Hui Li

Ronghua Ji

Jianhua Zhang

Xue Yuan

Kaiqun Hu

Lijun Qi
email: qilijun@cau.edu.cn

WEB-based Intelligent Diagnosis System for Cotton Diseases Control

Keywords: Cotton, Diseases, Intelligent Diagnosis System

Diseases control is always an issue in cotton production, the timely detection and effective control of diseases depend on, in most cases, an effective diagnosis system. Based on the distribution of cotton diseases in the main yielding areas of China in recent years, the main species and characters of cotton diseases were listed classified in the study and a database was established for this purpose. BP neural network as a decision-making system was used to establish an intelligent diagnosis model. Based on the model, a WEB-based Intelligent Diagnosis System for Cotton Diseases Control was developed. An experiment scheme was designed for the system test, in which 80 samples, including 8 main species of diseases, 10 samples in each sort were included.

The result showed the rate of correctness that system could identify the symptom was 89.5% in average, and the average running time for a diagnosis was 900ms.

Introduction

Cotton is playing a crucial role in the national agricultural economy of China. Cotton diseases and insects are the two main causes of damage on both quantity and quality of the cotton production. A timely detection of the pests and feasible controlling measures are cure to the effective cotton protection.

The researches concerning disease diagnosis systems for agricultural products started from the late seventies of the last century. The first disease diagnosis system, the soybean disease diagnosis expert system (Plandds), was developed by the University of Illinois (1978). Some additional work, the integrated expertise system for soybean pest control (SOYBUG) [START_REF] Nunninen | What Makes Expert Systems Survive Over 10 Years-Empirical Evaluation of several Engineering Applications[END_REF] , the expertise system for orchard management and pest control (POMME) were developed in the state of Florida. Japan has also conducted a lot of researches on disease diagnosis [START_REF] Grove | Internet-based Expert System[END_REF][START_REF] Mizoguchi | Expert System Research in Japan[END_REF][START_REF] Kozai | Intelligent Information Systems for Production Management in Agriculture and Horticulture[END_REF] . Comax-Cossym (Cotton Production Management System) developed by the Crop Simulation Center from Agricultural Research Service in the United States Department of Agriculture (USDA) is the most successful agricultural expertise system in the world, which improved the U.S. cotton production management [START_REF] Hallemmon | Comax: An Expert System for Cotton Crop Management[END_REF][START_REF] Jan | Cotton Advice You Can Bank On[END_REF] significantly.

The related researches in China are developing rapidly since last eighties. The Cotton Pests Management Expertise Decision Support system (COPMEDS) developed by the Institute of Zoology at Chinese Academy of Science was in the leading place [11] . Liang and Shi (1997) developed a cotton pest diagnostic system based on the BP neural network, which enhanced the running speed of the system by simplifying the neural network model [START_REF] Liang | BP Neural Network Based Diagnosis System for Cotton Pests (in Chinese)[END_REF] . Jiang et al. (1998) developed the Prototype of rule-based cotton pest management expert system by utilizing the expertise system development tool KA3 [START_REF] Feng | Diseases Diagnosis of Neural Network Expert System Design and Implementation[END_REF] . Zhang et al. (2005) designed the knowledge-model-based decision support system for cotton management .This system used system analysis and mathematical modeling to solve the issue of the overly-spread information in a huge knowledge data base, enabling decision-making processes to be more rapidly and simplified [START_REF] Zhang | Based on Knowledge Model of the Cotton Decision Support System (in Chinese)[END_REF] . Wang et al. (2008) designed a cotton fertilization expertise system based on Geographical Information System (GIS) in XinJiang. The system presented extensive research and discussion on the application of GIS technology to cotton fertilization expertise system [16] . Liu, etc. (2009) designed a Web-GIS-based expertise system to forecast agricultural diseases and pests [START_REF] Liu | Expert System Based on WebGIS for Forecast and Prediction of Agricultural Pests (in Chinese)[END_REF] .

The work presented in this paper is based on the description of the pattern of the cotton diseases, and the identification of the type and characteristic of the major disease problems during the recent years in

System Overview

The yield and the quality of cotton are affected significantly by the disease, which means farmers' great financial loss. As the lack of basic knowledge of cotton diseases detection and protection, the cotton farmers are longing for the guidance from experts since the occurrence of new and large variety of diseases. However, such experts are not always available for the emergencies. A diagnosis system is therefore considered effective in helping cotton farmers. 3. Management module: serviced for the system back-stage management, which was divided into three parts: disease management, message management and user management. 4. Expert online: the communication bridge between experts and users.

System Design

The system was a three-layer model with "data layer / application layer / interface layer". Where: Data layer was the bottom layer of the system, which was used for the storage of information needed by the system, and was consisted of database and knowledge base. Database was used to store the basic facts of cotton disease, description of problems, intermediate and final results during the diagnostic process, etc. Knowledge base was used to store knowledge and experience of cotton disease experts.

Application layer which included Web inference engine and application server could realize the inference and explanation functions of the system diagnosis. The BP neural network technology was used to execute the knowledge inference and diseases diagnosis based on the symptoms supplied by users.

Interface layer enabled the interaction of users and system. There were two types of users, ordinary and administrator, in the system. Ordinary users could query disease and input the required information by selecting disease factors, and the disease diagnosis and appropriate control methods were given to users in the form of interface. Administrator users managed the knowledge base and database.

The structure of cotton intelligent diagnosis system is shown in figure 2.

Cotton Disease Database

The database designed in this paper included the disease

Knowledge Base of cotton diseases

Knowledge base was used to store basic knowledge and practical experience of cotton disease experts.

Based on the investigation of the occurrence and the distribution of cotton diseases in recent years, there were more than 20 kinds of main cotton diseases in the main yielding areas of China. 8 kinds of diseases as the major disease of cotton were identified, such as, Cotton anthracnose, Cotton damping-off, Cotton Red rot disease and Cotton wilt, etc. Based on the analysis and summary of cotton disease symptoms, 5 categories and 32 symptoms were summarized, and which were numbered, so that each symptom of each category corresponded to a number. Cotton phytophthora boll rot a1 a3 b1 b4 d7 d8 e2 e8

Cotton angular leaf spot a1 a2 b1 b2 b3 c4 d7 e3

Cotton damping-off a1 a3 b2 b4 c1 d1 d6 d8 e2 e6 e7

Inference Engine of cotton diseases

The core of the cotton disease diagnosis system was the inference engine. Most of the existing reasoning models carried out the forward and backward reasoning by constructing a database of disease recognition features and using a standard query language (SQL). The process of knowledge acquisition and mimic expert reasoning for these models has grown an over-reliance on expert knowledge and experience, the practicality was poor and also with limitations.

Neural network was a massively parallel processing and self-learning, self-organizing, nonlinear dynamic system, which was particularly suited to complex non-deterministic causal reasoning, judging, identification, and classification problems. It had the self-learning function and cuold constantly enrich the content of the knowledge base. Therefore, BP neural network was used as the inference engine of cotton diseases in this system.

BP neural network was constructed as a disease inference engine based on the analysis and summary of cotton disease symptoms. There were 32 input nodes which were corresponding to 32 diseases in the BP neural network. According to empirical formula mn N  , the number of nodes N in the hidden layer was obtained as N=16. Then by errors comparison, the optimal number of neurons in the hidden layer was determined. By experimental verification of the system with 8 kinds of diseases (each with 10 samples), the average diagnostic accuracy was 89.5% and the average diagnosis time was 900ms.

Conclusion

A web-based intelligent diagnosis system for cotton diseases control was developed in this article.

Based on the research of cotton disease occurrence in recent years and on the analysis of the cotton diseases characteristics, a corresponding database was established and disease factors for the description of various diseases were obtained then. BP neural network combined with the Web technology was used as the disease inference engine in the system, so that the diagnostic accuracy and intelligence as well as the ability to solve the uncertain and ambiguous problems were enhanced.

Modelling results showed that the diagnosis system was high-accuracy and high-speed. The system was effective and easy to operate with the friendly interface. It was expected to show the high feasibility to the field situation by more practical tests.

  China. A Cotton Disease Intelligent Diagnosis and Decision Model was established, and a WEB-based Intelligent Diagnosis System for Cotton Diseases Control was developed with the BP neural network as the decision support system.

A

  Web-based Intelligent Diagnosis System for Cotton Diseases Control could realize the querying, diagnosing and online consulting of the main diseases during the whole growing process. Multiple users could access the system simultaneously. The system also could facilitate experts to input, update, modify and search data, information and rules associated with cotton intelligent diagnosis system knowledge and database. Web-based Intelligent Diagnosis System for Cotton Diseases Control was consisted of four separate modules: Query module, Diagnosis module, Management module, and Expert online. The scheme of the system is shown as figure 1.

Fig. 1 .

 1 Fig. 1. Scheme of the system function

Fig. 2 .

 2 Fig. 2. Structure of cotton intelligent diagnosis system

There were 8

 8 output nodes which were corresponding to 8 major diseases of cotton. The BP neural network was trained and validated by using Matlab in the article. The hidden layer transfer function was s-type tangent function tansig, the output layer transfer function was purelin, and the training method was Levenberg-Marquardt with the training function trainlm. Gradient descent momentum learning function learngdm was used as the learning function, and the performance function was mse.

Table 1 .

 1 table, disease picture table, user table and message table, which has played an important role in the development of Intelligent Diagnosis System for Cotton Diseases Control. Structure of disease table

	The structure of diseases table is as follows:
	List	Data Type	Non-empty	Notes
	dis_id	Int	Yes	Keyword
	name	Varchar(20)	Yes	Name
	region	Varchar(80)	No	Geographical
				place
	Pathogen	Varchar(160)	No	Pathogen
	law	Varchar(1000)	No	Occurrence
	prevention Varchar(1000)	No	Control
				methods
	germ	Varchar(300)	No	Germ

Table 2 .

 2 Cotton disease factorBased on the diseases factors listed in table 2, the factors corresponding to different diseases and the causalities of diseases were listed in the diseases table.

	Diseases period	Disease location	Lesion shape	Lesion color	Lesion state
	Factor Symptom	Factor Symptom Factor Symptom Factor Symptom Factor Symptom
	a1	Seeding stage	b1	Cotyledo ns	c1	Semi-circ circular ular or	d1	Brown	e1	Dot
	a2	Bud stage b2	Stem	c2	Near circle	d2	Dark brown	e2	Water stains
	a3	Boll stage b3	Leaf	c3	Irregular	d3	Red-brow n	e3	Grease-lik e
										Physalosp
			b4	Boll	c4	Polygon	d4	Tan	e4	ora black
										Dots
					c5	Spindle	d5	Pink	e5	Slime
							d6	Yellow	e6	Mold、 Floc
							d7	Dark green, black	e7	Fold Withered
							d8	Gray, white	e8	Mycelium
							d9	Deep blue	e9	Dwarf,Wit hered
										Color
							d0	Dark red	e0	changed, Reticulate
										d

Table 3 .

 3 Disease

	Name	Diseases period	Disease location	Lesion shape	Lesion color	Lesion state
	Cotton anthracnose	a1 a2 a3	b1 b2 b3 b4	c1 c2 c5	d2 d8 d0	e4 e5
	Cotton Rhizoctonia	a1 a2	b1 b2 b3	c3	d1 d4 d6	e7 e9 e0
	Cotton boll rot disease	a1 a3	b1 b2 b4	c1	d1 d5	e6 e7
	Cotton Hongfen disease	a1 a3	b1 b4	c1	d1 d5	e6
	Cotton wilt	a1	b1		d6	e0

Acknowledgements

We would like to thank Kaiqun Hu and Jianhua Zhang for their technical assistance. We would also like to acknowledge Ronghua Ji for her valuable suggestions on the improvement of the system.


Jinping Sun
email: jinpingsun1224@163.com

Yong Liang

Qin Yan

Chengming Zhang

Study Study Study Study on on on on the the the the Parameters Parameters Parameters Parameters' ' ' ' Acquisition Acquisition Acquisition Acquisition Method Method Method Method of of of of Di Di Di Distributed stributed stributed stributed Hydrological Hydrological Hydrological Hydrological Model Model Model Model Based Based Based Based on on on on RS RS RS RS

Parameters' acquisition is a key step for the distributed hydrological model when simulating a basin. Based on remote sensing technology, this article proposed a parameters acquisition method of distributed hydrological model. That is extracting the important ground objects' information from the images to obtain some parameters of the model. This article also used the hierarchical classification principle, and introduced the extraction method of several ground objects. At last, by means of TopModel, we simulated the Xueye Reservoir, Laiwu City in Shandong Province. The experiment shows that this method can effectively improve the accuracy of model simulation.

Presentation Presentation Presentation Presentation of of of of Questions Questions Questions Questions

Distributed hydrological model is a kind of hydrological model, the model's advantage is able to take into account vegetation, soil, topography and other factors' impact on the hydrological processes in the watershed, and the spatial differences that existent in these elements can more accurately reflect the actual movement of hydrological [START_REF] Liu Zhanyou | Comparison Research of Two Distributed Hydrologic Models in Flood Forecasting[END_REF] . So this model has been widespread concern in actual production. However, parameters used in distributed hydrological model generally have physical meaning, using the usual means of obtaining these parameters is more difficult, it limit s the application of distributed hydrological model. As the improvement of remote sensing technology, now we can easily access remote sensing image of high-resolution and high spectrum, obtaining the soil, vegetation, geomorphology, topography, land use and other information underlying surface based on remote sensing become an effective mean.

At present, there hasn't formulation of parameters' acquisition method on the distributed hydrological model, the parameters' acquisition only exists as necessary stage in the process of hydrological modeling. In the study of watershed distributed hydrological model based on spatial information technology, Ling Feng referred to the parameters' acquisition of distributed TOPMODEL, mainly obtained the ground cover and soil data. Based on remote sensing technology, this article studied the parameters' acquisition method distributed hydrological model.

TopModel TopModel TopModel TopModel

TOPMODEL is short for Topography Based Hydrological MODEL. It is a terrain-based semidistributed hydrological model, proposed by Beven and Kirky in 1979. TOPModel uses terrain space as the main structure, use terrain information (topographic index ln( α/tanβ ) or Soil -topographic index ln (α / Totanβ), α represents the drainage area of the unit contour length, tanβ represents the ground slope, T represents the soil permeability coefficient) to describe flow trends, based on the role of gravity drainage runoff along the slope movement principle, to simulate the runoff process [START_REF] Cheng Ji | Research on TOPMODEL in Basin Flood Forecasting[END_REF] . Since proposed, the model has been widely used.

2.1 2.1 2.1 2.1 The The The The basic basic basic basic structure structure structure structure of of of of the the the the model model model model TOPMODEL's calculation structure is shown in Figure 2.1, Model divide the watershed into vegetation root zone, unsaturated zone and saturated soil zone three levels. In the vertical direction, first, rain enter vegetation root zone through infiltration, some of the moisture that reach the region is lost through evapotranspiration , while the rest continue to filter down to reach the unsaturated soil zone; The moisture that reach the region first enter the saturated groundwater area at a certain rate, and then form soil midstream through lateral flow, and move in the form of soil midstream; If the water surface of saturated groundwater zone rise constantly, the stream that enter this part will show out the ground in the low-lying area, which will form the overland flow. (1) Calculation of evapotranspiration. The model believes that evapotranspiration only occurs in vegetation root zone, evaporation a E is calculated as:

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ - = i r i rz p i a S S E E max, , , 1 (2.1)
In this formula, i rz S , is the volume of water shortage of vegetation root zone of point i; (2) Calculation of infiltration rate. The model believes that moisture of unsaturated zone finally enter the saturated zone at a certain rate, which mainly depends on soil structure and the current water storage situation. Equation (2.2) gives the calculation method of infiltration rate at point i:

d i i uz i v t SD S q , , = (2.2)
In this formula: (3) Calculation of saturated groundwater. Model first proposed three assumptions: interflow always in a steady state, that is, the rate of interflow is equal to the rate of the upstream; the hydraulic gradient of saturated groundwater is equal to surface local slope; soil hydraulic conductivity can be expressed by negative exponential function of saturated ground water zone's water depth, on this basis, use the following method to calculate the depth of surface water:

i v q , represents
⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⋅ ⋅ ⋅ - = i i zm i T R S Z β α tan ln 0 (2.
3) (4) Saturation overland flow. Saturation overland flow will form when the depth between the saturated ground water and surface is negative, calculated as:

( ) [ ] { } i i i i us s A o z S t Q ∑ - ∆ = , 0 , max max 1 , (2.4)
Δt is the time step, i A is the effective runoff area.

(5) Interflow.

∫ = L i b dL q Q (2.5) (6)
Confluence. Confluence's calculation contains confluence's calculation of slope and confluence's calculation of riverway, the delay time that associated with distance is used to calculate slope confluence, and a method that based on average flood wave velocity is used to calculate river confluence. Formula (5.6) is used to calculate the needed time from one point to the watershed's outlet:

( ) [ ] ∑ = • = N i i i v x t 1 tan / β (2.6)
In the formula, i

x is a length, Tanβi is the slope of I in the N section flow path, v is a speed parameter, as a constant. In the actual calculations, we can use a dimensionless distribution function curve that is similar to unit normal to calculate the output process of runoff, assume the runoff is equal in the entire watershed, and then divide the main channel into different levels for confluence. ChVel: the effective rate of convergence of the main river.

The The The The parameters parameters parameters parameters' ' ' ' acquisition acquisition acquisition acquisition method method method method of of of of hydrological hydrological hydrological hydrological model model model model based based based based on on on on RS RS RS RS 3.1 3.1 3.1 3.1 The The The The data data data data of of of of MODIS MODIS MODIS MODIS and and and and ETM ETM ETM ETM

MODIS is the main detector of EOS-AM1 satellite. It belongs to a kind of high spectral sensor, whose band is discontinue, (spectral range from 0.4 to 14.5), and ground resolution is very low (the resolution is 250m, 500m, 1000m). The resolution of ETM + data is very high, but the number of bands is small (only 8 bands). Table 3.1 gives the characteristics of each band.

ETM ETM ETM ETM data data data data characteristics characteristics characteristics characteristicstable table table table

Wave Wave Wave

The The The The technology technology technology technology of of of of Image Image Image Image Fusion Fusion Fusion Fusion

Image fusion is a process that the multiple remote sensing images according to certain algorithms, in the prescribed geographic coordinate system, generate a new image. Through data fusion, on the one hand, we can remove the useless information pertinently, eliminate redundancy, and reduce data processing dramatically to improve the efficiency of data processing; on the other hand, we can also gather the useful information in the massive multi-source data to facilitate all kinds of information facilitate all kinds of information, and exert respective strengths to get more useful information. In this study, the MODIS data and ETM + data was fused to make the experimental data to reach the purpose of simple and information complementary.

In this study, the fusion wavelet transform-based, main steps are as follows: suppose A, B as the two original images, F is the fused image. First, transform each source image separately, to establish the small towers shape decomposition of the image; second, fuse each decomposition layer, different frequency components of each decomposition layer can be fused with different fusion operator, and finally get the fused wavelet pyramid; at last, carry out inverse wavelet transform on the fused wavelet pyramid (that is, the image reconstruction), the reconstructed image obtained is the fused image. Figure 3.1 is the fused image. In the application of multi-band remote sensing data, selecting a reasonable band combination or band operation to extract information is an important means to improve the classification accuracy and work efficiency. According to remote sensing Classification theory, each kind of ground objects can be characterized by spectral curve to show its unique internal features. In practice, due to a variety of sample imaging environment, makes the spectral information on images complex and changing. But for most remote sensing images, there are also several kinds of ground objects on a leading position, such as water, vegetation, rocks and soil . From the view of spectrum, these features of the ground objects can be characterized uniquely, the information that decides the basic types of images is always referred as a spectrum of information. Starting from the characteristics of remote sensing, we can use the Figure 3 Water nearly absorbs all of the primary energy in the near infrared band, so the reflected energy in this band is very little, while plants and soil absorbed little energy in this band, so with a high reflectance characteristics. This makes water has significant difference between vegetation and soil in this band. In this band, the water shows dark tones, while the soil and vegetation is able to show relatively bright colors. However, when in the mountains, this might change because of the effect of the shadow of the mountain , for short infrared , the reflection in the shade is very low, which makes the shadow region in the image also has a clear dark tone, thereby makes it difficult to correctly distinguish water bodies and shadows.

In remote sensing images, we select typical pixels for water body and the background ground objects such as woodlands, residential areas, farmland and shadow, and statistic the spectrum brightness values, as shown in Table 3 From the table we can see, in the band 2, water bodies can't easily distinguish with village and farmland; while easily distinguish with shadow and Woodland. In band 3, the water and farmland has significantly differen ce, while has some confusion with Woodland and shadow. In band 4, the water body and other background ground objects all have obvious differences. In the band 5 and band 7, the water can distinguish with Woodland, village and farmland, but easily confused with shadow. According to sampling points of the same ground objects in the same wavelength, we can do the mean statistics, and make objects spectrum diagram, as is shown in Figure 3.3. As we can see from the spectrum chart, the gray of water and shadow in the band 5 was significantly less than the band 2, while other background objects are the opposite. In the band 2 and 3, the gray value of water is generally greater than the shadow's gray value. While in the band 4 and 5, the gray value of the shadow is generally greater than the water's gray value, add the two bands, you can increase the difference between water body and shadow. Add the band 2 and 3, add band 4 and 5, the enhanced spectrum chart is shown in Figure 3.4. As we can see from the above chart, after the band combination, water and other background ground objects can distinguish more obviously. So, we can use the spectrum value of the combination of band 2 + band 3, band 4 + band 5 to compose feature space to extract water, woodland, village, farmland and shadow.

Parameters Parameters Parameters Parameters' ' ' ' acquisition acquisition acquisition acquisition

Remote sensing data (aerial photographs and satellite images) can provide information on spatial characteristics of watershed, is the most feasible method to describe hydrological variability, especially in the absence of regional ground-based observations. In distributed hydrological modeling, the application of remote sensing data can be summarized as follows: used as the input data and parameter estimation of the model, there are seven specific areas: 1) rainfall intensity's observation and spatial pattern; 2) the calculation of evapotranspiration and soil moisture retrieval; 3) snow cover; 4) groundwater; 5) land cover and land use classification; 6) water feature; 7) vegetation parameter extraction [6].

For each basin, because the condition of underlying surface is different, when carries on the simulation of the basin, different basin's model parameter ought to be able to reflect under the pad surface condition spatial variation. However, in practice, in addition to the differences in the terrain can take advantage of each basin's terrain exponential distribution function, model parameters that reflect the vegetation, soil conditions can not are difficult to obtain through actual measurements, you can only use hydrologic data to rate parameters.

The biggest storage capacity of vegetation root area SRmax is a pattern parameter, soil parameters can obtain by referring to elated content of the AGWA [START_REF] Yu Xing-Jie | The Development, Situation and Future of Distributed Hydrological Model[END_REF] model. The parameters related to soil is soil saturated hydraulic conductivity 0 T and soil hydraulic conductivity along with depth of soil change coefficient of attenuation M, the soil saturated hydraulic conductivity determined according to the soil texture:

0 clayey 0clayey sand 0sand silt 0silt T P T +P T +P T = × × × (3.1)
In the formula: clayey

Watershed Watershed Watershed Watershed Simulation Simulation Simulation Simulation

In order to verify the validity of the above parameters acquisition method, according to the extracted results, this article used TopModel and the actual data of Xueye Reservoir, carried out the simulation on Xueye Reservoir basin.

According to the classification rules of Chapter III , we extracted the fused image using the supervised classification methods in REDAS, and got the information of 6 kinds of ground objects. The result is as follows: From the simulation results we can see, besides the No. 20060730 flood, the uncertainty factor is all higher than 75%, and besides the No. 20060729 flood the runoff error is all smaller than the required 20%. The result reaches the B level, and we can say the method can be applied to practical production.

Conclusions Conclusions Conclusions Conclusions

Based on the hierarchical classification principle, this article studied the classification problem of remote sensing images, and finally proposed a parameters' acquisition method of distributed hydrological model. The method is to extract information that is relevant to distributed hydrological model parameters from remote sensing images. Compared with traditional methods of parameter determination, the method that using RS to obtain hydrological model parameters is more convenient [START_REF] Kang | Applying SWAT for TMDL programs to a small watershed containing rice paddy fields[END_REF] . At last, this article used TOPMODEL to simulate Xueye Reservoir, the result shows that the method has some practical value.

References References References References

Q

  Fig Fig Fig Fig. . . . 2.1 2.1 2.1 2.1 S S S Structure tructure tructure tructure frame frame frame frame diagram diagram diagram diagramof of of of TOPMODEL TOPMODEL TOPMODEL TOPMODEL

2.2 2 .2 2 .2 2 . 2 T

 2222 T T The he he he needed needed needed needed parameters parameters parameters parameters in in in in TopModel TopModel TopModel TopModel' ' ' 's s s s calculation calculation calculation calculation In TopModel, the main parameters used are in the following: SZM: the parameter of index conduction function; T0: when the soil reach saturated, T0 is the natural logarithm of soil effective conductivity; Td: time parameter of gravity drainage; SRmax: maximum water storage capacity of vegetation root zone; SRinit: initial soil moisture initial value of vegetation root zone; RV: the effective rate of surface overland flow;

  -0.690 30 4 Near IR 0.775-0.900 30 5 SWIR 1.550-1.750 30

Fig

  Fig Fig Fig Fig. . . . 3.1 3.1 3.1 3.1 the the the the fused fused fused fused image image image image

the the the hierarchical hierarchical hierarchical hierarchicalclassification classification classification classificationchart chart chart chart 3 . 3 . 3 . 3 .4 4 4 4

 33334 Fig Fig Fig Fig. . . . 3.2 3.2 3.2 3.2 the the the the hierarchical hierarchical hierarchical hierarchicalclassification classification classification classificationchart chart chart chart

3

 3 Fig Fig Fig Fig. . . . 3.3 3.3 3.3 3.3 Object Object Object Object Spectrum Spectrum Spectrum Spectrum Chart Chart Chart Chart

  Fig Fig Fig Fig. . . . 3.4 3.4 3.4 3.4 the the the the enhanced enhanced enhanced enhanced spectral spectral spectral spectral chart chart chart chart

  sand and silt in the soil, if the basin contains many kinds of different types the soils, then we can calculate by area weighted average of different soil types; 0 clayey T , 0sand T and 0 silt T represent the saturated hydraulic conductivities of the three material, and need to rate through the actual material.

  Experimental Experimental Experimental area area area area and and and and data data data data source source source sourceThe experiment was carried out in the Xueye Reservoir, Laiwu City in Shandong Province. Xueye reservoir is one large reservoir of Shandong Province, controls the drainage area of 444 square kilometers, has a total capacity of 221 million cubic meters, has useful storage capacity of 112 million cubic meters, and dead storage of 2.8 million cubic meters. The location of Reservoir is very superior, and the materials are very rich, which provide a favorable condition for the experiment.

Figure 4 .

 4 1 is the position diagram of Xueye Reservoir.

Fig

  Fig Fig Fig Fig. . . . 4.1 4.1 4.1 4.1 the the the the position position position position diagram diagram diagram diagramof of of of Xueye Xueye Xueye Xueye Reservoir Reservoir Reservoir Reservoir

Fig

  Fig Fig Fig Fig. . . . 4. 4. 4. 4.2 2 2 2 Classification Classification Classification ClassificationResults Results Results ResultsChart Chart Chart Chart

Fig 3 FigFigFigFig

 3 Fig Fig Fig Fig. . . . 4.3 4.3 4.3 4.3 the the the the flood flood flood flood simulation simulation simulation simulationsituation situation situation situation of of of of No. No. No. No. 20040615 20040615 20040615 20040615

Tab Tab Tab. . . . 3.2 3.2 3.2 3.2 the the the the spectrum spectrum spectrum spectrum brightness brightness brightness brightnessvalue value value value of of of of Water Water Water Water and and and and the the the the background background background backgroundground ground ground ground objects objects objects objects

  .2.

		Tab Band 2 Band 3 Band 4	Band 5		Band 7
		Values of samples	Mean	Values of samples	Mean	Values of samples	Mean	Values samples of	Mean	Values samples of	Mean
	Water	28,26,27 , 30,34	29	3,0,0,9, 14	5	13,12,12,1 5,15	13	6,6,8,5, 6	6	5,1,2,1 ,3	2
	Woodlan d	24,26,30 , 25,25	25	1,3,0,2,5 2	53,50,55,5 9,49	53	47,43,48 ,56,49	49	15,14,1 2,15,18	15
	villag e	31,32,29 , 29,31	30	14,17,14, 10,19	15	28,31,30,2 6,32	29	30,41,58 ,42,54	45	17,24,3 2,26,31	26
	Farmlan d	39,36,36 ,36,38, 33	36	41,32,37, 40,31	36	59,54,49,5 4,49	53	105,84,9 3,103,82	95	57,43,4 7,53,43	47
	Shadow	21,21,22 ,20,22	21	0,0,0,0,0 0	13,17,19, 17,20	17	10,7,13, 9,11	10	2,2,4,3 ,4


Yunliang Song
email: songyunliang@126.com

Bin Chen

Shushan Wang

Daoli Lu

Min Yang

The Novel Integrating Sphere Type Near-infrared Moisture Determination Instrument Based on LabVIEW

Keywords: NIR spectroscopy, moisture content, determination instrument, LabVIEW

Based on the technology of near-infrared spectroscopy, a novel nearinfrared moisture content determination instrument has been developed with three laser diodes of different wavelengths as light source, with one InGaAs detector as determination device, and with integrating sphere as sample holding device. Besides, LabVIEW virtual instrument program was employed for development of its operation instructions. This instrument has made full use of the integrating sphere system to remove the spectroscopic parts which are necessary in general spectrometers and has thus facilitated the improvement of system stability and signal noise ratio. Determination tests of moisture in sesame seeds and tea fresh leaves verified high precision of the instrument, showing that it can meet the primary requirement of moisture determination in practical production.

Introduction

Moisture widely exists in various natural substances. In medicine, food, tea, grain, tobacco, chemical and many other manufacturing industries, it's necessary to monitor the moisture content of raw materials and products in almost all aspects of processing. Hence, moisture measurement and control is an important aspect of the production, which directly affects product quality. Moisture content testing for wheat, corn, sesame seed, soybean, rapeseed and other agricultural products is directly related to product pricing and classification of seeds based on quality. The traditional methods of moisture content determination mainly drying method, resistance or capacitance measurement method and so on, are either testing process cumbersome, consuming too long time, or accuracy is not high enough, so cannot be used for non-contact measurement. Compared with these methods, Near-infrared (NIR) analysis technology has such advantages as non-destructive, fast, efficient, free of sample pretreatment, and suitable for process online analysis. NIR is electromagnetic waves whose wavelength range within 780 ~ 2 526 nm, the earliest recognized waves in the non-visible light region. And after first used to determine water content in grains, NIR spectroscopy was subsequently used in quality assessment and analysis of other agricultural products. It is one of the earliest used and most effective ways for moisture detection in agricultural materials. In recent years, the development and production of NIR analyzer has progressed substantially in China. However, the existing NIR instruments generally use halogen lamps as light sources, which need to install splitting device, thus making the path of rays constitution and the control circuit system complex, the instrument debugging process tedious, the construction cost high, and practical application of the instruments difficult. In addition, they generally lack such functions as remote diagnosis, and remote data real-time transmission and processing. Furthermore, they have certain technical short comings as poor instrumental consistency between various parameters and high model transfer error.

To solve these problems and to promote the application of NIR special instruments, a novel NIR moisture determination instrument has been developed with integrating sphere as sample holding device, with three laser diodes (LD) of different wavelengths as light source, with an InGaAs detector, and with operation instructions developed using LabVIEW virtual instrument program. Rapid and non-destructive determination of moisture content of sesame seeds and tea fresh leaves have been completed on it.

2

Overall Scheme Design

The test system consists of two parts: hardware and software. Hardware is mainly used for standard optical signals generating and sending, reflected signal with sample information receiving, work process controlling and status monitoring. Software includes control and monitor interface, data analysis, and results displaying, developed with high-level graphical programming language LabVIEW. Schematic and structure diagram of the integrating sphere type NIR moisture determination instrument are shown in Fig. 1 and Fig. 2. From the diagrams, a personal computer (PC) has been used to control the rotation interval of the 3 LD light sources inlayed in the integral ball along with InGaAs detector. The sample cell has been inserted into integral ball so as to eliminate measurement errors caused by the sample structural differences and realize body absorption, which makes full use of the structural characteristics of integrating sphere and good monochromaticity of LD to remove the spectroscopic parts and thus makes the device compact and stable. LD driven by match electric circuit is selected by the multiway switches to send out stable monochromatic light. The reflection light signal with material function is then collected by the integral ball and transformed to the InGaAs detector. Through the data processing circuit, the signal enters the data acquisition card and then to the computer to run following processing to obtain the moisture content of the tested sample. Hardware of the NIR moisture content determination instrument mainly consists of optical system and circuit system. As shown in Fig. 2, integrating sphere is the key component of the optical system. The integrating sphere is often used as a test sample devices in NIR test equipment, because it ① can collect most of the analysis light, greatly increasing the signal strength, improving signal to noise ratio; ② can eliminate the interference of reflection, scattering, polarization light, reducing the light incident impact of the shape and angle changes , making the instrument more stable and more reliable; ③ can reduce the impact on the measurement of uneven and spatial location as the sample changes, improving the measurement repeatability. However, traditionally the sample room was located outside the integrating sphere, the light spot size and intensity of optical signal greatly affected the results. As the average moisture content is always the goal of moisture content determination, the sample room can be inserted into the integrating sphere, which is not only able to take full advantage of the benefits, but also increased the light intensity, thus make the influence subject to the measurement spot size disappear. The circuit system mainly includes light source driving circuit, detector detection and processing circuit, operation control and status monitoring circuit. And the light source driving circuit is essential to ensure the instrument precision from the wavelength and power stability of the emitted light.

Optical System Design

Integrating sphere is the key component of the optical system. To design it, not only the inner surface of the coating should have high reflectivity, and the integrating sphere opening area should also meet the general requirements of the integrating sphere. In this design, coating material for integrating sphere inner surface used gold to ensure its reflectivity of NIR up to 99.5%. There are 5 openings in the integrating sphere, corresponding to the 3 light sources, a detector and a sample cell port. The diameter of the integrating sphere is 200 mm. The different center wavelengths have been selected based on test data analysis of full spectrum instrument, namely 1 310 nm, 1 450 nm and 1 550 nm. Here, 1 310 nm is used as reference wavelength, 1 450 nm as water absorption peak, and 1 550 nm as amended wave.

To eliminate temperature drift due to too long operation time, and revise operating point difference of the detector to receive signals from different light sources, the driving circuit was separately designed in view of various light sources to allow the light sources work in turn. In consideration of the semiconductor laser photo source's work principle and characteristic, one kind of simple feasible automatic power control (APC) driving circuit was used to realize the constant power control so as to remove the spectroscopic parts which are necessary in general spectrometers.

Detection and Processing Circuit for Detector

InGaAs photodiode sensitive in the long wavelength has been selected as detector. Yet its output current signal is very weak. Before being input to A/D transformation chip, there should be some processing circuits. As shown in Fig. 3, after the I/V transformation, chip DS1859 achieves the detector temperature control and filtering, amplifying processing, thus obtains voltage signal in the operating region of data acquisition card.

Monitoring Circuit

Equipment's work condition monitoring, includes environmental temperature testing, sample temperature testing, work status monitoring of light source and alarming. The current of the light sources are detected by Hall sensors, who can detect the current around the magnetic field without affecting work status of the instrument. And the temperature are detected by the digital temperature sensor DS18B20.

Data Acquisition Card

This system uses UA206D PCI bus A / D acquisition card sold by a company YouCai in Beijing. The card has two-way integrated D / A, with card-specific dynamic link library UA204.DLL, which provides simple and efficient acquisition and control functions to support various functions of UA206D. LabVIEW applications directly call Dynamic Link Library subroutines to realize a measurement process control and data acquisition.

Software System Design

Software of the NIR moisture determination instrument mainly realizes instrument control, condition monitoring and data processing. Data processing includes data collection, data storage, the results display and processing. The man-machine interface is used to detect and determine the user input information to form the testing process and calls the corresponding subroutine. Background data processing fulfils analysis and judgments of the returned measurement data, forms statements and then archives. Software system controls apparatus of spectrum scanning to the measured sample while the background data processing is doing the analysis to determine whether the result reaches to the corresponding target. As shown in Fig. 4, working process of the equipment contains: moisture content of continuous testing and monitoring, environmental temperature testing, sample temperature testing, work status monitoring of light source and alarming. The main interface will display the current operator status under the operator's rights and have different control responses on databases. During the test, the screen will display the progress of the main interface, show the data measured for some monitoring of the project as required in the interface, analyze data at the same time, and generate different forms of reporting results to the operator use. If requirements on sample are not satisfied, a warn message will appear.

Moisture Content Test Experiments

After assembly of hardware, design of software and debugging of the instrument, a test sample was scanned. Then, multi-dimensional linear regression (MLR) and polynomial regression algorithm was employed for establishing calibration model between relative value of system outputs and moisture content. Tea fresh leaf and sesame seed samples were used to carry out moisture content examination in the selected wavelength. The relative value of system outputs and moisture content has been established and survey examination has been carried out to the test sample.

With a group of samples of known moisture content, first scan the blank on this instrument to define instrument operating point, and then load the samples and scan them to obtain system outputs in various wavelengths. Finally, set up the calibration model between relative value of system outputs and moisture content with software NIRSA2.2. System relative output is calculated as:

i H = i I / R I . ( 1
)
where, i H is the system relative output in detection wavelength i, R I is the reflected light intensity in reference wavelength R, i I is the reflected light intensity in detection wavelength i. (R = 1 310nm, i = 1 450nm or 1 550nm )

Moisture Content Modeling Analysis of Tea Fresh Leaves and Sesame Seeds

The experiment altogether gathered 46 tea fresh leaf samples from the production line in a tea factory in Danyang, 30 samples as the adjustment collection, and 16 samples as the forecast collection. Scanning was carried out to examine separately to get the system relative outputs and actual moisture content of each sample, and water content measured according to GB / T 8304 -2002. 54 sesame seed samples were collected from the market in Zhenjiang, 36 samples as the adjustment collection, and 18 samples as the forecast collection. Water content was measured according to GB 5497-85, and was taken a similar testing process to the tea fresh leaves. Then the MLR model and the polynomial regression model were respectively established, and the model accuracy and error are shown in Table 1.

Table 1 shows that the integrating sphere type NIR water meter can meet the general moisture measurement requirements of the tea fresh leaves and sesame seeds. The model accuracy of tea fresh leaves is relatively poor, and the increase in precision from polynomial regression is also quite limited. This is mainly due to that the samples' loading tightness is difficult to control. As a result, the moisture content of tea fresh leaves spans a larger area, since working point adjustment device is difficult to achieve such a large measurement requirements. In sesame seeds modeling, the correlation coefficient of MLR model can reach above 0.90. That of polynomial regression model can reach 0.97, being sufficient to meet the accuracy requirement of the general test.

System Tests

Stability. Without any conditions changed, collect the reflected light intensity 13 times in 5-minute interval. The results are shown in Fig. 5, and the error analysis are shown in Table 2. Repeatability. Take 2 sesame seed samples, each sample surveys the blank first, then surveys continuously 10 times, and record system relative outputs, substitute into sesame seed moisture content examination model to calculate water content, to test the repeatability of instrument. The results are shown in Table 3.

Error Analysis. Stability and repeatability of the instrument were obtained under the same loading condition. Considering the results of the model of tea fresh leaves and sesame seeds, the conclusion comes to that the stability of the system is high, and the system could meet the requirements of the prediction accuracy basically though the repeatability is relatively poor. Because the once reflect light from the sample is directly irradiate the detector, and the error caused by the sample compactedness in different loading is received and amplified directly. Duplication sample loading can improve forecast accuracy effectively, but it will affect the test speed and ease of use of the instrument, and also bring difficult to the transformation of online detection. Therefore, an adjustment on the internal structure of sphere accordingly is expected.

Conclusions

Based on the technology of NIR spectroscopy and virtual instrument technology, a new moisture meter used for online analysis is developed. The results of system tests and moisture content detection experiment of tea fresh leaves and sesame seeds show that the instrument can meet the primary requirement of moisture determination in practical production. New application method of integrating sphere has greatly increased the signal intensity, enhanced signal to noise ratio of the instrument and reduced the influence caused by the changes of shape and angle of the reflected light, thus made the instrument more stable and reliable, as well as reduced the impact on measuring resulted from the sample unevenness and spatial changes, and improved the measurement repeatability. In addition, this system uses the rich software resources of computer to make some hardware software-based. To simplify the apparatus structure and shorten the development cycle of the instrument, it takes full advantage of the compatibility between LabVIEW software and non-NI hardware products, mostly uses domestic data acquisition cards to reduce costs. Users could create forecast model of water for different materials according to various needs, which can be easily ported to different applications. Meanwhile, the applications on remote diagnostics for the equipment failure also have been taken into account when designing the instruments.

Fig. 1 .

 1 Fig. 1. Schematic of the integrating sphere type NIR moisture determination instrument.

Fig. 2 .

 2 Fig. 2. Structure diagram of the integrating sphere type NIR moisture determination instrument

Fig. 3 .

 3 Fig. 3. Detector examination recuperation electric circuit..

Fig. 4 .

 4 Fig. 4. Virtual instrument front panel.

Fig. 5 .

 5 Fig. 5. Stability test results of the system.

Table 1 .

 1 Model precision of moisture measurement of tea fresh leaves and sesame seeds.

	Model	Tea Fresh Leaves	Sesame Seeds
	Precision	MLR	Polynomial	MLR	Polynomial
	Correlation	0.733 9	0.734 1	0.925 1	0.972 1
	Coefficient
	of Model
	Correlation	0.731 8	0.733 1	0.917 5	0.977 8
	Coefficient
	of Predict
	RMS Error	0.032 3	0.032 3	0.023 9	0.014 8
	Ave. Error	3.32%	3.31%	2.46%	1.66%
	Max. Error	8.11%	8.12%	8.91%	6.64%
	Pre. MSE	4.11%	4.10%	3.39%	2.52%

Table 2 .

 2 Test result errors of stability (Unit: mW).

	Error	Wavelength/nm
		1 310	1 450	1 550
	Max / Min	1.347	2.470	1.174
		7/1.345 7	1/2.463 8	4/1.172 7
	Extreme Difference	0.002 0	0.003 6	0.001 7
	Mean	1.347 0	2.466 6	1.173 5
	Standard Deviation	0.000 6	0.001 8	0.000 4

Table 3 .

 3 Test results of repeatability (%).

	Serial Number	Sample 1	Sample 2
	1	6.46	7.68
	2	6.29	7.89
	3	6.64	7.69
	4	6.45	7.76
	5	6.76	8.12
	6	6.47	7.67
	7	6.45	7.69
	8	6.75	7.76
	9	6.56	8.11
	10	6.47	7.67
	Max / Min	6.76/6.29	8.12/7.67
	Extreme Difference	0.47	0.45
	Mean	6.53	7.80
	Standard Deviation	0.15	0.18


Xinlei Li

, Gang Liu

Mingming Guo

Yin Liu

Fei Yang

A Circuit Module and CPLD Laser Ground Controller Based on RS485

The function of the controller is to adjust the rise and fall of the ground shovel constantly according to the signal of positional deviation transmitted from the laser receiver, and remove or fill in the soil of the farmland, thus reaching the aim of smoothing the farmland. So the controller is the core component of the laser-control-ground system. The speed of response and the control algorithm of the controller are in relation to the stability and work efficiency of this system. This research uses the RS485 module to transmit, enhancing the interference immunity of the system, raising the stability when it works in severe environment, replacing SCM with CPLD to carry out the function of logical control, cutting down the power consumption of the system. It interferes manually in the hydraulic control automatic mode via software, thus improving the stability and flexibility of the system. The function of adjusting the duty cycle of the controller output signal is added, making the controller able to work both in paddy field and dry land.

Keywords:RS485 Circuit CPLD Laser Ground Controller

Introduction

The purpose of designing the controller is to achieve the matching of the controller with different types of hydraulic systems, and improve the controller's response to hydraulic actuator, carrying out the ground operation in different landforms (paddy field of dry land) [START_REF] Lv | Improvement and Experimentation of Laser Controlled Land Leveling System[END_REF] . Through the summary of large amount of previous ground operation, this research made some improvements upon the original controller. The signal transmission between the controller and receiver uses RS485 bus transfer mode instead of the previous current transfer mode, adding the function of manual interference in the rise and fall of the ground shovel under the automatic mode, carrying out the function of manual and automatic interlocking via software instead of the previous interlocking relying on gate circuit, simplifying the circuit's area and raising the circuit's stability; adding the function of adjusting the duty cycle of the controller output signal, thus adapting to different hydraulic mechanisms and different working landforms [START_REF] Lin | Research and Development on Receiver and Controller for Laser Controlled Land Leveling System[END_REF] .

System Design for Hardware

Figure 1 Hardware Structural of Controller Figure 1 is the hardware structural of the controller. The receiver's signal is transmitted to the MAX485 circuit module of the controller via RS485 bus; MA485 converts the 485 electrical level to TTL level, then transmits it to CPLD to process; CPLD processes the position signal, then puts out the pilot lamp driving signal and hydraulic driving signal; the pilot lamp driving signal controls the on and off of the pilot lamp through the lamp driving circuit, displaying the level changes of the position; the hydraulic driving signal drives the hydraulic system to work via controlling the relay switch, adjusting the rise and fall of the ground shovel. Users can choose between the automatic mode and manual mode for the system via the mode-selecting module.

Main Control Unit and Peripheral Circuit Design

This research uses the CPLD Chip EPM240T from Alteral Company as the main control chip. This chip has 240 logical units, 8K-bytes flash with the instant system programming function (ISP), making it very convenient to modify the system's function. The chip supports 300M clock frequency at the most; the maximum time delay between pins is 4.5 ns, providing more advanced property for users; 100 pins TQFP with smaller volume; 3.3V system voltage and 1.8V core voltage with lower power consumptio [START_REF] Liao | FPGA Embedded Application and Development Technology Platinum Handbook[END_REF] n. CPLD programs' parallel processing is faster compared with 51 SCM, and the programs won't overflow; eliminating the peripheral devices like the Watch Dog, raising the stability and reliability of the system. Its inner unique structure fits well for disposing complicated combination logic circuit and sequential logic circuit. The power supply for the controller is from the tractor battery, with the voltage of 12V. The CPLD inside the controller needs a working voltage of 3.3V, and most chips like the pilot-lamp-driving circuit and the relay-driving module need a voltage of 5V, so it requires the powersupply-changeover module to convert the voltage from 12V to 5V and 3.3V. This research uses LM2575 powersupply-changeover chip to achieve the change from 12V to 5V, and uses LM117-3.3 to achieve the change from 12V to 3.3V. The power-supply-changeover circuit diagram is Figure 2. LM2575 is a kind of switching voltage regulator, with the features of small volume, high working efficiency, low heating value, etc. The maximum output voltage is 40V, maximum output current 1A, maximum voltage-stabilizing error 4%, with limiting-current circuit and overheating-protection circuit inside, etc. LM117-3.3 belongs to switching power-supply-changeover chips, with the features of heavy output current, high accurate output voltage, low ripple coefficient, etc. A tantalum capacitor of about 10u needs to be added to the outlet end of the power-supply-changeover when it is working, in order to filter the AC component in the direct voltage. And this research uses the electric relay to control the on-off of the power supply. VCC in Figure 3 is the power supply provided by the tractor's battery. When the switch S0 is on, the relay's normally open contact is on, out1 and out2 on, and the tractor's power supply accessed the controller.

RS485 Circuit Module Design

RS-485 works in the way of half-duplex, with one sending and the other receiving. The position signal of the receiver is sent to the controller by RS-485 bus [START_REF]SCM Typical Module Examples Guide People's Post and[END_REF] . RS-485 uses the way of differential-level-receiving to raise the capability of anti-interference, fit for working under severe condition. The baud rate of the serial communication is 9600bit/s, and the data format is composed of 1 start bit, 8 data bits and 1 stop bit. The CPLD system uses MAX485 chip to accomplish the communication between the controller and receiver. As the system voltage for CPLD is 3.3V, MAX3485 is used to achieve the RS485 level changeover, and MAX3485 working under the power supply of 3.3V is a RS-485 changeover chip. As Figure 4

Hydraulic System Driving Circuit

The magnetic valve of the hydraulic system is controlled by the relay. One end of the normally-open-contact of the relay is connected with the electrical source, the other end with the magnetic valve. In order to improve the reliability of controlling the hydraulic system, this research uses solid state relay as the component for driving the hydraulic system. The solid state relay has a long operational life with high reliability. It is able to work under the circumstance of heavy impact and shock, because there is no mechanical part inside. At the same time, the solid state relay dose not have a input coil or contact arc or rebound, thus reducing the electromagnetic interference [START_REF]Changpeng Jia Properties and Applications of Solid State Relay[END_REF] .

Figure 5 is the circuit of solid state relay driving hydraulic magnetic valve. CPLD controls the on-off of the relay via controlling the on-off of the transistor, thus carrying out the function of controlling the on-off of the hydraulic magnetic valve. For example, when the up port is high level, the Transistor Q1 is on; the Relay U11's normally open contact is closed (Port 4 and Port 3 are on), supplying power for the hydraulic system magnetic valve and driving the hydraulic system to lift the ground shovel. Likewise, when the down port is high level, the hydraulic system will lower the ground shovel. To prevent an excessive dash current when the relay is switched on, R3 is added as a current-limiting resistance. At the output end of the relay, an RC absorbing circuit is added, which can effectively restrain the transient voltage added to the relay and the exponential growth rate of the voltage. Also a piezo-resistor with given clamp voltage is linked to the output end, thus protecting the relay effectively [START_REF] Peng | Discussion about Some Problems in Application of Solid State Relay[END_REF] . PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.cn

Position Pilot Lamp Driving Circuit

The position pilot lamp can show the level of the ground of the time, which is convenient for users to conduct ground operations correctly accordingly. The controller makes judgment according to the information about position transmitted from the laser receiver, and then drives the transistors on or off, thus control the pilot lamp on or off. Figure 6 is the position pilot lamp driving circuit. The transistors used here are NPN 9013 low power transistors; the maximum working current of the collector is 500mA; the working temperature is from -55℃ to 150℃; the voltage drop between collector and base is around 0.3V when it is working in saturation region. The operational principle of the driving circuit: when the land level is higher than the reference plane, the receiver sends the position signal of the area to the controller; then the controller sends electrical level to HighLight port, Transistor Q6 conducted and LED5 on. There are 3 pilot lamps representing high, middle and low. So the information of the land level can be shown though the 3 lamps, providing the base of judgment for ground operation for users.

Software Design Framework

This research uses VHDL hardware description language to program CPLD. The main functions of the controller are as follows: in the automatic mode, disposing according to the signals from the receiver, outputting driving signals for the hydraulic system and pilot lamp; in the manual mode, checking the signals from the rise button or the fall button, outputting controlling signals for the hydraulic system. The difficulty in the software design is to carry out UART serial function via hardware logical unit, receiving the serial data from the receiver, outputting hydraulic controlling signals whose duty cycle are adjustable, raising the speed of response and stability of the hydraulic system; the duty cycle of the hydraulic controlling signals from the controller can be selected artificially so that the controller can match different hydraulic systems and different ground shovel devices.

The receiver uses 36 photocells in all that are divided into 4 rows; each row includes 9 photocells, with 5 groups of signal inputs. The 5 groups of signals are amplified and reshaped through the circuit, and then are sent to Pin P2.0-P2.4 of the SCM; The SCM encodes the input signals, sends the data to Chip MAX485 through the serial interface, and to the controller through the signal wire after changing the TTL electrical level to 485 electrical level. The controller uses Chip MAX485 to transform the sent signals to TTL electrical level, and then sends it to CPLD to deal with.

In the laser receiver, every two contiguous photocells are 1.5mm apart, so there are two photocells at most receiving laser signals at the same time, thus there are 9 effective states for the 5 groups of input signals. Table 1 is the signal codes for effective states of the laser receiver and the controlling signals' frequencies and duty cycles from the controller to the hydraulic system.

Software Control Flow

Figure 9 is the flow chart of software control of the controller. The controller begins to work upon power-on, and the circuit begins to check whether the position signal sent from the receiver is effective or not (no input is regarded as ineffective). When the signal is effective, the controller judges whether the working mode at the moment is automatic or manual. If it is the manual mode, artificial direct intervention in the output of hydraulic controlling signals is allowed; if it is the automatic mode, the controller disposes the input effective signals, outputting controlling signals to the hydraulic system and driving signals to the pilot lamps. Through the schematic diagram, we can see that UART is mainly composed of four parts: baud-rate generator, sending module, receiving module and comparing module. The baud-rate generator generates a local clock signal that is higher than the baud rate, sampling RXD constantly, making the receiver and the controller keep pace with each other. The receiving module of UART receives the serial signals from RXD and transforms them into parallel data. The sending module transforms the parallel data to be output into TXD serial-output signals according to the basic frame format [START_REF] Wu | Computer and CPLD Serial Port Correspondence[END_REF] .

Baud-rate Generator Module

The baud-rate generator is actually a module that divides the frequency of the system clock. After frequency division, it outputs the clock signal of sampling RXD [START_REF] Liu | EDA Technology and Application Practice of VHDL Circuit Development[END_REF] . The clock frequency of the External Crystal Oscillator used for CPLD in this research is 3.6864MHZ, and the baud rate of asynchronous serial communication is 9600bit/S, so it needs to divide the frequency of the system clock to get a clock of 9600HZ. In order to improve the fault-tolerance disposal of the system, it requires the output clock of the baud-rate generator be N times of the baud rate of the actual serial data, and N can be 6、16、32、64. In this design, N is 16. Figure 9 Figure 12 is the program flow chart of the asynchronous receiving module: sig1 is the sign of receiving interruption; when sig1 is in low electrical level, it indicates that the receiving process has not been started up, thus checking the electrical level of RX. When RX is in a low electrical level, sig2 begins to count; if RX is in low electrical level in consecutive 8 times of sampling, it indicates that this is the start bits, thus starting up the receiving process, receiving one bit of data every 16 receiving clock until it completes receiving all the 11 bits. The parallel-output end is a serial-to-parallel end, and the output data will change as the bits shift because it does not have the latch function [START_REF]Xianning Zhan VHDL Detailed Interpretation and Application Practice[END_REF] .

Asynchronous Sending Module

The logic diagram of the asynchronous sending module is shown in Figure 13. In the module of Tx Module, indata [7..0] are the 8-bit data-input end. CS is the chip-selection signal, which is effective in low electrical level. Wr is the output-permission, which is effective in high electrical level. Clk is the clock-input signal, providing clock signals for sending data. Txd is the serial-sending end for sending serial data. Ti is sending-interruption signal, which means sending when it is in high electrical level, and unused in low electrical level. The program flow chart of this module is shown in Figure 14. When the rising edge of the clock signal comes, if CS is in low electrical level, it will check whether the signal written into wr is effective. If wr is effective, it indicates that the asynchronous sending module has data to be sent; if sig_buffer is 0, it means that the sending is not busy, and it can continue reading in data; if sig_buffer is 1, it indicates that the asynchronous serial-sending module is sending data right now, which is unable to respond to the order from the controller, and the control has to wait.

PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.cn 16 is the RTL diagram after synthesizing the adjustable-duty-cycle of hydraulic controlling signal module, and the simulation diagram is shown in Figure 17. RTL is a created gate-level net list after translating the hardware description language (VHDL), and a crucial step in transforming the high-level description to hardware circuit. In the simulation diagram, sig [4..0] are the position-signal-input end; clk is the clock-signal-input end; Modle Chose is the mode-selecting-input end, and 1 means dry-land mode, 0 paddy-field mode. Clkdown and clkraise are hydraulic-controlling-signal-output end. From the simulation diagram, when mode=1 (dry-land mode) and the position signal is 11110 (high-level land), Clkdown outputs a falling-control signal with the duty-cycle of 60%. When mode=0 and the position signal is 01111 (low-level land), Clkraise outputs a rising-control signal with the duty-cycle of 100%.

Mode-selecting Circuit

The mode-selecting circuit is mainly used to judge whether to control the hydraulic system in automatic mode or in manual mode. The mode-selecting function of this circuit is achieved mainly through the CPLD software, and the function of manual intervention in the up and down of the ground shovel in the automatic mode is added, providing some convenience for users to operate according to the practical situation. Figure 18 is the RTL diagram of the logic-judgment module. There are 6 input signals in the circuit. Figure 19 is the sequential simulation diagram of this module.

Conclusion

This essay firstly introduces the overall design and the hardware structure design of the controller. Then it respectively aims at the controlling unit, the power convert module, the pilot-lamp-driving module, the hydraulicdriving module, the RS485 communication module, and makes some designs for these hardware circuits. Finally it tries some optimum designs for the software of the controller, using the hardware description language VHDL to carry out UART module, and developing the mode-selecting module and duty-cycle-adjusting module. The controller can choose proper hydraulic-controlling signals according to different landforms so as to fit in with both paddy field and dry land. The main results is:

1. CPLD was used as the logic control chip of this system. It enhances the system's ability to control. Its control capability is better than existing controller with MCU and its power consumption is further reduced.

2. The online changing ability of CPLD could enable quick update of the function of the controller, therefore makes it possible for the users to choose the most suitable software for the terrain, therefore maximize its efficiency.

3. Within CPLD, by programing with software, the interlock of automatic mode and manual mode is realized, therefore decreases the magnitude of the circuit and increases its stability.

4. Transmission by RS-485 can bus increases the stability and reliability of the working process.

Figure 3

 3 Figure 3 Power Switch of Controller

Figure 4

 4 Figure 4 RS485 Communication Module

Figure 5

 5 Figure 5 Hydraulic-System-Driving Circuit

Figure 6

 6 Figure 6 Position-Pilot-Lamp-Driving Circuit

Figure 7

 7 Figure 7 Software-Control Flow Chart of Controller

Figure 8

 8 Figure 8 Basic Principle Diagram of UART

FredivFigure 9 Figure 11

 911 Figure 9 Logic Diagram of Baud-rate Generator

Figure 12 Flow

 12 Figure 12 Flow Chart of Asynchronous Receiving Module

Figure 13

 13 Figure 13 Logic Diagram of Asynchronous Sending Module

Figure 14 Figure 15

 1415 Figure 14 Software Flow Chart of Asynchronous Sending Module 5.2 Adjustable-Duty-Cycle of Hydraulic Controlling Signal Module The adjustable-duty-cycle of hydraulic controlling signal module outputs PWM signals of different duty cycles mainly according to the different ground modes and different position signals chosen by users. Figure 15 is the logic diagram of this module. Sig[4..0] receive position signals from the receiver; Mode is the ground-modeselecting-input end, and users can select hydraulic controlling signals of different duty cycles according to different conditions of ground work (paddy field or dry land). Clk is the clock-input end; Clkdown is the falling-signaloutput end after frequency division, and ClkRaise is the rising-signal-output end after frequency division. Clk Div Module signal4..0] Modle Choose[1..0] Clkin

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Figure 16

 16 Figure 16 Adjustable-duty-cycle of Hydraulic Controlling Signal Module

Figure 18

 18 Figure 18 Integrated Result of Logical Judgment

  shows, Port A and B of MAX485 are linked to resistances, forming the differential voltage input; RO is the input end, and signals run into CPLD via RO when CON485 is linked to high electrical level. DI is the output end, and signals run out via DI when CON485 is linked to low electrical level. As the controller only receives signals from the receiver, there is no need to send signals to the receiver, thus DI is not used. The MAX3485 chip has 8 pins, and their functions are as follows: RO: Output end of the receiver, if A is 200mV higher than B, RO is high level; if A is 200mV lower than B, RO is low level. RE: Strobe end of the receiver output. If RE is low, RO is effective; if RE is high, RO is in high-impedance state. DE: Strobe end of the driver output. If DE is high, the driving output A and B are effective; if DE is low, they are in high-impedance state. DI: Output end of the driver. If DI is low, it will force the output low; if DI is high, it will force the output high. GND: Ground terminal. B: Input of inverting receiver and output of inverting driver. A: Input of inphase receiver and output of inphase driver.\ VCC: Positive electrode

Table 1

 1 Signal State of Receiver and Controller Ports

	Output Signal of Controller

Acknowledgement

This research is sponsored by the project 2008BAB38B06 ,2009BAC55B01-1and.2009GB23600511.All of the mentioned support is gratefully acknowledged.


Junjing Yuan
email: yuan.junjing@163.com

Jian Zhang

Hongzhen Cai

Design of Decision Support System for Mechanical Conservation Tillage

Keywords: decision support system, subsystem, evaluation index 1

This paper designed decision support system for mechanical conservation tillage, which includes three subsystems, such as subsystem of development potential estimation, subsystem of optimum model or suitable models selection, and subsystem of economy benefit estimation. In different area, it is necessary to alter, increase or decrease some evaluation index to accommodate the agricultural production. This decision support system is based on the ordinary technique such as .NET Framework and SQL Server 2005, which includes three kinds of function and four levels.

Introduction

Mechanical conservation tillage is a new agricultural production mode [1][2], and the development of this tillage is becoming the national economy and the people's livelihood issues stage by stage. How to spread mechanical conservation tillage effectively in China is a difficulty. As an assistant decision-making means [3][4], the decision support system for experimentation model of mechanical conservation tillage is focused by the relative organizations. According to the characteristics of investigation area, production practices, projection pursuit, fuzzy comprehensive evaluation and dynamic estimation of economic benefits [1,2,5], this paper designed the experimentation model system for mechanical conservation tillage and develops the decision support system. With the help of the decision support system, it is much easier to finish the estimation of spreading, the selection of experimentation model and the dynamic estimation of economic benefits with low cost and high efficiency.

2

Demand analysis

In the program of popularization and actualization of new tillage, the decision support system is an available tool to solve some problems. The details are as follows.

(1) The design of system is an available approach to the popularization or farm examination of mechanical conversation tillage in the double-crop one year regions;

(2) In the system, it is necessary to construct the databank, model bank and knowledge bank, which are the foundation of memory, query, renovation and management;

(3) The system is useful to conform the degree of difficulty in the program of examination or popularization of the mechanical conservation tillage;

(4) The system is helpful to conform the suitable model of the optimum model by comparing the situation of field, technology, and tillage approach;

(5) The system is available to analyze the sensitivity and estimate the economic benefits dynamically;

(6) The functions the decision support system, such as renovation of databank, extension of system and improvement of model, should be convenient and rapid.

The decision support system for mechanical conservation tillage in the double-crop one year regions is an effective and assistant tool to solve some problems, and the system includes the following three levels, the first level provides some relative data and information to the decision-maker and analyzes the data and information, the second level is focus on the decision problems and draw out the decision program, the third level is to analyze and estimate the decision program by the human-computer interaction.

3

System design [6] In the program of designing the decision support system, the designer should abide by the following three principles. Firstly, practicability is the basic principal of the system, which makes the system functions and decision demand matching. Secondly, the system is easy to learn and use, and the friendly human-computer interaction is required. Thirdly, the favorable expansions and maintenances of system should be taken into account.

System structure

Agricultural production involved many factors and the randomicity of some indexes determined the complexity of the system. This paper designs a decision support system, which is a visual software and includes some functions, such as data analysis, potential evaluation, adaptability estimation and model selection, economic benefit estimation and sensitivity analysis, and so on. Fig. 1 describes the main structure of the system.

System flow

The decision support provides the basic function, such as file management, information query, statistics analysis, user management, system help, result output and printing, etc. In this paper, the author designs the system according to the project theory, and the system development flow is described in Fig. 2.

System frame

The decision support system is on the bases of data collection and science management, and the large volume of data and the complex interface are difficult problems in the program of constructing the system successfully. Reasonable system structure ensures the decision support system for mechanical conservation to be practiced. The system frame includes data layer, logic layer, component layer and function layer, and its details are in Fig. 3.

System development platform

This decision support system is developed on the platform of.NET Framework 2.0 and SQL Server 2005, which integrates three models, such as potential estimation model based on projection pursuit, adaptability estimation and model selection model based on fuzzy comprehensive evaluation, economic benefit estimation and sensitivity analysis model based on the time value of money.

.NET Framework includes two main components: common language runtime library and class library. common language runtime library is a software engine used to load applications, confirm them to run without any errors, verify the security license, execute them and clear them when work is finished, and NET framework class library is a reusable type set that tight integrated with common language runtime library [7].

SQL Server defined an xml data type that could be used either as a data type in database columns or as literals in queries. SQL Server introduced a method of allowing usage of database connections for multiple purposes [8]. And it can be used to monitor the health of a server instance, diagnose problems, and tune performance. For relational data, SQL Server has been augmented with error handling features and support for recursive queries with common table expressions.

Conclusion

According to the above methods and steps, the author researches and develops the decision support system for mechanical conservation tillage, which is an available assistant tool to analyze data and information for the decision-maker. The system adopts .NET+ SQL Server which satisfies the demand functions, and the platform is in common use and easy to learn.

Fig. 1 .

 1 Fig. 1. Structure of decision support system

Fig. 2 .

 2 Fig. 2. Flow of decision support system development

Fig. 3 .

 3 Fig. 3. Frame of decision support system


Huamao Zhou

Chao Zhou

Honghui Rao

Yande Liu

FAST DISCRIMINATION OF NANFENG MANDARIN VARIETIES BASED ON NEAR INFRARED SPECTROSCOPY TECHNIQUE

Keywords: Vis/NIR spectroscopy, Nafeng mandarin fruit, varieties, SIMCA, BPNN

The potential of visible and near infrared (Vis/NIR) spectroscopy was investigated for discriminating the varieties of Nanfeng mandarin fruit nondestructively. The spectra were collected by a spectrophotometer in the wavelength range of 600-1040 nm. Relationship between the spectra and Nafeng mandarin varieties was established using principal component analysis (PCA), supervised independent modeling of class analogy (SIMCA) and backward propagation neural network (BPNN). By comparison the best result was obtained by BPNN with recognition rate of 97.5%. The results suggested Vis/NIR spectroscopy combination with BPNN was a new approach to discriminate of the varieties of Nanfeng mandarin fruit nondestructively.

Introduction

Visible and near infrared (Vis/NIR) spectroscopy combination with chemometric techniques provides a powerful tool for monitoring the variation of processes for quality control purpose. The fact that samples requires virtually no treatment, and the expeditiousness with which spectra can be obtained, have made NIRS a preferred choice over traditional techniques involving lengthy sample conditioning procedures.

The characteristics of the absorption bands in Vis/NIR spectra are dictated both by the chemical composition of the sample and by physical properties such as color, firmness, size and defect. As a result, the Vis/NIR technique can be used to discrimination both internal and external quality, however, it is difficult to detect accurately and stably. In order to obtain accurate and simple calibration models, one can be used mathematical pretreatments to minimize unwanted spectral contributions in determining the quality of the samples. These appealing features of the Vis/NIR technique have been fostered its use in a variety of fields including the meat, fruits and vegetables, grain and grain products, dairy products, oils, fish and fish products industries [START_REF] Huang | Near infrared spectroscopy for on/in-line monitoring of quality in foods and beerages : A review[J][END_REF]).

Nanfeng mandarin is popular for its high internal and external qualities with respect to sweetness, vitamin C, color, shape. But the quality is different to different varieties, and Vis/NIR has been applied to discrimination varieties, such as Chinese bayberry [START_REF] Li | Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy[END_REF]), fruit vinegars [START_REF] Liu | Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis[END_REF]), finishing oil [START_REF] Blanco | Classification and quantitation of finishing oils by near infrared spectroscopy[END_REF], tea [START_REF] Chen | Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration[END_REF]) and transgenic tomatoes [START_REF] Xie | Discrimination of transgenic tomatoes based on visible/near-infrared spectra[END_REF]), which are important to quality control and customer choice.

The objectives of this work are to evaluate the use of Vis/NIR in discriminating the varieties of Nanfeng mandarin fruit including Daguoxi, Xiaoguoxi, Guihuadixi and Zaoshuxi, and the methods of principal component analysis (PCA), supervised independent modeling of class analogy (SIMCA) and principal component analysisback propagation neural network (PCA-BPNN) are attempt to discriminate varieties accurately.

2

Materials and methods

Nanfeng mandarin fruit

A total 320 Nanfeng mandarin samples were harvested from a local orchard in Jiangxi province. There were four varieties of Daguoxi, Xiaoguoxi, Guihuadixi and Zaoshuxi. The varieties of Daguoxi, Xiaoguoxi and Guihuadixi are cultivated in Nanfeng, Jiangxi. Zaoshuxi is cultivated in Xunwu, Jiangxi. By comparison, Daguoxi is heavier than other varieties, fruit index of Xiaoguoxi is different from others, and the maturity of Zaoshuxi is earlier. Guihuadixi is a variety mutated from Xiaoguoxi. The Statistics of physical and chemical properties of four varieties are listed in Table 1. Base on these features, it is possible to discriminate the four varieties using destructive method, and NIRS is a rapid, accurate and stable tool to discriminate varieties nondestructively. Then 240 samples were selected to establish or train models including 60 samples of every variety, and others were for prediction.

They were placed in airtight polyethylene bags and stored in an ice filled refrigerator to keep at cold temperature (4±1℃). All fruit samples were allowed to equilibrate to room temperature (20℃) before NIRS measurement. Fruit Index is defined as the ratio between horizontal and vertical diameter of fruit.

) ( The system was consisted of a tungsten halogen lamp (24V/50W), CCD spectrometer (USB4000), an optical fiber (SMA905), USB data line and PC. The CCD spectrometer was 3648 pixel photodiode array. The wavelength range of the spectrometer was 400-1040nm with a 0.2nm sampling interval. The angle was about 30º between the lamp axis and the vertical line. The horizontal distance was about 150mm between the center of lamp and fruit hold.

Vis/NIR spectra were collected and transformed by OOIBase32 software (Oceanoptics INC., USA), from three positions marked with a circle beforehand on each mandarin around equatorial position. The data was analyzed by 'Unscrambler v9.5' software (CAMO AS, Trondheim, Norway) and Matlab 7.0 software (Mathworks, Natick, USA).

Results and Discussion

PCA Classification

PCA was performed to examine qualitative differences between the four kinds of Nanfeng mandarins by the raw spectra of 600-1040nm. Fig. 2 shows the three dimensional (3D) principal component score plot using first three score vectors, PC1, PC2, PC3 derived from raw spectra of samples. The initial three factors, which account for the most spectral variations 98% (77%, 19%, 2% for the first three principal components, PC1, PC3, respectively) related to the characteristic and indicated as positive or negative, are used to make differentiation clearer. From this figure, we can find that the samples are divided into two groups. One group is Xiaoguixi Nanfeng mandarin, others are Daguoxi, Guihuadixi and Zaoshuxi Nanfeng mandarins which overlapped each other. It can be assumed that the samples can be differentiated by using PCA in term of varieties. PCA gives very important information about the basic data structure regarding a potential capability of separation of objects. The result suggests that four varieties could not be separated clearly by PCA method. So SIMCA and PCA-BPNN were attempted to improve separation.

SIMCA Classification

When building SIMCA model, the four classification models are built by PCA analysis, respectively, including Daguoxi, Xiaoguoxi, Guihuadixi and Zaoshuxi Nanfeng mandarins. The samples of test set was selected randomly, the assignment of the samples can be seen for Table 2. The appropriate number of principal component factors would be concerned in building SIMCA model. In our study the number of PCs is selected by root mean square error of cross validation, and the appropriate number of PCs is 3 for four varieties, respectively (Fig. 3). At 99% confidence level, the best results of identification are achieved, that Zaoshuixi and Guihuadixi could not be discriminated well, So PCA-BPNN method is applied to improved separation.

PCA-BP Classification

As show in Table 3, the first seven PCs are considered to explain the original variables. And these variables are not interacting from each other. The first seven PCs, which can explain the 99.881% of variables, were selected as the input vectors to build the PCA-BPNN model. The output vectors of these samples were assumed to be 1, 2, 3 and 4. And 1 was denoted as Daguoxi mandarin, 2 was denoted as Xiaoguoxi mandarin, 3 was denoted as Guihuadixi mandarin and 4 was denoted as Zaoshuxi mandarin. Adjusting by many times, the optimal nodes of the hidden layer of neural network came up to 9. Therefore the seven PCs as the input neurons were connected with 9 hidden neurons randomly; the node of output neuron layer was defined as a vector of [START_REF] Huang | Near infrared spectroscopy for on/in-line monitoring of quality in foods and beerages : A review[J][END_REF][START_REF] Li | Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy[END_REF][START_REF] Liu | Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis[END_REF][START_REF] Blanco | Classification and quantitation of finishing oils by near infrared spectroscopy[END_REF]. The transfer function of hidden layer was tansig function. The transfer function of output layer was purelin function. The train function was trainlm, the threshold value was 0.08. The whole samples were separated randomly into train and test set (240:80) randomly. Fig. 4 is the plots of the prediction errors of the 80 samples in the test set. The abscissa represents the 80 samples in order. The ordinate represents the error between the test output vectors and standard vectors of these samples. It can be found that the error of almost all samples was be close to zero, expect the NO.7, 15, 35 and 54 samples. The error of the No. 7, 15, 35 and 54 were -0.00017, -0.00118, 0.000126 and 0.000844 respectively. The threshold value was 0.06, so two samples was classified inaccuracy. The total recognition rate is 97.5% for the test set.

Conclusions

The contribution of this work is to present a rapid and non-destructive approach for discriminating different varieties of Nanfeng mandarin. In this research, quantitative analysis for varieties of Nanfeng mandarin fruit was carried out. By means of PCA, SIMCA and PCA-BPNN techniques, a relation was established between transmission spectra and varieties of Nanfeng mandarin fruit. The PCA-BPNN shows a better data prediction performance. And the recognition rate of 97.5% achieved. It is proved to be appropriate and successful for species discrimination. This study lays a foundation for further research to develop a portable apparatus in discriminating the different species.

Fig. 1 .

 1 Fig. 1. The schematic of Nanfeng mandarin NIRS acquisition device

Fig. 2 .

 2 Fig. 2. Three dimensional score plot of the first three principal components for four varieties of Nanfeng mandarins

Fig. 3 .

 3 Fig. 3. The relationship between RMSECV and principal in model

Fig. 4 .

 4 Fig. 4. Error of the model in the test set

Table 1 .

 1 Statistics of physics and chemical properties of four varieties of Nanfeng mandarins

						Physics properties
	Varities	Producing Area Number		Weight(g)	Fruit Index	Color( E Δ )
				Mean	Max.	Min. Mean Max. Min. Mean Max. Min.
	Daguoxi	Nanfeng,Jiangxi	80	45.08 64.26 30.39 0.68 0.76 0.58 78.69 80.97 72.00
	Xiguoxi	Nanfeng,Jiangxi	80	23.16 33.47 15.68 1.17 1.51 0.81 78.09 81.05 74.52
	Guihuadixi Nanfeng,Jiangxi	80	27.35 38.23 20.77 0.70 0.78 0.60 78.70 80.63 69.01
	Zaoshuxi	Xunwu,Jiangxi	80	23.59 32.90 17.16 0.68 0.75 0.59 76.54 78.87 74.27
						Chemical properties
	Varities	Producing Area Number	Souble Solid Content ( O Brix)	Titration Acidity (%)	Vitamin C (mg/100g)
				Mean	Max.	Min. Mean Max. Min. Mean Max. Min.
	Daguoxi	Nanfeng,Jiangxi	80	13.28 17.00	9.60 0.42 0.74 0.22 13.05 28.29 4.09
	Xiguoxi	Nanfeng,Jiangxi	80	14.46 19.90 11.80 0.56 0.68 0.31 18.00 25.42 7.53
	Guihuadixi Nanfeng,Jiangxi	80	13.59 15.20 10.40 0.50 0.68 0.32 17.91 26.01 10.30
	Zaoshuxi	Xunwu,Jiangxi	80	18.00 19.80 15.60 0.51 0.64 0.39 15.39 22.78 11.94

Table 2 .

 2 The results of classification by SIMCA

	varieties	Sample numbers	PCs	The identification rate in test set Daguoxi Xiaoguoxi Guihuadixi Zaoshuxi

Table 3 .

 3 The statistics of PCs and accumulative reliabilities

		PC	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9 PC10
	Ar(%) 77.585 95.468 98.078 99.670 99.796 99.845 99.881 99.896 99.906 99.915
					PC: Principal component; Ar: Accumulative reliabilities.
		0.16
		0.14
		0.12
	Error of test	0.02 0.04 0.06 0.08 0.1
			0
		-0.02	1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
		-0.04
							80 samples in test set

Acknowledgement

The authors gratefully acknowledge the financial support provided by the National Science and Technology Support Plan (2008BAD96B04) and Natural Science Foundation of Jiangxi Provincial Education (GJJ08514) and Youth Fund of Jiangxi Agricultural University (2533).