Dengwei Wang
email: wangdw@nercita.org.cn

Tian'en Chen

Chi Zhang
email: czhangc@nercita.org.cn

Li Gao

Li Jiang

Research of the Early Warning Model of Grape Disease and Insect Based on Rough Neural Network

Keywords: rough set, artificial neural network, grape disease and insect, early warning model

The grape is one of the four fruits in the world and its cultivation area and production has been ranked first in the world. The area is growing in our country after the reform and opening up which is significant for the rural economic development and farmers' income. However, the growing of grape diseases and insect pests has become one of the important problems in the development of grape planting industry. In the paper, intensive and overall surveys and studies of the research progress of the early warning model of the disease and insect are made firstly and then the comparison and analysis of rough set and artificial neural network are presented. Finally, we collected a large amount of data from grape planting base of the grape and wine engineering technology research and development center in Beijing. Based on the real-time sensing data technologies of Internet of things and the intelligent grape early warning model based on rough neural network established in the paper, we did a number of experiments and its validity was verified. The model can provide beneficial references for the research of other crops diseases and insect pests.

Introduction

The farming population is numerous and agriculture holds dominant position in the national economy. However, the level of agricultural science and technology information is relative backwardness in agriculture [START_REF] Li | Agricultural plant diseases and insect pests of remote diagnosis and early warning technology[END_REF]. How to increase the technological content and ensure the ability to resist the disaster in the course of agricultural production have become a development trend and research focus. The greenhouse fruit and vegetable disease and especially the explosive diseases must be conducted before the disease symptom displays. Therefore, the research pays attention to emphasize on giving out the alarm before the disease symptom is shown, such as we can send out alarm through collecting and analyzing information as well as the weather forecast before the symptom is shown [START_REF] Yang | Early warning model for cucumber downy mildew in unheated greenhouses[END_REF][START_REF] Shtienberg | Incorporation of weather forecasting in integrated, biologicalchemical management of Botrytis cinerea[J][END_REF][START_REF] Shtienberg | Polyethylene mulch modifies greenhouse microclimate and reduces infection of Phytophthora infestans in tomato and Pseudoperonospora cubensis in cucumber[J][END_REF][START_REF] Vincelli | Relationship of participation probability to infection potential of Botrytis squamosa on onion[J][END_REF].

At present, several countries such as Israel, America, Japan, and Spain put more research on greenhouse crop disease early warning system. In China, the introduction of agricultural early warning has elaborated the basic theoretical knowledge, as well as the natural disaster warning [START_REF] Tao | an Introduction to Agricultural Warning[END_REF]. In recent years, the early-warning analysis theory of the macroeconomic and regional forest resource field has been applied to agriculture field [START_REF] Gu | The Design of early warning index system for the economic and social benefits in China[END_REF]. For example, LiXin Bai [START_REF] Bo | Research on the index system of the cotton bollworm disaster monitoring and early warning and the risk warning level[END_REF] has built bollworm disaster early warning index system based on the evaluation method. According to the local climatic environment and the actual situation of crop planting in Beijing, Lili [START_REF] Li | Research on the early warning and forecasting system of pear diseases and insect pests based on radial basis network and support vector machine[END_REF] set up pear plant diseases early warning system. Ming Li and ChunJiang Zhao [START_REF] Li | Towards developing an early warning system for cucumber diseases for greenhouse in China[M]//Computer And Computing Technologies In Agriculture[END_REF] studied cucumber downy mildew early warning system in the sunlight greenhouse environment. Shuwen Liu and Qingwei Wang [START_REF] Liu | Research on the grape disease diagnosis systems based on Fuzzy Neural Network[END_REF] studied the grapes diagnosis based on neural network and Yingfeng Cui and Shiping Wang [START_REF] Cui | Comparison of the intelligent diagnosis network models for grape diseases[END_REF] constructed grape intelligent diagnosis network model. However, taking into account the application cost and other factors, there is less intelligent and practical early warning system in agriculture and still less for large area promotion. In recent years, with the rapid development of networking and cloud computing technology and combined with network real time sensing data, there is a huge advantage for us to do facility agriculture disease early warning based on agricultural cloud services platform. It is still in the primary stage of the research for early warning analysis and decision based on a large number of real-time sensing data in agriculture. So it is necessary to further improve the algorithm model of early warning analysis and the precision.

The grape downy mildew is one of the most serious diseases for the grape [START_REF] Li | Research progress of the Grape Downy Mildew prediction models and early warning technologies[END_REF]. Its incidence is closely related to the light, temperature, humidity in the microenvironment. So we took the grape in the area of yanqing country in Beijing as an example and collected real-time sensing data in grape micro environment through various sensing nodes and wireless communication network and then researched and constructed the grape diseases and insect pest warning model based on rough neural network which provides service for the intelligent analysis of grape diseases and insect pests, the agricultural precision production such as prediction and warning decision, the visual management and Intelligent decision analysis [START_REF] Wang | Research of the Early Warning Analysis of Crop Diseases and Insect Pests[M]//Computer and Computing Technologies in Agriculture VII[END_REF].

the Construction of Rough Neural Network-based Early Warning Model

Comparison of Rough Set and BP Artificial Neural Network

BP neural network has strong anti-interference ability and processes large amounts of information in parallel and arbitrarily approximates non-linear function. But it can't simplify information and reduce input data. In addition, the built network is more complicated if there is large amounts of input data. Rough set theory doesn't need prior knowledge to remove redundant information without additional data in the process of data analysis and processing, but also execute algorithm in parallel. However, its anti-interference ability is insufficient [START_REF] Zhang | Research on forest pest forecasting model and algorithm based on Rough Set Theory[END_REF][START_REF] Xing | Research on the data fusion method based on rough set and neural network[END_REF][START_REF] Hu | Rough set theory and its application in the neural network[END_REF][START_REF] Mao | Rough set theory, algorithm and Application[END_REF].

There exist complementary relationships between rough set theory and BP neural network in many aspects. For instances, we combine them and obtain a process that concentrates the knowledge reduction and no prior knowledge and strong antiinterference performance which can improve accuracy and noise resistance of the system. The result of the comparing analysis is as shown in table 1-1: maintenance is difficult when data size is large the learning time is long and it is easy to be trapped in local minimum when data size is large integration capability the integration with various software calculation methods is easily and closely the integration with the fuzzy set, the rough set and genetic algorithm is easily and closely generalization ability weaker strong generalization ability

the Constructing Idea of RS-BPANNs Model

According to their features we build the early warning model based on rough set and BP artificial neural network (hereinafter referred to as RS-BPANNs). We collect the grape diseases information case, experimental data and expert experience over the years to build an attribute decision-based matrix which is used to reduce the attribute and then design suitable BP Neural Network to train the model. The model will become knowledge rules to judge the crop disease when it achieves certain accuracy.

The knowledge model library is perfected through feedback information of diagnosis results and finally the accuracy of the early warning is improved. The decision knowledge acquisition model based on RS-BPANNs is shown in figure 1

the Constructing Steps of RS-BPANNs Model

The paper takes grape downy mildew as an example to build RS-BPANNs-based early warning model. The rough set theory is used to reduce the attributes of the training sample data and then determine the relationship among attributes. The module is divided into two parts: the preprocessing of the rough set and the artificial neural network learning. The whole process includes the following four steps: 1) Build the grape downy mildew case database: the way to obtain comprehensive grape downy mildew cases is various and the cases in the paper are mostly from document literature, expert discussion and experimental data at the grape planting base of the grape and wine engineering technology research and development center in Beijing that are saved in the form of vector. The data which is described using natural language can be converted into the corresponding data and is saved into the database in the form of matrix.

2) Express the case data through the decision table . A lot of original judgment information is represented by the decision table. Among them, C represents condition attribute value. D represents the decision attribute value. all grape downy mildew cases are represented as U. V is the range of attribute values. The representation of the original case decision table is T=(U,C,D,V). The value is equal to 0 or 1.

3) Reduce the decision table: take out the redundant attributes without changing the classification of the decision table. The reduction is realized through calculating the positive region of the decision attribute of the condition attributes in the paper. Remove C j . Otherwise, reserved 5. Ergodic all attributes 6. Output a reduction that C is relative to D The main idea of the reduction algorithm is that the condition can be removed if a condition in the set C is removed and it will not affect the decision result. Otherwise, the condition is reserved. The operation is complete until generating a reduction about C.

4) The obtaining rules of neural network: take the condition attributes and decision attributes gotten after attribute reduction as the input variables and output variables of the neural network. We use the neural network to train the sample and to learn and finally get a decision information model.

On the basis of the decision information model, feedback information is added when the plant diseases and insect pests of early warning system is realized in order to record the validity of the decision information and count the accuracy of the trained decision information model, which make the decision information easy managed and optimized. The whole process of the system is shown as the figure 12 The above table shows that whether grape downy mildew appears or not in the various greenhouse environmental factors. There are 100 items data which constitute the case table. Among them, U is a finite case set.

D C A  
, C is the condition attribute set. Through the study of grape downy mildew material, there is the stipulation that it is low when air temperature is below 18 o C and high when it is above 25 o C. It is low when the humidity is below 50% and high when it is above 90%. It is low when the soil temperature is below 10 o C and high when it is above 20 o C. (We concluded that the surface air temperature is generally 5 o C higher than the soil temperature when air temperature is relatively stable from the data gathered by sensor without the case of air temperature shock.). It is low if soil humidity is below 50% and high for above 80%. It is low if light intensity is under 30000 lx and high for above 40000 lx. It is low if CO 2 the concentration is under 1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 0 1

x 2

1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 1

x 3

1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1

x 4

1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0

x 5

1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1

x 6

1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1

x 7

1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0

x 8

1 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1

x 9

1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 1 1

x 10 1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1

x 11
1 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 1

… … … … … … … … … … … … … … … … … … … … … X 100
1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 It can be seen from the above table that the object x3、x5、x10 are the same instance because their attribute value domains are same. According to the attribute reduction method only one case is retained and the other needs removing. And classify each equivalence relation relative to the domain U after dealing with the duplicate lines.

Count the dependence degree of D relative to C and get pos c (D)={x 1 ,x 2 ,x 3 ,x 4 , x 6 ,x 7 ,x 8 , x 9 ,x 11 ,…} Finally, we get the final reduced table which includes the key factors that affect the grape downy mildew which is shown in table 2-4. The design of BP neural network is mainly divided into the following steps:

1) The design of the input layer and output layer: It should be based on application need. The number of the output node is 1 in the experiment and there are 19 properties for the objects in input layer. But the number of the core attributes is taken as the number of input layer after attribute reduction. The number of the input node N is set as 12.

2) The design of the hidden layer: It includes the design of the layer number and the neuron number each layer which mainly relies on experience. It can increase the processing capacity of neurons if we add the amount of the hidden layer. The paper uses three layers BP neural network structure. The experimental results show that single layer can meet the demand in the determination of the boundary problem of the small network.

Through the above process we can extract the knowledge rules in grape berries period:

If C 3 is between 18 o C and 25 o C /1 and C 7 > 90%/1 and C 13 > 80%/1 and C 14 < 30000lx Then D(Downy mildew) We can get the knowledge rules that grape downy mildew is easily appear in every period at grape growth stage which can guide the judgment of grape downy mildew. At the same time, according to the feedback information knowledge rules are optimized.

Comparison and Analysis

The paper makes comparison and analysis of BP artificial neural network model and rough BP artificial neural network model through training and testing the same sample and then analyzing of the test results of each model and the length of training time.

It is concluded that the combination of the rough set and BP neural network can decrease the redundancy attributes and optimize the structure of neural network and shorten the training time and also improve the effect of training. Therefore, the model based on the RS-BPANNs has certain advantage. The following table is the results of the two models in the aspect of the training time and accuracy.

Conclusions

The facility agriculture is very important in the process of agricultural modernization in our country. Taking advantage of Internet of things, we can obtain real-time information of the infrastructure environment and build early warning model to make decision analysis of facility crop diseases whose purpose is to realize the timeliness, accuracy, convenience and practicality of the facility agriculture disease early warning.

The paper is mainly for facility agriculture and constructs a grape diseases early warning model based on rough neural network. The paper gets the effectiveness of the early warning model and provides a reference for the construction of plant diseases early warning model through analyzing the real-time sensor data from the grape downy mildew.

Fig 1 - 1 .

 11 Fig 1-1. Decision knowledge acquisition model based on RS-BPANNs

3 .

 3 The input condition attribute set is represented as C={C1,C2,...,Cn} and the decision attribute set is D={D1,D2,...,Dn}.The steps of reduction algorithm are as follows: 1. Calculate the positive region of D, pos c (D) 2. Remove attributes C j from C, C w =C-{C j } Calculate the positive region of C w ,

1 )

 1 So we get the conclusion that decision attribute depends entirely on condition attributes and then calculate the relationship between pos c (D) and (

Table 1 -1. the comparison of the rough set and BP neural network Rough set theory BP neural network self-learning ability

 1

		weaker	stronger, with increasing
			learning network
	the reasoning method	serial reasoning way	implement parallel
			computing
	data processing	qualitative and	quantitatively
		quantitatively
	prior knowledge	without prior knowledge	need experience and
			multiple trial before the
			network structure designed
	knowledge	by neatly	connotative,
	representation		through parameters
	noise proof	affected by noise	good ability of resisting
			noise
	redundant data	determine the attribute	almost cannot be handled
	processing	and value of the decision
		table
	rule discovery method	determine the	through a nonlinear mapping
		relationships between data
		in the reduction process
	interpretability	easier	the transparency is poor in
			knowledge acquisition and
			knowledge reasoning process
	knowledge maintenance

  -1:

						feedback
	training samples	disease informatio n case				information(t hreshold)
	discretization	the number of condition attributes	the original decision table	attribute reduction	BPANNs	the model disease that crop easily gets

  .The paper takes grape downy mildew as example to analyze rough neural networkbased early warning model. The original data is gathered from air temperature and humidity, soil temperature and moisture(the depth of 20cm), light intensity and CO 2 concentration collected every 15 minutes time. The following data shown in table 2-1 is acquired from grape berries period in greenhouse and the acquisition time is from 7 am to 5 pm and the data collected every time is done average processing.

	2. Experimental Analysis
	2.1 Data Acquisition
				acquire
				environmental
	save/read			data			collect sample
							data
						disease
	databas	e	save/read	data preprocessing		diagnosis recordsd atabase	data decision
							table
						decision
					save/read	feedback information
				the disease early		database
				warning decision			attribute reduction
					save/read
					disease
	real-time database about growth environment		disease prevention and control guidance	prevention and control guidance	disease early warning model library	BPANNs training
					database
						save
							disease warning
							rules
		Figure 1-2. the flow of the plant diseases early warning system

Table 2 -

 2 It shows that the change process of various greenhouse environmental factors in grape berries period during one day. Organizing the sensors' data and feedback information from sensor, we obtain the original decision table shown in table 2-2.

			1. The data gotten in grape berries period
	time	air	air	soil	air	light	CO 2
		temperature	humidity	temperature	moisture	intensity	/μL/L
		/ o C	/%	/ o C	/%	/lx
	7	15.9	92.1	10.2	35.8	0	445
	8	17.3	91.6	12.5	36.4	24625	379
	9	21.8	68.7	15.6	36.4	38652	324
	10	26.5	51.7	21.7	36.9	45689	254
	11	29.5	45.2	25.5	35.7	47895	278
	12	32.8	48.9	28.8	35.7	50127	201
	13	28.3	57.4	23.7	35.9	51257	156
	14	28.2	55.8	23.8	35.7	38784	158
	15	27.6	64.9	22.1	35.7	21784	121
	16	24.3	67.5	19.0	35.7	7523	122
	17	20.3	83.7	16.4	35.7	0	154
		Table 2-2. Grape downy mildew original decision table
	air	air	soil	air	light	CO 2	Downy
	temperat	humidity	temperature	humidity	intensity	concentration	mildew
	ure / o C	/%	/ o C	/%	/lx	/μL/L
	19.2	94.7	10.7	66.5	0	388	Y
	23.5	97.3	16.4	80.1	0	421	Y

Table 2 - 3 .

 23 . The range of the condition attribute value is 0 represents that do not belong to the property and 1 is on behalf of belonging to the attribute. C 1 represents the berries period, C 2 represents that the air temperature is low, C 3 represents that air temperature is just right, C 4 represents the air temperature is high. C 5 , C[START_REF] Tao | an Introduction to Agricultural Warning[END_REF] and C 7 respectively represents air humidity is low, moderate and high. C 8 , C 9 and C 10 respectively represents soil temperature is low, moderate and high. C 11 , C 12 and C 13 respectively represents soil moisture is low, moderate and high. C 14 , C 15 and C 16 respectively C 17 , C 18 and C 19 respectively represents the concentration of CO 2 is low, moderate and high. The case data table of the samples is shown in the table 2-3. Grape Downy mildew case data

	100	L 		1 -L	but high

Table 2 - 4 .

 24 The data gotten after the attributes reduction

	U	C1	C2	C3	C4	C5	C6	C7	C 11	C 12	C 13	C 14	C 15

Table 2 - 5 .

 25 Result comparison of different methods

	the training time(s)	accuracy %

Acknowledgments

The research was supported by Beijing Natural Science Foundation of China (key project) -Research of adaptive the key technology and application model in agricultural cloud service (4151001), and the Technology innovation ability construction Projects of Beijing Academy of agriculture and forestry science -the research and application of the network service technology for the vegetable seeds Display and promotion (KJCX20140416), all the support is gratefully acknowledged.


Xiaowei Teng

Yansheng Dong

Lumin Meng

The Study of Winter Wheat Biomass Estimation Model Based on Hyperspectral Remote Sensing

Keywords: winter wheat, biomass, hyperspectral remote sensing, TINDVI, LWCI

Biomass plays an important role in crop growth and yield formation. The study of biomass has been expanded to remote sensing sphere, which provides more ways to the obtainment of crop biomass. To carry out the study of winter wheat biomass estimation model, the field experiments were conducted at Rougu test area and Wugong test area, Shanxi Province in the cropping season 2013-2014. The biomass estimation model was based on the Time-Integrated Value of NDVI (TINDVI) and Leaf Water Content Index (LWCI), which was used to predict the winter wheat biomass. And the model was validated with the ground measured biomass. The results

showed that the determination coefficient (R 2 ) and root mean square error (RMSE) between the measured and the estimated biomass were 0.7949 and 2.689 t/ha, respectively. The estimated biomass was exactly similar to the field measured biomass, therefore this model had a good application prospect.

Introduction

The information of crop growing and health condition is essential to the optimization of crop production [START_REF] Kross | Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops[J][END_REF] . Biomass is an important indicator of crop condition monitoring [START_REF] Du | Overview on Monitoring Crop Biomass with Remote Sensing[END_REF] . The quantity of biomass affects the grain yield directly [START_REF] Ma | A Study on Accumulation and Increment Distribution of Biomass of Summer Maize in Zhengzhou [J][END_REF] . Many methods can be used to monitor crop biomass, including the estimation of remote sensing information, the prediction of crop model based on remote sensing and so on.

In recent years, a large number of scholars build numerous regression models of biomass estimation with the correlativity between remote sensing information and measured biomass, which have proved that the agronomy parameters just like Biomass and Leaf N could be evaluated. He Cheng used three kinds of data, the Thematic Mapper data, the 30 meters Digital Elevation Model data and field observation data, to get the functional relation which was possibly applied between vegetation biomass and remote sensing image information [START_REF] He | The Inversion Processing of Vegetation Biomass along Yongding River Based on Multispectral Information[END_REF] . Liu Ming used ten spectral vegetation indices to estimate the LAI and biomass, and the consequences indicated that the correlations between ten vegetation indices and LAI, aboveground biomass were significant, thus using vegetation indexes to inverse LAI and aboveground biomass was feasible [START_REF] Liu | Estimation of Leaf Area Index and Aboveground Biomass of Spring Maize by MODIS-NDVI[END_REF] . Gao Shuai designed the microwave and optical remote sensing integrated vegetation indexes with the RADARSAT-2 and HJ-1 data, and the results showed that compared with original methods, these vegetation indexes had a better evaluating performance with the structure parameters just like maize LAI, height and biomass [START_REF] Gao | Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2[J][END_REF] . Calera et al monitored the growth of corn and barley by applied remote sensing data, and the results showed that the value of TINDVI was linearly related to dry biomass [START_REF] Calera | Monitoring barley and corn growth from remote sensing data at field scale[J][END_REF] . With the purpose of establishing a spatial-temporal model for future TINDVI, Andreas Westergaard-Nielsen combined TINDVI and observed temperatures with a downscaled regional climate model (HIRHAM5) [START_REF] Westergaard-Nielsen | Greenlandic sheep farming controlled by vegetation response today and at the end of the 21st Century[END_REF] . Hunt mentioned a leaf water content index(LWCI) , which was calculated by Landsat near infrared band reflectance and shortwave infrared band reflectance, and proved that the leaf water content index had a good correlation with the relative water content [START_REF] Hunt | Measurement of leaf relative water content by infrared reflectance[END_REF] . Daeha Kim mentioned a biomass estimation model, which was based on the studies of Calera and Hunt [START_REF] Kim | Validating FAO AquaCrop using Landsat images and regional crop information[END_REF] .

Research of remote sensing technology for biomass estimation has been undertaken by many predecessors, and they have made corresponding progress, which has made a significant contribution.

Site description

Field experiments were implemented in the Rougu test region and Wugong test region, Shanxi Province, China(Fig. 1). The test sites are located in central of Shanxi GuanZhong Plain, which is a typical arid and semi-arid region. The average temperature of summer is 26.1℃, and the average temperature of winter is -1.2℃. The average rainfall of the test sites is 635.1mm. The main crops of the two areas are winter wheat and summer maize. Most of the winter wheat is sowed in the middle of October, and is harvested in early June. Five times field experiments were carried out and the measured agronomy parameters(Table 1) were obtained which included wheat canopy spectra, aboveground biomass and yield. The wheat canopy spectra was measured by the America ASD FieldSpec FR 2500 field spectral radiometer. The aboveground biomass and yield were measured by wheat samples obtained from each plot.

Fig. 1 The study area

3.1Model Description

Combining the previous researches [START_REF] Calera | Monitoring barley and corn growth from remote sensing data at field scale[J][END_REF][START_REF] Hunt | Measurement of leaf relative water content by infrared reflectance[END_REF] , Daeha Kim [START_REF] Kim | Validating FAO AquaCrop using Landsat images and regional crop information[END_REF] invented the biomass model which was based on TM, ETM and OLI images, and involved the growth of TINDVI and the influence of water stress. The core principle of this model is that it translates the value of TINDVI into biomass under comprehensively considering the water stress coefficient. The equation for calculating biomass production is as follows:

ASD B m W TINDVI    (1)
Where m is conversion coefficient from the value of TINDVI to biomass. W is the water pressure coefficient or water pressure factor. B ASD is biomass estimated from hyperspectral remote sensing. NDVI is the Normal Differential Vegetation Index, which is computed from reflectances of red band and near infrared band [START_REF] Rouse | Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation[END_REF] . TINDVI is the Time Integrated Value of NDVI. And it is calculated as: Where t 0 is the start time of returning green stage, and t 1 is the time when biomass is estimated. NDVI soil is NDVI of bare soil. RED and NIR are red band reflectances and near infrared band reflectances, respectively. R 890 and R 670 are the reflectances of 890th band and 670th band, respectively.

For dry leaves, the reflectance of shortwave infrared band is almost equal to the reflectance of near infrared band [START_REF] Thomas | Estimating leaf water content by reflectance measurements[END_REF][START_REF] Rock | In machine processing of remotely sensed data symposium[END_REF][START_REF] Knipling | Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation[END_REF] . For green leaves, near infrared band has the maximum reflectance of the six Thematic Mapper band, whereas the shortwave infrared band reflectance is reduced because of absorption by water [START_REF] Knipling | Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation[END_REF][START_REF] Tucker | Remote Sensing of Leaf Water Content in the Near Infrared[END_REF] . So that the difference between the shortwave infrared band reflectance and the near infrared band reflectance for the green leaves should be equivalent to the water absorptance in the green leaves [START_REF] Hunt | Measurement of leaf relative water content by infrared reflectance[END_REF] . Absorbance is usually calculated by -log(1-a), where a is the absorptance [START_REF] Nobel | Transpiration stream of desert species: resistances and capacitances for a C3, a C4, and a CAM plant[J][END_REF] . So that the biomass model regards the LWCI as the water pressure coefficient, and the water pressure coefficient [START_REF] Hunt | Measurement of leaf relative water content by infrared reflectance[END_REF] is defined by NIR and SWIR as:

    log 1 log 1 FT NIR SWIR W LWCI NIR SWIR            ( 4
)
Where SWIR is the reflectance of shortwave infrared band. This study uses the 890th and 1610th band reflectances of hyperspectral remote sensing instead of NIR and SWIR. The subscript means that the reflectances of these leaves are in the state without water stress.

The biomass conversion coefficient is associated with the region and the cultivar of winter wheat, which is determined by the value of TINDVI and the conservative biomass at mature. The equation of biomass conversion coefficient m is as follows:

  h B m E W TINDVI   ) (5)
Where B h is the conservative biomass at mature, and E[W×TINDVI] is the average of W× TINDVI in the region.

model verification

The R 2 and RMSE were applied to evaluate the biomass estimation results. The more the R 2 close to 1, the more the consistency well. And the more the RMSE close to 0, the more the error small.

RMSE is calculated based on Eq.( 6).

n i i I M RMSE n     (6)

Results and Discussions

The estimated winter wheat biomass in the cropping season 2013-2014 was validated with the field measured biomass. In the analysis, the R 2 between estimated biomass and measured biomass in the period of Mar05-06, Mar28-29, Apr27 and May16-17 were 0.2836, 0.2042, 0.3412 and 0.1976, respectively (Fig. 2). The estimated biomass precision was slightly lower in single period. The maximum and minimum correlation coefficients were 0.3412 and 0.1976, respectively. The biomass estimation precisions were in the range of acceptable precision. The R 2 and RMSE between the estimated and measured biomass in the whole period of winter wheat were 0.7949 and 2.689 t/ha, respectively (Fig. 3). The whole period of winter wheat estimated biomass had a higher correlation with the measured biomass. Measured value(t/ha) n=136 R² = 0.7949 RMSE=2.689 t/ha Fig. 3 The relational graph between estimated biomass and measured biomass in the whole period of winter wheat Thus, the correlations between estimated biomass and measured biomass suggested that this biomass estimation model was viable. In this study, the TINDVI and LWCI were applied to estimate the biomass of winter wheat, which has more advantages than the biomass regression models. Because it can be applied to different areas. Moreover, it combined the former studies by considering both the TINDVI growth and the effect of water stress on biomass growth [10] . But this model had little consideration on rainfall data and irrigation data. Only four biomass tests were carried out in the cropping season 2013-2014, which lead to a deviation from the NDVI real situation curve. So that the each times single predicted precisions were slightly lower. This model is a new biomass model, thus the further research is required to verify and perfect the model.

Conclusion

This study carried out winter wheat biomass estimation based on a new biomass model through hyperspectral remote sensing, and it was verified by the field experimental data from Rougu test area and Wugong test area. The estimated wheat biomass had a higher correlation with the field measured biomass. Certainly, more field experiments should be carried out. In order to furtherly verify and perfect the biomass model, the meteorological data and field management data should also be considered. Finally, the model should be applied to remote sensing images. This paper may be valuable in guiding further study about crop biomass estimation.

  However, most of the estimation models are not universal, and are only suitable for local area, which goes against the popularization of these models. Daeha Kim mentioned a biomass model based on the value of NDVI and crop information data, but the model was not been validated by the field measured biomass. The paper adopts the biomass model of Daeha Kim, and to estimate the biomass collected at Rougu test area and Wugong test area with the hyperspectral remote sensing, hoping that the model can be applied in different areas.

Fig. 2

 2 Fig.2The relationship between estimated biomass and measured biomass in the period of Mar05-06(a), Mar28-29(b), Apr27(c) andMay16-17(d)

Table 1

 1 The testing time and growth period of winter wheat in the cropping season 2013-2014

	Testing time	Mar05-06	Mar28-29	Apr27	May16-17	Jun10
	Growth	Returning green stage	Jointing stage	Heading	Pustulation	Mature period
	period			period	period
	3 Methods

Acknowledgments

The work is supported by the National Science Foundation (NNSF) of China (41401476).


Xuexia Yuan

Yujian Yang

Yong Zhang

Study on Landscape Sensitivity and Diversity Analysis in Yucheng city

Keywords: Country-level, landscape sensitivity, land use, scale effect

Landsat ETM image located in Yucheng city in 2002 was interpreted by RS image extraction technology and classification method. Moreover, landscape ecology theories were applied as well as ArcGIS and Fragstats4 to choose the reasonable landscape indices including Contagion Index(CONTAG), Patch Density(PD), Landscape Shape Index(LSI), Perimeter Area Fractal Dimension(PAFRAC), Shannon's Diversity Index(SHDI), Shannon's Evenness Index(SHEI). The study results showed that correlation between the landscape index was significant at the 0.01 confidence level, the relationship rule was revealed between ecological index based on statistics model. The typical scale effect was selected, including 5m, 10m, 15m, 20m, 25m, 30m, 40m, 50m, 60m, 70m, 80m, 90m, 100m, 110m, 120m, 150m, 180m and 210m. The series results were clarified by PD 、LSI、PAFRAC、CONTAG、SHDI and SHEI in response to the different scales, or 18 different scales. The detailed results showed that the decreasing trend was presented from 5m scale to 210m scale for each index. Furthermore, we also analyzed the scale effects for different landscape index. Finally, based on image by the change of LSI, PAFRAC, SHAPE-MN and AI on 30m pixel scale, we emphatically analyzed the LSI, PAFRAC, SHAPE-MN, AI of 12 landscapes. Further, according to the new classification, for the 12 landscapes in Yucheng city, they are Arable-land, Grassland, Traffic and Transmission Land Use, Residential land, Public management and service land, Commercial service land, Garden plot land, Mine and storage land, Woodland, Water and water facility land, Special land and other land we explored and explained the ecological significance of different landscapes in the case city, Especially, landscape sensitivity, fragmentation and complexity of landscape spatial pattern and diversity.

Introduction

Numerous studies have showed that Landscape is not only the typical scale dependence, but geographical and historical interactions related to ecological system. Obviously, landscape diversity index of Land-use types for grain size and scale changes in response to different sensitivities. At present, land use landscape pattern From Xiamen City, Guangzhou, Shanghai City, Pingyin County in Jinan and Jinghe Watershed landscape had showed landscape diversity and sensitivity of landscape pattern, which had an important impact on landscape pattern of land-use types whether between the landscape pattern index, or between different scales of landscape index [START_REF] Yanhui | Temporal and spatial distribution of cropland-population-grain system and pressure index on cropland in Jinghe watershed[END_REF][START_REF] Li-Hua | Spatial scale effect of urban landuse landscape pattern in Shanghai[END_REF][START_REF] Yinjiang | 3S-based Analysis on Guangzhou's Landscape Pattern[END_REF][START_REF] Kuan-Jin | Analysis on the Landscape Pattern of Land Use in the Middle Rolling Area of Pingyin County[END_REF] . From the scale perspective, there is better foundation in Yucheng city, one of the network stations of CAS. With the development of urbanization, information and modernization, land-use landscape pattern and process evolution made rapid changes. Therefore, the case of Yucheng city has great promotion value for the more similar country-level scale in China. Further retrieval of previous literatures have shown that land use landscape diversity and sensitivity of Yucheng City was not retrieved according to the new land use classification standard, the study had few in related landscape analysis, the current research situation is not commensurate with its status for Yucheng city, in the meanwhile, it is not conducive to the landscape of the overall planning and the process of urbanization process in Yucheng City.

Materials and Methods

Study Area Situation

Yellow-Huaihe Rivers Plain is the largest plain in China and an important area of grain, cotton, oil. Yucheng city is the part of Yellow-Huaihe Rivers alluvial Plain and located in the northwest of Shandong province in China, between 116º22′11″-116 º45′00″E and 36º41′36″-37º12′13″N. The total area of the city is 990km2, the study area belongs to semi-moisture monsoon climate area and has on average 2639.7 hours of sunshine per year. The total radiation of sun is 124.8K/cm2. The average temperature per year is 13.1℃, the 200 frost-free days, over 10℃ and over 15℃ in accumulated temperature are 4441℃ and 3898℃ in the study area, which provides plenty of thermal conditions, the average rainfall per year is 666mm. The study area is the part of Yellow-Huaihe Rivers alluvial Plain, the site physiognomy is comparatively complex, there are 7 kinds of landforms in all, including flood land plateau, high land, even land, low-lying land, shallow land, sector crack land and arenaceous river channel. On the basis of topography, landform, parent material and climate, there are two kinds of soil types, Fluvo-Aquic soil and Solonchak. saltaffected lands are small distributed in the study area, dynamic changes of land use, especially temporal and spatial changes of salt-affected lands, has an important role to improve land quality and promote agricultural sustainable development in the study area [5].

This paper depends on ETM image combined with land-use other data. In order to improve the accuracy of RS image, we referred to the data, 1:50 000 topography map in scale and other spatial maps. The same scale (1:50 000) maps, such as groundwater salinity map, groundwater depth map, soil organic matter content map, soil texture and configuration map. In the study, the author carried out the new land-use classification system, involved in 12 landscapes, they are Arable-land, Grassland, Traffic and Transmission Land Use, Residential land, Public management and service land, Commercial service land, Garden plot land, Mine and storage land, Woodland, Water and water facility land, Special land and other land, respectively.

Scale Effect System and Landscape Significance

Different landscape index has different ecology significance, the selected landscape index which is applied to analyze the scale effect is illustrated in Table 1 [START_REF] Zhenrong | Landscape Ecology[END_REF] .

LSI LSI ≥ 1

The index is to measure shape complexity of a certain patch through calculating the deviation of its shape from circle or square of the same area. The more complex and irregular the patch shape is, the higher LSI value is.

PAFRAC 1 ≤ PAFRAC ≤ 2
The index to some extent reflects the degree of human disturbance, and indicates the relationship between shape and area of landscapes consisting of patches, and the index at the landscape level is identical to the class level.

SHDI SHDI ≥ 0

The index reflected the diversity of land-use landscape, and it is in response to heterogeneity, and especially sensitive to the non-balanced distribution of all patches, Shannon's index is somewhat more sensitive to rare patch types than Simpson's diversity index.

SHEI 0 ≤ SHEI ≤ 1
The index reflected the diversity of land-use landscape. Shannon's evenness index is expressed such that an even distribution of area among patch types results in maximum evenness. SHDI = 0 when the landscape contains only 1 patch. SHDI = 1 when distribution of area among patch types is perfectly even.

Results and Analysis

Correlation Analysis of Landscape Index

The correlation degree analysis results of six selected index in Yucheng city were showed in reflected between SHEI and SHDI, the more significant correlation coefficient was 0.999.

Sensitivity Analysis and Spatial Pattern of 12 Landscapes

The typical scale effect was selected and explored in the study, including5m,10m,15m,20m,25m,30m,40m,50m,60m,70m,80m,90m,100m,110m,120 m,150m,180m and 210m. the series results were clarified by PD、LSI、PAFRAC、 CONTAG、SHDI and SHEI responding to the different scales, or 18 different scales, the results were clarified in Table .3. According to Tab.3 results, the decreasing trend was presented from 5m scale to 210m scale for the index, LSI and CONTAG. The increasing change of PD index was illustrated in the case area from 5m scale to 210m scale, furthermore, there was an important point, reflected on 25m scale, or the decreasing trend of PD index was presented from 25m scale to 210m scale. The increasing change of PAFRAC was taken place from 5m scale to 180m scale, but on the key 50m scale, the abruptly decreasing change was represented from 180m scale to 210m scale. For SHDI and SHEI, obviously, the change rule was consistent, the stable status from 5m scale to 50m scale, but the fluctuation was formed from 50m scale to 210m scale. Undoubtedly, diversity, fragmentation and hierarchy of landscape located in Yucheng city were interpreted by the change of LSI, PAFRAC, SHAPE-MN and AI landscape index on 30m scale based on RS image pixel. The detailed tendency of landscape index was shown from Fig. 1 to Fig. 6. Obviously, it is crucial for land-use landscape pattern and spatial process evolution in the case study [7,8.9] . In terms of LSI, the drastically changed from the high value (70.814) to the small value(7.376). Water and water facility land had the high value is 71.814, which meant that Water and water facility land had the most complicate shape and was most influenced by various interventions, which showed the most complex diversity according to LSI ecological significance. Obviously, there are all kinds of Water and water facility land in the case region, including Rivers, water, lake water, water reservoir, ponds, coastal beach, inland beach, ditches, glaciers and permanent snow landform, which explained the complicated landscape types. From the shape matrix perspective, LSI value of Mine and storage land is less than 10, and indicate the landscape types is simple, Mine and storage land had the second most complicate shape, while Public management and service land LSI has the lowest value, which indicated that it had the simplest shapes and were influenced by human interventions [START_REF] Fu Bojie | Principle and Application of Landscape Ecology(Second Edition)[END_REF] .

Perimeter-area fractal dimension (PAFRAC) to some extent reflects the degree of human disturbance, and indicates the relationship between shape and area of landscapes consisting of patches, and the index at the landscape level is equal to the class level. For PAFRAC, Traffic and Transmission Land Use had the high value is 1.545, the value decreased step by step, PAFRAC is slight fluctuation centered on 1.2 of 11 landscapes, except 1.6 of Traffic and Transmission Land Use [START_REF] Fu Bojie | Principle and Application of Landscape Ecology(Second Edition)[END_REF] . SHAPE-MN MN (Mean) equals the sumof the corresponding patch metric values, which is divided by the total number of patches. In terms of SHAPE_MN, Traffic and Transmission Land Use had the high value is 3.552, drastically change presented, the second high value is 1.675 of Public management and service land, the value decreased step by step for other landscapes, Arable-land, Residential land, Water and water facility land, 0ther land, Grassland, Commercial service land, Woodland, Mine and storage land, Special land, Garden plot land. Area index (AI) does not have a lot of interpretive value about evaluating landscape pattern, but it is important which defines the extent of the landscape. Moreover, many class and landscape metrics computations use total landscape area for. AI of Public management and service land, had the high value is 93.911, and AI of 12 landscapes to some extent presents the variability and gradient characteristics. basically the kind of change can be divided into three levels in accordance with the area index value, the first gradient change is mainly involved in the three landscapes, Arable-land, Residential land, Public management and service land. The second, Grassland, Woodland, Water and water facility land, Commercial service land, Traffic and Transmission Land Use, other land. The third gradient change included the three landscapes, Garden plot land, Mine and storage land, Special land. As the precious analysis, the landscape index (LSI, PAFRAC, SHAPE-MN, AI) results showed that they has a certain change and variability for each landscape, especially the landscape is in response to the scale, which indicated the scale effects. From the value of the index perspective, is not steep, or the ecological significance of landscape index indicated has the smooth change, AI has a certain hierarchical characteristics, PAFRAC has a small change, LSI and SHAPE-MN reflected the real landscape, in accordance with own characteristics, and there is no obvious stratification change.

Conclusions

In a word, the main contents and conclusions are as follows:

We also definitely understand and investigate the question how changing scale, such as grain size affects pattern analysis. The ecological significance of CONTAG, PD, LSI, PAFRAC, SHDI, SHEI indicated that had a certain scale effects of 18 scales in Yucheng city, though the different degree and different curve. CONTAG changed slightly, which indicated the stable landscape equilibrium. PD has increased from 5m scale to 30m scale, but decreased from 30m scale to 210m scale, so fragmentation of regional landscapes had presented fluctuation. In terms of LSI and PAFRAC increased from small scale to big scale. SHDI and SHEI changed slightly and showed the balanced landscape diversity, landscape types were evenly distributed, landscape fragmentation and heterogeneity changed slightly [11,12,13]. We may detect or identify characteristics scales and hierarchical levels to understand and predict ecological phenomena. Based on image by the change of LSI, PAFRAC, SHAPE-MN and AI on 30m pixel scale, we also emphatically analyzed the LSI, PAFRAC, SHAPE-MN, AI of 12 landscapes. Further, we explored and explained the ecological significance of different landscapes in the case city. Applying the principles of the landscape ecology, the paper analyzes the landscape diversity and sensitivity of 12 landscapes, points out that landscape sensitivity is the landscape systems response to disturbance at different spatial scales, and reveals that landscape spatial pattern and ecological processes of interaction couple of natural factor and Human disturbance.

Fig. 1 .Fig. 2 .Fig. 4 .Fig. 5 .Fig. 6 .

 12456 Fig.1. Sensitivity of PD on different scale

Fig. 7 .Fig. 8 .Fig. 10 .

 7810 Fig.7. LSI change for 12 landscapes

Table 1 .

 1 Ecological significance and range of Index

	Index	Range	Ecological significance
	CONTAG	0	<	Contagion is inversely related to edge density, When edge density
		CONTAG ≤	is very low, for example, when a single class occupies a very large
		100

percentage of the landscape, contagion is high, and vice versa. The index reflects fragmentation and complexity of landscape spatial pattern.

PD

PD > 0 Number of patches of a certain landscape element per unit area, the index reflects density degree and difference of landscape spatial pattern.

Table 2 .

 2 Table.2, which disclosed features and changes tendency of land-use landscape spatial patterns. Ecological significance and range of Index

		PD	LSI	PAFRA	CONTA	SHD	SHEI
				C	G	I
	PD	1	.956( ** )	-.776 **	.845 **	.267	.259
	LSI	.956 **	1	-.911 **	.961 **	.236	.227
	PAFRAC	-	-.911 **	1	-.976 **	-.076	-.064
		.776 **
	CONTAG	.845 **	.961 **	-.976 **	1	.144	.135
	SHDI	.267	.236	-.076	.144	1	.999 **
	SHEI	.259	.227	-.064	.135	.999 *

* 1 *: Correlation is significant at the 0.05 level (2-tailed). **: Correlation is significant at the 0.01 level (2-tailed).

Table. 2 summarized the correlation results for the key variables. 6 ecology index (PD, LSI, PAFRAC, CONTAG, SHDI and SHEI) was significant at the 0.01 level, the correlation coefficient was 0.956,-0.776 and 0.845 between PD and LSI, PD and PAFRAC, PAFRAC and CONTAG, respectively. There is significant Correlation between LSI and PD, LSI and PAFRAC, LSI and CONTAG, PAFRAC and CONTAG, the correlation coefficient was 0.956, -0.911 0.961 and -0.976 at the 0.01 level, especially, the significant coefficient was

Table . 3

 . Different scale statistics of landscape index

		PD	LSI	PAFRAC CONTAG	SHDI	SHEI
	m	20.214	80.748	1.2236	68.7214	1.4008	0.5637
	10m	21.089	79.648	1.2436	66.3961	1.4009	0.5638
	15m	22.302	78.447	1.2539	64.4111	1.4007	0.5637
	20m	23.438	77.129	1.2645	62.6548	1.401	0.5638
	25m	24.333	75.517	1.2734	61.109	1.4012	0.5639
	30m	24.232	73.683	1.2836	59.7725	1.4007	0.5637
	40m	22.935	70.169	1.2975	57.4559	1.4015	0.564
	50m	21.249	66.605	1.3097	55.6154	1.4017	0.5641
	60m	19.315	63.080	1.3123	54.1374	1.4019	0.5642
	70m	17.267	59.763	1.3179	52.9574	1.4005	0.5636
	80m	15.441	56.732	1.3205	51.9548	1.4002	0.5635
	90m	13.775	53.954	1.3236	51.0507	1.4022	0.5643
	100m	12.327	51.266	1.3272	50.4467	1.3987	0.5629
	110m	11.429	49.507	1.3254	49.6355	1.401	0.5638
	120m	10.231	47.157	1.3334	49.1382	1.401	0.5638
	150m	7.8704	42.043	1.3446	47.6594	1.4044	0.5652
	180m	6.1825	38.049	1.3665	46.8699	1.3991	0.5631
	210m	4.9978	34.393	1.3276	46.2924	1.3975	0.5624
	At present, it is widely used that Landscape TM/ETM images were interpreted on
	country level,whose resolution is 30m. So 30m pixel scale was considered and used

Table . 4 Index values of 12 landscapes AI SHAPE_MN LSI PAFRAC

 .

	Arable-land		93.0069	1.6684	58.8861	1.3356
	Grassland		73.6023	1.2772	58.7977	1.2842
	Traffic	and	70.3847	3.552	13.2989	1.5453
	Transmission Land Use
	Residential land		82.5141	1.4287	56.4615	1.2425
	Public management	93.9111	1.6752	7.3756	1.3004
	and service land
	Commercial service	75.7625	1.2616	62.1716	1.2248
	land
	Garden plot land		54.1405	1.1434	25.3056	1.2437
	Mine and storage	56.0564	1.1846	67.5817	1.2749
	land
	Woodland		69.2063	1.2298	48.6987	1.2637
	Water and water		74.8341	1.3313	71.8142	1.4143
	facility land
	Special land		50.6402	1.1802	17.831	1.2847
	0ther land		72.7473	1.2921	63.5423	1.2915

Acknowledgements

The work was supported by State Key Laboratory of Soil and Sustainable Agriculture (Institute of Soil Science, Chinese Academy of Sciences) (grant no. 0812201221).


Jiabo Sun

Wenjie Feng

Xiaoyan Zhang

Luyan Niu

Yanzhong Liu

Research and Design of Shandong Province Animal Epidemic Prevention System Based on GIS

Keywords: animal epidemic prevention, GIS, information system

Animal husbandry has become one of the important pillars of agricultural economy. In the meantime, along with the rapid development of animal husbandry, all kinds of animal disease hazardous to health of livestock and poultry appear, such as High Pathogenic Avian Influenza (HPAI) and Foot and Mouth Disease (FMD). Therefore, establishment of animal epidemic prevention system is very necessary. Geographic Information System (GIS) has been widely used both in the field of disease control and prevention and livestock and poultry epidemic prevention for its strong ability of spatial analysis and visualization analysis. Despite all this, provincial livestock and poultry epidemic prevention system based on GIS is rare. To obtain animal epidemic information accurately and timely, and improve the mechanism of animal outbreaks, combing with the reality of Shandong province, an animal epidemic prevention and early warning forecast system is developed. The purpose of the system, the system structure, function module, and development platform are analyzed. Combined with GIS, handheld mobile GIS/GPS, and GPRS/CDMA, the system has proved its flexibility, stability, convenience, and easy extensibility, assuring effective implementation of animal epidemic monitoring.

Introduction

Animal by-products economic development is one of the important direction of our country's rural economic development. In recent years, animal husbandry in Shandong province keeps the sustainable and healthy development, for example, animal husbandry output value, the total output of meat, eggs and milk, livestock products export and other major economic indicators among the top. Animal husbandry has gradually transformed from subsistence to commercial, from traditional to modern, and has become the most dynamic and potential of the pillar industries of the rural economy, and is also an important way of farmers to get rich . Meanwhile, animal disease has become an important factor which restricts animal husbandry economy rapid growth, prevents animal by-products to expand exports, and threatens quality and safety of animal origin. Major animal disease prevention and control not only directly affects the animal husbandry development and farmers' income, but also relates to people's physical health, public health security and social stability. Therefore, to strengthen animal epidemic prevention system and animal disease traceability system construction, improve animal epidemic prevention, monitoring, quarantine supervision system, establish animal disease risk assessment and early warning forecast mechanism is of great significance, which is also the necessary premise of to achieve animal husbandry quality security, disease security and ecological security. The application of information technology in livestock and poultry epidemic prevention, supervision and management has become the trend of the development of animal husbandry industry. By introducing professional data management module, establishing basic database, executing digital management, livestock and poultry epidemic prevention information platform is developed. GIS is an auxiliary space data information management system, combining unique visual effect, the map geographical analysis function, and database operations together [START_REF] Jinfeng | Spatial decision support system for controlling the outbreak and spread of animal epidemics[END_REF]. It is an important means of realizing digital decision-making management, and provides strong theoretical support and technical support for epidemic prevention and control from the perspective of spatial evolution process analysis and forecast. GIS is mainly used in the field of animal epidemic prevention, which can be summarized as the following respects: (1) production of livestock epidemic spatial distribution thematic map, which means visually displaying epidemic analysis results in the form of space distribution thematic map and statistical charts, on the basis of GIS spatial analysis and visual display; (2) forecast of animal epidemic situation and development trend, which means using the multi-factor comprehensive analysis and statistical prediction model to predict and evaluate popular trend, space accumulation and dispersion model, and outbreak impact in a certain time; (3) control and planning of Animal epidemic prevention resources, which means optimizing the resources distribution of epidemic prevention with the help of GIS; (4) animal husbandry and outbreak early warning, forecast, and information publishing, which means the use of GIS for risk factors analysis and positioning, and early warning in related areas. Domestic and international numerous agriculture departments have been using GIS for animal epidemic monitoring and early warning. The United States established the National Animal Health Report System (NAHRS) in 1996. Australia's National Animal Health Information System (NAHIS) is fully functional and works well. Since 1991, New Zealand began to build information system for emergency animal disease control Epiman-IMS [START_REF] Lin | Research and design of GIS based on animal disease information system[END_REF][START_REF] Changhua | Digital monitor and control system for major animal epidemic disease in China [J][END_REF]. In China, the national center for epidemiological studies was established and put into special funds to establish the national animal health information system in 2000, an integration of animal health information management and animal heath GIS. National SARS control and warning system was built in 2003, which supplied a modern means of prevention and control work. Chongqing city major animal epidemics GIS system was established in 2011. The application of GIS in the health filed at home and abroad has proved that animal husbandry GIS system is the inevitable trend for the scientific and automatic animal epidemic information management [START_REF] Preriffer | Geographical information system as a tool in epidemiological assessment and wildlife disease management [J][END_REF][START_REF] Xie Lianjun | Design and implementation of animal husbandry information system based on GIS [J][END_REF][START_REF] Jianjun | Design of a construction project for the emergency command system [J][END_REF]. Although, there is a lack of domestic provincial animal husbandry outbreak early warning system based on GIS. The existing livestock and poultry epidemic monitoring mechanism in Shandong province is given priority to artificial investigation and reports submit, which is free from the technology of GIS, GPS, and big data, reducing the sensitivity, timeliness, and traceability of the epidemic monitoring.

System Framework

System Objectives

As the first step to develop the system, we identify the core requirements and objectives the system seeks to meet, which includes four aspects: ( There are three common development modes, which are Client/Server (C/S), Browse/Server (B/S), and Mobile client/Server (M/S). Here, we integrate C/S, B/S and M/S together, provide data sharing based on the unique data source, switch to appropriate mode when facing different users and application purposes, which guarantee the integrity and flexibility of the system.

Spatial Database and Non-Spatial Database

Livestock epidemic multi-source information database contains two major data types:

(1) Spatial Database, which can be divided into basic geographic information and thematic geographic information about animal epidemic information, the former contains administrative boundary, roadway, and river system, while the latter contains the distribution of species group, livestock and poultry farms, disease communication media, and epidemic prevention resources.

(2) Non-Spatial Database, which covers a multitude of data types, for example, attribute data (the density of livestock and poultry, disease characteristics, traffic condition, environmental condition, et al.), image , and video.

System Architecture

As is shown in Fig. 1, the overall structure of provincial animal epidemic prevention system includes four levels: The first level is the foundation layer, including a series of hardware and software, such as host device 3 System Function

LERCS

This subsystem is based on the embedded terminal device and can be used to acquire and transmit typical epidemical information. The standards of epidemic information collection are in accordance with the existing epidemic reporting requirement. Information acquired by LERCS refers to disease information, epidemic information, and animal die information in the form of image, video, QR code, and character. Meanwhile, information acquired can be transmitted in two means, one of which is by connecting the terminal device to data server with USB, the other is uploading to data server remotely by GPRS network [START_REF] Jun | Design and implementation of intelligent terminal system for collecting animal epidemic disease data[END_REF]. Location mapping can precisely localize epidemic sampling points. LERCS function module is shown in Figure .2.

LERCS

LEMES

Based on the basic geographic information, the spatial distribution of modern animal husbandry demonstration county, standardization of livestock and poultry breeding demonstration county, and no prescribed animal epidemics demonstration area in Shandong province, can be visually displayed on the map. By receiving animal epidemic information from LERCS, this subsystem takes full advantage of GIS spatial analysis, statistical analysis, and thematic map display to predict epidemic information and improve the capability of disease prevention and control. LEMES function module is shown in Figure .3.

LEMES

Fig. 3. Basic function module of LEMES

The most specific performance of statistic analysis and visual display is composed of distance analysis, area analysis, information query and statistics, and thematic mapping basically composite. Based on the above function, livestock and poultry relevant information can be queried, summarized, analyzed and fed back in the form of thematic map and statistical graph. After clear about data format, data type, data security safeguard, data transmission mechanism, epidemic information can be acquired accurately, timely, and diversely, which guarantees character, images, video and other related information about the outbreak field can be obtained by epidemic data reception function. Epidemic monitor and early warning function utilizes a series of GIS spatial analysis, such as density analysis, clustering analysis and dynamic analysis, to make an overall assessment of the distribution of epidemic prevention resources and breeding resources. Through the analysis and data-mining of real-time data and historical data, distribution, development and perniciousness of epidemic information can be monitored and predicted. At the same time, it is very feasible that by using the simulation method, changing the conditions of nature, geography, ecology, meteorology, transportation, virtually setting related factors such as population structure and breeding density, the key epidemic control and prevention area and the optimization control strategy can be determined [START_REF] Jong | Design and implementation of a seamless and comprehensive integrated media device interface system for outpatient electronic medical records in a general hospital[END_REF].

LEEDSS

When there are major outbreaks, LEEDSS is able to acquire animal epidemical information accurately, timely and exhaustively, which enables a rapid definition of epidemical area of influence and grading of epidemic situation. After that, distance analysis can be utilized to summarize distribution of livestock and poultry farms and their attribute information in different level of distance range, dynamically display locations of disease management department, persons chiefly in charge and their contact information, and at last give an optimal path to the outbreak site. Meanwhile, base on mobile GIS and wireless transmission technology, outbreak sites can be located in the electric map. The scene of the real-time monitoring information will also be transmitted to emergency command staff. LEEDSS function module is shown in Fig. 4.

Real-time Outbreak Decision Feedback Prevention Resource Map Orientation

Emergency Dispatching Mobile GIS Desktop GIS

Animal Breeding Information Epidemic Prevention Information

Basic Geographic Information

Integrated Information Management

Epidemic Analysis

Fig. 4. Basic function module of LEEDSS

The characteristics of animal husbandry can be summarized as multi-subject, small scale modal, decentralized decision-making and scattered farmers. Due to subjective decision-making and traditional report filling method, in the actual epidemic prevention and control work, there will be a variety of problems, for example, relevant decisions is not conformed with actual fact, acquisition of epidemical information is not real-time. Thus, this paper builds up animal epidemic prevention system at the provincial level, respectively developing LERCS, LEMES, and LEEDSS in the three perspectives of epidemical information acquisition, epidemical early warning and monitoring, and epidemical prevention and control decision support. Relevant information about basic geographic information, animal breeding information, and epidemic prevention information is combined together to summarize animal husbandry and outbreak investigation and monitoring data of the whole province. This provides a scientific basis for government regulators, monitoring institutions at all levels, scientific research units, farmers about livestock and poultry epidemic prevention. Provincial animal husbandry and outbreak early warning forecast system established in this study has presented limitations to some extent. In order to guarantee the accuracy of the early warning analysis, livestock epidemic prediction model needs to be further researched and improved with the combination of real-time and historical epidemical data, growth environment, breeding habits, and common epidemical disease for different kinds of livestock and poultry.

Fig. 2 .

 2 Fig. 2. Basic function module of LERCS

  , operating system, database, Internet, Intranet, VPN; The second level is the data layer, containing spatial database and nonspatial database which are established in the form of vector data, image data, video data and statistical data; The third level is the support layer, which supplies developing environment, GIS platform, workflow engine, security opponents, short message service to construct livestock epidemic early warning mechanism; The fourth level is the business layer, respectively developed in the mode of C/S, B/S and M/S, and divided into three subsystems, LERCS, LEMES, and LEEDSS.

		LERCS	LEMES		LEEDSS
		Animal Epidemic Prevention
	Level4		System
		Desktop GIS	WebGIS	Mobile GIS
		ArcEngine	Visual C#.NET		Oracle
	Level3	Security Engine	Work Flow Engine	SMS
		Coding	Transmission	Management
		Standard	Standard	Mechanism
	Level2
		Vector	Image	Video	Attribute
	Level1		主机
		Host	Operating System Database
	Fig. 1. The Architecture of Animal Epidemic Prevention System

Acknowledgment

This work was supported by the National Science and Technology Support Program (2014BAD08B05-02).


Yande Liu

Yifan Li

Zhiyuan Gong

Establishment and Optimization of Model for Detecting Epidermal Thickness in Newhall navel orange

Keywords: :Newhall navel orange, Near-infrared diffuse transmittance spectra, Epidermal thickness, Variable selection

Diffuse transmittance spectra in the near-infrared scope as a prevalent sensitivity method carried out to test epidermal thickness of 'Gannan' navel oranges. In order to lay a good foundation for accurateand rapid online classification, variable sel--ection methods was intervened for navel orange model optimization. In spectral range of 900~1650nm, navel orange in thick skin depth chosen arbitrarily were set up the qualitative models for both calibration and prognostication sets in this experiment. Firstly, different pretreatment methods such as the Savitzky-Golay, the first derivative and so on were compared by PLS modeling results. Then GA and SPA were brought in to improve predictive models. Compared with results, light scattering can be effectively eliminated by the standard normal variate transformation (SNV). Moreover, fewer variables and model optimization were carried out by GA. The supreme calibration model procured with GA-PLS approach had the Rp of 0.864, RMSEP of 0.290, R C of 0.882 and RMSEC of 0.264. The experiment showed the detection of epidermal thickness of navel orange is completely feasible.

1.Introduction

Navel orange is a kind of comprehensive nutritional food, containing various essential nutrients in human body. A great source of vitamin C and carotenoids can be taken in through eating navel orange. The ripe navel orange is a popular fruit for its benefits, such as: seedless, juicy flesh, tasting good et al. When placed indoor, they can radiate enticing aroma and the rind's color and lustre is charming. [1] Near infrared spectrum detection technology has been used for a wide range of applications, especially in the quality test of thin skin fruit such as apple, peach ,pear et al. [2] In general, the average consumers take cortical thickness as one of the important considerations when they buy orange, water melon and other thick skin fruits. In the process of fruit grading, if the skin is too thick, the fruit can't be treated as optimal fruit. Even if the internal quality of fruit is excellent, the fruit also be degraded, as peel thickness directly influences its edible rate. Currently, the application of near infrared spectral detecting the thickness of the fruit skin also has not been reported. As a consequence, in this paper the navel orange peel thick testing research was discussed. Through a variety of spectral data processing method to establish the optima navel orange skin thickness detection model of Newhall Navel Orange . The researchers at home and abroad processed a series of researches in near infrared spectrum technology area detecting internal quality of both thin skin fruit and thick skin fruit. [3][4][5][6] Taking the application of 3 different wavelength of LED lights to estimate SSC and size of Shuijing pears ,PLS and LS -SVM models were established. The model obtained by PLS was preferable than that of LS -SVM. The R of forecast set was respectively 0.86 and 0.90 for soluble solids content and size. (Liu et al.2010). [7] In order to implement modern management for pear orchards, the technology of NIR was applicated in the quality detection for Dangshan pear. PLS , LS -SVM and GRNN model was established to portend the SSC of Dangshan pear. Then uninformative variables elimination (UVE) was introduced to simplify the former models. UVE-LSSVM had a great advantage in terms of regression model. (Wang et al.2013). [8] In the wavelength of 1200-2200nm, transmission spectrum of the mangos were collected for measurement during SSC and potential of hydrogen (PH) experiments. Infinite variety of pretreatment techniques was employed to process all wave spectral for the processing efforts. MLR based on PLS was applied to build calibration models. (Shyam et al, 2012 ). [9] In the wavelength of 400-1000nm, visible/near-infrared spectroscopy of valencia oranges were applied to conduct the SSC and TA experiments. The prediction models especially the model of the fruits taste best characteristic value stand (BrimA) on PLS and PCR are developed. Based on these results, visible/near-infrared spectroscopy technology is a promising and feasible method for detecting the BrimA of valencia oranges. (Jamshidi et al,2012 ). [10] Analyzing the above scholars' researches comprehensively, relevant testing models are established using fruits in regular shape、smooth surface and high internal quality. [11][12][13][14][15][16][17][18] The experiment take Newhall navel oranges which have thick wooden peel and poor uniformity of internal quality as objects of study. After different spectral preprocessing methods and the variable selection method, the raw navel orange spectrum data was to establish the optimal PLS prediction model. Compared with the origin model, it promotes the discriminative and forecasting ability.

2.Materials and methods

Materials

One hundred twenty samples of Newhall were procured from a fruit wholesale marketplhce in NanChang (NanChang, China) in January, 2015. The samples were wiped up with distilled water and then naturally dried. All samples were be numbered consecutively and stored in the experimental environment at room temperature and 60% relative humidity (RH) for 24h. Spectral collection were carried out on the next day and SSC measurement performed soon thereafter. Newhall navel oranges were divided by proportion of 3:1in the light of calibration and prediction [19] .

Spectral collection

Portable fruit quality detection device based on Android system consisted mainly of optical module, table computer with spectrum acquisition unit and power supply unit was set up. The optical module (Micro-NIR 1700, JDSU) kept an account of wavelength from 950nm to 2150 nm. The table computer with Atom Z2580 processor displayed the measurement results on a 9.7 inch capacitive touch screen. The JDSU Micro-NIR and table computer are workable under 5V model via 10000 mA portable power source .

Reference spectral of a white Teflon tile and dark current were gathered before spectral acquisition of test samples. Diffuse reflection spectra of Newhall were gathered at about 20 C. Acquisition spectrum evenly picked around the equator equidistantly (approximately 120). Optical gain had been set to ' low gain ' .

Measurement of soluble solid content and skin depth

In the spectral acquisition area, several drops of filter juice extracted by manual compression were used for soluble solid content measurement using the PR-101αCat refracometer. Observed value of skin depth was conducted after soluble solid content measurement via vernier caliper immediately. When the flesh of the orange was removed from the peel clearly, the peel was to be hold pressure level off to measure the skin depth. The thickness value at three equidistant positions were recorded exactly during the experiment. The average value of the three marked points was taken as the thickness values of navel orange skin.

Data processing and model evaluation

All of the R、RMSEC and RMSEP together assess the performance of the model built in the experiment. The number of the best principle component achieved when coupled with the RMSEP which had reached the minimum value. [20-22] In this paper, all the RMSEC and RMSEP were processed by the formula (1) and ( 2) respectively.

   

Results and discussion

Analysis of Newhall navel orange

Fig. 3 is the original absorbance spectra chart of 120 ripe Newhall navel orange. Fig. 4 is the original absorbance spectra chart of 120 ripe Washington navel orange. It is obviously that two kinds of samples have similar spectrum shape and location of wave crest and trough. Because of the O -H, C -H or N -H stretching vibration [23][24] , there is a obvious absorption peak near the location of 970、1090、1220、1285、1470 nm.

Wavelength(nm)

Fig. 3 The original spectra of ripe Newhall navel orange

Measurement results of soluble solid content and skin depth

The samples of Newhall navel orange had certain representativeness as the sizes of them contain the dimension from small size to large size. Form the table1. available, the skin thickness range of calibration set is 1.98~5.57mm meanwhile the prediction set is 2.63~5.43mm. The data structure make better fit for prediction set to predict using the model calibration set made.

Comparison of the results of spectral preprocessing

Table 3 illustrates that the model PLS established through base line、1st derivatives 、SNV and MSC improve the model prediction compared with the model built by raw spectral data. The prediction abilities of SNV also with MSC after pretreatment, while SNV had the better result. Take Newhall Navel Orange as research objects to discuss the outcome. The result showed that the Rc reached 0.883 and of prediction set reached 0.815. It is better to see that RMSEC was 0.268 and of PMSEP was 0.333. Therefore, the process of spectra band screening in the further adopted the spectra data preprocessed by SNV.

Table 3 The effect of different pretreatment methods on PLS modeling results for Newhall

Method of spectral band selection

To test and attest the practicability of near infrared diffuse reflection technology to inspect Newhall Navel Orange. In the hope of providing the theoretical basis for online detection through setting up a corresponding optimal prediction model simultaneously. As a consequence, testing requires not only high accuracy, but also higher detection efficiency. Spectral band selection methods are needed so as to acquire fewer variables and good prediction ability. Genetic algorithm is an algorithm proposed on basis of imitating the preferred choice of biology and genetic principle.

With the advantages of high efficiency of global searching, keeping the optimal variables, eliminating worse variables, establishing model more conveniently and predicting more accurately. According to the reference [25][START_REF] W X Zhu | Transactions of the Chinese society for agricultural machinery[END_REF][START_REF] Zhang | Transactions of the Chinese society for agricultural machinery[END_REF] , in the process of GA variable screening of 127 wavelength points, the main parameters are set as follows.

Initial value of the group was 30; Adaptive crossover probability and mutation probabilities was set to 0. illustrated that the minimum value corresponding to the RMSECV was 0.292 when the variable number was 13. For this reason, the 13 variables was the characteristics screened out by GA method. The processed result can be seen from figure 3. The variable frequency was more than 11 times which were chosen as characteristic variable of the model in all 127 variables. This method is based on the minimum RMSECV generally choose the characteristic variables within 20. Calculation procedure and slection of numbers of variables of SPA consult to relevant literature [START_REF] Kamruzzaman | [END_REF][29][30] . The leading eigenvalue is set to 20 and the least eigenvalue is 1. Figure 6 was the diagram of characteristic variables of screened out by applying SPA for epidermal thickness of Newhall Navel Orange. There were 6 variables filtered out from the total variables.

wavelength variables

Comparison of models

Table 4 provides a comparison of forecasting results of different forecasting models made by spectral data processed through method of GA and SPA which were the method of artificial screening in the multi-variable analysis and full spectral of samples. For the PLS model of Newhall navel orange skin thick in mature stage, research findings shows that GA-PLS models outperformed the SPA-PLS models and full wave band models. SPA-PLS models had the least spectral variables but the ability to predict relatively weakened. The reason may be that some useful variables are eliminated, so that the model precision is lower. It can be observed that GA-PLS models made the variables from 127 down to 13 and improved Rp from 0.815 to 0.815 at the same time. It's very gratifying to see that RMSEP reduced from 0.333 to 0.290. So from the experimental results , GA-PLS models increased the optimization efficiency of spectral models and produced the best Rp and RMSEP for calibration models. Performance of 30 navel oranges peel thickness in calibration set had been presented in figure 7.

Finally practical data are compared to predicted ones to assess the quality of calibration models .

Conclusion

This study aimed to establish prediction model to detect peel thickness of Newhall navel orange accurately. To make a thorought acquire for the means of simplifying and improving model, comparison of spectral preprocessing methods and the band selection method of GA and SPA were introduced. The predictions suggested that the best model with Rc 、Rp 、RMSEP and RMSEC of 0.882 、 0.864、 0.290 and 0.296 were established through spectral data processed by SNV and spectral variables screened out by GA. Based on these results, SNV was a effective way to greatly reduce the effects of light scattering. Instead of using full spectral data, GA models simplified models and improved the ability to predict with decreasing the extent of the spectral variables .

Fig. 1

 1 Fig. 1 Collecting the effective reference and dark current



  Where y i was the model's actual measured value; ŷ i was model's predictive value; f was used as dependent variables

Fig. 4 Fig. 5

 45 Fig.4 Relation between RMSECV and wavelength variables for epidermal thickness of Newhall navel orange

Fig. 6

 6 Fig.6 Variables selected by SPA for epidermal thickness of Newhall navel orange

Fig. 7

 7 Fig. 7 Comparison of predicted values and measured values of 'Gannan' navel orange in prediction set by GA-PLS mode

Table 1 .

 1

	Calibration set	9.70	18.83	14.37	1.97	13.68
	Prediction	10.50	18.63	14.43	1.99	13.81
	set

Sugar measurement results of Newhall navel orange Sample sets Min(Brix) Max(Brix) Mea(Brix) SD (Brix) CV(%)

Table 2 .

 2 Skin thickness measurement results of Newhall navel orange

	Sample sets Min(Brix) Max(Brix) Mea(Brix) SD (Brix) CV(%)
	Calibration set	1.98	5.57	3.72	0.57	15.42
	Prediction	2.63	5.43	3.76	0.58	15.58
	set

Table 4 Modeling results and comparison of different PLS

 4

	model	variables	R C	RMSEC	R P	RMSEP
	PLS	127	0.883	0.268	0.815	0.333
	GA-PLS	13	0.882	0.269	0.864	0.290
	SPA-PLS	6	0.859	0.293	0.811	0.339

ACKNOWLEDGMENT

The authors gratefully acknowledge the financial support provided by National High Technology Research and Development Program of China (863 Program).