Yuli Chen

Zhiyou Zhang

Yan Liu

Yan Zhu
email: yanzhu@njau.edu.cn

Hongxin Cao
email: caohongxin@hotmail.com

Models of Dry Matter Production and Yield Formation for the Protected Tomato

Keywords: protected tomato, dry matter production, yield formation, biomass, economic coefficient, models Foundation Information: sub-topic

【Objective】 In order to quantify the yield formation of protected tomato, 【Method】 the field experiments on varieties and fertilizer were conducted in 2009 and 2010, and cultivars: (B1) American mole 1 (early maturing), (B2) Chaoshijifanqiedawang (late maturing), and (B3) American 903 (medium maturing) were adopted; The models of dry matter production and yield formation for protected tomato were built by analyzing the relationships between yield and the number of fruit letting and the mean fruit weight, between yield and biomass and the economic coefficient at harvest, and between the mean fruit weight and economic coefficient and biomass of different varieties and fertilizer levels in accordance with the theory of yield formation.

Independent experiments data was used to validate the models. 【Result】 The results showed that root mean squared error (RMSE), mean absolute error (X de ), and the determined coefficient (R 2 ) between the simulated and measured values of dry matter production was 363.135kg/ha (n=63), 79.016kg/ha, and 0.900, respectively, and RMSE, X de , and R 2 between the simulated and measured values of yield based on yield components was 186.842g per plant (n=36), -1.069g per plant, and 0.854, respectively, and RMSE, X de , and R 2 between the simulated and measured values of yield based on economic coefficient was 137.302g per plant (n=27), 21.170g per plant, and 0.785, respectively.【Conclusion】 It indicated that the dry matter production and yield formation under different varieties and fertilizer levels for protected tomato could be well simulated by these models.

Introduction

Crop growth models are one of the powerful tools to support the optimum regulation for production environment and cultivation management of the protected crops, and tomato is one of the main protected crops [START_REF] Ni | Simulation of leaf area and dry matter production in greenhouse tomato[END_REF] . So, it has an important role in the digital regulation management of protected tomato production to build yield formation models. Nowadays, there have been many reports on the yield formation models of field crops, however, the research of protected crops is not more [START_REF] Zhang | Dynamic simulation on dry matter partitioning and yield formation in cotton[END_REF][START_REF] Zheng | Simulation models of the photosynthetic production and yield formation in maize[END_REF][START_REF] Wang | Study on the simulation model of the yield and grain quality formation of wheat[END_REF] , and the most reports about tomato yield were research on the relationships between yield formation and environmental factors in cultivation physiology and cultivation practice. For example, Song et al. [START_REF] Song | Tomato production laws and its correlation to the environmental factors in modern greenhouse[END_REF] researched the tomato yield formation rules and its correlation to the environmental factors in modern greenhouse, Liu et al. [START_REF] Liu | Effects of soil moisture regime on greenhouse tomato yield and its formation under drip irrigation[END_REF] studied effects of soil moisture stress on greenhouse tomato yield and its formation under drip irrigation, Chen [START_REF] Chen | Research on tomato fruit development and yield formation model under facility condition[END_REF] built the simulation models of relationship between individual plant yield and physiological development time. However, the models were with more empirical, less mechanistic. Ni et al. [START_REF] Ni | Simulation of greenhouse tomato dry matter partitioning and yield prediction[END_REF] established the models of greenhouse tomato dry matter partition and yield prediction based on relationships between partitioning coefficient and harvest index and product of radiation by quantity of heat, and tested by various varieties, substratum, and sites, which had high precision and less parameters. Yang [START_REF] Yang | Simulation of tomato fruit individual growth rule based on revised logistic model[END_REF] studied the influence of growth environments on tomato fruits yield using the functional structural plant models, GreenLab, based on source-sink relationship with greenhouse environment factors such as temperature, humidity, light intensity, and so on. These models were more suitable for protected greenhouse, but less suitable for the environments of plastic shed. The objective of this research was to simulate protected tomato yield formation under various varieties and fertilization levels based on yield components factors and economic index, build protected tomato dry matter production and yield formation simulation models in accordance with the principle of yield formation, and provide a theoretical basis for growth and yield prediction as well as cultivation management and environment regulation of protected tomato.

Materials and Methods

Materials

This study used 3 tomato cultivars representing wide variation in maturing characteristics, and they are: (B1) American mole 1 (early maturing, determinate growth type, good disease resistance, growth period 100~110days), (B2) Chaoshijifanqiedawang (late maturing, sub-determinate growth type, super large fruit type, growth period 109~119days), and (B3) American 903 (medium maturing, determinate growth type, strong growth, growth period 106~116days).

Methods

The experiments were conducted in plastic shed with 80m long, 9.8m width, and horse liver soil (the total nitrogen, 0.239 g/Kg; total phosphorus, 1.297 g/Kg; available phosphorus, 202 mg/Kg; and pH, 6.344 in pre-planting in soils) at Suoshi village in Nanjing from July to October of 2009 and from March to June of 2010. The experiments was a split plot design with three whole-plot treatments arranged in a randomized complete block design with three blocks and three sub-plot treatments. The whole-plot factors were the fertilizer levels: A1 (CK: 1/2 F N ), A2 (Normal: F N ), and A3 (High fertilizer: 3/2 F N ), and the sub-plot factors were varieties (B1, B2, and B3), with 3 replications and 27 plots (2.96×4.6m 2 ). F N was the normal fertilizer level: compound fertilizer (N:P:K=16%:16%:17%) 750 kg•ha -1 , in that 40 percent of this fertilizers was basal, 60 percent was top dressing applied in early fruit stage and maximum fruit number stage. The planting density was 3-4 plants/m 2 , and the other cultivation practices were the same as the conventional high yield field.

Data Acquisition

After planting, the representative samples were taken every 7d from seedling to flowering and every 14d during fruit period. Three representative plants selected in each treatment were separated into organs after determining fruits number, dried in 30 min. at 105℃, then at 80℃ until reaching a constant weight, measured using a 0.001 g electro-level, and leaf area was determined by method of dry weight. HOBO-H8 was placed in three different positions in the plastic shed to collect environment elements automatically every 10 s, including air temperature, relative humidity and dew point, the absolute humidity, and light intensity, etc. In that average values every 15 minutes were recorded.

At the main growth period of tomato, ECA-PB0402 was used in measuring photosynthetic rate (including CO 2 concentrations, relative humidity (RH), canopy temperature (TC), leaf temperature (TL), net photosynthetic rate (Pn), and photosynthetically available radiation (PAR), etc.) of the top three leaves on plants tagged in each treatment around midday. According to the data, light response curve was made, and the max photosynthetic rate value (Pmax) was confirmed.

Data treatments

In this study, Excel.2007and SigmaPlot V 10.0 were used to analysis experimental data. The experiment data in 2009 were applied to model establishment and parameter determination, and the experiment data in 2010 were applied to model verification.

Model verification

Simulation values were calculated using Visual C++6.0, and model precision was verified using root mean squared error (RMSE), mean absolute error (X de ), the determined coefficient (R 2 ), and 1:1 plotting between measured values and simulated values. If X de and RMSE were smaller and R 2 was larger, the simulated values were better agree with measured values, i.e. the deviation between simulated values and measured values was smaller, and simulation results of model were more accurate and reliable. The calculation formula of RMSE and X de can be expressed as follows:

where OBS i is measured values, SIM i is simulated values, de is absolute error, and n is sample numbers.

Results

Model Description

Dry matter production simulation of protected tomato

The calculation of leaf photosynthetic rate

The leaf photosynthetic rate was expressed by negative exponential model [START_REF] Cao | Crop system simulation and intelligent management[END_REF][START_REF] Spitters | A simple and universal crop growth simulator: SUCROS87[END_REF][START_REF] Nederhoff | A dynamic simulation model for greenhouse cucumber (Cucumis sativus L.): validation of the submodel for crop photosynthesis[END_REF][START_REF] Goudriaan | Modelling protential crop growth processes: textbook with excercises[END_REF] :

(1

)
where Pg is leaf photosynthetic rate in kg CO

2 •ha -1 •h -1 , Pmax is single leaf maximum photosynthetic rate in kg CO 2 •ha -1 •h -1 ,
with the ranges from 20 to 50 kg CO 2 •ha -1 •h -1 at the weak light and usual carbon dioxide concentration in greenhouse, and it was 37 kg CO 2 •ha -1 •h -1 in accordance with the observation data. ξ is the initial slope of photosynthesis-light responsive curve, called initial light utilized efficiency

in kg CO 2 •ha -1 •h -1 /J•m -2 •s -1 ,
in other words, at the early stage of leaf received light, the quantity of carbon dioxide fixed by unit area leaf in ha when it absorbed 1J•m -2 •s -1 PAR at unit time in h, and it was always regarded as a constant under the weak light of greenhouse [START_REF] Cao | Crop system simulation and intelligent management[END_REF][START_REF] Schapendonk | Fruit growth of cucumber in relation to assimilate supply and sink activity[END_REF][START_REF] Challa | Ep Heuvelink: Photosynthesis driven crop growth models for greenhouse cultivation: advances and bottle-necks[END_REF] . ξ equals to 0.40 kg [START_REF] Goudriaan | A simple and fast numerical method for the computation of daily totals of crop photosynthesis[END_REF] , and PAR is photosynthetically active radiation in this paper in J•m -2 •s -1 .

CO 2 •ha -1 •h -1 /J•m -2 •s -1

The calculation of canopy photosynthesis

Canopy photosynthesis is the total photosynthesis of all plant leaves on unit area. According to the research of Goudriaan [START_REF] Goudriaan | Modelling protential crop growth processes: textbook with excercises[END_REF][START_REF] Spitters | Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis. PartⅡ: Calculation of canopy photosynthesis[END_REF] , Gauss Integral was applied to compute the canopy photosynthesis rate in this paper, and it can be calculated as follows:

(2)

(3) (4) (5) (6)
where LGUSS i is canopy depth of gauss layer, DIS i is distance coefficient of gauss integral (table 1), LAI is leaf area index, i is layer number of canopy layers, L i is the quantity of PAR of arriving the i th layer, k is the extinction coefficient of canopy (it equals to 0.8 [START_REF] Goudriaan | A simple and fast numerical method for the computation of daily totals of crop photosynthesis[END_REF] in this paper), Pg i is the instantaneous photosynthesis rate of the i th layer in kg CO 2 •ha -1 •h -1 , Pg t is the instantaneous photosynthesis rate of all canopy at the time of t in kg CO 2 •ha -1 •h -1 , WT i is weight of gauss integral (table 1), DTGA is the total photosynthetic amount of one day in kgCO 2 •ha -1 •d -1 , and DL is day length in h. Table 1. The Gaussian weight and distances for the method of 3 points [START_REF] Goudriaan | A simple and fast numerical method for the computation of daily totals of crop photosynthesis[END_REF] i 1 2 3 DIS i 0.1127 0.5000 0.8873 WT i 0.2778 0.4444 0.2778

The simulation of protected tomato leaf area index (LAI)

The protected tomato LAI continuously increased with the adding of biomass in accordance with the data in 2009, and it's changes like as power function (Fig. 1).

Fig.1. The relationship between leaf area index and biomass for different varieties

The figure 1 showed: LAI of B1, B2 and B3 continuously increased as a power function with the raising of per plant biomass, and during growth and development of B2 (late maturing) and B3

(medium maturing), the changes in LAI were almost the same, B2 was appreciably higher than B3, and the two obviously lower than B1(early maturing). With the further raising of biomass, LAI of three protected tomato varieties also tended to stable.

According to the relationship between LAI and per plant biomass of protected tomato, the changes in LAI with the per plant biomass of different protected tomato varieties can be expressed as follow:

(

) 7
where a 1 and b 1 are parameters, and DW is total dry matter weight of per plant. All parameters and its statistical test are showed in table 2. Note: *P<0.05 and **P<0.01, the follows were as the same.

The calculation of respiration

Respiration includes maintenance respiration and growth respiration generally. The former is the energy needed by living organism maintaining its normal biochemical and physiological process, and it can be computed by formula (8) in accordance with the research of Spitters et al. [START_REF] Spitters | Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis. PartⅡ: Calculation of canopy photosynthesis[END_REF] . The later is the energy needed by organic matter synthesis, plant growth as well as metabolism consumption in plant, in other words, it is partial photosynthesis consumed in the process of carbon dioxide translating into CH 2 O, and it is considered at the formula (9) for calculating dry matter increment.

(

) 8
where Rm is consumption of plants maintenance respiration in kg CH 2 O•ha -1 •d -1 , Rm (T 25 ) is the maintenance respiration coefficient at 25℃ (it equals to 0.015 kg CH 2 O•kg -1 DM•d -1 [START_REF] Spitters | Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis. PartⅡ: Calculation of canopy photosynthesis[END_REF] in this paper), W is the total dry matter weight in kg DM•ha -1 , and T is daily average temperature in ℃.

The calculation of dry matter production

Dry matter increment can be expressed as follow:

where ΔW is dry matter increment in kg DM•ha -1 •d -1 , ASRQ is the conversion coefficient which is from CH 2 O to dry matter (it equals to 1.43 kg CH 2 O•kg -1 DM [START_REF] Spitters | Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis. PartⅡ: Calculation of canopy photosynthesis[END_REF] in this paper), 30/44 is the molecular weight conversion coefficient which is from CO 2 to CH 2 O, DTGA is daily total photosynthate in kg CO 2 •ha -1 •d -1 , F(N) is nitrogen influencing factor, and it can be computed as follows [START_REF] Gao | Foundation of agricultural modeling science. Tianma book limited company[END_REF][START_REF] Lian | The research of dynamic relationship between tomato yield formation and meteorological element in plastic shed[END_REF] : The total biomass on any day can be computed by initial dry matter and daily dry matter increment, ΔW , and the formula was follow: (13) where Biomass i+1 is the total dry matter of the (i+1) th day in kg DM•ha -1 , Biomass i is the total dry matter of the i th day in kg DM•ha -1 .

(10) (11)

Yield formation models for protected tomato

The model of yield formation for protected tomato based on the method of yield component

The yield formation of protected tomato can be determined by per plant fruit number and mean fruit weight. Therefore, the model of yield formation for protected tomato can be expressed as follow:

(

) 14
where Y is per plant yield, FN is per plant fruit number, and MFW is mean fruit weight.

(1) The model of per plant fruit number for protected tomato Individual plant fruit number (FN) is the result of balance between per plant potential fruit number (PFN) and per plant fruit abscission number (DFN). Therefore, it can be computed as follow:

(

) 15
where PFN can be estimated by per plant flower number (FLN) and the ratio of FN to total flower number per plant (PFLN), and the formula is as follow:

where FLN can be estimated by per plant bud number (BN) and the ratio of PFLN to total bud number per plant (PBN), PFLN can be computed by FN and per plant maximum flower number (FLNMAX), and FLNMAX is a variety parameter. Therefore, FLN and PFLN can be computed respectively as follows:

(17) (18) where BN can be decided by varieties and environment factors, and PBN can be computed as follow:

(

) 19
where BNMAX is per plant maximum buds number, can be regarded as a variety parameter.

It has been analyzed in the other paper because of its complexity.

(2) The model of mean fruit weight for protected tomato

The protected tomato mean fruit weight continuously increased with the raising of biomass in accordance with the data in 2009, and it's increasing was the same as a power function (Fig. 2).

Fig.2. The relationship between mean fruit weight and biomass for different varieties

The figure 2 showed: when per plant biomass of B1, B2, and B3 all achieved about 14g, it started to set fruit, and changes in mean fruit weight of three varieties with per plant biomass were basically similar, and the same as a power function. However, the growth rate and extreme value were different obviously, all in all, medium maturing B3>late maturing B2>early maturing B1, the maximum mean fruit weight of B1 and B2 were similar (about 200g), and all higher than B3 (about 160g).

According to the relationship between mean fruit weight and per plant biomass of protected tomato, the changes in mean fruit weight with the per plant biomass of different protected tomato varieties can be expressed as follow:

(

) 20
where DW is dry weight in per plant, it is computed section 3.1.2.2, a 2 and b 2 are all parameters. All parameters and its statistical test are showed in table 3.

The model of yield formation for protected tomato based on economic coefficient method

Economic coefficient was a vital standard to measure crop yield, and tomato yield simulation model based on economic coefficient method was expressed as follow:

(

) 21
where Y is yield per plant, DW is biomass per plant and EC represents economic coefficient per plant at harvest.

(1) The per plant biomass model for protected tomato

The formation of biomass per plant was the joint action of photosynthesis and respiration of protected tomato. And its model could be expressed as follow: (22) where DB i+1 and DB i were total dry weight per plant in the (i+1) th day and the i th day, respectively, Δ DW is increment of dry weight per plant. In that the harvest date of protected tomato in 2010 field trials was from 2 to 6 July.

(2) The calculation of per plant economic coefficient for protected tomato Economic coefficient per plant is an important index for crop production. It can be calculated as follow:

(23

)
where EC is economic coefficient per plant, Y is mean yield per plant and DW is biomass per plant at harvest. Economic coefficient per plant of treatments showed in table 4. According to table 4, B3 had a higher economic coefficient per plant than B1 and B2 under different fertilizer levels. As the raising of fertilizer rate, the highest economic coefficients of B1 and B2 can be gained at medium fertilizer levels, while B3 was at high fertilizer level.

Model verification

The model verification of population dry matter production for protected tomato

The model verification of population dry matter production for protected tomato

The population dry matter production model was verified by independent data in 2010 (Fig. 3). The figure 3 showed: RMSE, X de , and R 2 of between the measured and simulated values for protected tomato population dry matter were 363.135kg/ha (n=63), 79.016kg/ha, and 0.900, respectively. The correlation coefficients (r) was 0.949 (r 0.01 (61) =0.322) with 0.01 significant level. Therefore, the measured values agree well with the simulated values.

The model verification of LAI for protected tomato

The leaf area index model was verified by independent data in 2010 (Fig. 4). The figure 4 The mean fruit weight model was verified by independent data in 2010 (Fig. 6). The figure 6 showed: RMSE, X de , and R 2 of between the measured and simulated values for mean fruit weight of different protected tomato B1, B2, and B3 were 10.308g (n=12), 3.806g, and 0.768; 9.434g (n=12), 0.625g, and 0.932; 8.402g (n=12), 0.524, and 0.819, respectively. The correlation coefficients (r) were 0.877 (r 0.01 (10) =0.517), 0.965 (r 0.01 (10) =0.517), and 0.905 (r 0.01 (10) =0.517) with 0.01 significant level, respectively. Therefore, it had better consistency between the measured and the simulated values.

The model verification of yield formation for protected tomato based on the method of economic coefficient

The yield formation model based on the method of economic coefficient was verified by independent data in 2010 (Fig. 7). The figure 7 showed: RMSE, X de , and R 2 of between the measured and simulated values for protected tomato yield formation were 137.302g per plant (n=27), 21.170g per plant, and 0.785, respectively. The correlation coefficients (r) was 0.924 (r 0.01 (25) =0.487) with 0.01 significant level. Therefore, it had better consistency between the measured and the simulated values.

Discussion

The yield of two kind of yield formation models were all theoretical yield in this paper, however, actual yield and theoretical yield had a certain difference because of the effects of environments factors such as light, temperature as well as water, and so on and the quality factors such as malformed fruit, disease fruit, pest fruit, cracked fruit as well as minimal fruit, and so on, and the actual yield was the sixty percent of theoretical yield. In addition, the actual LAI of protected tomato was smaller than simulated LAI because of the influence of planting density and artificial pruning, but the results of simulation were good. The yield formation models based on yield component factors and economic coefficient were built by analyzed the influence of varieties and fertilizer on yield formation of protected tomato.

Compared with the results reported, it had a better mechanism, and simulated results. Moreover, the structure of plastic shed, genetic effect factors of tomato as well as environmental factors all affected the yield formation certainly. Lian et al. [START_REF] Fadhl | Heterosis and inheritance of productive characters in tomato[END_REF] researched the dynamic relationship between tomato yield formation and meteorological element in plastic shed, Fadhl [START_REF] Han | Prediction model for harvest date and yield of Brassica chinensis L.in plastic tunnels[END_REF] studied the heterosis of related traits and genetic effect of tomato yield, Han et al. [START_REF] Diao | Model for simulation of growth and yield of greenhouse sweet pepper[END_REF] built the harvest date and yield prediction models of Brassica L.in planted in plastic shed covered with insect-proof screens. In addition, Diao et al. [23] simulated the yield formation of greenhouse sweet pepper using harvest index (it is a ratio of harvested fruit dry matter weight to total fruit dry matter weight). Further research should comprehensively considerate these factors, specially introduce temperature, light, soil water and so on in population dry matter production, establish the quantitative relationship between them and yield component factors, increase the feasibility and precision of models, and supply theoretical basis and technical support for cultivation management of protected tomato. In this paper, the phosphorus and potassium influencing factors were not studied because of the restricted of experiment conditions. Besides, the models established in this paper dealt with three varieties, and had a suitable environment, the results of these models was good, but the corresponding conditions would be used to verify and revise it if these models were applied to any other conditions.

Conclusions

The yield formation models based on yield component factors and economic coefficient were built respectively by analyzing the relationships among yield formation factors, varieties, and biomass as well as among yield and per plant biomass at harvest and per plant economic coefficient in accordance with rules of yield formation, and using the field experiment data of protected tomato in 2009 and 2010, and it included mean fruit weight model, per plant economic coefficient model, LAI model, dry matter production model, and so on. These models were verified using independent experiment data, the statistical analysis (RMSE, X de and R 2 ) and 1:1 diagram all showed: the models could simulate the yield formation of different varieties and fertilizer levels well, and the X de of yield formation based on economic coefficient larger than that of based on yield formation factors, while the RMSE and R 2 were all smaller than the later. Conclusively, the simulation results of yield formation model based on yield component factors was better than that of based on economic coefficient.

( 12 )

 12 where SN and Nc are the 0-30cm topsoil total nitrogen storage in g•m -2 and content in g•kg-1 , respectively; CKN is the nitrogen rate of CK in kg•ha -1 (it equals to 60 kg•hm -2 in this paper), RFN is nitrogen rate in kg•ha -1 (it includes high nitrogen level and normal nitrogen level), the compound fertilizer amount of high fertilizer, CK, and the normal levels is 1125 kg•ha -1 , 375 kg•ha -1 , and 750 kg•ha -1 , respectively (nitrogen rate is 180 kg•ha -1 , 60 kg•ha -1 , and 120 kg•ha -1 , respectively), TNP is nitrogen requirement of high yield level (it equals to 189.321 kg•ha -1 in this paper). γ is the bulk density (1.3 g•cm -3 ), H is the thickness (30 cm), and δ2mm is the <2mm fraction (%) of soil. CURN is nitrogen use efficiency (%). NBY and NCK is the nitrogen absorbed by tomato plant at the RFN and CK levels in kg•ha -1 , respectively.

Fig. 3 .

 3 Fig.3. The 1:1 plotting comparison between observed and simulated total dry weight for protected tomato population

  showed: RMSE, X de , and R 2 of between the measured and simulated values for LAI of different protected tomato B1, B2, and B3 were 0.144 (n=24), 0.051, and 0.868; 0.109 (n=24), 0.048, and 0.912; 0.137 (n=24), 0.051, and 0.894, respectively. The correlation coefficients (r) were 0.932 (r 0.01 (22) =0.517), 0.955 (r 0.01 (22) =0.517), and 0.946 (r 0.01 (22) =0.517) with 0.01 significant level, respectively. Therefore, the measured values agree well with the simulated values.

Fig. 4 . 3 . 2 . 2 ( 1 )

 43221 Fig.4. The 1:1 plotting comparison between observed and simulated LAI values

Fig. 5 .

 5 Fig.5. The 1:1 plotting comparison between observed and simulated Y values

Fig. 6 .

 6 Fig.6. The 1:1 plotting comparison between observed and simulated MFW values

Fig. 7 .

 7 Fig.7. The 1:1 plotting comparison between observed and simulated Y values

Table 2 .

 2 Analysis of variance for LAI models and its coefficient test

	Varieties	Correlation	F	a1	b1
		coefficient
	B1	0.984 **	439.226 **	0.067 **	0.630 **
	B2	0.971 **	262.439 **	0.083 **	0.554 **
	B3	0.971 **	234.101 **	0.058 **	0.650 **

Table 3 .

 3 Analysis of variance for the mean fruit weight models and its coefficient test

	Varieties	Correlation	F	a 2	b 2
		coefficient
	B1	0.952 **	378.588 **	1.321×10 -3*	2.579 **
	B2	0.908 **	779.857 **	3.785×10 -4*	2.903 **
	B3	0.923 **	53.586 **	1.102×10 -3*	2.649 **

Table 4 .

 4 The economic coefficient of different treatments in 2009

	Treatments	A1B1	A1B2	A1B3	A2B1	A2B2	A2B3	A3B1	A3B2	A3B3
	Economic	12.840	11.576	18.424	15.710	12.366	16.879	12.992	10.780	19.671
	coefficient


Hong Zhou

Bingwu Liu

Pingping Dong

The Technology System Framework of the Internet of Things and its Application Research in Agriculture

Keywords: the internet of things, intelligent agriculture, information collection, ZigBee, Sensor

Directing at the current development condition of the internet of things and based on the available technology analysis of the internet of things, the paper makes analysis and research on the internet of things in terms of technological levels and systems. Started from three aspects, respectively, data collection, network service, date fusion and computation, the paper analyzed the technologies like RFID, ZigBee, sensors, Cloud Computing and so on, based on which the paper further brought forth the technological system framework of the internet of things. By combining the technology system framework, the paper carried out the application research in aspect of intelligent agriculture and raised the system construction of production monitoring system of agriculture standardization which is based on the internet of things. Moreover, the paper carried analysis and research works on the sensor nodes of the system, made analysis and discussion on the various technologies involved.

Introduction

Internet of Things refers to a network allows a series of intelligent activities like identification, positioning, tracking, monitoring and management by linking devices like RFID, Smart Sense, GPS (Global Positioning System) and 2-D Code, etc. in objects to wireless network via interfaces to endow objects with intelligence, therefore realize the communication and dialogue between human and objects as well as objects and objects.

IOT and relevant technologies have already been highly values by enterprises and in academic circles both inside and outside the country. Moreover, series of research and exploring works have been launched. However, both inside and outside the country, IOT's research and development works still remain in a preliminary condition and their architecture and system model haven't been developed. There are certain blindness in the research and development of the IOT technology. In terms of the proper definition, the fundamental principles, the architecture and the system Supported by Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR200906210), Beijing Social Science Project(09BAJG258), Beijing-funded project to develop talents(PYZZ090420001451) model of IOT, there are plenty of questions to be considered and discussed. Based on the current IOT technology analysis, by analyzing and discussing the technological levels and systems of IOT, the paper is going to start the research on the architecture and the framework of IOT.

Started from the intelligent transportation, logistic scheduling and tracing and base station monitoring, IOT extends its application domain to public oriented personal medical treatment, intelligent home furnishing and so on, and its applications can be found in all walks of life. However, being in the preliminary stage and asking for innovations, IOT hasn't been popularized in large scales. The IOT industry covers sensors, transmission tunnels, computation and process, industrial application and so on, which involves technologies like RFID, sensors, wireless network transmission, computation with high-performance, intelligent control and so on. The paper is going to unfold the introduction by combing the application of IOT in the monitoring system for the agricultural standardized production.

Key Technologies of IOT

Internet of Things is an open architecture with a variety of technical supports, including radio frequency identification technology, middleware technology, logistics management, e-commerce technology and so on. It involves three key technologies:

(1) sensing technology--obtain the object information simultaneously and accurately through the use of RFID, sensor, two-dimensional product code and other equipments and technologies; (2) information transfer technology--deliver real-time and accurate object information by using the deep integration between a variety of telecommunications networks and the Internet; (3) intelligent processing technology-analyze and sort out large amounts of information and data as well as implement intelligent control of the goods through cloud computing and fuzzy intelligent recognition technology. Considering the content of IOT, in the following part, we are mainly focused on analyzing and researching the information collection technology (ID identification, location information), network service technology (wireless sensor networks, core network element and so on), data merger and computation technology (Cloud computing, mass data merger) and so on.

Technology of Information Collection

The gathering technology of IOT is sensing technology which is the foundation of IOT. Currently, information collection mainly depends on electronic tags and sensors, etc. In sensing technology, the electronic tag is used to standardize the identification of the collected information; and the data acquisition and device control are realized through radio frequency identification and two-dimensional code readers, etc. In data collection and processing stages, it mainly uses various types of sensor technologies, RFID, two-dimensional code and other information gathering techniques to collect data and then receive the control signals from upper receiver, respond, and complete the corresponding actions to process information.

RFID is a non-contact automatic identification technology and it identifies targets automatically and collects relevant data by radio-frequency signal. By affixing electronic labels to the objects, RFID realizes high efficient and flexible management and it becomes the most critical technology for IOT. Typical RFID system is comprised by electronic labels, readers and information process system. Without the manual involvement, the identification process is capable of all kinds of severe environment. RFID is able to identify high-speed traveling object and multiple labels at the same time, with great convenience and efficiency in operation. By absorbing technologies like internet, communication and so on, RFID realizes the object tracing and the information sharing all around the world.

Currently, in the field of RFID, most of researches are based on label cost, uniform technology standard, key technology and so on. Besides RFID, the sensor technology is another frequently applied technology in the ITO. Sensor refers to devices and equipment which are able to perceive the determined test object and the transform it into useful signals in accordance with certain rules. In most cases, the sensor is comprised by sensitive element and conversion element, which are able to detect, perceive external signals, physical and chemical and physical conditions. Meanwhile, the sensor technology is the premise of the sensor node technology.

Technology of Network communication

In communication technologies of IOT, there are a variety of technologies to choose. They are mainly divided into two types, wired technology (e.g. DSL, PON, etc.) and wireless technology (e.g. CDMA, GPRS, IEEE 802.11a/b/g and WLAN, etc.), which are relatively mature. Referring to the implementation of IOT, the wireless sensor network technology is particularly important.

WSN is a network system which integrates distributed data collection, transmission and process technology and it is widely concerned for its network method and installation method with low cost microminiaturization, feasibility, reliability and flexibility and its capacity for moving object. By sensor node and networks spread in different place, IOT is able to perceive the world. The network construction of WSN can be classified into physical layer, data link layer, internet layer, transmission layer and application layer. The basic composition of sensor's network node includes sense unit, process unit, communication unit and energy unit.

In the technologies of IOT's network and communication, ZigBee is not only a technology between wireless labeling technology and Bluetooth, but also a bidirectional wireless communication technology with close range, low complexity, low power consumption, low cost. ZigBee is applicable in the domain of automatic control and long-range control. ZigBee, as a transmission standard, can be applied in multiple frequencies and working segments and is widely applied for its low power consumption and low cost. In domains of safety system, sensor network, industrial monitoring and other IOT areas, ZigBee has great development space. Moreover, by integrating IPv6 technology into the IOT, we can realize the end to end communication with the current network equipment and improve the retransmission efficiency, which further enhance the safety of information transmission.

Technology of data fusion and computing

IOT is comprised by numerous nodes in the sensor network. In the process of information perception, it is not feasible to adopt the method of single nodes transmitting the date to the sink independently. Because there is enormous redundant information, they would waste much communication width and precious energy resource. Besides, they would lower the efficiency of information collection and affect the timeliness of information collection. For this reason, we need to adopt data merger and intelligent technology to deal with the problem, during which distributed data merger and cloud computing are involved.

Cloud computing is one of distributed computing. By automatically dissembling the huge computing and processing processors into countless subprograms, it handled these subprograms to the huge system composed by multiple servers for searching, computing and analysis, after which it transfers the procession result to the user. Via this technology, web service providers is able to process millions and millions, even billions and millions of information, and thus be capable of net service as powerful as "super computer". By taking advantage of the low cost, super powerful procession ability and storage ability of Cloud Computing Center, and the everywhere information collection of IOT, we combined them together, and enable all kinds of objects to exchange and communicate, thus realizing the intelligent identification. By means of visualization technologies, single server can support multiple virtual machines in running multiple operating systems, and therefore improving the use ratio of servers. Cloud Computing is a service which provides those dynamic, scalable and visualized computing resources by internet.

Visualization, elastic range extension, distributed storage, distributed computing and multiple lessees are critical technologies of Cloud Computing. Distributed storage aims at the target of satisfying the storage requirement which can't be satisfied by single server by utilizing the storage resources of multiple servers inside the cloud environment, and its characteristic is that storage resource is able to be expressed abstractly and be managed in uniform. Moreover, it is capable of ensuring the security and reliability and other requirements generated in the data read-write and operation.

Processor Architecture of IOT System

Based on the research and analysis on IOT's critical technologies, in the general technological system architecture of IOT, we shall ensure the size of IOT (only with certain size can we enable the object intelligence to play their role), mobility (we need to ensure the object can realize instant communication under moving condition even with high speed ) and security (as for those which involve national security, trade secret and personal privacy, core technology with proprietary intellectual property rights are necessary) The application of IOT is still relatively fragmented in China, and there is no large-scale, systematic development trend, so a scalable and open architecture of IOT should be established in the industrial chain technology as well as application and formation system in ideal aspects in order to break through the barriers of application in large-scale and promote the IOT industry's transition from the start-up period to the growth period. The overall technical architecture of IOT includes data acquisition layer, information exchange layer and application layer. The overall technical architecture of IOT is shown in Figure 1.

Data collection layer consists of two-dimensional code tags and readers, RFID tags and readers, cameras, sensors, GPS, sensor gateways, sensor networks and other equipment and technologies. In this layer, it mainly solves the issues like data collection and object identification, etc. It is composed by the various types of acquisition and control modules, and the main function is to complete IOT's information perception, data collection and the control of facilities. It is an important foundation of IOT.

Fig. 1. The technical architecture of IOT

The layer of information exchange is based on the network of IOT and communication technologies, such as mobile communication network and the Internet, which is a converged network formed by a variety of communication networks and the Internet. It includes information center, management center of IOT, expert systems and cloud computing platform, which are responsible for the massive part of Intelligent Information Processing. Therefore, the network layer not only requires the ability to operate the network, but also the ability to enhance operational efficiency of information. It is the infrastructure to make the IOT become a universal service.

Application layer refers to solutions of integrating IOT technologies with industrial technical expertise to achieve a wide range of application with intelligent technologies. Through the application layer, IOT ultimately realizes deep integration with information technology and industrial professional technologies. The application layer lies on the top which is the ultimate goal of IOT applications. It consists of a variety of servers and its main functions include the collection, transformation and analysis of the gathered data as well as the adaptation and triggers of things for users. The key issue in this layer is socialization of information's sharing and information security. In the process of agricultural production, the most critical part is the true time data collection in terms of temperature, moisture, carbon dioxide content, and soil temperature and soil moisture content. By making use of the IOT platform and GPRS/TD, by means of SMS, WEB, WAP and other methods, we can make the users dealing with agricultural production acquire these real-time information. Monitoring System for Agricultural Standardized Production based on IOT aims at the target of making information collection towards several indexes in crop growing and carrying out systemic monitoring towards the plantation area, crop pattern, crop growing, the breaking out and development of agricultural damages, crop output and so on.

Business

Fig.2. the system architecture of Monitoring system

The monitoring system is composed by the wireless sensor monitoring network and distant monitoring information system. ZigBee sensor nodes set in the plastics tents or greenhouse collect the critical index in crop growing like air temperature, moisture, soil temperature, moisture, illumination intensity, CO2 concentration and so on. Moreover, by means of ZigBee short range wireless communication technologies, we can realize the date transmission and transmit all the node date to the base state where we can carry out TCP/IP packaging on the data we've received, after which we can send them to the remote control center by means of GORS. The control center receives data, analyze and express them. Moreover, they can make parameter setting. Once environmental parameters go beyond the set values, it is possible to make acousto-optic alarming or message alarming. By making use of cell phone or remote computer, researchers can make real-time control on the environmental conditions and information in the crop growing spot. The architecture of monitoring system is shown by the Picture 2.

Monitoring system is composed by three parts: (2)gate way. Located in the edge of sensor network. Realizing the interconnection and communication between the sensor network and internet. In the gateway, we can realize the conversion from the sensor network protocol to the internet protocol. (3) Monitoring and management center of agriculture environment (user), being responsible for the information storage, procession, evaluation and so on. One management center usually is capable of managing multiple monitoring areas. Remote control and PDA users are able to visit the data of the environmental monitoring center by means of internet and they can make real-time inquiry via the center.

Fig.3. Structural framework figure of the sensor nodes

Sensor network is basically comprised by sensor board which is set with sensors of air temperature and moisture, soil moisture and temperature, soil PH value, light intensity and CO2 concentration. These sensors can be selected and set in the sensor board. The sensor board makes adjustment on the input signals of sensors which is collected by the terminal node of wireless sensor network. The general architecture of sensor node is shown by picture 3. Temperature and moisture sensors are more and more widely applied in the areas of industrial and agricultural production, whether, environment protection and so on. We've selected the SHT11, a digital moisture and temperature sensor with high-accuracy, which is produced by Sensirion in Switzerland. The sensor is a new typed and digital relative moisture and temperature sensor with two passes of serial interface. The sensor has monolithic calibration and can be used to measure relative moisture, temperature, dew point and other parameter, with characteristics of digital output, adjustment free, standardization free, peripheral circuit free, good exchanges. As for the air moisture and temperature sensor, we've adopted SHT11, digital moisture and temperature sensor with high accuracy, which is produced by SENSIRION in Switzerland. As for the soil temperature moisture sensor, we've adopted the LS-100, soil moisture sensor, which is produced by domestic companies. As for the CO2 concentration sensor, we've adopted KCD-AN, produced by Korean companies.

Data procession module is comprised by micro processor, data storage circuit and embedded operation system and it is the core component of sensor node. Moreover, it is responsible for data's storage and procession, scheduling system tasks, carrying out the communication protocols and so on. Wireless communication module is responsible for the communication among the sensor nodes and between the nodes and the station, information exchange and control among sensor nodes and the data collected by the receiving and sending station. When design the system, we adopted CC2420 ship which supports ZigBee protocols, with a frequency band of 2.4 GH z.

Conclusion

The paper has analyzed the current situation of IOT and made analytical research on the major technologies of IOT. Directing at the practical requirement of national agricultural environment, we've design a agricultural standardized production monitoring system based on the IOT. The system can realize the automatic configuration and self organized transmission of information collected node, realized the real-time collection, transmission, expression and storage of agricultural environmental information. By applying it to the construction agriculture, we can better solve the defect in the traditional monitoring system of green house and plastic tents and make people acquire agricultural environment information at any time. Moreover, we can realize the remote, real-time and accurate monitoring on the agricultural environment. In many aspects, IOT has displayed its promising research and application values. What is more, its application in agriculture has become one of hotspots because it constructed a precious technological platform for the precision agriculture in turning from demonstration to the practices. IOT enables traditional agricultural pattern to evolve into a information network centered production mode and brings automation, networking and intelligentializing to the agricultural production.

( 1 )

 1 sensor node, sending the information like atmosphere collected by the senor in periodicity to the monitoring and management center of agricultural environment by means of multiple

  adopted DSSS(Direct Sequence Spread Spectrum)under the specification of IEEE 802.25.4.


Zhifang Chen

Jinglei Wang

Jingsheng Sun

Aiwang Duan

Zugui Liu

Ni Song

Xiaofei Liu

Water-saving Irrigation Management and Decision Support System Based on WEBGIS

Keywords: WEBGIS, SuperMap IS.net, Irrigation management, Spatial analysis

To improve the management of irrigation district is a key to develop water-saving agriculture. Based on the basic principles of water-saving irrigation, and using the method of Modified Penman Monteith and water balance equation, The system of irrigation management and decision support based on WEBGIS was developed,which is according to the soil moisture, crop growth status, meteorology change and the physiological and ecological index of crop that is monitored, and the situation of water supply and crop water-saving irrigation system were also combined with the system. With the help of the GIS, the system under discussion can predict the water requirement of the irrigation district so as to optimize the allocation of the water resources, to provide a decision support for irrigation managers, and to finally realize desirable irrigation practice. Besides, it will promote the automation, informationization, and intelligent of the irrigation district management.

Introduction

As the basic resource, water is indispensable to social development. However,more and more problems in relation to water arise with the increase of population and the rapid economic development in China.

Among other things, the conflict between water supply and demand becomes more and more serious.

Accordingly, how to improve the management of the irrigation area, how to use the water resource in an effective and safe way, and how to yield the greatest returns on the regional water resource are becoming a new task [START_REF] Shengrong | The present situation and countermeasures of water resources in China--The shortage of water resources restricts the economic and social development of our country[END_REF] .Fortunately,modern technological development, in particular,Computer, Database,Networks, and Geographic Information System, makes possible the precision management of water resources for the agricultural irrigation [START_REF] Wang | Application of GIS technique to the management in irrigation districts[END_REF] .

As a new computer-based technology, GIS is a technological system for the management and study of the spacial database.With the support of both the software and hardware,GIS could effectively manage the spacial database in accordance with the geographical coordinates or the spacial location and obtain needed information based on an analysis of all the related factors and finally show the result of processing in the form of data, graph or map. When applied to the management and decision support of the irrigation area, it could predict more precisely the water demand of the irrigation area and also play an important role in improving management, optimizing allocation of the water resource, and realizing a desirable irrigation. Besides, a combination of GIS, the Networks and the expert system of irrigation management could promote the informationization and intelligent of the irrigation district management [START_REF] Xing | Study of decision support system (DSS) in irrigation districts based on GIS[END_REF] .

The GIS has been widely applied to the city traffic control, electric energy and cadastre management in foreign countries. Rather, it has been mainly used in the river basin management, flood control and disaster alleviation,as well as soil and water conservation in China. Anyway, it is scarcely used in the management and decision support of irrigation.With the help of SuperMap Deskpro 2008, SuperMap IS .NET 2008, Microsoft Visual Studio 2010 and Microsoft SQL Server 2008, the system under discussion could be used to improve the management and decision support system of irrigation when the management mode of irrigation is combined with GIS, in particular, its mass data input and pre-processing, spacial analysis and visual expression.

System Architecture

Centering on service, three architectures -the presentation layer,the business logic layer and the data layer, are established as follows [START_REF] Hu | Research on Informationization Construction in Village and Township Based on WebGIS[END_REF][START_REF] Rui | A System for Forestry Resources Management and Decision-Support Based on WebGIS and Portal[END_REF][START_REF] Chen | Design and implementation of property management information systembased on WebGIS[END_REF] :

The client browser is HTML and could receive ordinary HTML webpage. Its task is to read the contents in WEBGIS related to ASP.NET webpage and to require the map data. Then the server transmits all the requirements to the client through the HTTP protocol and all this will be shown on the browser.

The business logic layer is made up of two parts: the Web server software, such as IIS(Microsoft Internet Information Server)and MTS(Microsoft Transaction Server), and the server components based on GIS. When the WEBGIS receives the requirements from the browser, it could process, analyze and compute with the help of GIS Server components. If the data of GIS data server is needed, it could require the GIS data server.

The data layer could complete the definition, storage and retrieval, integrity constraint of data, and realize the functions of inquiry, modification, update of the database. It sends the processing results to the WEBGIS server after it has received the data requirement from the WEBGIS server.

The system architecture is shown in figure 1. [START_REF] Zhou | The tutorial of database application and development.The press of China railway[END_REF] . By using the spacial database engine of SuperMap SDE+,it makes possible visit to the spacial data. The programming environment is Microsoft Visual

ASP

Studio 2010 [START_REF] Bin | The development practice of web application system (Visual C# 2008). The press of China railway[END_REF][START_REF] Fuqiang | The programming tutorial of Visual C#.net.The press of People's post and telegraph[END_REF] ,and the background uses C# for programming. Taking the SuperMap [START_REF]The User's Manual of SuperMap Deskpro[END_REF][START_REF]The User's Manual of SuperMap IS[END_REF] as the main developing tool, The system makes possible the management of agricultural water information and the visual expression of irrigation decision support.

Design of database

Data is the most important part of GIS system and database is the center of all the data. Database includes basic database, spacial database and attribute database [START_REF] Dongsheng | The design and implementation of agricultural information system based on WebGIS[END_REF][START_REF] Yong | Design of Information System of Korla Fragrant Pear Based on WebGIS[END_REF][START_REF] Pang | Design and Realization of Farmland Soil Information Management Platform Based on WebGIS[END_REF] .

The basic database includes data related to soil type, crop type, crop coefficient, crop growth stage, irrigation and drainage, field monitoring and so on.

Attribute database is an important part of the system. It is the core tool to realize data processing, analysis, assessment, and storage of the decision functions.

Spacial database is the basis of the GIS system. The spacial geographical data are obtained by processing the scanned graph which is stored in the form of grid data structure. In order to improve the image quality of the grid data, we use other image processing software for further treatment, such as graphic joining together, noise reduction, refined, etc. Finally the grid data is transformed to the vector data for need. Spacial data and attribute data are processed in SuperMap deskpro 2008 in which modifying or editing data could also be conducted if the SDB is used. If the spacial vector data of the system is voluminous, the spacial data and the attribute data could be easily managed in the basic database. When the vector data is not so voluminous, the spacial vector data would be managed in the form of documents. In this case, the attribute data could be linked to the basic database to realize the remote dynamic linkage in order to enhance the efficiency of the system.

Take the meteorological data as an example. The data structure designed in this paper is shown in Table 1 and part of the data diagram in Figure 2. Fig. 2 The data relations of some tables

Design and realization of the system

Based on the collection materials and survey, the system makes use of many technologies and the functions of GIS provided by SuperMap IS.NET to establish a GIS-based supporting system for the visual water resources management and decision support of irrigation district according to the management of the irrigation information and the requirement of the irrigation decision support. By the system, the manager could grasp the general situation of the irrigation area and could integrate, analyze, and show the scattered materials. It could increase the quantity and quality of the information that the manager obtains, and quickly and easily realize the visual expression and web publishing of the irrigation decision so as to provide the technological support for scientific irrigation [START_REF] Zhou | Design and Implementation of Administrative System for Ocean Map Services Based on ArcGIS Server[END_REF][START_REF] Zhifang | Study On Water-saving Irrigation Management and Decision Support System[END_REF][START_REF] Da-Qiang | Design and Realization of Xi′an Urban Geographic Information Query System Based on SuperMap IS[END_REF][START_REF] Jun | Disaster Situation Map Issue System of the Wenchuan Earthquake Based on SuperMap IS[END_REF][START_REF] Bing | Developed of irrigation decision support system based on WebGIS[END_REF][START_REF] Yijie | Design and Implementation of Map Website Based on SuperMap IS[END_REF] .

Map Display

Create a webpage with the name of map.aspx as the main window of the map. Switch this webpage to the design page and add Mapcontrol. Click the right key of the mouse and choose'Load Map'in the menu. Input the IP address of GIS server and the port of the map service in the editor of MapControl.

Click'Validate'to verify. If the connectivity is succeeded, there will be a notice about it in the below and all the names of the maps which will be issued have been released. Choose the map needing to be released. Click 'Preview' and the image of the map will appear. Click 'OK' and the function of map display will be okay. See Figure 3 as following.

Fig. 3 Map Display-the diagram of Haihe river basin

Map View

Map view could be used to zoom in or zoom out the map, to do the map random,and to show the whole map. These functions are developed by using the SuperMap controls.

Zoom in or zoom out the map

When the usres browse the maps,they will do the different operation on the different ratio graphics.

When one user selects the tools of zoom in or zoom out the map,click the mouse on the map,the map will be magnified or minified correspondingly.

Map random

When the map is shown on the client,due to the limit of the display window size,the whole map often fails to display on the window,if the users want to browse the entire map, they need move the mouse on the map,when randomming on the map,the client need again get the data from the server,but,the new data which are obtainde from the server need wait for a certain amount of time.

View changes

When the map is be magnified,minified or random,the view of the map will change.If the users want to return to the front view or the back view,they only need click the button of the previous view or the back view.

The function of map view could be performed by adding any of the controls mentioned above to the design page(Figure 4).

Query and retrieval of information

To inquire and locate the spacial information is one of the basic functions of GIS. There are mainly two modes of query and retrieval based on the GIS. One is to search through the attribute information and to locate the result; the other is to search for the attribute information related to the target through the map. The former is called 'Property Map Search', and the latter is called 'Map Property Search'.

Property Map Search is a kind of fuzzy query. Namely, all the information with the keywords will be shown in the layers of the map when one or more keywords are inputted. The inquiry results will be displayed in the result show box and the target information will be shown in a highlighted way in the map.

Property Map Search

The following controls should be added in the main window in the system: 'DropDownList1, DropDownList2 and DropDownList3' for inputting the keywords, 'Button' whose ID name is 'Search' for submitting, and 'Gridview' for showing the result list. The key codes are as follows.

Map Property Search

Another way of spacial information query is to search the target-related attribute information through map, which is called 'Map Property Search'. SuperMap IS.NET provides several ways of map query including query by clicking the target map, query by rectangular box selecting sphere, query by circle selecting sphere, query by polygon selecting sphere, and query by selecting buffer sphere.

Map Property Search released by the system requires that the Button is added to the main window of the map. The Value of the button is set as 'select'. Click 'select' to make the map in the state of selection and make a rectangle by pushing the left key of the mouse on the map. The results will be shown in Gridview in the bottom right of the map window and the eligible targets will be shown at the time in the window map in the highlighted way. The key codes are as follows.

Spacial analysis

Spacial variability of the soil moisture is common and complex. To get a full understanding of the spacial distribution of the crop water requirement and the soil moisture is of great significance for irrigation decision support. Spacial analysis could extract and transmit the spacial information. As the basis of comprehensive geosciences analysis mode, the spacial analysis provides a basic tool for establishing a complicated mode. Spacial analysis mode is a kind of mathematics mode used in GIS spacial analysis. This study adopts the method of point kriging, which is also named general kriging method, to interpolate the soil moisture data. Taking the regionalized variables theory as the basis and the variograms as the main tool, the kriging method is a science which can be used to study the natural phenomena that is random, structural,and spacially correlation and dependence. It is a fast developing field in modern measurement geography. Kriging interpolation could describe the spacial distribution of the soil moisture in a directly perceived way.

Based on the data computed by adopting the irrigation management and decision support system, combination the experiment results of field and modern technology, and using GIS for the technical support means, the system could analyze the spatial distribution characteristics of soil moisture.

First,the point data of soil moisture are visualized through the SuperMap deskpro 2008, and the data are integrated to the Chinese map which has been adjusted in the same projection coordinate system. Then using grid as the basic unit, the data such as crop water requirement and soil moisture being quantified are transformed into the grid distribution. Through adopting the WebControls and AjaxControls of SuperMap IS.NET 2008 to establish spacial geographical mode and to integrate the complicated spacial analyzing process, the work efficiency is improved and more attention from those concerned is attracted to the procedure and methods of solving problems.

Visualization of irrigation results

Irrigation prediction is the main contents of field water management, which predicting the irrigation date and irrigation quota of crops in certain condition. Based on the basic principles of water-saving irrigation, and using the method of Modified Penman Monteith and water balance equation, The system of irrigation management and decision support based on WEBGIS is developed,which is according to the soil moisture, crop growth status, meteorology change and the physiological and ecological index of crop that is monitored, and the situation of water supply and crop water-saving irrigation system are also combined with the system.Using GIS,the information of irrigation area and the result of the decision support are visualized. Figure 8 is the spacial distribution chart of the forecast results of soil moisture for winter wheat in Guangli Irrigation District of Henan Province.

Conclusion

The paper combines the management and decision support mode of irrigation area with such functions of GIS as inputting and pre-processing of mass data, spacial analysis, and visual expression. It greatly helps users to improve the management of the irrigation area, use the water resources more effectively, predict the water requirement of the irrigation area more precisely, optimize allocation of the water resources, make better decision related to irrigation, and realize the automatic management of the irrigation area.

What to be noted is that there are some problems to which more attention should be directed. They include: (1) the relation between the attribute data of databases and the structure of the data table should be further optimized so as to reduce undesirable data and enhance the efficiency of query and retrieval of the system; (2) the application of GIS to decision support of irrigation is shown by two-dimensional graphics.Therefore, three-dimensional presentation should be developed so that decision related to irrigation could be more directly perceived and users could conduct the spacial analysis more favorably.

Fig. 4

 4 Fig.4 Map View

  this.DropDownList2.SelectedItem.Text.ToString(); string Stadia = this.DropDownList1.SelectedValue.ToString(); string StadiaCode = this.DropDownList3.SelectedValue.ToString(); querysql = "Province_ Name like '*" + Promary + "*' and Stadia_Name like '" + Stadia + "' and Stadia_Code like '" + StadiaCode + "' ";//and Year_ Frequency > " + min + " and Year_ Frequency < " + max + ""; QueryParam param = new QueryParam(); QueryLayer querylayer = new QueryLayer(); querylayer.ReturnFields = new string[8]; querylayer.ReturnFields[0] = " Province_ Name "; querylayer.ReturnFields[1] = " Stadia_Name"; querylayer.ReturnFields[2] = " Sowing "; querylayer.ReturnFields[3] = " Overwintering "; querylayer.ReturnFields[4] = " Turning_green "; querylayer.ReturnFields[5] = " Jointing "; querylayer.ReturnFields[6] = "Heading"; querylayer.ReturnFields[7] = "Maturity"; querylayer.Name = "Growth stage of winter wheat @5"; querylayer.WhereClause = querysql; param.Layers = new QueryLayer[1]; param.Layers[0] = querylayer; param.Highlight.HighlightResult = true; ResultSet Rs = MapControl1.QueryBySQL(param); ds = Rs.ToDataSet(); gvStadia.DataSource = ds; gvStadia.DataBind(); } The interface of growth stage of winter wheat through the Property Map Search is shown in Figure 5.

Fig. 5

 5 Fig.5 Property Map Search-the diagram of growth stage of winter wheat

  .Highlight.HighlightResult = true; e.Params.Layers = new QueryLayer[1]; e.Params.Layers[0] = new QueryLayer(); e.Params.Layers[0].Name = "ET 0 @5"; e.Params.Layers[0].ReturnFields = new string[4]; e.Params.Layers[0].ReturnFields[0] = "Stadia Name"; e.Params.Layers[0].ReturnFields[1] = "Stadia Code"; e.Params.Layers[0].ReturnFields[2] = "Year_ Frequency"; e.Params.Layers[0].ReturnFields[3] = "ET 0 "; } The interface of inquiring the reference crop water requirement(ET 0 ) through the Map Property Search is shown in Figure 6.

Fig. 6

 6 Fig.6 Map Property Search -the diagram of ET 0

Figure 7

 7 Figure 7 is the spacial distribution chart of the soil moisture for winter wheat in Guangli Irrigation District.

Fig. 7

 7 Fig.7 The spacial distribution chart of the soil moisture for winter wheat in Guangli Irrigation District

Fig. 8

 8 Fig.8 The spacial distribution chart of the results of soil moisture for winter wheat in Guangli Irrigation District

Table 1

 1 Data structure of meteorological data sheet

	Field Name	Data Type	Note
	Guid	Int	Serial number
	StadiaCode	Varchar	Station code
	Date	Datetime	Date
	Rainfall	Float	Rainfall
	AvgWind	Float	Average wind velocity
	TemMax	Float	Maximum temperature
	TemMin	Float	Minimum temperature
	AvgTem	Float	Average temperature
	Suntime	Float	Sunshine duration
	Moisture	Float	Relative humidity

ACKNOWLEDGEMENTS

We acknowledge the financial supports by the Key Lab for Crop Water Requirement and Regulation of Ministry of Agriculture Foundation(CWRR201006),the National Natural Science Foundation of China(51079154),the earmarked fund for China Agriculture Research System (CARS-3-1-30).

ABOUT THE AUTHOR(S)

Corresponding Author:Jinglei Wang(1972-),male,Kaifeng,Henan,China,Associate Professor,Master's tutor;mainly engaged in water-saving irrigation and spatial variation in crop water element.

First Author:Zhifang Chen(1981-),female,Xinxiang,Henan,China,mainly engaged in computer applications in water-saving irrigation area.


Laiqing Ji

Xinrong Cheng

Li Kang

Daoliang Li

Daiyi Li

Kaiyi Wang

Yingyi Chen
email: chyingyi@126.com

A SVM-Based Text Classification System for Knowledge Organization Method of Crop Cultivation

Keywords: Support Vector Machine (SVM), Text Classification, organization method, crop

The organization of crop cultivation practices is still far from completion, and Web Resources are not used adequately. This paper proposed a method, based on SVM, to organize the knowledge of crop cultivation practices efficiently from Web Resources. The knowledge organization method of crop cultivation was proposed with Good Agricultural Practices (GAP) in the application of the crop cultivation practices. It is that how to organize the existing crop cultivation knowledge, according to the requirements of crop cultivation practices. It mainly includes a text classification method and a search strategy on the knowledge of crop cultivation. For the text classification method, it used a text classification method based on SVM Decision Tree; for the search strategy, it used a strategy, organized by Ontology and custom knowledge bases. The experiment shows that performance of the proposed text classification method and the knowledge organization method with wheat, is workable and feasible.

Introduction

The amount of information about the knowledge of crop cultivation in Web pages is huge and the information is disorganized, so that it is a big workload to organize the related knowledge and not sufficient to use it, resulting in a number of waste of the useful resources. So it is a meaningful thing to organize the related knowledge [START_REF] Usama | Data Mining and knowledge discovery: making sense out of data[END_REF] .

To solve these problems, a lot of methods were proposed both at home and abroad. However, these methods [START_REF] Hand | Intelligent Data Analysis: Issue and Opportunities[END_REF] only collect the content from the Web Resource, not classifying the collected content and getting what we truly need. The search engine is a retrieval tool for the network information, but the performance of traditional search engine is unsatisfactory [START_REF] Jiang | Building Knowledge base for Consulting System on Agricultural Practical Techniques[END_REF] . By analyzing Web documents to get the related knowledge, Text mining, as an important branch of data mining, can help have a better use of Web knowledge, based on information retrieval, data mining and knowledge management and so on.

This paper proposed a method on how to organize the knowledge for more effectively organizing and analyzing the documents about the related knowledge. This method involved in how to classify the documents and set the search strategies on the knowledge of crop cultivation.

The knowledge organization method

Knowledge characteristics analysis

The complexity of crop cultivation brings about the complexity of the related knowledge organization, and there is not a very clear knowledge or practice to guide the process of crop cultivation. However, the current research for crop cultivation practices, mainly combined with Good Agricultural Practices (GAP), has also had some progress. In this paper, it mainly referred the document "Combinable crops control points and compliance criteria" in GAP as required.

With the analysis of GAP as a prerequisite, it divided the process of crop cultivation into three phases: pre-production, in-production and post-production. The pre-production mainly consisted of land selection and seed selection. The land selection mainly referred the land type, the time of sowing and so on, and the seed selection mainly referred varietal characteristics, suitable areas for planting, output, resistance, price and so on. The in-production mainly referred sowing and seedling raising, fertilization, irrigation, pest and disease control and so on. For different crops in different areas, the detailed operating of crop cultivation may be different. For example, some crops just need seedling raising, not sowing; the crop cultivation in outdoor and greenhouse is different. The post-production mainly consisted of agricultural harvest, storage and so on.

The knowledge organization model

Although the knowledge of crop cultivation is numerous and messy with the above analysis, it has certain law so that we can organize it with the knowledge of GAP. The following graphic shows this method.

Categories of crop

Selection of the concrete crop

Fig.1. Knowledge organization model of crop cultivation

As shown in Fig. 1,the kernel of this model is the text classification method and a search strategy on the related knowledge of crop cultivation. For the strategy in this model, it mainly includes the retrieve key provided by Ontology knowledge database and self-defining knowledge database. For the search strategy for Ontology knowledge database, it is domain Ontology built in accordance with the request of GAP; for the search strategy for self-defining knowledge database, its setting rule is that the key in the self-defining knowledge database can stand for the most common characteristics in some category.

Taking the wheat as an example, the key in the search strategy of pest and disease control stands for the following: the symptom and their solutions. These solutions mainly refer the irrigation time, the irrigation methods and so on. Because the setting of the search strategy directly affects the accuracy rate of the related knowledge retrieve, we should analyze a lot of related texts, and summarize the most common characteristics of these texts, then make the related search strategy, using the key which can best represent the characteristics.

Text classification method of crop cultivation

Support Vector Machine (SVM)

Based on the minimization principle to structure risk, SVM is a non-linear method. It maps the limited training data(Input Vector) to high dimension feature space, resulting in transforming the problem of the non-linear separability in the sample spaces into the problem of linear separability in the characteristic spaces. In other words, it is how to search the best linear classification Hyperplane. For the finding this Hyperplane, SVM is realized by the Support Vector (SV) and the decision boundary.

The text classification ideology and algorithm

Through analyzing the related knowledge characteristic of crop cultivation, we know that the knowledge classification is the multi-class issue. As we know, SVM can only solve the two-class classification, so that we need graft many SVMS onto one to realize the knowledge classification of crop cultivation.

Through analyzing the flaws of algorithm and the related knowledge characteristics, this paper proposed a method, combined binary decision tree with SVM, to realize the related knowledge classification of crop cultivation. Every non-leaf node refers to a SVM classifier and every leaf node refers to the final classified category. The text classification algorithm of crop cultivation, and the algorithm were described as Fig. 3 and Fig.4 [START_REF] Osuna | Training support vector machines: An application to face detection[END_REF][START_REF] Osuna | Training support vector machines: An application to face detection[END_REF] :

Tranining

Results and discussions

The text classification of crop cultivation is the kernel of the related knowledge organization.

According to the actual requirement of the related document arrangement, this paper divided the crop cultivation into six critical control points: land selection, seed selection(seedling raising),fertilization, irrigation, pest and disease control ,harvest. It extracted the related content, using the knowledge of Web crawling and Web analysis, finally classified the content, and made these documents be the training samples in experiment.

It mainly adopt the recall ratio(r) and the precision ratio(p) to evaluate the quality of text classification, and it also refers to the comprehensive evaluation indexes of the recall ratio and precision ratio [START_REF] Rao | Research and implementation of Web Text Classification[END_REF] :F1,its mathematical formula is:

2 1= rp F rp   [10].
Taking the pest and disease control of wheat as an example. For the search strategy of the pest and disease control, it mainly includes the symptoms of pest and disease control, the name of the medicines, the usage and dosage of the medicines and so on. Let the number of the related texts about the pest and disease control be num2; Let the number of the related texts about the pest and disease control of wheat be num2; Let the number of the texts including all of the search strategies on the pest and disease control of wheat be num3.

Let M be the reference values of the specialists, and the specific definition of M is that it is the ratio of the number of the text, including all of the search strategy in a category, and the number of the related texts in a category. It is shown by the definition of M , M =num3 / num1.It shows the result of the experiment about the text classification method of crop cultivation and the knowledge organization method of the pest and disease control to wheat in the following table1.

Conclusions

On the premise of the related knowledge characteristics analysis of crop cultivation, this paper proposed the knowledge organization model of the crop cultivation. In this model, it mainly described the principle how to set the search strategy and the SVM-Based text classification ideology and algorithm. This paper had the related test. The experiment result shows that the organization method is workable and feasible, and it can effectively provide the data for the document arrangement of crop cultivation practices. The experiment result also shows that it needs a lot of data to achieve the effectiveness, or the effectiveness is not apparent.

Fig. 2 .

 2 Fig.2. Knowledge classification model of crop cultivation

Select SVM 1 Fig. 3 .Fig. 4 .

 134 Fig.3. Training Algorithm(n>2)

Acknowledgements

Funding for this research was provided by National Key Technology R&D Program. The project name is Study on the comprehensive evaluation system of crop cultivation practices. The project number is 2009BADB6B02-01.