid interpolation to calculate annual precipitation distribution of Beijing mountain area, china and the result showed the error tolerance was within 40mm.

Chongqing tobacco planting region, which locates in the southwest China and mostly is covered by hilly and mountains. Understanding the spatial pattern of precipitation especially increasing the accuracy of spatial prediction would be very important for local tobacco planting precise plan and management. In this study, we present an application of cokriging, small grid interpolation, regression kriging respectively for modeling precipitation in January (least), June (most) and the whole year of this region. Topographic variables including elevation, altitude, latitude, slope and aspect were used as auxiliary data. Mean squared error (MSE) and root mean squared standardized effect (RMMSE) as performance evaluation indicators were investigated to determine which approach is appropriate for predicting regional precipitation spatial distribution within different periods.

This paper is organized as follows. Following the introduction, some theory and methods used in this study are described in Section 2 .The study area and data resources are depicted in Section 3. In section 4, results and discussion are illustrated. Conclusions are finally made in Section 5.

Theory and Method

Cokriging

Multivariate geostatistics comprise a set of techniques and estimators which use the spatial variability, multivariate variables and correlation of a continuous spaceddistributed phenomenon to predict unsampled locations. In this section, we briefly introduce three methods used in this paper.

( ) ( ) ( ) { } [ ] 2 2 1 h x z x z h u u uu + - = γ (1)
Where h is a vector, the lag. For v also, the expected difference is zero and its autovariogram is ( )

h vv γ .The two variables have a cross-variogram ( ) h uv γ , defined as ( ) ( ) ( ) { } () ( ) { } [ ] h x z x z h x Z x Z E h v v u u uv + - + - = 2 1 γ (2)
Which describes the way in which u is spatially related to v .

To compute the usual cross-variogram, there must be sites where both u and v have been measured. The experimental cross-variogram ( ) ^h r uv can be estimated by

( ) ( ) ( ) ( ) { } ( ) ( ) ( ) { } h x z x z h x z x z h m h i v i v h m i i u i u uv + - + - = ∑ =1 ^2 1 γ (3)
Where u z and v z have been measured at sites i x and h

x i + , and ( )

h m
is the number of pairs of data points separated by the particular lag vector h .

The cross-variogram can be modeled in the same way as the autovariogram but there is an added condition. Any linear combination of the variables itself should be a regionalized variable, and its variance must be positive or zero. This is ensured if we adopt the linear model of coregionalization. For any pair of variables u and v , the variogram is

( ) ( ) h g b h k k k k uv uv ∑ = = 1 γ (4)
Where the k uv b represents the variances, for example the nugget or sill variances.

( )

h g k
is the spatial autocorrelation function which must be the same for both analyzed variables.

The ordinary punctual cokriging prediction of the primary variable

∧ u Z is obtained from the linear sum ( ) ( ) i ok l V i n i il x z x l u z ∑∑ = = = 1 1 0 ^λ ( 5
)
Where there are V variables, V l ,..., 2 , 1 = of which u is the one to be predicted and the subscript i refers to the sites of which there is l n in the search neighborhood where the variable l has been measured. The il λ represents the weights, which in the case of 'classical 'cokriging (Goovaerts, 1997) satisfy . The weights i λ are inserted into Eq. ( 5) to estimate ^0 ) ( x Z u .

u l u l l n i il ≠ = ⎪ ⎩ ⎪ ⎨ ⎧ = ∑ = , 0 , 1 1 λ (6)

Small grid simulation

Small grid simulation is based on a linear regression between a target variable such as certain climatic property ( Z ) and a secondary or third variable such as elevation or slope ( i Y ).The regression model so obtained is used to predict Z to the locations of the prediction grid at which i Y is known. The residuals from the regression ε are interpolated deterministically to the prediction grid. Since there is still no any deterministic interpolation proved to fit all variables. In this paper, we used IDW and Spline respectively and determined the proper one by calibration and comparison. The predicted values ^R Z and the interpolated values of the residuals ε are summed to

give the predicted values of the target variable Ẑ .

( ) ( ) ( )

x x Z x Z R ε + = (7)

Regression Kriging

Odeh et al [START_REF] Odeh | Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression kriging[END_REF]described three types of regression kriging: model A, B and C which are the developments of the general theme. In this study, we used model C. Like small grid simulation, the method is also based on a linear regression between a target variable and a secondary or third variable. But the residuals from the regression ε are kriged stochastically to the prediction grid using the variogram computed from the residuals. The predicted values ^R Z and the kriged values of the residuals ôk ( ) ( ) ( )

x x Z x Z ok R RK ε + = (8)

Performance evaluation indicators

In order to evaluate the performance of different multivariate geostatistical methods, mean squared error (MSE) and root mean squared standardized effect (RMSSE) in this paper, are used as performance measure indicators. The mean squared error (MSE) is expressed as

( ) ( ) [ ] ∑ = - = l j i i x z x z l MSE 1 2 1 1 (9)
The root mean squared standardized effect (RMSSE) is expressed as

( ) ( ) [ ] ∑ = - = l i i i x z x z RMSSE 1 2 2 1 l 1 ( 10
)
Where

( ) i x z 1
is standardized site true value and

( ) i x z 2
represents standardized site prediction value, l is the number of validation sites. When MSE is more close to 0 and RMSSE more approaches 1, the accuracy of prediction is regarded better.

The Study Area and Data Source

3.1

The study area

The study area is located in the east part of Chongqing between north latitude 28°09′and 32°12′and east longitudes 106°23′and 110°11′(Fig. 1).The landform of this area is dominated by hills, low mountains and medium mountains. Generally, it is undulating with slopes ranging from 0 to 84 °and altitudes within the range from 100 to 2750.92 m. Due to weak transportation and poor economy development, tobacco planting has been one of local dominant crops productions.

Data sources

Precipitation data were obtained from 34 meteorology stations of the Chongqing Meteorological Institute monitored monthly from 1977 to 2006, and 13 additional stations belonging to other organizations, irregularly dispersed throughout the whole region. From the observations, 10 sites randomly selected, were kept aside for validation. As precipitation of this region is strongly seasonal (Fig. 2.), and in this study we just model mean monthly precipitation for the least rainfall (January), the richest rainfall (July), as well as the whole year.

A DEM was constructed using ArcMAP Version 9.2 (ESRI).50×50m DEM data were derived from automated image matching of scanned panchromatic aerial photograph based on AUNDEM. Primary topology attributes including elevation, slope and aspect. Latitude and longitude were available by Binary ACCII interpolation.

Fig. 2. Mean monthly precipitations of the stations over past thirty years in study area 4 Results and Discussion

Exploratory data analysis

The Pearson correlation coefficients were calculated between precipitations and the independent topographic variables to determine the influence of different terrain elements on rainfalls. It was shown in Table 1.For precipitations within three different periods, they were all related to elevation and aspect. And precipitation generally increased with elevation, but aspect otherwise inversely. For longitude, only annual precipitation showed strong positive correlation and both precipitation in January and that in July rather showed negative correlation whereas annual precipitation is likely to be indirect. Except precipitation of July, the precipitations of other periods had no obvious links with slope.

Table 1. Bivariate correlations between the independent topographic variables and precipitation data within different periods Fig. 5. Spatial predictions for precipitations within different periods with regression kriging

Discussion

The prediction residuals and MSEs as well as RMMSEs for the precipitations of January, June and Annual were given in the Table 4 for each method of spatial prediction

In terms of prediction residuals, for precipitation of January, cokriging was the smallest, followed by the small grid (Spline interpolation), small grid (IDW interpolation), and last was the regression kriging. And cokriging could explain 58% of total variation. The MSEs Variation for different methods showed same trend. The RMSSE of being close to 1 was ranked by cokriging, small grid (Spline interpolation), small grid (IDW interpolation) and regression kriging. Fig. 3, Fig. 4 and Fig. 5 have showed the spatial prediction maps with different methods. Generally, the major patterns of variation were evident but the detail was different.

Table 4 showed that small grid (IDW interpolation) was the most accurate method of prediction for precipitation of June, which could explain the 72% of total variation, followed by small grid (Spline interpolation), cokriging. And the regression kriging was the worst in this spatial variability of January, June and Annual mean case. The less accurate predictions from regression kriging probably reflected regression model less good determined coefficient For annual precipitation, the prediction accuracy rank showed similar pattern with June but the least accuracy method was cokriging and the explained percentage of total variation by small grid (IDW interpolation) simulation was 61%.

Conclusion

Understanding the spatial variation of climatic factors is very important for crops planting planning and management. Increasing the accuracy of the spatial predictions of precipitation with the aid of available ancillary data is quiet environmental typically in complex terrain mountain areas, where topographic factors usually have influence on precipitation spatial pattern. This study predicted the spatial distributions of precipitations within different periods with three multivariate geostatistical methods including cokriging, small grid and regression kriging in Chongqing tobacco planting area. And the methods showed good advantage which enabled us to describe 58-72% of the precipitation. However, it should be noted that although ancillary data have been proved to contribute to improving prediction results, there is still no single optimal method for all regions. The coregionalization and the relations between the deterministic components of the variation still should be carefully examined before deciding on the most appropriate methods of prediction.

  There are non-bias conditions and the weights il λ which minimize the estimation variance of ^u Z for a point 0x are found by solving the kriging system for all V

  to give the predicted values of the target variable RK Z . in the author index.

Fig. 1 .

 1 Fig. 1. Location of the study area

Table 4 .

 4 Prediction accuracy at validation points TSS represents the total sum of residuals b SSR represents the sum of squares of residuals

	Item		Cokriging	Small Grid(Spline)	Small Grid( IDW)	Regression Kriging
		TSS a	10947	10947	10947	10947
	January	SSR b	4598	5255	5692	6568
		MSE	0.074	0.087	0.092	0.093
		RMSSE	0.891	0.851	0.871	0.832
		TSS a	961037	961037	961037	961037
	June	SSR b	451687	374804	269090	470908
		MSE	0.034	0.031	0.027	0.072
		RMSSE	0.911	0.952	0.973	0.897
		TSS a	32921134	32921134	32921134	32921134
	Annual	SSR b	17448201	15143722	12839242	16131356
		MSE	0.065	0.051	0.042	0.057
		RMSSE	0.872	0.897	0.925	0.887

a

JanuaryJuly

Acknowledgments

The authors would like to thank the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education of China (No.706049), Fundamental Research Funds for the Central Universities (XDJK2009C032) and Chongqing Science& Technology Commission Research Funds (CSTC, 2009BB1115) as well as Chongqing Tobacco Corporation for financially supporting this research.

Cokriging

Experimental cross-and auto-variograms were obtained by applying Eq. ( 4) using the longitude, altitude, elevation, slope and aspect at the observation sites for precipitations. It was shown as in Table 2. It was indicated that the two sets variograms of elevation, aspect for precipitation within different periods were all bounded and were fitted by Gaussian, Spherical and Exponential, Exponential, Exponential as well as Spherical respectively. The variograms of longitude for precipitation of January and June were both unbounded, whereas it was bounded for annual precipitation with the range set to 116.19 km. The variogram of slope was bounded for precipitation of July whereas it was unbounded for both June and Annual. Under the ArcGIS platform, altitude, elevation and aspect were associated as covariances for precipitation of January and altitude, elevation, slope as well as aspect were associated as covariances for precipitation of July. Longitude, elevation and aspect acted as covariances for the spatial prediction of annual precipitation. The whole prediction maps were shown in Fig. 3.

Small grid interpolation

Linear regressions of precipitations of January, July and Annual were done. The equations were depicted as follows:

Where 1 y , 2 y and 3 y represents precipitation of January, July and Annual respectively,

represents longitude, altitude, elevation, slope and aspect. The residuals of regression models calculations were interpolated by IDW and Spline respectively, and then summarized with regression predicted values. The whole spatial prediction maps were displayed in Fig. 4.

Regression Kriging

Regression modeling is the same with small grid interpolation, and an experimental variogram was computed on the residuals of precipitations within different periods from the regressions at each site. It was shown in Table 3. It was found that the highest determined coefficient (R 2 ) existed in Gaussian, Exponential and Gaussian model for precipitation of January, June, Annual respectively. The whole spatial prediction maps were displayed in Fig. 5.


Yu Zang

Zhongxiang Zhu

Zhenghe Song

Enrong Mao

Virtual Reality and the Application in Virtual Experiment for Agricultural Equipment

Keywords: Virtual reality (VR), Agricultural equipment, Virtual experiment, Test repetition, Real-time test

The meaning and characteristics of the virtual experiment and the significance of applying virtual prototype into agricultural equipment were analyzed in this paper. Then a kind of virtual experiments system platform on agricultural equipments was founded by using MultiGen Creator, Vega Prime software and VC++ programming language. According to the functions of the virtual experiment system, the structure and all components of the system were introduced. Furthermore, the methods of network design, synchronous driving, image edge-blending and geometric calibration, and the key technologies such as tractors dynamics modeling, experiment reappearance and real-time test of virtual experiment were discussed. Finally, based on three-dimension geometry model and Vega Prime, the digitalization of tractor testing ground and real-time performance simulation were realized, which provides a new research means and technical method for the tractor performance testing. The test results show that the platform could make the observers immersed among the virtual environment to experience testing process directly and intuitively, and could achieve the real-time interaction between users and environment. This study proves stable operation of the system with reliability and validity, its velocity can reach 30 frames/s and the delay time is 0.025s.

Introduction

Virtual reality is a kind of simulated environment which can create and experience the virtual world. It is simulation system as well as 3D dynamic scene fused multi-source information [START_REF] Zou Xiangjun | The Development and Prospects of Virtual Reality[END_REF]. Using three-dimensional computer graphics, interactive devices, and high-resolution display, a virtual world can be realized in which imaginary objects can be picked up as if they are in the physical world. Therefore, virtual environment, immersion, tactile feedback (a response to user input) and interaction are the most important points during experiencing the virtual reality. Indeed, the significance and advantages of rapid product development and cost saving have been recognized in recent years. For this, countries in the world are making a study of virtual reality and its applications, and many researchers have worked on developing various techniques while priority research areas were changed from technique at first to using VR at present and what VR can do for people at the same time. As a kind of practical technology, virtual reality is well-known and is currently investigated for practical use in various industrial fields such as computer graphics, CAD, CAM, CIM, robotics, medical/health care, architectural design, entertainment, education, multimedia, games and so on [2~5]. Many companies have launched commercial products which can be used for developing VR application systems. However, its application in virtual experiment for agricultural equipment is not widely used. The final objective of this study is to introduce VR technology and establish virtual experiment system for agricultural equipment based on VR.

2

Virtual experiment for agricultural equipment based on VR

What is virtual experiment

During virtual experiment, software is used to generate kinds of virtual environment instead of part or all of hardware in the computer system. In this condition, customers could complete all scheduled projects as if they are in the true environment, and the experimental results obtained from virtual experiment are close to real test results. As a matter of fact, there are several means of realization, such as computer simulation technology, virtual instrument technology and virtual reality technology [START_REF] Fachao | Virtual Test Technology and the Application on the Vehicle[END_REF]. Specifically, there are many advantages of virtual experiment, which can be roughly categorized into four areas: (1) overcoming danger and inconvenience during the real experiment; (2) avoiding destructive effect and property losses during using physical objects; (3) unlimited number of tests and repeatability of the results; (4) good interactivity between users and environment. Therefore, from the view of virtual prototype, as a typical VR application in design and manufacturing industry, virtual experiment could realize multi-mode virtual environment including audibility, visibility and sense of touch by VR technology with input/output device, while users could do experiments, operate machines interactively, and inspect or evaluate the product performance.

Significance of the application in virtual experiment for agricultural equipment

In recent years, new models of agricultural equipment are invented continuously, but the tractor performance tests are always the same, just by using a real vehicle and analyzing data collected in physical test site. As a result, the tests in this way not only pollute the environment, but also consume a large amount of manpower and resources. Further more, it is liable to injure testing personnel during operating, and the test period is long relatively. In particular, the affects on test results caused by non-objective factors cannot be avoided during tests, for example, the skilled level of operators.

Consequently, the significance of applying VR technology into agricultural equipment experiment is great, especially for large-scale agricultural machinery, because of its complex structure, long period of production design and high cost. With VR in test, designers could simulate the performance and modify the products, while customers could understand the properties and functions of the products directly. In brief, virtual experiment for agricultural equipment, which helps to organize the preparation work for production, plays an important role in safe reliability, shortening product-manufacturing period, enhancing product quality, reducing product cost and tractor innovative design.

3

The construction of virtual experiment system for agricultural equipment

System function

Actually, agricultural equipment virtual experiment is a kind of performance simulation on various working conditions in virtual test scene, which maps the simulation data of the theoretical model or practical data obtained from operation to the computer screen and exports the result as the form of graphics and moving points. By means of various interaction methods of VR, researchers could feel tractor performance in different conditions, and they experience personally as if doing the actual test. Therefore, the system functions are as follows:

(1) visualization of physical model and testing grounding environment;

(2) response to collision or display and extraction of the tractor driving attitude or geographic coordinates in real time;

(3) point of view switch and input control with interactive devices; (4) kinematics and dynamics analysis and calculation of tractor model, researchers could simulate traction, braking, and steering control performance according to the results;

(5) network communication interface so as to do real-time data transmission.

System structure

According to the testing requirements and operating characteristics of the tractors, virtual experiment for agricultural equipment makes simulation with virtual prototyping technique and follows the principles of openness, extensibility and repeatability. Users could control the tractor models in a virtual environment and do some experiments during virtual tests. On the basis of the characteristics of tractor virtual test system, the system is divided into several parts as follows (Fig. 1), every part transmits information through interface parameters.

System hardware composition

This system adopts PC-driven and Client/Server system mode, which consists of one server, six IPC, three-channel passive projection system, stereo glasses, steering wheel, joystick, spaceball and other devices, and all of them are connected to a LAN. The projectors are controlled by the IPC, and each of two projectors displays the same channel, but one shows for left eye and the other for right eye through the polarizers. Observers look at the scene which is made up of six pictures by wearing the stereo glasses, so as to restoration the images to stereo effect, which are divided into three parts.

Implementation of virtual experiment visual system

Virtual experiment environment modeling

Virtual experiment environment is created based on geometric model, which is computer-graphics-based. First of all, designers need to abstract the real scene so as to use polygons to construct the three-dimensional geometric model of the virtual landscape (including terrain, buildings, trees, etc.). Second, designers must finish the light and material of models in virtual environment. Third, it is necessary to set the control parameters and complete the texture mapping. Finally, researchers render the visual images in real-time by output devices in order to complete drawing the whole scene. Fig. 2 is the flow diagram by which the tractor virtual proving ground was constructed. In this system, several typical roads were set up in virtual proving ground, such as ring test track, alternating vibration road, farm road, driver's vision test road, washboard road and so on. All of the test roads are provided for tractor virtual experiment.

Fig. 2. Flowsheet of virtual environment based on geometric model

During modeling, visual effect and real-time performance should be given overall consideration. On one hand, the simple model will impact on the reality of the simulation, and on the other hand, complex environment will affect the system operation in real time. Therefore, by the premise of guarantee of system running, some critical technologies were used to set up the models as far as possible to meet the visual authenticity of scene model.

(1) Billboard: Billboard is to map a texture to a plane -a single polygon which rotates within the simulation, so that the image of the model always faces the eyepoint. When viewed from ordinary perspectives in a scene, such symmetrical models appear realistic enough, while using only a small amount of system resources so as to increase the running speed. The trees and signs were constructed on basis of the billboard technology in the system.

(2) Texture: Textures are bitmapped images that are mapped onto polygons. However, when the 2D texture is applied to a 3D model, the u and v coordinates of the texture image are mapped to the x, y, and z coordinates of the model. Creator provides seven kinds of texture mapping, such as three-point put tool, four-point put tool, surface project texture tool, spherical project texture, radial project texture tool, environment map texture tool and geo-put texture tool. With textures viewer will experience a photo-realistic appearance without increasing the polygon count. In this system, texture technology was carried through from beginning to end.

(3) Level of Detail: Levels of detail (LODs) are sets of models that represent the same object or terrain area with varying degrees of complexity. The real-time system selects one of the LODs to display, depending on the distance from the eyepoint to the LOD and the number of polygons the real-time system can process. If the eyepoint is far away from an object, the object is displayed as a low LOD, containing relatively few polygons. As the eyepoint moves towards the object, the real-time system replaces it with increasingly complex LODs. LODs play an important part in optimizing a visual scene for drawing, so it was used to render the terrain to improve operation velocity. The 3D scene in virtual experiment system is shown in Fig. 3.

System network design

The system selected Master/Slave mode, because several PC were used in internet to drive the virtual environment. The server is Master node and IPC is Slave node. Fig. 4 is the flow diagram of network system. This structure has the some features:

(1) Each of nodes has the same application program and data copy, the system only need to update the interactive information, control instructions and the scene change information in run-time.

(2) The Master node is responsible for receiving information, handling user interaction information, and transmitting threads, input (keyboard, mouse, joystick, etc.) and other user data to the slave applications.

Synchronous driven in real-time

Distributed rendering utilities (drUtil) of VP were used in this system. The principle is to receive input from the server and to send rendering information and time-stamped data packets through drUtil in real-time, so as to respond to the control and synchronize the master's scene graph with the slave's scene graph among three channels. Main code: // Set synchronous mark vuDistributed::setSyncEnable(vuDistributed::SYNC_LABEL_ PRE_SWAPBUFFERS, true ); vuDistributed::setSyncEnable(vuDistributed::SYNC_LABEL_ POST_SWAPBUFFERS, false ); // Synchronize ACF vuDistributed::setSyncEnable(vuDistributed::SYNC_LABEL_ ACF,true); // Synchronize the time vuDistributed::setSyncEnable(vuDistributed::SYNC_LABEL_ TIME,true); // Synchronize window message vuDistributed::setSyncEnable( vuDistributed::SYNC_LABEL _MESSAGES, true );

Correction and Fusion

Geometric distortion correction is one of the key technologies in virtual reality projection system. However, a kind of geometric correction algorithm based on viewpoint locations and texture mapping was proposed in this system, so that the image distortion correction for single projection screen and alignment correction for multi-projection screen could be completed. The principle is shown in Fig. 5. Furthermore, in order to solve uneven brightness caused by overlapping area between two projectors, the method of color edge fusion based on weight was used, that is, each pixel in bright band is given a preset weight, and the color component gray value of corresponding pixels will multiplied by default weight while rendering the image, thus the high light parts in overlapping area between two adjacent channels are processed to smooth brightness.

Fig. 5. Schematic diagram of image geometric calibration Design and development of system software

The system used Visual C++.net of Microsoft company as integrated develop environment. The virtual experiment system for agricultural equipment runs on the Multigen Creator and Vega Prime as modeling and visual simulation software, with OPENGL and other auxiliary software, and it fits for Windows XP operating systems.

Dynamic model of tractor

To realize the interactive control tractor in virtual experiment, the system established a tractor dynamic model of tractor for real-time simulation, including the whole vehicle model, engine model, transmission system model, brake system model, steering system model and tire model. In this model, driver, tractor and virtual environment constitute the closed-loop system, as shown in Fig. 6, in order to achieve steering, acceleration / deceleration, braking and other acts of simulated operation. As a result, the driver could control the tractor in real time and experience the control effects in a virtual environment. The original projector image

Correction of original image

The correct projection image after correction

Implementation of tractor virtual experiment

The system calls VP class function in VC++.net so as to achieve the management and calls of the scene, view control, steering control, and texture control during virtual experiments. For example, the researchers could finish changing the position or rotation of the tractor model by class function called vpTransform in VP, whose getTranslate () function and getRotate () function mainly return the position and orientation information of controlled objects or motion models. Otherwise, if the information of x, y, z coordinates, h (heading angle), p (pitch angle), and r (roll angle) are changed during interaction process, setTranslate () function and setRotate () function will be used to set the new position and orientation. Therefore, the parameters change of tractor motion could be realized in virtual environment. //Assign the data to tractor model after obtain the data from linked list m_tractor->setTranslate(r->cog_ster [START_REF] Zou Xiangjun | The Development and Prospects of Virtual Reality[END_REF],r->cog_ster [START_REF] Jianping | Application of Virtual Reality Technology in Urban 3D Geosciences Modeling[END_REF],r->cog_ster [START_REF] Fei | The Application of Virtual Reality in Driving Simulator[END_REF],false); m_tractor->setRotate(-r->cog_ster [START_REF] Yu | Design and Develop of Virtual Reality Technology in Program-Based Learning[END_REF],r->cog_ster [START_REF] Kaizhan | Virtual Reality Technology and Its Application in Agricultural Machinery Design[END_REF],r->cog_ster [START_REF] Fachao | Virtual Test Technology and the Application on the Vehicle[END_REF],false); ……

The system can reappear the tractor real experiment, such as braking performance, traction performance and steering performance. In addition, researchers can control playing speed and process depending on requirement, and display the speedacceleration -time curve. The system operation interface is shown as Fig. 7. Researchers test the virtual experiment system for agricultural equipment by carrying out a certain type of tractor virtual experiment. The simulation effect is shown as Fig. 8. There are three kinds of virtual scenes for selection, such as road test scene, field test scene and washboard road test scene. The current information, for instance, speed, acceleration and front wheel angle of the tractor, will be displayed on the interface dynamically. However, the experiment results will be stored in the database in order to process data and reappear the experiment. The experiment results show stable operation of the system with the reliability and validity and its velocity can reach 30 frames/s and the delay time is 0.025s.

Conclusions

In this study, virtual reality was applied into agricultural equipment by multi-channel immersive virtual reality technique and tractor kinetic theory was used as well based on traditional method of tractor performance testing in the system. It realized the three-dimensional display of the models and could drive the models in real-time to complete the experiment reappearance and real-time test of the tractor. The test results show that the virtual experiment for agricultural equipment, which achieves the real-time interaction between users and environment, could make the observers immersed among the virtual environment to experience testing process directly and intuitively. This research provides a new method to the tractor performance test, and this way plays an important role in safe reliability, shortening product-manufacturing period, enhancing product quality and production efficiency, avoiding the physical modelmaking, reducing product cost and tractor innovative design.

Fig. 1 .

 1 Fig. 1. Structural framework of virtual experiment system

Fig. 3 .

 3 Fig. 3. 3D scene model in virtual experiment system

Fig. 4 .

 4 Fig. 4. Flowsheet of network system

Fig. 6 .

 6 Fig. 6. Closed-loop of tractor dynamic model

Fig. 7 .

 7 Fig. 7. Operation interface of experiment reappearance 5.2.2. Real-time test. In the part of real-time test, the operator can drive the tractor by interactive manipulation by switching the view point and using steering wheel, joy stick, spaceball and other virtual devices so as to simulate the field operations. During driving interactively, the driving attitude and the information about location coordinate of the tractor are shown and extracted. Otherwise, the system can response to collision detection when the tractor is moving. Take the steering wheel turning as example and the concrete program codes are shown as follows: /* Judge input device type*/ if(m_nRealTestInputType==1) // Judge whether the input device is tractor drive test-bed m_strcDriveTracData=g_strcDriveTracData; // Transmit signal data /* Read the sensor signals from tractor drive test-bed and calibrate the data*/ x=(BYTE)(Read_lpBuffer[In88+2]); // Read the steering wheel sensor signal from memory address dFangxiang=360*x/240; // Convert the unit to degree if(dFangxiang>648)dFangxiang=648; else if(dFangxiang<-648)dFangxiang=-648; // Limit the range of signal from -648 to +648 dFangxiang=450*360*dFangxiang/240/648; //Limit the range of steering wheel from -450° to +450° g_strcDriveTracData.m_Fangxiang=dFangxiang; /* Find the node of steering wheel*/ vpObject* m_tractor; // Define the tractor as an object m_tractor=vpObject::find("myObject_Tractor"); // Get the object of tractor vsDOF *m_SteerDof; // Define the freedom of steering wheel vsNode *myNode; // Define a node

Fig. 8 .

 8 Fig. 8. Operation interface of virtual experiment

5.2.1. Test repetition.

  In the part of test repetition, tractor performance was analyzed by virtual prototype, and the results were read by linked list to realize the test repetition. The linked list data structure of tractor components is defined as follow:

	struct ReplayDataList
	{
	double cog_ster[37]; //cog_ster [37] represents the time, x,
	y, z coordinates and h, p, r angle of the centroid component
	ReplayDataList *next;
	ReplayDataList()
	{
	for(int i=0;i<37;i++)
	cog_ster[i]=0.0;
	next=NULL;
	}
	};

ACKNOWLEDGEMENTS

This research was supported by National Key Technology R&D Program during the 11th Five-Year Plan Period of China (No. 2006BAD11A01) and Special Fund for Agro-scientific Research in the Public Interest (No. 200903050-5-1).