Lingling Liu

Bo Zhao

Yinqiao Zhang

Xiaochao Zhang

Design and Experiment of NIR Wheat Quality Quick Detection System

Keywords: NIR spectroscopy, Wheat quality, Quick detection system, Quantitative analysis, Predictive performance

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Near infrared spectroscopy (NIRS) technique is characterized by simple operation, short period and environmental friendliness. Compared with Kjeldahl nitrogen method, NIRS technique can detect protein of wheat quickly and nondestructively, which is meaningful for wheat quality inspection, processing and breeding. NIRS technique is constituted by NIR instrument, chemometrics software and calibration model [START_REF] Yan-Lu | Foundation and application of near-infrared spectroscopy analysis[END_REF]. As NIRS instrument is becoming miniature, professional, steady and excellent, domestic and foreign famous companies have developed kinds of the special purpose instruments focused on inspecting grain, feed, soil and oil [START_REF] Xiao-Li | Present situation and prospect of near infrared spectrometer in China and abroad[END_REF] . Domestic researchers have already gained some achievements in wheat spectroscopy research, but our research is still far lower than international level [START_REF] Xiao-Chao | Near infrared spectral analysis technology and its application in modern agriculture[END_REF] . In order to analysis wheat quality professionally and quickly, NIR-WQDS was developed on grating technology and it works in the 900-1700nm region. In this paper 46 wheat samples were detected by MPA spectroscopy and NIR-WQDS to test the performance of NIR-WQDS.

2

Development of system

NIR-WQDS includes optical system with fixed grating, CCD drive and signal acquisition system, CCD temperature control system, light source control system, sample rotator control system, and computer software system. NIR absorption spectra are recorded in the 900-1700nm (11111.1-5887.8cm -1 ) range. CCD is controlled to work at 0℃ and its temperature controlling precision is ±0.1℃. The light intensity accuracy can be controlled within 0.02% and the SNR is 4000:1. The system structure of NIR-WQDS is shown in figure 1.

Following are working principles of NIR-WQDS spectroscopy:

(1) Light source control system is based on current negative feedback. It controls light intensity of source and provides stable near infrared light to the whole instrument.

(2) After light concentrated and collimated, it penetrates wheat sample directly in a sample rotator. Then, diffuse transition light is obtained.

(3) In optical system, diffuse transition light is concentrated and collimated. Then, light is projected onto fixed optical grating and a continuum spectrum is produced. (1) To collect more hydrogen information in wheat groups, spectrum wavelength ranges from 900nm to 1700nm.

(2) To eliminate influence caused by temperature shift and ensure high measurement precision, CCD temperature control system was designed to ensure CCD's working temperature low and steady.

(3) Light intensity control system was designed to ensure stable light with accuracy 0.02%, which reduces error from light intensity shift.

(4) Wheat sample is heterogeneous and inhomogeneous; therefore sample rotator was designed to eliminate error by increasing number of scans [START_REF] Yan-Lu | Foundation and application of near-infrared spectroscopy analysis[END_REF] . sciences. After milling, the samples were sealed in plastic bags. Protein standard values of samples were measured by national standard method, and it varies representatively and widely from 9.830% to 16.94% with a 14.18% average. MPA was selected as the target analyzer, which is a fourier transform infrared spectrometer produced by Bruker Optics Company. It has a scanning range of 12800-3600 cm -1 . The NIR spectra of 46 wheat powder samples were collected using quartz sample cup on sample rotator in this experiment. The main parameters and setting of two spectrometers are shown in table 1.

experiment methods

The spectra were acquired with MPA and NIR-WQDS at room temperature (20~25 ℃) respectively. In order to decreasing operation differences, both instruments were started at the same time for about half an hour. After completing all measurements, the obtained data was processed into absorbance value by using MATLAB 7.0 software. Wheat NIR spectral data was finally analyzed using OPUS 6.5 software. In OPUS6.5, partial least squares (PLS) regression was used to build calibration models.

Results and analysis

Peprocessing and modeling methods

In this paper, PLS were selected for building equation between spectra and protein standard values. R 2 , RMSECV and RDP were computed to select the best model.

Higher R 2 represents that the predictive ability of model is better. RMSECV (cross validation) and RMSEP (external validation) indicate the rate of deviation between predictive value and true value. When they get smaller, models get better. RPD can state the stability of model. A model has a good stability when its RPD>3.

In order to eliminate differences, OPUS 6.5 software was used to process spectra obtained by two instruments and to build PLS calibration model. More excellent evaluating indicators and wider modeling wavelength ranges are used to select a better calibration model [START_REF] Jie | Analysis of soil nutrient content based on near infrared reflectance spectroscopy in Beijing region[END_REF] . Table .2 shows preprocessing algorithm, wavelength ranges and rank when modeling.

Table.2.

Preprocessing methods, spectral range and rank of the calibration models.

Results and analysis

The relationship between predictive values and standard values in both models is shown in Fig. 3. We can acquire that both models have an excellent goodness of fit, but the linear correlation of MPA is more significant than that of NIR-WDQS. predictability and stability [START_REF] Xiao-Chao | Near infrared spectral analysis technology and its application in modern agriculture[END_REF] . It proves that NIR-WQDS spectroscopy has a good performance with high stability, accuracy and repeatability and it is practical to inspect wheat quality.

Conclusion

NIR wheat quality quick detection system was designed based on grating technology in a spectra range of 900-1700nm. The spectrometer consists of optical system with fixed grating, CCD drive and signal acquisition system, CCD temperature control system, light source control system, sample rotator control system, and computer software system. In this paper, the instrument is proven to be accurate, stable and reliable by analyzing wheat protein content in 46 wheat samples.

The spectra acquired by NIR-WQDS are thorny with spike-waves at extreme ends, which may be caused by noise from slightest vibration of sample rotator, or precision of temperature control system. To improve performance of NIR-WQD, suggestions of the next step are put forward as follows:

(1) Continuing to reduce working temperature of CCD detectors to low dark current.

(2) Adding more processing algorithms and modeling algorithms to perfecting supporting computer software system.

( 4 )( 5 )Fig. 1 .

 451 Fig. 1. Structure of NIR-WQDS

4. 1 Fig. 2 .

 12 Fig. 2. 46 wheat sample spectra: (a) MPA (b) NIR wheat quality quick detection system

Fig. 3 .

 3 Fig. 3. The relationship between prediction value and standard value: (a) MPA (b) NIR-WQDS

Table 1 .

 1 Main parameters and setting of two instruments

	Spectrometer	Spectra Range (cm -1 )	Sample Cup	Detector	Modeling software
	NIR-WQDS	11111.1-5887.8	quartz	InGaAs	OPUS6.5
	MPA	12800-3600	quartz	PbS	OPUS6.5

Table .

 .

	3. According to
	models acquired from NIR-WQDS and MPA, R 2 are 94.44% and 95.95%, RMSECV
	are 0.3460, 0.2950, RMSEP are 0.2430, 0.2270, and RPD are 4.240 and 4.460,
	respectively. According to experiment results, forecast performance of NIR-WQDS is
	good but less excellent than MPA. In the NIR-WQDS, there is a significant linear
	correlation between standard values and prediction values and the model gets a good

Table .

 .

		3. Optimization results of both models
	Instrument	R 2	RMSECV	RMESP	RPD
	NIR-WQDS	94.44%	0.3460	0.2430	4.240
	MPA	95.95%	0.2950	0.2270	4.970

This work was supported by national science and technology support program for the 12th five-year plan (2011BAD20B06), and national basic research program of China(2010CB735707)

Materials and methods

experiment materials


Juan Liu

Xiaoli Zhao

Shuping Xiong

Xinming Ma
email: xinmingma@126.com

Yanfeng Wang

Jing Wang

Research on Prediction Model and Characteristic Parameters on Dry Matter Accumulation in Wheat Based on Normalized Method and Grey System

Keywords: Wheat, Dry matter accumulation, Simulation model, Characteristic parameters

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

Introduction

Wheat is one of the most important grain crops in China; over 95% of its yield is based on photosynthesis. Dry matter accumulation is the final product of wheat photosynthesis, and closely related to the yield formation [START_REF] Yao | Studies on dry matter accumulation, partitioning and translocation in winter wheat under sprinkler and surface irrigation conditions[END_REF][START_REF] Dordas | Dry matter and nitrogen accumulation, partitioning, and retranslocation in safflower (Carthamus tinctorius L.) as affected by nitrogen fertilization[END_REF]. Regulation and control of many cultivation factors affect the wheat dry matter accumulation, among them density is the important factor which affect the wheat growth and development and yield [START_REF] Hiltbrunner | Are seeding densities an opportunity to increase grain yield of winter wheat in a living mulch of white clover? Field[END_REF]. So it has great significance to clear the high yield or even super high yield in wheat by investigating the relationship between density and dry matter accumulation. Dry matter accumulation appears S-shape during the whole life of wheat, and has obvious differences in different densities [START_REF] Su | The relationships between soluble sugar contents of plant and kernel and dry matter accumulation in different high yielding wheat genotypes[END_REF]. There are great differences among those tillering abilities of wheat in different cultivars, so it is necessary to study the evolution law of dry matter accumulation process with different densities and tillering abilities of wheat cultivars.

Crop simulation technology can quantitatively describe the process of crop growth [START_REF] Cao | Discussion on development of crop models[END_REF]. At present, there has been created some simulation models on photosynthetic production and dry matter accumulation of wheat [START_REF] Liu | A simulation model of photosynthetic production and dry matter accumulation in wheat[END_REF][START_REF] Zheng | Numerical study of wheat photosynthetic production[END_REF], but those models have stronger mechanism, and more parameters which were more difficulty to get, so their practicability was obvious limited. Empirical models mostly used Richards or Logistic equation to simulate the process of dry matter accumulation in wheat [START_REF] Goudriaan | Modelling Potential Crop Growth Processes. Textbook with Exercises[END_REF][START_REF] Cao | Modelling phasic development in wheat: a conceptual integration of physiological components[END_REF], However, some other researchers hold that the time scale, which measured on the process of crop growth and development, should be selected reasonably [START_REF] Lü | Studies on Effects of Ecological Factors on Growth of Maize and Establishment of Climate Ecology Model and Appraisement System[END_REF]. For example, ecological variables such as accumulated temperature, are more representative than time variables that of days, and they can also reflect the status of crop growth better. In order to investigate the relationship between density, tillering ability and dry matter accumulation of wheat, provide the theoretical basis for prediction on wheat growth and culture of high yield, making research based on field experiment, and taking the accumulated temperature as time scale, a prediction model, which is suitable for dynamic dry matter accumulation with different densities and tillering abilities of wheat, was tried to create with the method of normalization [START_REF] Zhang | Establishment and test of LAI dynamic simulation model for high yield population[END_REF].

Materials and Methods

Experimental Design

Field experiments were conducted during 2009~2011 at the Experimental Station of the Henan Agricultural University, Zhengzhou, PR China. The cultivars studied were medium tillering ability and high earing rate wheat (YM49-198), stronger tillering ability and low earing rate wheat (LKAZ8) and stronger tillering ability and high earing rate wheat (YZ4110). Seeds were sown on 14th October 2009 and 15th October 2010, which produced with densities of 750, 000, 2, 250, 000, and 3, 750, 000 plants hm -2 in randomized block design. The field was also managed as same as high yield wheat field.

Measure Items and Methods

Choosing 200 strains wheat seedling in uniform growth state at three leaf period of wheat, marking those wheat seedling with label, and sampling every 10 days from three leaf period to wintering stage, every 30 days from wintering stage to returning green stage, and every 10 days from returning green stage to maturity, dry matter of 10~20 samples were measured each time. Weighing and calculating the dry matter accumulation of aerial part in unit land area (DMA for short).

Meteorological Data

The meteorological data was come from Meteorological Bureau of Henan, mainly including maximum temperature, minimum temperature daily during the growth period of wheat and so on. Calculation method of daily mean temperature is using an equation

T i =(T imax +T (i+1)min )/2
, where T i is mean temperature of the i day, T imax is the maximum temperature of the i day and T (i+1)min is the minimum temperature of the i+1 day. Calculation method of accumulated temperature is AT i = ∑T i , where AT i is the accumulated temperature of the i day, while if T i ≤0℃ or T i ≥35℃, then T i =0℃ [START_REF] Xiao | Calculation and stability of accumulated temperatures in the growing season of winter wheat[END_REF].

Normalized Treatment of Data

Define the maximum dry matter accumulation (DMA max ) the maximum accumulated temperature (AT max ) to 1 (using the data of 2009~2010), the relative dry matter accumulation (RDMA i ) and relative accumulated temperature (RAT i ) were obtained with the method of normalization [START_REF] Zhang | Establishment and test of LAI dynamic simulation model for high yield population[END_REF], using the equations as follows:

RDMA i =DMA i /DMA max , RAT i =AT i /AT max .
The DMA i is the dry matter accumulation in different growth stages, RDMA i is the relative dry matter accumulation in different growth stages, RAT i is the relative accumulated temperature in different growth stages. The value of RDMA i and RAT i were 0~1. The experiment data have been fitted by soft of Curve Expert 1.38, statistical analyzed by SPASS 13.0, and mapped by Sigmaplot in this paper.

Model

Using the experiment data of 2010~2011 as data, the accuracy of simulation model of RDMA was tested [START_REF] Fu | Optimal model for dynamiccharacteristics of grain weight commonly used in wheat and maize[END_REF].

Results and Analysis

Established and Screened of RDMA Models

Treating AT and DMA that from seeding stage to maturity period of wheat by the method of normalization, and fitting the relationship between RAT and RDMA by soft of Curve Expert 1.38, five mathematical equations in Table 1 which have higher correlation coefficient were got. The limit value of the 5 fitting equations show that simulation equations of (1), ( 2), (3) could not explain the DMA process, but (4) and ( 5) could reflect the law of DMA dynamic changes. Equation ( 5) is a special case of (4), and it could be transformed from (4) when d=1. So equation ( 4), the Richards equation was chosen to simulate the relationship of RDMA and RAT in this paper. The Richards curve equation was fitted as follows:

Validation of RDMA Models in Wheat

The RDMA models of wheat were tested using the data of 2010~2011 as validation data, Fig. 1 shows that the simulated values exhibited significant positive correlation with the measured values, correlation coefficients were all more than 0.97. The accuracies of simulation were all over 0.91, and increased with the increase of density. Through the deduced of Richards equation, characteristic parameters with biological significance would be obtained, and they could be used to analyze the dynamic characteristics of DMA in wheat quantitatively.

Characteristic Parameters Analysis of Wheat

After derivation calculus to the equation y=a/(1+e (b-cx) ) 1/d , variation rate of RDMA equation was got as follows:

Mean relative growth rate of DMA and the maximum relative accumulation rate of dry matter during the whole growth were obtained by integral to equation [START_REF] Zheng | Numerical study of wheat photosynthetic production[END_REF]. Meanwhile, the characteristic parameters, such as RAT and RDMA, at the fastest stage were also derived [START_REF] Li | Effect of sowing-date and planting density on dry matter accumulation dynamic and establishment of its simulated model in maize[END_REF][START_REF] Li | Dry matter accumulation in rice aboveground part: quantitative simulation[END_REF].

0 1 2( 2) a dy a c G dx a dx d      . ( 8
) ( ln ) / ARAT b d c  . ( 9
) max ( 1)/ (1 ) d d ac AR d     . ( 10
) 1/ (1 ) d a ARDMA d   . ( 11
)
Where G is the mean relative growth rate of DMA, ARAT is RAT in occurrence time of maximum rate of DMA, AR max is the maximum relative accumulating rate of dry matter accumulation, ARDMA is RDMA in occurrence time of maximum rate of dry matter accumulation. The values of these characteristic parameters were obtained by using the value of a, b, c, d in equation ( 4), G=1.26, ARAT=0.53, AR max =1.86, ARDMA=0.44. So RAT is 0.53 when AR max appears, and at this time DMA was almost half of total DMA. The actual accumulated temperature in occurrence time of maximum rate of dry matter accumulation (ARAT ′ ), actual dry matter accumulation in occurrence time of maximum rate of dry matter accumulation (ARDMA′), mean actual growth rate of dry matter accumulation (G′), and maximum actual accumulating rate of dry matter accumulation (AR max ′) could be derived by using the value of DMA max and AT max when AR max appears.

In order to determine the relative importance of characteristic parameters to dry matter weight, using the grey system comprehensive relationship degree to analyze the relationship between dry matter weight and characteristic parameters. Consider the dry matter weight as system characteristic behavior sequence:

( (1), (2),..., ( ))

i i i i X x x x n  . ( 12
)
Consider characteristic parameters as related factors sequence: ( (1), (2),..., ( ))

j j j j X x x x n  . ( 13
)
Calculated grey comprehensive relationship degree between each of them. Grey comprehensive relationship degree not only embodied the similar degree between the two sequences, but also reflected the approaching of the change rate with characteristic behavior sequence and related factors sequence relative to the starting point respectively. Meanwhile, it was a quantitative index to characterize more comprehensive if the relationship of sequences are close, and the calculation formula is 0 0 0 (1 )

i i i        , [0,1]   . ( 14
)
Where 0i

 is absolute correlation degree, 0i  is relative correlation degree, and 0.5   . From Table 2, the results of correlation degree analysis show that the correlation coefficients between dry matter weight and characteristic parameters of wheat ARAT′, AR max ′, ARDMA′, G′were all above 0.6. The correlation coefficients of ARDMA′and dry matter weight was 0.861, and then decreased in order of: AR max ′ , G ′ and ARAT ′ . Therefore, the effect of ARDMA ′ on dry matter production was most important, higher ARDMA′plays a very important role to improve dry matter weight of wheat. Wu et al [START_REF] Wu | A dynamical model of photosynthetic production and dry matter accumulation in wheat under increment UV-B radiation[END_REF] established DMA mechanism models, which had stronger interpretability and the parameters had physical meaning. However there were too many parameters in the model. Empirical models with less parameters, but they were only used to describe. Crop growth model should be established by combined the method of mechanism and empirical [START_REF] Cao | Discussion on development of crop models[END_REF]. Analyzing the characteristics of dynamic DMA in wheat, mainly using time variable as the scale of growth and development in the past [START_REF] Hou | Establishment of dry matter accumulation dynamic simulation model and analysis of growth characteristic for high-yielding population of spring maize[END_REF]. Therefore, using the normalized method and making accumulated temperature as time scale. Richards equation was established in this paper as 0.2776 4.6558 1/0.1130 1.1435 / ( 1)

x y e     (r=0.9927), it improved generality of the model, overcome the defects of model parameters changes that cultivation measures and varieties caused. The Richards equation has less parameters and good biological significance. The production process of wheat could be predicted by DMA which be simulated through the model and accumulated temperature in any growth period.

Research on Characteristic Parameters of Dry Matter Accumulation in Wheat

By deduced of simulation equation, characteristic parameters with biological significance would be obtained. Dynamic characteristics of crop dry matter accumulation have been analyzed by many scholars [START_REF] Huang | Photosynthetic characteristics during grain filling stage of summer maize hybrids with high yield potential of 15000 kg ha -1[END_REF][START_REF] Li | Dynamic analysis on response of dry matter accumulation and partitioning to nitrogen fertilizer in wheat cultivars with different plant types[END_REF]. As the results show, RAT at the maximum rate of dry matter accumulation was 0.53, meanwhile, DMA was almost half of total DMA. This proved that strengthening field management in early stage of wheat growth, cultivating sound seedlings and constructing proper population structure were important to high yield of wheat. Correlation coefficient of grey comprehensive relationship degree between AR max ′, ARAT′, G′and final dry matter weight were more than 0.6. The highest correlation coefficient was between ARDMA ′ and dry matter weight which was 0.861 proved that ARDMA′ effects the dry matter weight most. A higher ARDMA′ played an important role in stabilizing and improving dry matter weight of wheat.

Fig. 1 .

 1 Fig. 1. Relationship between the simulated and measured dry matter accumulation of wheat

Table 1 .

 1 Relative dry matter accumulation models of wheat

	y		1.1435 / (1		e	0.2776 4.6558 1/0.1130 ) x  	.	(6)

1

In these equations x and y denoted RAT and RDMA, respectively. ** denoted P<0.01. a denoted relative maximum increment; b denoted initial value of parameter; c denoted growth rate parameters; d denoted shape parameter; The same below.

Table 2 .

 2 Analysis on dry matter weight and characteristic parameters of wheat based on grey comprehensive correlative degree

4 Conclusion and Discussion

	4.1
	Item Dry matter weight	ARAT′ 0.647	Correlation coefficients ARDMA′ AR max ′ 0.724 0.861	G′ 0.718

Established Model and Its Characteristics of Wheat DMA

Acknowledgments. This research was supported by grants from the Special Research Foundation of Agriculture Industry (201103001), National Natural Science Fund (30771266) and the System of Industrial Technologies for Modern Agriculture of Henan Province (S2010-01-G04).


Ping Yang

Yujian Yang

Spatial Optimization and Mode Analysis of Primary Industry Structure in Yellow River Delta

Keywords: Industry structure, Yellow-river Delta, Spatial analysis, Universal Kriging, New mode

With the support of the total output of primary industry, secondary industry and tertiary industry and industry value of cultivating industry, forestry industry, animal husbandry industry and fishery industry of 19 country-level units in Yellow River Delta in 2007, the paper calculated the variation index of economic development based on country level, the MDS analysis was explored for primary industry, secondary industry and tertiary industry and compared with spatial difference characteristics of 19 country-level units in Yellow River Delta. The study carried out the analysis of combination analysis of primary, secondary and tertiary industry, and optimized the cultivating industry, forestry, animal husbandry and fishery industry. Autocorrelation characteristics was clarified by Moran`s I calculation, the new development mode of agricultural industrial structure was systemically concluded that the different mode presented in the different country units. The results showed that the industry evolution stage of primary, secondary and tertiary industry structure of 19 country units is in "secondary, primary and tertiary industries " stage and " secondary, primary and tertiary industries " stage according to industry structure stages theory, the obvious characteristics is that the cultivating industry value has spatial variability based on classical statistics combinig with universal Kriging. The emphasis of industry adjustment should be the focus on the secondary industry driving transformation into the primary, secondary and tertiary industries jointly driving economic growth, straight-pushing mode and tilting mode were put forward to new ideas about promoting industrial optimization and coordination development in Yellow River Delta. The study drew a conclusion that the measures were took to optimize industrial structure of cultivating industry, forestry industry, animal husbandry and fishery industry by combing straight-pushing mode with tilting mode, during the coordination development, focused on association effects of land and marine industry, extended agriculture industrialization chain and formed the diversification mode.

Introduction

Industry structure was influenced by historical factors, natural factors and social& economic factors, and its spatial heterogeneity is very close to the unbalance economic development. Differences of regional industrial structure which reflected the coordination of regional economic development is the result of long-term evolution a variety of factors, optimizing and upgrading of industry structure is helpful for the target of the regional economic development [START_REF] Gonghao | Regional analysis and planning[END_REF] . Accelerating transformation and adjustment of industry structure is important opportunity promoting development of rural areas and agriculture in Yellow River Delta, and adjustment of agriculture structure is also essential of rural areas and agriculture economic development.

The spatio-temporal model is important method for economic and social life study of the clear alternative to traditional analysis model. The spatial variability characteristics of industries value of primary, secondary and tertiary industry, and the cultivating industry, forestry industry, animal husbandry industry and fishery industry was explored and analyzed in Yellow River Delta in 2007, spatial difference of industry structure was developed to classify into different combination types, the continuity surface was used to establish model and prediction of the industries value. Further, the study provided the foundation for grasping the spatial difference of industry structure. On this basis, new mode was put forward to adjustment and upgrading of industry structure in Yellow River Delta.

2

Material and Methodology

Overview in Yellow river delta

The yellow river delta was taken shape on the basis of the yellow river alluvial plain and the adjacent sea regions in the north parts of Shandong province, extension the round regions, including Dongying city, binzhou city, Hanting district, Shouguang city, Changyi city in Weifang city, Leling and Qingyun in Dezhou city, Gaoqing in Zibo city and Laizhou inYantai city. The total area is 26500Km2, the 1/6 of the total area in Shandong province. Speeding up the high-efficient and high-ecological development in yellow river delta, is not only promoting the regional economic development in Shandong, but also accelerating open-up of Binhai new district in Tianjin, at the same time, Establishment of ecological zone accelerating the development of Bohai economic zone and cultivating new economic growth pole in the lower reaches of the Yellow River. Some countries in Yellow River Delta are a crossing and overlay regions located in "high-efficient ecological zone in Yellow river delta and the blue economic zone of peninsula". It is crucial issue for adjustment and upgrading of industry structure during the constructing high-efficient ecological zone in Yellow river delta, not only beneficial from of the industry significance from macro perspective, but also engaging in high-efficiency, high-ecological and highindustrialization [START_REF]Development and reform committee of Shandong province, the high-efficient and highecological economic zone in Yellow River Delta[END_REF] .

Universal Kriging

The Ji wheat 22 yield obtained from the actual yield of the observation area, with the Universal Kriging is a high precision prediction method one of Kriging methods, theoretical explanations of the method and technical aspects referred to the relevant literatures [3]. On the whole, this method not only removed the trend effect, but also has obvious characteristics of the residual prediction, the mathematical expectation of the regional variables was regarded as function of spatial position, effectively dealing with non-stationary, or drift characteristics of the regional variable. During the universal Kriging analysis. The general steps, firstly, analysis of data trends that exist in original data to the fit-goodness model. Secondly, the residual data (original data minus the trend data) was used to develop Kriging analysis. Finally, the comprehensive analysis of trend surface analysis and residual analysis of Kriging using the second-order trend surface equation, the final result is obtained by the separation of trend effects.

3

Results and Analysis

Characteristics Analysis of Industry Structure in Yellow River Delta

Variation index effectively reflected the difference of country-level economy in Yellow River Delta, as the following description:

t t ti t Y N Y Y S / ] / ) ( [ 2 / 1 2   
Where, N stands for country-level units number in Yellow River Delta, ti Y represents per GDP in the ith country in tth , the larger variation index indicated the more difference of economic development in the regions [START_REF] Dianting | The advanced tutorial of regional analysis and planning[END_REF][START_REF] Binhui | Multivariate statistical analysis and modeling of R language[END_REF] . As is illustrated in Figure 1, the variation index results of 19 country-level units in Yellow river delta referred to the following sequences: Kenli > Guangrao>Yangxin> Zouping> Dongying> Leling>Huimin>Hekou>Gaoqing>Qingyun> Laizhou> Zhanhua>Hanting> Bincheng>Shouguang> Changyi>Wudi> Boxing> Lijin,according to the sequence, the difference of economic development level presented smaller and smaller trend, the weak heterogeneity of economic development and the strong similarity, the difference of variation index reflected the spatial pattern of economic development and industry structure, the results indicated that there is balanced development characteristics, and is not in polarization situation of industry structure pattern in Kenli country, Guangrao country and Yangxin country. The relatively large heterogeneity difference of the regional economic developmen in Wudi Country, Boxing Country and Lijin Country.

For the status, transformation and upgrading of industry structure should be utilized by the different policy and measures. Multidimensional scaling method (multidimensional scaling, MDS) is a generic term that includes many different specific types, which can be classified according to the form of two-dimensional figure, its results was generally validated by the stress, the stress is less than 0.2, which showed the reasonable MDS results, the stress is more than 0.05, or equal to 0.05 and the stress is less than 0.1 or equal to 0.1, the results showed that MDS results are considered of a good sequence, the stress is less than 0.05, with good representation.By the R system based on open-source software, the MDS value of primary industry, secondary industry, tertiary industry was calculated in Yellow River Delta, referred to figure 2. MDS can explain the coordination degree of industry structure and the similarity relationship of industry values [START_REF] Binhui | Multivariate statistical analysis and modeling of R language[END_REF][START_REF] Weidong | Application of multivariate statistical analysis[END_REF] .Generally, the total industry values and the coordination degree has the similarity characteristics located in the same quadrant or the near interval distance of country-level units, the results of MDS showed that there is similar to industry values mode about Shouguang and zouping, Guangrao and Laizhou,Wudi and Dongying,Laoling and Hanting and zhanhua, Boxing and wudi, Huimin gaoqing qingyun yangxin liji and hekou, Changyi and kenli has the special mode characteristics. In summary, the relationship between coordination goodness of industry structure and the total value of the primary, secondary industry and tertiary industry is very complex, the results also clearly showed that the goodness of industry structure is probably poor in the developed country units, but in the smaller total values country units, there is coordination goodness of industry structure. So industry restructuring should be in accordance with industry structure development and evolution in essence, and be treated differently.

In the study, the proportion of agricultural industrial structure and primary industry, secondary industry, tertiary industry was the following description in Yellow River Delta. In the definition the proportion of 0-25% is lower level structure, at the scope from 25% to 50% is defined as the lower level structure, the proportion scope of 50% -75% is defined as the high level, but the proportion of 75%-100% is regarded as the higher structure [START_REF] Lijun | Geographical information and the multi-objective planning in the regional scale[END_REF] . According to the above classification, the types combination of industry structure is classified into Figure 3, as follows:

Low level +lower level+ lower level, the proportion of the primary industry is more than or equal to 0%, less than 25%; the proportion of the secondary industry is more than or equal to 25%, less than 50%; the proportion of the tertiary industry is more than or equal to 25%, less than 50%.The industry structure attributes to the type in Leling country, Qingyun country, Zhanhua country, the obvious characteristics is the lower value of the primary industry and the considerable proportion of the secondary industry and tertiary industry. The development emphasis in the next step is integration with city economy, increasing of the total economy of the secondary industry and tertiary industry.

Low level +higher level+ low level. The proportion of the primary industry is more than or equal to 0%, less than 25%; the proportion of the secondary industry is more than or equal to 25%, less than 75%; the proportion of the tertiary industry is more than or equal to 0%, less than 25%. The industry structure belongs to this type in Changyi country, Guangrao country, Lijin country, Hekou district, Yangxin country, Wudi country, Huimin country. The characteristics is the higher proportion of secondary industry, the lower proportion of primary industry and the tertiary industry, the development focus is on increasing proportion of tertiary industry based on industrial foundation.

Low level +higher level+ lower level. The proportion of the primary industry is more than or equal to 0%, less than 25%; the proportion of the secondary industry is more than or equal to 50%, less than 75%; the proportion of the tertiary industry is more than or equal to 25%, less than 50%. The industry structure belongs to this type in Laizhou city, Shouguang country, Dongying district, Gaoqing country, Bincheng district, Boxing, Hanting country. The characteristics is the higher proportion of the secondary industry and the tertiary industry, the lower proportion of the primary industry. The emphasis is increasing development of tertiary industry, strengthening the second industry and decreasing environmental damage.

Low level +high level+ lower level. The proportion of the primary industry is more than or equal to 0%, less than 25%; the proportion of the secondary industry is more than or equal to 75%, less than 100%; the proportion of the tertiary industry is more than or equal to 0%, less than 25%. The industry structure belongs to this type in Zouping country and Kenli country. The characteristics is the lower proportion of the primary industry and the tertiary industry, and the higher proportion of the secondary industry, the focus of development is upgrading the second industrial, accelerating the tertiary industry development.

During the evolution process of industry structure, adjustment and upgrading of it in developed regional showed the obvious evolution stages, " primary, secondary and tertiary industry "→" secondary, primary and tertiary industry "→" secondary, tertiary and primary industry "→ " tertiary, secondary and primary industry ", the industry structure of "tertiary, secondary and primary industry" was regarded as the goodness level of industry structure. By calculation, there is " secondary, primary and tertiary industry " industry development stage in Lijin country, the industry evolution stage is " secondary, tertiary and primary industry " in Changyi city, Laizhou city, Shouguang country, Zouping city, Guangrao country, Kenli country, Hekou district, Dongying district, Yangxin country, Wudi country, Gaoqing country, Leling city, Qingyun country, Zhanhua country, Huimin country, Bincheng district, Boxing country, Hanting district. So the achievement of the superior level of industry structure was considered of the following country-level units: Laizhou city, Shouguang city, Dongying district, Gaoqing country, Leling city, Qingyun country, Zhanhua country, Bincheng district and Boxing country. In a word, three industry restructuring should depend on "high-efficient, ecological and economic zone plan in Yellow River Delta " and "the memorandum about joint promoting eco-agriculture demonstration zones in Yellow River Delta", the industry structure was carried out to mainly rely on the driving of secondary industry transformation into the collaboration driving of the primary industry, second industry and the tertiary industry, based on structural optimization and upgrading the joint development of the region and highlighting intensive utilization of resources, eco-efficient industrial system was preliminary constructed .

Optimization and Variability of Primary Industry Structure

Optimization Analysis of Primary Industry Structure

"Agriculture, forestry, animal husbandry and fishery" industry is one of the 16 major industries reflecting the economic function [START_REF] Wang Jinfeng | Spatial ananlysis tutorial[END_REF] . The proportion of cultivating industry, forestry industry, animal husbandry and fishery industry value in Yellow River Delta showed that cultivating industry is the superior industry in Changyi city, Shouguang city, Zouping country, Guangrao country, Lijin country, Yangxin country, Gaoqing country, Leling, Qingyun country, Huimin, Bincheng district, Boxing country. Fishery industry is relatively prominent industry in Laizhou city, Kenli country and Hekou district. Industry structure types of cultivating industry, forestry industry, animal husbandry industry and fishery industry in Yellow River Delta is illustrated in Figure 3, as follows:

Higher level+ low level +lower level+ low level. Cultivating industry is prominent industry in Changyi, Zouping, Guangrao, Yangxin, Gaoqing, Leling, Qingyun, these country-level units are not only grain and cotton production base, but also the relatively good animal husbandry. Adjustment and upgrading of industry structure centered "Number 1" file in 2011, expanded the effective irrigation area, promoted high-efficient and high-quality of cultivating industry, moreover, the industry chain of vegetable, winter jujube was developed and extended, animal husbandry base was planned to construct, animal epidemic prevention system was built and the dairy chain was extended to construct the relatively reasonable economic structure.,

Lower level + low level +lower level+ low level. The industry structure belongs to the type in Dongying, Lijin, Wudi country, Hanting. For the four countries, Industrialization development should be added to the contents, which included "high technology, low resource consumption, little environmental pollution", so the new path of agriculture industrialization of information leading industrialization, industrialization promoting information was constructed to accelerate upgrading and adjustment of agricultural industry structure.

Higher level+low level+lower level+low level. Cultivating industry is prominent industry in Shouguang, Zhanhua, Huimin, Bincheng, Boxing. At the premise of ensuring the stable growth of grain, high quality and high efficient economic crops should be positive developed and fodder crops s was gradually constructed to transform cultivating structure into ternary structure of grain crop, economic crop and fodder crop.

Lower level +low level +lower level+ lower level. High salinization content and fragile ecological environment is gradually improved in Kenli Country, with the comprehensive technology development of saline and alkaline land reclamation and water-saving demonstration development. In the next step, restructuring of industry structure should emphasize the coordinated development of agricultural industries centered industry effects of 1 adding t 1 is more than 2. For the new planning of the ecological function in the country, economic development and industry adjustment are the important thing, improving wetland ecological areas and cultivation of fishery industry, construction of aquaculture bases, the introduction of high-yield, highquality, high stress resistance breeding new varieties of sea water are the other emphasis in Yellow River Delta.

Lower level+ low level +lower level+ lower level. the kind of industry structure represented the good fishery foundation in Laizhou city and Hekou district, the aquaculture base, which is ecological health of the breeding base should be constructed in the country-level units, and the upgrading and restructuring of marine industries and traditional industries promoted the regional economic development.

There is obvious agglomeration characteristics of industries value of primary, secondary and tertiary industry, and the cultivating industry, forestry, animal husbandry and fishery industry, which suggested spatial association characteristics of industry values existed in 19 country-level units. Moran's I was used to analyze the cluster pattern of industries value. Theory and technology referred to the correlation literatures (Anselin,2004;YujianYang,2010), Moran's I value is equal to 0.1034, 0.0484 and -0.0761 of primary industry value, secondary industry value and tertiary industry value, respectively, and Moran's I value is equal to -0.0083, 0.0989, 0.2090, 0.2202 and -0.0761 of primary industry value, secondary industry value and tertiary industry value, respectively. The biggest Moran's I value indicated the highest spatial autocorrelation characteristics, so the study results showed that animal husbandry value and the primary industry have the higher spatial autocorrelation, other industries has the lower autocorrelation characteristics, The autocorrelation characteristics of industries value was caused by the complicated factors, such as natural factors, social & economic factors and historical factors [START_REF] Anselin | Advances in spatial econometrics : methodology , tools and appl ications[END_REF][START_REF] Yang | The spatial and temporal prognosis of oilseed yield in Shandong Province Springer, computer and computing technologies in agriculture Ⅳ[END_REF] .

Spatial Variability of Cultivating Industry Value

The classical statistics analysis was developed for the value of cultivating industry, forestry industry, animal husbandry industry and fishery industry of 19 country units in Yellow River Delta, the range reflected the regional difference of industry value, fishery industry value > forestry industry value > cultivating industry value > animal husbandry industry value, and the variation coefficient is 0.657,1.028,0.807,0.537, respectively, for cultivating industry value, fishery industry value, forestry value and animal husbandry value, the results showed that there existed in the big difference among industry values. The normality suggested that distribution of the above industry value is higher than normal distribution, the large standard deviation and coefficient of variation showed that relatively large differences existed in agriculture industrialization. The large skewness coefficient indicates the mean value of four industry value is the right side of the peak value, showing a positive (right) skewness, kurtosis coefficients is more than 0 showed that the distribution of four industry value is higher than normality, kurtosis coefficient of fishery industry and fishery industry value is more than 3 showed the fitting curve is steep.

The analysis of the Pearson`s correlation coefficient among cultivating industry value, forestry industry value, animal husbandry value, and fishery industry value showed that the value is equal to 0.886 between cultivating industry value and animal husbandry value in confidence interval of 99%, the value is equal to 0.532 between forestry industry value and fishery industry value in confidence interval of 95% and the value is equal to 0.463 between animal husbandry value and fishery industry value in confidence interval of 95%. The striking result is that the better correlation characteristics among cultivating industry value, forestry industry value, animal husbandry value, and fishery industry value.

The spatial variability of industries value is not able to be clarified by the classical statistics, but using geostatistics combining with GIS. The continued surface of industry value using Universal Kriging referred to Figure 4. The results reflected the total distribution of cultivating industry value, based on the centroid coordinates of 19 country-level units, not only considering of sample size, shape, relationships and spatial location distribution, but also the non-stationary characteristics of industry value of samplings, centroid coordinates is calculated by the average weighted method and the formular is the following description:

     n i i n i i i G W X W X 1 1 ,      n i i n i i i G W Y W Y 1 1 Where G X 、 G Y represents the centroid coordinates; i X , i Y stands for the discrete coordinates of ith objective, i
W represents the weight of ith objective.

Mode Analysis of Primary Industry Structure

There is a lack of proper balance and the low ratio among forestry industry, animal husbandry industry and fishery industry in Yellow River Delta and are still serious structural problems during the economy development. There are a lack of competitive industries and products, the low-quality varieties and the lag status of agricultural products processing and circulation, and so on. To solve these issues, we should improve the organization degree of agricultural industry, and build a new mode for regional industrial development [11] .The new mode of the regional development combing straight-pushing mode with tilting mode was applied in the economy development in Yellow River Delta. Straight-pushing development mode emphasized the synchronous development of industries, or balanced development in the balanced regions of industry proportion, for example, Kenli country. Tilting development mode emphasized the development of the leading industry, which is the ideal regional model of industrial development comparative with straight-pushing development mode, the mode is suitable to Changyi city, Zouping country, Guangrao Country, Yangxin Country, Gaoqing Country, Laoling city and Qingyun country, dominant industry of cultivating industry. So tilting mode was also applied in the superior level of fishery industry in Laizhou, Kenli and Hekou [START_REF] Chuanmin | collaboration development model of Country economic industry[END_REF] .

We will continue to follow a new path of building industrial zone involved in marine industries and land industries, moreover, and we should accelerate the industry clustering concentrating on upgrading the industrial structure. The non-linear characteristics of 1 adding 1 is more than 2 from the association effects of industry, so we should emphasize the achievement development of industry clustering regions in Changyi, Shouguang, Laizhou, Wudi, Zhanhua and Bincheng district. With the support of economic technical development area, high-efficient and high-ecological agricultural demonstration zone, modern industrial clusters which has distinctive, specialized characteristics and complete service platform was constructed on basis of industry chain as a link, and industry demonstration area as the main object [START_REF] Zhang Minglin | Growth mechanism of agricultural industrialization chain[END_REF] . Agricultural industrial chain is loose, fragile in Yellow River Delta, and the regional chain is identical, short and narrow, the lack of diversity. Considering of it, the agricultural industrialization mode should combine with the service mode of agricultural information, such as "professional market (company) and farmers," "farmers and cooperative economic organization", "leading enterprise and farmers". The variety and quality of the agricultural products, the regional pattern of agriculture was carried out to adjust in all aspects, achieve the strategic adjustment and upgrading of agricultural structure, finally, the country industry model was formed, industry association characteristics of modern agriculture demonstration site of China of "Shouguang city mode" and industrial clusters of "Zouping city mode" [START_REF] Guangshun | economic and industrial pattern of marine industry[END_REF][START_REF] Pingnan | Harmonious development -path of China countleveldevelopment[END_REF] .

Conclusions

In a word, the low level of industrial structure, the bigger proportion of traditional agriculture, the low-tech industry and the behind service industry were reflected in Yellow River Delta. There is the difference of country economy in 19 country-level units from variation index and multidimensional scaling index analysis. As is known to us, industrial structure types reflected the degree of industry optimization and provided the foundation of industry structure adjustment. By analyzing the combination types of industry structure of primary, secondary and tertiary industry, and the cultivating industry, forestry, animal husbandry and fishery industry of agricultural industrial structure, we drew a conclusion that the type of primary, secondary and tertiary industry structure of 19 country units located in Yellow River Delta is in the industrial structure "second, first and third" stage and "second, third and first" stage according to industry structure stages theory, which explained the necessity of upgrading and adjustment of industry structure. The results also showed that the value of cultivating industry has spatial variability based on classical statistics combining with universal Kriging, and the continued surface from point scale to the regional scale, the results also indicated that the optimization of industry structure in Yellow River Delta should take some important measures, such as the optimization of the agricultural industry chain and the boosting effects of the rural areas and agriculture information, the study also put forward to new ideas which straight-pushing model is combing with favorable model of country economy about promoting industrial optimization and coordination development in Yellow River Delta. Correspondingly, we should accelerate efforts to upgrade the industrial structure and make independent innovations, and the emphasis of industry adjustment should be the focus on the secondary industry driving development transformation into the primary, secondary and tertiary industry jointly driving development, achievement of the transition from secondary industry serving as the major driving farce to primary, secondary and tertiary industries jointly driving economic growth, and accelerating the development of modern industrial system, integrating IT application with industrialization.

Fig. 1 .

 1 Fig. 1. The curve linear of 19 country units (left).

Fig. 2 .

 2 Fig. 2. The location plot based on multidimensional scaling analysis (right).

Fig. 3 .

 3 Fig. 3. Industry structure types of 19 country units in Yellow River Delta in 2007 (left).

Fig. 4 .

 4 Fig. 4. The spatial prediction map of cultivating industry value of 19 country units in Yellow River Delta in 2007 (right).


Mingquan Wu

Jie Wang

Ni Yao

Zhongwei Hou

Changyao Wang

Data Quality Evaluation of ZY-1 02C Satellite

Keywords: ZY-1 02C satellite, Data quality evolution, Signal to Noise Ratio, Entropy, Definition

ZY-1 02C satellite is a new Chinese civil remote sensing satellite launched on December 22, 2011. It is the highest resolution civil remote sensing satellite in China which carried panchromatic/multispectral sensor and high resolution sensor. Compared with image of SPOT5 satellite, the radiation quality of ZY-1 02C sensors data was evaluated. The result showed: the SNR of ZY-1 02C is higher than SPOT5 panchromatic band; the power spectrum of ZY-1 02C is close to SPOT5 panchromatic band; however, the mean value and variance of ZY-1 02C data are smaller than SPOT5 panchromatic band, while the skewness and kurtosis of ZY-1 02C data are higher than SPOT5 panchromatic band; the definition of ZY-1 02C data is much smaller than SPOT5 panchromatic band and the entropy is a little smaller than SPOT5 panchromatic band.

Introduction

Chinese resource satellite (ZY-1 02C) is a new civil remote sensing satellite which was launched on December 22, 2011. It weighs about 2100 kg, 3 years design life, and equipped with panchromatic/multispectral sensor (P/MS) and High-resolution sensor (HR). Its mainly usage is to acquire high-resolution panchromatic and multispectral data for land resources survey and monitoring, disaster prevention and mitigation, forestry, water conservancy, ecological environment, and the state of major projects monitoring.

Data quality evolution is not only an important technology to inspect the design of satellite sensors, but also an important theoretical reference for remote sensing satellite sensor development and band selection [START_REF] Rui | Data quality evaluation of chinese HJ CCD sensor[END_REF]. In the last decades, several approaches have been proposed in this study area. Yang YY et.al. [START_REF] Yuanyuan | Reseach on the quality of exceed images provided by CBERS-1 Satellite[END_REF] evaluated extended service image of CBERS-01, Wang J et.al. [START_REF] Jing | The quality evaluation of the Image Date of CBERS-1No.2 Satellite[END_REF] evaluated image of CBERS-02, Zhou YB et.al. [START_REF] Yuji | CBERS-02B CCD image data quality evaluation and application potential for vegetation classification[END_REF] and Zhang JL et.al. [START_REF] Zhang Jielin | The image quality evaluation of CBERS-02B and the application of the satellite of the interpretation of ore-forming factors of uranium deposits[END_REF] evaluated image of CBERS-02B, Liu R et.al. [START_REF] Yang Zhongdong | CBERS-1's CCD image quality evaluating and cross calibrating study[END_REF] evaluated image of HJ-1 satellite, Zhang JL et.al. [START_REF] Yuji | Image quality evaluation of EO-1 Hyperion sensor[END_REF] evaluated image of Hyperion sensor onboard on EO-1satellite, Wang QJ et.al. [START_REF] Qinjun | Quality evaluation of LISS3 image from IRS-P6 satellite[END_REF] evaluated image of LISS3 sensor onboard on IRS-P6 satellite, G H Thomson [START_REF] Thomson | Evaluation of Russian Arkon-2 Earth observation satellite[END_REF] evaluated image of Arkon-2 satellite. Most of those methods are based on image engineering. We would like to draw your attention to the fact that it is not possible to modify a paper in any way, once it has been published. This applies to both the printed book and the online version of the publication. Every detail, including the order of the names of the authors, should be checked before the paper is sent to the Volume Editors.

Study Area

The test area covers a surface of about 104 km 2 and covered by images of SPOT5 and ZY-01 02C satellite. It is located in the north-east of Beijing, China (40.05°-40.58°N ,116.51°-117.21°E) (fig. 1). Most of the area is flat and devoted to agriculture, with a prevalence of winter wheat, corn and urban areas.

Data Introduction

Three ZY-1 02C images and one SPOT5 image were used in this study (Table 1). All of those data were acquired in clear shy conditions and covered the same area.

     1 0 1 N j j x N x Mean . (1)         1 0 2 1 1 N j j x x N Variance . (2)              1 0 3 1 N j j Variance x x N Skewness . (3)               1 0 4 3 1 N j j Variance x x N Kurtosis (4)
Where N is the number of pixels, j is the index of pixels, and

x is the value of pixel j. Signal to Noise Ratio. Signal to Noise Ratio is the ratio of signal and noise in image. There are many different methods to calculate the image SNR, and different methods have different result. Some research showed that the image SNR and land cover closely related [START_REF] Jiangui | Estimation of signal to noise ratio of remote sensing images[END_REF]. Variance method can determine the SNR level of sensors, but the method is influenced by surface target. However, using this method between different sensors in the same area can be used to compare the SNR level between different remote sensing sensors. The calculation formula of SNR by variance method is followed:

Variance Mean SNR /  . ( 5
)
Sharpness. The sharpness reflects the sensitivity of the image edge change. The more distinctive of color contrast of the image junction, the clearer of the image. A improved point sharpness algorithm was used to calculate the image sharpness1. The calculation formula is followed:

n m dx df p n m i       1 8 1 /  . ( 6
)
Where p is the sharpness, m and n is the line and row number of pixels, df is the amplitude of intensity change, dx is the distance increment between pixels. Information content. Information content is a indictor that reflect the rich degree of information contained in images. It always measured by information entropy. The calculation formula of entropy is followed:

    n i i i P P H 1 log . ( 7
)
Where H is the information entropy, i is the value of pixels, i P is the probability of value i. Due to the cross correlation between adjacent bands, the redundant information must be removed when calculating total entropy of multiband images [START_REF] Zongjian | Measurement of information and uncertainty of remote sensing and GIS data[END_REF]. Power spectrum. Image power spectrum reflects the image texture features. When the image quality the loss of high frequency components will lead to blur of the image space texture and the image power spectrum of each component will fall. Thus, the comparative evaluation of image quality can be evaluated by comparing the image power spectrum12. Image power spectrum can calculate by Fourier transform. The calculation formula is followed:

          1 0 1 0 2 , m u n v v u v u F sum . ( 8
)
Where sum is image power spectrum, u and v is the index of pixels,   v u F , ˆis the value of pixel (u, v) after Fourier transform.

5

Result and Analysis

Result of Qualitative Evaluation

Through visual interpretation approach, we can found out that both SPOT5 image and ZY-1 02C images have a good visual effect (Fig. 2). Land cover types like farm land, water and cities were very clear. However, those land cover types have a more strong contrast in SPOT5 image.

Result of Quantitative Evaluation

Parameters of radiation precision, Signal to Noise Ratio, sharpness, information content, and power spectrum were calculated to evaluate the radiation quality of ZY-1 02C data and SPOT5 PAN band image (table 3). By comparing parameters of mean, variance,skewness and kurtosis, we found that the mean and variance of SPOT5 PAN band image is higher than any ZY-1 02C images, while the skewness and kurtosis are smaller. This showed that the value distribution of SPOT5 PAN band image is more smooth and broad, which is coincide with the result of histogram comparison.

The SNR of ZY-1 02C images are higher than SPOT5 PAN band image. The green band image of ZY-1 02C MS sensor has the highest SNR. It is 53% higher than SPOT5 PAN band image, while the red and near-infrared band is 26% and 30% higher than SPOT5 PAN band image. The SNR of pan band of ZY-1 02C is 25% higher than SPOT5 PAN band image. The HR band of ZY-1 02C has a very close SNR with SPOT5 PAN band, which is only 8% higher than SPOT5 PAN band.

The sharpness of SPOT5 PAN band is more higher than any ZY-1 02C images. The HR band of ZY-1 02C has the highest sharpness in ZY-1 02C images. However, its sharpness is only 51% of the sharpness of SPOT5 PAN band.

Images of those two satellites have a close power spectrum. The power spectrum of SPOT5 PAN band is a little higher than images of ZY-1 02C.

The SPOT5 PAN band has higher entropy than any ZY-1 02C images. The entropy of SPOT5 PAN band is about 21%、23%、33%、27% and 27% higher than the entropy of HR band, pan band, green, red and near-infrared band. Even the total entropy of MS images is also a little lower than the entropy of SPOT5 PAN band.

6

Conclusions and Discussion

Conclusions

Comparing with SPOT5 PAN band, the quality of ZY-1 02C images is similar to SPOT5 PAN band. Each data has distinct advantages and disadvantages.

The SNR of ZY-1 02C images are higher than SPOT5 PAN band. And the Power spectrum of ZY-1 02C images is close to SPOT5 PAN band. However, due to the histogram of ZY-1 02C images is too narrow and abrupt, the ZY-1 02C images cannot distinguish the differences of the surface reflectance very well. The sharpness of ZY-1 02C images is lower than SPOT5 PAN band. Moreover, the entropy of ZY-1 02C images are lower than SPOT5 PAN band.

The ZY-1 02C has not blue band images. This will limit the application of ZY-1 02C data in water monitoring. However, ZY-1 02C have two Panchromatic bands. So the panchromatic data acquisition ability of ZY-1 02C satellite is stronger than SPOT5 satellite.

Discussion

Due to the lack of higher-resolution topographic maps, the positioning accuracy of ZY-1 02C satellite was not evaluated in this paper. However, the positioning accuracy is a very important index of satellite image quality. So, we will carry out work in this area when conditions are ripe. Due to data constraints, only SPOT5 PAN band was used as a comparative data. And comparison of multi-spectral bands between SPOT5 and ZY-1 02C satellite was not carried out. The difference of spatial resolution may influence the result of some comparison between SPOT5 PAN band and multispectral bands of ZY-1 02C satellite. Moreover, the acquire date of SPOT5 PAN band is five years earlier than ZY-1 02C images. The change of land cover was not considered in the evolution which has a certain extent effect to evolution result.

Fig. 1 .

 1 Fig. 1. Location of the study area.

Fig. 2 .Fig. 2 .

 22 Fig. 2. Images of ZY-102C and SPOT5.The histogram of SPOT5 PAN band image, and images of ZY-1 02C were calculated by ENVI 4.3 (fig.3). By comparing histogram of those images, we found out that the quality of SPOT5 PAN band image is better than ZY-1 02C images. The histogram of SPOT5 PAN band image is more broad and more smooth, while the histogram of ZY-1 02C images were narrow.

Table 1 .

 1 Images used in this study.

	Satellite	ZY-1 02C				SPOT5
	Sensor		02C-MS	02C-PAN	02C-HR	PAN	Sensor
	Path/row	1/107	1/107	395/107	281/269	Path/row
	Acquisition time	2012/02/01 2012/02/01 2012/02/13 2007/04/24 Acquisition time
	Spatial resolution 10m	5 m	2.36 m	2.5 m	Spatial resolution
	Sensor		02C-MS	02C-PAN	02C-HR	PAN	Sensor
	3.1	ZY-1 02C Satellite
	ZY-1 02C is a high spatial resolution civil remote sensing satellite. It carried one
	panchromatic/multispectral sensor (P/MS) and two High-resolution cameras (HR)
	(Table 2).

Table 2 .

 2 Basic parameters of sensors carried on ZY-1 02C and SPOT5 satellite.Image quality of satellite data can be evaluated qualitatively by visual interpretation approach or histogram comparison method. Visual interpretation method judged the image quality is good or bad directly by naked eyes, while histogram comparison method determined the image quality by histogram comparison. Images with good quality have a smoother and broader histogram. Radiation precision is an indicator that reflects the radiation situation of images. It can be measured by parameters like mean, variance,skewness and kurtosis. The calculation formulas are as follows:

	Satellite	ZY-1 02C			SPOT5
	Sensor	P/MS PAN	MS	HR	PAN	MS
							0.43-0.47
	Bandwidth(μm)	0.51-0.85	0.52-0.59 0.63-0.69	0.50-0.80	0.49-0.69	0.49-0.61 0.61-0.68
				0.77-0.89			0.78-0.89
	Spatial resolution	5 m	10m	2.36 m	2.5 m or 5m	10m
	Swath Width	60km		54km	60km
	Revisit Time(day)	3-5		3-5	2-3
	3.2	SPOT5 Satellite
	4	Approach
	4.1	Qualitative Evaluation

SPOT5 satellite is the fifth satellite of France SPOT series. It carried two High Resolution Geometric (HRG) sensors, one High-Resolution Stereoscopic (HRS) imaging instrument, and one Vegetation sensor.

Please note that, if your email address is given in your paper, it will also be included in the meta data of the online version.

Table 3 .

 3 Quantitative evaluation results of radiation quality.

	Parameters	SPOT	HR	Pan	MUX Green	Red	NIR
	Mean	135.917	57.246	52.990	56.905	49.918	47.813
	Variance	882.993	134.722	101.315	66.393	85.967	64.759
	Skewness	-0.087	0.984	1.023	1.698	1.058	0.286
	Kurtosis	-0.284	3.449	3.742	7.058	3.435	1.866
	SNR	4.574	4.932	5.264	6.984	5.384	5.942
	Sharpness	33.115	16.339	19.899	17.586	20.800	17.812
	Power spectrum	115.096	107.559	100.855	95.394	94.314	93.914
	Entropy	6.816	5.388	5.280	4.596	4.970	5.003
	Total entropy	6.816	5.388	5.280	6.649

Acknowledgments. This work was supported by the opening foundation of Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, and the Major State Basic Research Development Program of China (2010CB950603).


Hongxin Cao
email: caohongxin@hotmail.com

Yuwang Yang

Zhiyuan Pei

Wenyu Zhang

Daokuo Ge

Yiran Sha

Weixin Zhang

Kunya Fu

Yan Liu

Yuli Chen

Wenyu Zhang

Weixin Zhang

Hongjun Dai

Hainan Zhang

Intellectualized Identifying and Precision Control System for Horticultural Crop Diseases Based on Small Unmanned Aerial Vehicle

Keywords: Spectrum, identification, horticultural crop diseases, precision control, unmanned aerial vehicle (UAV)

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

Introduction

The control for diseases and insect pests is the key to horticultural crop production with high yielding, good quality, better benefit, ecology, and safety, and the traditional control for diseases and insect pests depends on manned or machined application of pesticide, which has some obvious disadvantages such as bigger work intensity, high cost, and environmental problems, etc. [START_REF] Zhang | The Pollution of Agricultural Environment with Chemical Pesticide and its Control[END_REF][START_REF]China's Pesticide Pollution and Its Being Problems and Suggestions[END_REF][START_REF] Zhu | Controlling the Environmental Pollution of Chemical Pesticide, Carrying out Consecutions Protection[END_REF][START_REF] Tu | Thinking of Problems of Agriculture Chemicals and Circumstance[END_REF] .

In recent years, rapid progress on the studies of unmanned aerial vehicle (UAV) spraying facility has been made in Japan and USA, etc. The first UAV in the world was created by Yamaha Company in Japan in 1990, which was mainly used in bestrewing pesticide [START_REF] Zhang | Application of remote control helicopter in Japan agriculture[END_REF] . Since 90's in 20 century, the remote control helicopter has been used in prevention and cure for diseases and insect pests of field crops, fruit tree, and vegetables, proper fertilization and quality management of field crops, and estimation of crop growth status (rice) [START_REF] Sun | Multi-purposes unmanned helicopter in agriculture[END_REF][START_REF] Hunt | Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[END_REF] . Because the UAV has some advantages such as small in scale, agility in grasp, and good effect in spray, etc., it has been into one of development trend that the UAV is used in agricultural production. The studies of aviation spraying of UAV were also done in USA [START_REF] Gong | Aviation spraying pesticide technology in modern agriculture[END_REF] . In china, the application of UAV spraying is not adaptable with needs of modern agricultural development, and has a big gap comparing with that of overseas [START_REF] Gong | Aviation spraying pesticide technology in modern agriculture[END_REF] .

The objectives of this study is to explore the UAV remote sensing identifying technology for horticultural crop diseases, to combine it with UAV spraying, and to create intellectualized identifying and precision control system for horticultural crop diseases based on small UAV.

Materials and Methods

Materials

The experiment for spectral capture was carried out in peach garden of headquarters in Jiangsu Academy of Agricultural Sciences from May to Nov. in 2012, the peach leaf blade was taken as material, and the peach shot-hole disease was taken as object for spectral capture.

Methods

Canopy spectral measurement: The spectral reflectance of the disease leaf and the happy leaf in the same peach, and the disease peach and the happy peach in the same peach garden and time were measured using MSR16R Multispectral Radiometry (16 wave bands, spectral ranges from 460 nm to 1700 nm, and manufactured by Cropscan, Inc. in USA) in the fine day's 10:00 a.m.-2:00 p.m. with above 45°solar elevation angle, the radiometry probe surface was paralleled to peach canopy or target leaf with 0.2 m upright distance, and the spectral reflectance adjusting of standard whiteboard was done around each measurement.

Expert identification of disease leaf: The severity of disease is composed of the size and number of disease spot in a leaf or a peach, in that the size and number were divided into 3 levels according to expert identification.

Analysis and using of data: First, the ranges of sensitive waveband for disease parameters were found based on correlation between disease parameters and single waveband spectral reflectance of disease leaf and peach, and the vegetation indices (VI) composed of sensitive waveband were computed based on existing research; Second, the optimum VI of the most sensitivity to disease parameters were screened out through analyzing correlation between disease parameters and vegetation indices, and the corresponding regression equations were established, in that correlation analysis, and VI computing were done using OFFICE.2007 and SPSS v 16.0.

Concept design

First, the standard spectral reflectance of disease leaf in peach garden was captured using spectral radiometry, and the spectral reflectance of peach garden canopy was measured using spectral probe carried by small UAV. Second, the information captured was wirelessly sent to ground, analysed, and identified. Third, the intellectualized identifying and precision control system for horticultural crop diseases based on identifying-pesticide spraying integration was set up through combining the spectrum-based identifying for crop diseases and the UAV-based pesticide spraying system (Fig. 1).

Small unmanned aerial vehicle (UAV)

The capture of spectral reflectance carried by small UAV of peach garden canopy UAV-based pesticide spraying system Wirelessly being sent to ground control station or UAV

Analysis and identification on ground control station

The standard spectral reflectance of disease leaf UAV-based precision pesticide spraying Fig. 1 Concept design of the intellectualized identifying and precision control system for horticultural crop diseases based on identifying-pesticide spraying integration

Results

The intellectualized identifying and precision control system for horticultural crop diseases based on small UAV mainly included identifying system for horticultural crop diseases, spraying pesticide system carried by small UAV, and the ground mobile control station system.

Identifying system for horticultural crop diseases

The identifying system included the part carried by small UAV, and the ground mobile control station.

The spectral measurement device carried by small UAV for horticultural crop diseases. The focal plane probe with 200 mm large caliber, inclined 45°lens structure, large light area, and stable turning was used in spectral probe, and the head of spectral measurement probe was composed of the imaging optics system, the bearing flat, and spectral probe. The scanning spectral ranges from visible to infra-red (430-1030 nm, 60 wavebands with 10 nm resolution) were selected, its verification examples were shown in Fig. 2. The linear detector-scanning program was applied in modularizing spectral meter system, and the spectral resolution, space resolution, sensitivity, and volume and weight were considered in the development of spectral measurement section. The GPS system was applied in UAV positioning, and the dynamic precision of position system can go to the meter level. The peg-top stable flat was introduced into UAV in order to insure pose stability of the spectral measurement device.

Fig. 2 Verification for the spectral measurement device carried by small UAV Communication between the small UAV and the ground mobile control station. Due to the large number of spectral data needed to be wirelessly transferred between the small UAV and the ground mobile control station, the bandwidth for communication should be larger. At the same time, the various statements from the ground mobile control station needed to be incepted by UAV, but because the formats of statements were to a certainty, its length was finitude, and the demand to communication bandwidth was not high. The independent uplink and downlink channel were adopted by the system, and the asymmetric bandwidths were used in transfer to avoid interfere between data and statements transfer each other. Under the normal condition, this can provide less than 3.5 Mbps of the uplink speed, and 54 Mbps of the downlink speed. The communication frequency with 2.4GHz was selected, and the wireless transceiver system developed by ourselves was used in communication device.

The real-time transaction for spectral data and identifying of peach diseases.

There is a powerful computer system in the ground mobile control station, which mainly was used in pre-processing for the spectral data, such as GPS data processing and composite, calibration processing, bad line repair, pixel registration, histogram calculation, contrast stretching, regional amplification, spectral curve display, data capture, and data format conversion, etc. The spectral data from the UAV was real-timely processed, the comparison of the final spectral data and the standard spectral reflectance of disease leaf were done after a series of processing steps, and the identifying information of peach diseases were obtained.

Spraying pesticide system carried by small UAV

The impact factors of UAV spraying effect included spraying device, target plant, atomization degree for droplet, droplet flow transport properties, and droplet penetrability, etc.

The development of UAV spraying device. It included self-regulation for amount

of spray, droplet diameter, and nozzle structure, etc. so as to fulfill low volume, ultra-low volume, and electrostatic spraying, and to help to realize double times atomization when the high speed airflow were passed through spraying nozzle.

Droplet atomization.

It can be divided into two modes, centrifugal type and hydraulic atomization. The centrifugal atomization nozzle was selected in UAV spraying device, and driven by generator in UAV. The size of droplet can be regulated using rotate speed and the dial structure of nozzle.

In addition, the atomization droplet was effected by airflow speed, state, and outside atmosphere, etc. in the flight UAV. The droplet atomization was studied in order to obtain the optimum particle size and movement state.

Sedimentation process. In the droplet transport and sedimentation processes, due to space of flow was very complex, and it let droplets to collide and polymerize each other, which resulted in movement of droplets with huge randomicity. Therefore, the mathematic models of space of flow were developed through experiments in order to capture the integrative flow state of droplets in the space of flow.

Effects of meteorological conditions on spraying.

The optimum time of spraying was one of important factors for raising droplet target rate, and under the different regions, and terrain conditions, the effects of temperature and wind speed, swirl from rub of airflow and crop, etc. on distribution of droplet on crop were studied.

Effects of droplet size and number on spraying. The droplet coverage was related to many factors, especially rotor vortex, and flight velocity in UAV. The droplet size depends on the target state sprayed, the optimization of droplet size can help to raise target coverage, and use pesticide availably.

Establishment of UAV operating instructions for spraying technology.

The UAV operating instructions for spraying technology were established according to the relevant FAO's rule to ensure security for flight and operators, spraying device stability, and operating precision, and reduce the negative effects on environment, nearby crop, and human and livestock.

The ground mobile control station system

The ground mobile control station system included the ground mobile control center carried by vehicle, the ground remote control and measurement system for UAV, the ground control station system for spraying pesticide, and the ground spectral signal incept system, etc., in that the ground remote control and measurement system for UAV were composed of industrial control computer, remote control transmitter, telemetry receiver, remoter, and power box, etc.

Test of the system

Test of the system mainly included the identifying precision of peach diseases, and the spraying pesticide effect, and the spraying pesticide effect was checked through comparison the spraying pesticide position carried by UAV and the observed disease position in peach garden. Some sub-systems were initially tested in laboratory and outside.

Discussion and Conclusions

Discussion

Identifying system for horticultural crop diseases. The previous studies showed that there were some shape and physiological changes in leaf such as becoming yellow, wilt, and withered, etc. when plant were stressed by diseases, which was visible changes easily to be identified. In addition, red light, infra-red, "blue shift", and "red shift", etc. all need to be considered in this study [START_REF] Huang | Hyperspectral character of stripe rust on winter wheat and monitoring by remote sensing[END_REF] . Size and volume of device carried by small UAV. In order to reduce loading of the small UAV, the spectral data captured by small UAV were transferred to the ground control station, and real-timely processed in the ground control station. The optic structure of spectral system carried by the UAV were elaborateelaborately designed, the whole spectral system weight was less than 1 kg, and its volume was less than 0.001 m 3 (0.10m×0.10m×0.10m). Spraying technology in lower level. The spraying technology in lower level is the key to the intellectualized identifying and precision control system for horticultural crop diseases based on small UAV. Therefore, the centrifugal atomization nozzle was used in small UAV spraying device to adapt to spraying pesticide in lower level, and raise control efficiency. The system need to be test under different conditions. The study is still in the initial stage, the various sub-systems were not integrated into the intellectualized identifying and precision control system for horticultural crop diseases, and tested under different conditions.

Conclusions

The intellectualized identifying and precision control system for horticultural crop diseases were developed, mainly including the identifying system for horticultural crop diseases, spraying pesticide system carried by small UAV, and the ground mobile control station system. Some sub-systems, e.g. the spectral measurement device carried by small UAV, were initially tested in laboratory and outside.

Fund [CX(11)2042], and Jiangsu Province Scientific Technology Supporting Program [BE2012386-1].