Yanmin Ren

Yongxia Yang

Yuchun Pan

Yu Liu

Yunbing Gao

Xiumei Tang

Zhixuan Zeng

Study on Cultivated Land Concentrated Areas Delineation Based on GIS and Mathematical Morphology --A Case of Miyun County and Pinggu District in Beijing

Keywords: mathematical morphology, concentrated and connective, GIS, cultivated land

The basic farmland within the protection areas was required to be of high-quality and connective in the general plans for land use. A new method was developed to delineate the boundaries of high-quality, concentrated and connective cultivated land. Based on mathematical morphology principles and GIS methods, the high quality of cultivated land blocks could be identified through dilation and erosion operations according to this rule that which inside distance was less than threshold d 1 and the number of blocks was larger than 3. This method was validated with the farmland classification data of Miyun County and Pinggu District in Beijing City, and 91% of high-quality, concentrated and the connective cultivated land could be identified by this method, so it would provide the reference method for delineating the basic farmland areas scientifically and reasonably in the general plans for land use.

Introduction

Cultivated land resources were scarce and it was a basic state policy that everyone should cherish and utilize rationally every inch of the farmland in China [START_REF]General land use planning regulations for examination and approval[END_REF] . Basic farmland protection system was set up and basic farmland regions were delineated to execute strict protection rules [START_REF]Regulations on the Protection of Basic Farmland[END_REF] . Basic farmland was the arable land which was of high-quality, good land geographic conditions, complete water conservancy facilities, concentrated and connective etc [START_REF] Sangyi | GIS based connectivity analysis and its application in prime farmland protection planning[END_REF] . A lot of researches had been done on how to delimit basic farmland, and much emphasis had been put on the principles of quality, the distance from the farmland to the settlement. However, the distribution and concentration of the farmlands were usually ignored in these principles. These principles made the cultivated land blocks scattered, and it was difficult to realize scale management of agriculture and furthermore, it resulted in that a large number of scattered basic farmland had been transferred. Basic farmland protection areas planning introduced the principle of " high-quality and concentrated ", and it would focus on protecting the blocks of high-quality and concentration, reducing flow, realizing the scale of agriculture as well as controlling the non-point pollution. As a result, the concentrated and connective concept in the space planning of basic farmland was helpful to realize the management of "high-quality, concentration and connective ".

At present, the researches about connectivity at home and abroad mostly focused on Landscape ecology and ecological protection and other fields, and rarely on cultivated land protection [START_REF] Marullia | GIS methodology for assessing ecological connectivity: Application to Barcelona Metropolitan Area[END_REF] . In recent years, more and more researches were concerned about connectivity in China, such as: Yun Wenju, Zhou Shangyi [START_REF] Wenju | Protect high-quality cultivated land Concentrated and connectively[END_REF] proposed the methods for measuring space connectivity by calculating the link index of blocks with grids; Duan Gang [START_REF] Gang | Study on the method of basis farmland Protection spatial Planning in basis of agricultural land classification[END_REF] raised a method based on the buffer analysis of vector; Guo Zihan [START_REF] Zihan | GIS-based farmland connectivity analysis methods research and system implementation[END_REF] determined whether the cultivated land was connected by setting up distance threshold for the grid length. These methods were helpful to select the cultivated land areas which were of high-quality, concentrated and connective. However, too long calculating time and the distance threshold which was difficult to ascertain had a significant impact on delimiting concentrated areas in the study area.

As a new subject of image processing and analysis, the principal theory and methods of Mathematical Morphology had been successful applied in the fields such as the biomedical, remote telemetry, highway and transportation, mechanical engineering and weather etc. For example, Li Deren [START_REF] Deren | Mathematical Morphology and its application binary image analysis[END_REF] analyzed Chinese characters of scanned binary images; Feng Qingzhi [START_REF] Qingzhi | The method of image processing and application based on mathematical morphology[END_REF] used these theories and methods in image enhancement and detection for license plates and complicated fingerprints; Wu Dan [START_REF] Wu Dan | The application and prospect of Mathematical Morphology[END_REF] had studied application status in GIS and recognition and the position of image mark; Zhang Qingnian [START_REF] Zhang Qingnian | Identification and generalization of area clusters by mathematical morphology[END_REF] proposed the method of identification and generalization of clusters by mathematical morphology and proved to be quite effective.

Considering that the cultivated land concentrated area could be regarded as area clusters, this research referenced Zhang Qingnian's methods on identifying area clusters. Firstly, the basic operations of mathematical morphology were introduced. Secondly, the identifying methods were probed to delineate concentrated area of cultivated land based on mathematical morphology. Thirdly, take the farmland classification data of Miyun County and Pinggu District in Beijing City for example, this method's feasibility was verified and it would provide the reference method for delineating basic farmland protection areas scientifically and reasonably in the general plans for land use.

2

Theory and methodology

Basic operations of mathematical morphology

The areal geographical phenomenon of discontinuous distribution was suitable to be expressed by raster [START_REF] Zhang Qingnian | Identification and generalization of area clusters by mathematical morphology[END_REF] . Mathematical morphology could analyze the structure of data and extract features through transforming the raster data structure [START_REF] Fei | Application of mathematical Morphlogy in GIS spatial analysis[END_REF] . Grid-based areal features were expressed in polygon, called blocks. The kind of map data was easily processed using the mathematical morphological methods.

Mathematical morphology was a nonlinear image processing and analysis theory, which discarded the traditional view of value modeling and analysis. It described and analyzed the image from the perspective of set operations, focusing on the geometric structure of image, in order to extract the targets' size, shape, connectivity, convexity, smooth , directional characteristics and so on [START_REF] Zhang Qingnian | Identification and generalization of area clusters by mathematical morphology[END_REF] . In some sense, mathematical morphology opened the new theory and the new method of image processing, analysis and identification. The operations allowed for efficient handling of grid data in morphological transform and pattern recognition, as well as other types of data.

Basic operations of mathematical morphology included dilation erosion and opening, closing, thinning, thickening, top-hat transform and low-hat transform derived from dilation and erosion. Among them, dilation and erosion were the most basic operators of mathematical morphology. Because the other operators could be derived by dilation and erosion, this paper just introduced the two operators.

X was input image, B was structural element,

(1)

dilation: bB { : , } [ ] X B x b x X b B X b       
Geometrically, dilation was the union of all calculation results with input image X translation of b.

(2)

erosion: bB { : b , } [ ] X B x x X b B X b       
Geometrically, erosion was the union of all calculation results that input image X was translated -b, also was composed of all points within input image X after structural element B was translated x.

Operation results of dilation and erosion were shown in fig. 1 Fig. 1

dilation and erosion

(a) Input Image X (b) Structural element B (c) dilation, X  B (d) erosion, X  B

2.2

Identifying cultivated land concentrated areas by mathematical morphology "Connectivity" is an indicator which showed the spatial distance and distribution of cultivated land blocks with the same property. When the distances among cultivated land blocks were smaller than the threshold value, they could be considered to be connective [START_REF] Wenju | Protect high-quality cultivated land Concentrated and connectively[END_REF] . "Clusters" was a group of features whose distances were less than the threshold value L, and the clusters were usually defined by human eyes. In this paper, a new method of identifying cultivated land block clusters, based on mathematical morphology, was developed to identify and delineate connective cultivated land regions.

Cultivated land clusters were identified based on two criterion, (1) the distances among features inside clusters; (2) the ratio between distances inside and outside cluster. Cultivated land blocks were considered as connective when their maximum distance d1 inside clusters was less than the distances mean value d2 outside clusters, while the block number of cluster was not less than 3.

Four steps were included to implement this method, as were shown as follows:

(1) Determinating the distance threshold d1 of concentrated areas. Firstly, cultivated land blocks were selected randomly as a cluster in the image. Secondly, the distances from each block to other blocks inside clusters were calculated. Thirdly, the maximum distance value was considered as the threshold of concentrated areas. The implement methods were as: There were n blocks Pk(k∈(1,n)) in the image X, the formula was needed to be looped until . B was the structural element with radius of 1, w was the number of loops, dk was the minimum distance between the block Pk and the others(k∈(1,n)) which equaled with w, the maximum of dk was considered as the threshold of concentrated areas d1. Image GH was assigned by X.

(2) Identifying the blocks aggregation which internal distance was less than threshold d1 and the number of blocks was not less than 3. Firstly, Image PR was dilated by structural element Br with radius of d1/2, and the arithmetic expression was . Image PR was composed of blocks Ck (whose number was h)(k∈(1,h)). Secondly, the aggregation inside Ck could be got through (k∈(1,h)). The number of blocks n inside was counted, and judged whether n was less than 3 or not. If n<3, the blocks were considered scattered and they would be deleted from GH, then they could be expressed as . Otherwise, the blocks were considered as connective, and the aggregation was initialized.

(3) Judging whether the blocks aggregation met with the criteria d1/d2≤1/2. The formula was needed to iterate by structural element B with radius of 1 until it met with this criteria . The variables were as follows: w was the iteration number, d2 was the minimum distance between the initialized aggregation and the other surrounding blocks, which was equal to w. If d1/d2≤1/2, PCk was seen as clusters.

(4) Identifying the aggregation for which inside distance was smaller than before by reducing d1 in the rest of the blocks. The identified clusters were deleted from image GH and it could be expressed by the formula GH=GH-PC, after uniting with all recognized clusters through (k∈(1,w )).

If , d1 was assigned with the value of d1-d*2% . Go back to Step 2 when d1≥ Ds*St/Ss to identify the clusters with smaller inside distance in the rest of the aggregation until all clusters inside image X were iterated through. Among them, Ds was the minimum distance discriminated by human's eyes in the target image. Ss and St were the scale denominators of the original image and the target image, respectively.

The operation flow to identify the cultivated land concentrated areas was shown in Fig. 2

k k PC C X k h   k PC = ? Y  2 1 2 max( , )( (1, )) k d d d d k h   1 2 / 1/ 2? d d  n<3 ? Delete scatered blocks ( ) ( ) (1, ) k k Y P B X P k n     Y N N Y Y N N Y GH GH PC   = ? GH  Y N End Y N Delete scatered blocks ( (1, )) k GH GH PC k h    1 1 2 max( , ) (1,
)

k d d d d k n   1 1 2% d d d    1 / ? s t s d D S S  

Application and demonstration

The method based on dilation and erosion operators of mathematical morphology, was applied and validated by identifying the concentrated areas of cultivated land in Miyun County and Pinggu District with the help of the Classification of Agricultural Land Map. Classification of Agricultural Land Map in was Miyun County and Pinggu District in Beijing City was shown in Fig. 3 (a) and its coordinate projecting system was Beijing 54. The agricultural land classes were determined by the project team according to the technical line of "Regulation for Classification of Agricultural Land" [15] . The regulation was the result of comprehensive evaluation in accordance with local natural factors including climate, soil, topographty as well as the utilization level and so on. The utilized agriculture land was ranged from 6 to 21grades and total area was 52548 hectares. According to the standard of protecting basic farmland, it was defined that the farmland above 11 grades was the high-quality cultivated land (In fact, the high-quality grade could be defined by local land administration departments). The high -quality agriculture land was ranged from 12 to 21 grades and total area was 42799 hectares, accounting for 81.45% of the total utilized agriculture land area.

The steps to delineate the concentrated area of high-quality cultivated land were described as follows.

Step 1: The farmland blocks above 11 grades (high quality) were extracted based on their property value, and they were shown in Fig. 3 (b).

Step 2: Identifying the concentrated areas of cultivated land based on mathematical morphology. After rasterizing the resulting image in step 1, the operation flow in Fig. 2 were to run to identify the cultivated land concentrated areas. Finally, 3 concentrated areas were identified, and they were expressed by different colors in Fig. 3 (c). The 3 concentrated areas were 13647 hectares, 4008 hectares, 14953 hectares, respectively. The result showed that the delineated concentrated areas of high-quality cultivated land was 32608 hectares which was accounting for 91% of the total highquality cultivated farm land area (35862 hectares) in Miyun County and Pinggu District in Beijing City. It showed that this method was feasible and precise result was attained. Therefore, it was helpful for delineating basic farmland protection areas.

Conclusion and Discussion

Protecting basic farmland was an elementary state policy in china. A new method was developed in this research to delineate the connective concentrated basic farmland based on mathematical morphology principles and GIS technology. Furthermore, this method was validated with the farmland classification data of Miyun County and Pinggu District in Beijing City and 91% of the connective and concentrated farmland could be identified by this method. The achievements of this research were as follows:

(1) the method was able to locate the position of the connective cultivated land and it could be identified satisfactorily with great computational efficiency and high computational accuracy; (2) delineating concentrated and connective cultivated land was helpful to consolidate basic farmland as well as to control basic farmland transformation. Therefore, it was of significant meaning to protect and supervise basic farmland. At present, there had been few studies on the connectivity of cultivated land, and the attempt was made based on mathematical morphology in this paper, it showed that the method was effective to delineate connective and concentrated cultivated land. However, further efforts should be made on these aspects: (1) the maximum threshold value of cultivated land block area should be defined, for the reason that when the area of cultivated land were large enough to management connectively, it was not meaningful to delineate any more;(2) the number of the scattered cultivated land should be further considered according to different situations. In this paper, the blocks number was set 3, when the scattered cultivated land were less than 3, they were not taken into the concentrated and connective the aggregates. When the area of cultivated land blocks was large, the error would be amplified, so improvement should be made for this method in the further researches latterly.

Fig. 2

 2 Fig.2 operation flow to identify the cultivated land concentrated areas

Fig. 3

 3 Delineate concentrated areas of high-quality cultivated land (a)the map of utilized classes ,(b)the map of high-quality,(c) the map of concentrated areas of high-quality cultivated land

  below.

						preparation
				Delineate study area and choose cultivated land of high-quality	软件ArcGIS9.3
		Initial Image X(including n blocks Pk)
	Determinating the
	distance threshold d 1
	of concentrated area
						= ? Y 
		Image GH is assigned by X
		PR GH B  	Expanded PR by structural element B r with radius of d 1 /2
			r
	Identifying the aggregate which interatomic spacing is	( (1, ))	Conform the aggregate and Statis the number of blocks n inside
	less than d 1 and the
	number of blocks is
	greater than or equal to 3
				Conform the initial clusters
			( Y P B  	) (	)( (1, )) X PC k h  
				k		k
	identify the aggregate
	Which equation meets
	d 1 /d 2 ≤1/2
		1 PC PC PC 	2	PC	k	Conform the recognized concertrated areas
		Delete the recognized concertrated areas from image GH
	Identifying the
	aggregate by
	reducing d 1 in the
	rest of the blocks.

Acknowledgments. This work was supported by the National Science-technology Support Projects for the 12th five-year plan under Grant No.2011BAD04B0302.

Projects for the 12th five-year plan, No.2011BAD04B0302.


Wenshen Jia

Wenfu Wu

Fang Li

Ligang Pan

Zhihong Ma

Miao Gao

Jihua Wang

Design and Implementation of Rapid Grading Platform for Shape and Diameter of Oranges Based on Visual C#.NET

Keywords:

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

Introduction

Machine vision technology is a computer image processing technology. It is simulate the human visual system to extract information from the image of objective things. Processing of such information achieves detection, measurement and control for the target. The use of machine vision technology for agricultural grading and detection means to use custom-built computer software computing processing of agricultural products image and effective decomposition of the image information. Through the machine vision, the agricultural products of pixel, boundary, shape and so on, has obtained its own characteristics, with a certain pattern to match the image. Finally get to determine the agricultural products quality [START_REF] Li | A study on navel orange grading system based on computer vision[END_REF] . Navel orange is one of the largest fruits of our production. At the same time, it is also an important trade fruit. Because of the detection and the grading technology is backward, causing the listed level of navel orange mixed, which affect its commodity value and in particularly cause the lack of competitiveness in the international market. At present, the navel orange grading relies on manual completed in Chinese. The grading results are less consistently and less efficient due to individual diversity of workers. Using machine vision to classify navel orange, which is objectivity strong, stable standard, consistency, high efficiency, and lossless. It is one of the effective ways to solve the artificial grading problem, and machine vision technology is also a hot research topic in agricultural applications [START_REF] Brosnan | Improving quality inspection of food products by computer visiona review[J][END_REF] . Dimension is one of the main quality parameters in navel orange, also one of the important bases for grading. The sales clerks usually sell navel orange with the dimension grading. Depending on machine vision technology we can obtain the navel orange image, and select the relevant parameters. We can obtain data like volume, weight, diameter of the navel orange and so on.

We develop a grading of software for the navel orange based on machine vision technology. And in accordance with the CAC standards, using diameter of navel orange, we can grade the navel orange.

Test materials

Test specimen: Navel orange Producing area: Southern Jiangxi, purchased yuanfang supermarket in Changchun 2 System Design

Software Design

The system use C # language development, a new programming language, which originate from .NET [START_REF] Anderson | Back in the studio-Visual Studio 2008[END_REF] . C # language evolve from C and C++, that is a Simple, modern, type-safe and object-oriented language. C # is designed for a wide range of enterprise applications running on the .NET platform. .NET Framework includes the common language runtime (CLR) and .NET Framework Class Library (FCL). FCL provides trusteeship applications, and writes object-oriented API [START_REF] Christian | Professional C# 4.0 and[END_REF] . When we write .NET Framework applications, we do not consider to the Windows API, MFC, ATL, COM or other tools and technologies. .NET provides great convenience to programmer, so they only focus on the algorithm in the program is enough. Relative to C and C++ program development, writing the same piece of code, C # is not only the development cycle is short, a small amount of code, and readability. C # requires a lower level of the programmer, and the entry time is shorter. So that the programmer only focus on the algorithm of the digital image, that is greatly improving the efficiency of software development, is very conducive to the application of image processing.

Image Acquisition & Pretreatment

Image Acquisition

Image acquisition for navel orange use light box which have circular fluorescent lamp and white background. The circular fluorescent lamp first preheats 5 minutes, to ensure the stability of light. Each navel orange collects one picture, and collected a total of 10 pictures.

Image Pretreatment

In order to facilitate detection and extraction of image features for navel oranges, the original image using Photoshop CS5 software batch program for unified clipping zoom 512 x 512 pixels image.

Navel orange shape extraction

The detection of edge is often uses the airspace differential operator. It is completed through templates and image convolution. There is always a gray edge between two adjacent areas of different gray values. Gray edge is caused by mutation of gray values. The discontinuous of gray values can be easily detected by seeking first-order and second-order derivative of gray values. Existing local edge detection methods include first-order derivative(Sobel Operator, Roberts Operator), second-order derivative(Laplace Operator), template operations(Prewitt Operator, Kirsch Operator, Robinson Operator) and so on [START_REF] He | A study on the improved algorithm for Sobel on image edge detection[END_REF] . This program use Sobel Operator algorithm.

Sobel Operator weight discrete data and it is use small convolution template (Fig 1). It use convolution of template and corresponding image data as the similar results. The two direction templates are horizontal direction edge detection and vertical edge detection, respectively. In the image processing the steps of using Sobel operator to detect image edges are as follows [START_REF] Gonzalez | Digital Image Processing[END_REF] : 1)

1 2 1 0 0 0 -1 -2 -1 1 2 1 0 0 0 -1 -2 -1
Respectively, both directions template along the image move from one pixel to another pixel, and the center pixel of the template coincide with a location in the image.

2)

The coefficients within the template and its corresponding image pixel by multiplying.

3)

Get the sum of all the multiplied values.

4)

Assign the maximum of the two convolutions to the pixel value of the corresponding template center location, as the pixel of gray value. Standard median filter [START_REF] Gou | Jia X H Digital Image Processing, Edge Detection Technique[END_REF] (SMF) , which mainly depend on the quick sort algorithm and is a nonlinear filtering method with less blurred edges , not only remove or reduce the random noise and pulse interference, but also retain edge information. After median filtering navel orange shape is clearly visible, it provides a basis for the further identification of the navel orange appearance grade.

Navel orange diameter converted

Any color can be expressed by the tristimulus values. The three kinds of stimuli are called as three kinds of primary colors, usually refers to red, green and blue [8][9] . In 1931 the CIE-RGB color system selects the red (R) for the 700 nm, green (G) for the 546.1 nm, blue (B) for the 435.8 nm. Navel orange generally was orange so the blue component sensitive, which blue pixel value, is less than 220 after the test images judged to be the navel orange part. The effect is very good.

As shown in Fig 2 (d), the difference between navel orange and circular is only 1120 pixels, the different area of navel orange and circular only 1.4% of the total area. Therefore, it is feasible to derive the diameter of navel orange from circle formula.

The camera lens is 380 mm away from the bottom of the light box. As the lens away from the sample is fixed height, therefore, as long as the statistical proportion of navel orange pixels share of the whole image can be converted navel orange cross-sectional area. accordance with the largest cross-sectional area of the navel orange, its diameter could be calculated through area formula of a circle. Table 1 shows the level of navel orange graded by diameter in accordance with the CAC standard [10] .

Conclusions

This paper studied on navel orange grading detection based on machine vision technologies, and established a navel orange image acquisition system. Through the navel image analysis and processing, this paper realizes the accurately distinguish between navel orange and the background based on Sobel Operator algorithm and standard median filter. Through the detection of the blue band of the image, this method can quickly distinguish between navel orange and the background, and convert the diameter of the navel orange. This method improves the efficiency of image processing, and is of great significance for online rapid detection. The results show that the detection using machine vision technology based on C # program for navel oranges' shape grading is feasible and provide a new idea for the grading detection of navel orange.

2. 1

 1 Hardware Architecture Computer:Hasee Core i3 380M,Memory 6G, Operating System Win7 Ultimate Light box size: 500 mm×500 mm×400 mm, white background Light: PAK T5, tricolor circular fluorescent lamp, Power 40W, Color Temperature 6500K, placed on the light box at the top of the central Camera: Logitech C270, 300 million pixels, the camera installed in the center of the light, Mounting Height: 380 mm from the lens to light box bottom.

Fig. 1 .

 1 Fig. 1. Sobel operator convolution template

Fig 2 (

 2 b) by the navel orange original image in Fig 2 (a), calculated after Sobel Operator method. Fig 2 (c) median filtering images by edge detection image in Fig 2 (b), calculated after median filtering algorithm.

Fig. 2 .

 2 Fig. 2. Median filtered image

Table 1 .

 1 CAC Navel orange grading standards

	Level	Diameter (mm)
	0	92 -110
	1	87 -100
	2	84 -96
	3	81 -92
	4	77 -88
	5	73 -84
	6	70 -80
	7	67 -76
	8	64 -73
	9	62 -70
	10	60 -68
	11	58 -66
	12	56 -63
	13	53 -60

This work is supported by Research and Demonstration of Authenticity Identification and Quality Safety Traceability Technology of Agricultural Products(201203046) funded by Special Fund for Agro-scientific Research in the Public Interest as well as the Restoration Technology Integration and Demonstration on Disaster destroyed and Wastewater Irrigated farmland(2011BAD04B04) funded by the National Key Technology R&D Program.

Acknowledgment

This work is supported by Research and Demonstration of Authenticity Identification and Quality Safety Traceability Technology of Agricultural Products(201203046) funded by Special Fund for Agro-scientific Research in the Public Interest as well as the Restoration Technology Integration and Demonstration on Disaster destroyed and Wastewater Irrigated farmland (2011BAD04B04) funded by the National Key Technology R&D Program.


Qinxiang Wang

Zhenbo Li

Jinxing Wang

Shuangxi Liu

Daoliang Li

A fast processing method of foreign fiber images based on HSV color space

Keywords: Cotton, Foreign fibers, S channel, Image segmentation

Traditionally, it was hard for image segmentation to suit the cotton image segmentation of foreign fibers. To solve this problem, this paper proposed an image segmentation method of foreign fibers based on HSV color space. The value of foreign fibers images' S channel was enhanced in this method to improve the contrast of foreign fiber and its background which help the subsequent image segmentation. The result of experiment shows that the method could highlight the images of foreign fibers, speed up subsequent image segmentation and realize fast image segmentation.

Introduction

Foreign fibers in cotton refer to the fibers mix in the raw cotton production, process and transportation. These fibers are non-cotton fibers and colored fibers which terribly affected the cotton and its production [START_REF] Li | Design of a sophisticated foreign fiber separator[END_REF]. Most of the cotton enterprises pick out foreign fibers artificially, which waste both time and energy [START_REF] Li | Features and application of online foreign fiber detecting and clearing device[END_REF]. Nowadays, Machine vision technology is the main method to process online automatic detection of foreign fibers.

The aim of image segmentation is to partition the image into meaningful connected components to extract the features of the objects [START_REF] Zhang | A fast segmentation method for high-resolution color images of foreign fibers in cotton[END_REF].The segmentation results are the foundation of all subsequent image analysis and understanding, such as object representation and description, feature measurement, object classification, and scene interpretation, etc. Thus, throughout the image processing, image segmentation is an extremely important aspect. Various image segmentation methods are reported in the literature [START_REF] Bakker | A vision based row detection system for sugar beet[END_REF][START_REF] Kim | Fast image segmentation based on multiresolution analysis andwavelets[END_REF][START_REF] Pichel | Image segmentation based on merging of sub-optimal segmentations[END_REF]. In recent years, researchers have developed more efficient but also more complicated methods for segmentation. The methods mentioned above may work well in their specific context, but they are not capable of segmenting images of cotton foreign fibers because of the low contrast [START_REF] Zhang | A fast segmentation method for high-resolution color images of foreign fibers in cotton[END_REF].

As a result, a non-linear enhancement method based on S channel of cotton images is proposed in this paper. First, the RGB color space of the original images is transformed into HSV, and the values of S channel are extracted. Second, the non-linear enhancement model is constructed via saturation histogram analysis.

Finally, the best thresholding is got through the iterative method.

2

Samples and Methods

Sample Preparation

The foreign fibers used in this research, including feather, hair, hemp rope, plastic film, polypropylene twine, colored thread, pieces of cloth, etc., as shown in Fig. 1, were collected from cotton mills. Adequate pure lint without foreign fibers was also prepared for making the lint layer.

Fig. 1 Foreign fiber samples.

Image acquisition

Put the prepared foreign fibers into sufficient lint without any of them before and opened the lint completely with opening machine. In the meantime, negative pressure was produced in the pipe through the fan, which leads the lint moving in it. Finally, an online scan industrial camera was used to scan these layer samples and acquired 24 bit true color images.

Choose of color space

Color spaces consist of many types such as RGB, HSV, YCbCr, CMY, etc. Acquired foreign fiber images are based on the RGB color space, a common color presentation method which is not suitable for human visual system. While HSV, describes the color with hue, saturation and value from the aspect of human visual system, is advantageous to the process and recognition of images. Hue refers to different colors, saturation refers to the various shades and value refers to the degree of lightness and darkness.

The conversion from RGB to HSV can be completed with the following formula

    ; 2 ; 3 1 min( ) () max , , GB H GB S R G B R G B V R G B                         (1) In (1),          0.5 arccos 1 2 2 R G R B R G R B G B                        
, R, G, B respectively response to different color component in collected images of foreign fibers in cotton; H, S, V, respectively response to saturation and brightness component of images after converted [START_REF] Wei | Research on the Segmentation Strategy of the Cotton Images on the Natural Condition Based upon the HSV Color-Space Model[END_REF].

Saturation component enhancing method

The three typical types of the foreign fiber images and their saturation images are shown as Fig. 2. (3)

2.5

Method of image segmentation

The identification of cotton foreign fibers is carried out through real-time monitoring.

However, image segmentation threshold value will change when image collection environment changes. Therefore, this paper chooses iteration method to calculate threshold value of foreign fibers automatically on the premise of accuracy and speed.

Image segmentation procedures of iteration method are described as follows [START_REF] Deng | Application Research on Segmentation Algorithm for Sun-image Based on Iteration Threshold[END_REF].

(1) To calculate the maximum saturation value Zmax and minimum saturation value Zmin of the saturation image, we define the original thresholding T 0 ={Tk|k=0} to be

max min 0 2 ZZ T   (4)
(2) Segment the image into target and background according to thresholding Tk, and calculate their average saturation value (Zo and ZB).

( , ) ( , ) ( , ) ( , ) ( , ) ( . ) ( . ) ( , ) ( . ) ( . ) k
O B k k k Z i j T Z i j T Z i j T Z i j T Z i j N i j Z N i j Z i j N i j Z N i j             (5)
Z(i , j) is the image saturation value of the position (i, j), N(i, j) is the weight coefficient of position (i, j), generally take N(i, j)=1.0.

(3) New thresholding Tk+1 is Calculated.

1 2 OB k ZZ T    (5)
(4) Repeat steps (2) and (3), until the difference between Tk and Tk+1 is smaller than defined parameter or reach certain iteration times.

(5) Compare the thresholding Tk+1 with the saturation value of each pixel, if the saturation value is bigger than Tk+1, the pixels will be set as target, otherwise, the pixels can be considered as the background. The formula is as following

1 1 0, ( , ) ( , )
1, ( , )

k k f i j T g i j f i j T         (6) 3
Results and analysis

Analysis of enhanced result of foreign fiber in cotton

The images of foreign fiber shown in Fig. 2 are chosen as the input images. The results show that histogram equalization method can enhance not only the foreign fiber targets but also the background. Therefore, it cannot increase the contrast between targets and background. The difficulty of segmentation hasn't been reduced.

On the contrast, conclusion can be drawn that the segmentation method which is proposed in this paper can remain targets' details, in the meantime, inhibit the background effectively.

Analysis of image segmentation results

The contrastive result of 1000 images between direct iteration method and iteration based on enhancing method is shown in Fig. 4. The direct iteration method can be success to handle the sliced foreign fibers such as paper and plastic pieces but not to the linear such as polypropylene yarn. The main reason is that the size of linear fibers in images is so small that the background is missed to recognize it as an object easily.

Consequently, the approach proposed in this paper is more effective for segmentation of foreign fiber.

Fig. 2

 2 Fig. 2 (a) Three typical types of the foreign fiber images, (b) Foreign fiber saturation images. Image enhancement can be divided to two kinds, space domain enhancement and transform domain enhancement. Machine vision system always chooses space domain enhancement due to the requirement of speed.Denote the saturation value of original image in the position of (x, y) to be S(x, y), the enhanced saturation value to be I(x, y) 3 2 ( , ) ( , ) I x y S x y S 

Fig. 3

 3 Fig. 3 (a) Enhanced images by histogram equalization, (b) Enhanced images based on the method of this paper.

Fig. 4

 4 Fig. 4 Processed images using the iteration method: (a) segmentation results of the original saturation images, (b) segmentation results of the enhanced saturation images.

Acknowledgement

This research was supported by Student's Science & Technology Innovation Fund of Huazhong Agricultural University -Research on the prediction of the acoustic performance for vehicle-use sound packages based on FE-SEA hybrid model‖under grant Nos. 11028

Conclusions

(1) To speed up enhancement, according to the features of foreign fiber images, we adopt air space method to enhance foreign fiber images.

(2) This research adopts iteration method to segment images and obtains accuracy segment results which avoid the inaccuracy results created by direct iteration method.

Biography

Qinxiang Wang is from Weifang city, Shandong province. He is dedicating the master degree in Shandong Agricultural University and majoring in image processing and pattern recognition.


Jiaran Zhang

Daoliang Li
email: li_daoliang@yahoo.com

Cong Wang

Qisheng Ding

An Intelligent Four-Electrode Conductivity Sensor for Aquaculture

Keywords: four-electrode, water-conductivity, intelligent sensor

Conductivity is regard as a key technical parameter in modern intensive fish farming management. The water conductivity sensors are sophisticated devices used in the aquaculture monitoring field to understand the effects of climate changes on fish ponds. In this paper a new four-electrode smart sensor is proposed for water conductivity measurements of aquaculture monitoring.The main advantages of these sensors include a high precision, a good stability and an intrinsic capability to minimize errors caused by polarization. A temperature sensor is also included in the system to measure the water temperature and, thus, compensate the water-conductivity temperature dependence. The prototype developed is appropriate for conductivity measurement in the range of 0-50mS/cm, 0-40 ℃. A cure relationship was found between the out-put value of each standard solution measured by the sensor and the electric conductivity concentration.

Introduction

As we all known, fish require levels of salinity (salt), hardness (Calcium and Magnesium) and low levels of various nutrients like Phosphorus, Ammonium and nitrate to maintain their daily lives. Therefore, understanding the concentrating of these ionized chemicals in water of fishponds is critical to successful aquaculture.

Electrical conductivity is commonly used in hydroponics, aquaculture systems to monitor the amount of salts, nutrients or impurities in the water [START_REF] Wei | Design of an Intelligent Electrical Conductivity Sensor for Aquaculture[END_REF] . The nominal values of water-conductivity are generally used to measure the concentration of ionized chemicals in water, though it does not distinguish individual concentrations of different ionic chemicals mixed in water. Water-conductivity measurements are of paramount importance in water-quality monitor of aquaculture since high or low conductivity levels, relative to their nominal values, can be used to detect the environmental changes of aquaculture fishpond.

Temperature is also an important variable when conductivity measurements are concerned. Temperature is itself a water quality parameter and an influence variable that affects conductivity measurements [START_REF]Standard Test Methods for Electrical Conductivity and Resistivity of Water, D1125[END_REF] . For raw water the temperature coefficient is about 2% per ℃. This means that acceptable conductivity measurement accuracy implies temperature measurement in order to obtain a temperature compensated conductivity measurement for a given reference temperature, typically 25 ℃ or 20 ℃. [3] There are two main types of conductivity sensors: electrode (or contacting sensors) and toroidal or inductive sensors [START_REF] Fougere | New non-external field inductive conductivity sensor (NXIC) for long term deployments in biologically active regions[END_REF][5] [6] . Electrode sensors contain two, three, or four electrodes. The conductivity (σ) is directly proportional to the conductance (1/R). The proportionality coefficient (KC) depends on the geometry of the sensor that must be designed according to the target conductivity range [7][8] . Toroidal or inductive sensors usually contain two coils, sealed within a nonconductive housing. The first coil induces an electrical current in the water, while the second coil detects the magnitude of the induced current, which is proportional to the conductivity of the solution.

[9]

As far as water conductivity measurement by electrodes is concerned, several solutions have been proposed [ 10 ] , and many commercial types of equipment are available from many manufacturers [11][12] . Well known limitations of a two or three electrodes sensor have been identified in literature [ 13 ] --The main problems associated with water-conductivity measurements are sensitivity to polarization effects in the situation of long time power, measurement selectivity, and too expensive price to application of aquaculture in china. Also this measurement solution implies a large number of repeated calibration procedures are required as the fouling drawback.

In this paper, the attention is focused on the smart four-electrode conductivity sensor for water-quality monitoring in aquaculture. The main advantages associated with the designed sensing unit are its high precision, an intrinsic capability to minimize errors caused by polarization. Another important advantage of the proposed solution for conductivity measurements are its simple, accurate method of temperature compensation, good linear behavior that enables the calibration of conductivity sensor to have the better accuracy by using several standard solutions. This paper includes the design of water-conductivity measurement hardware, description of temperature compensation, calibration of conductivity sensor and capability test experiments of the developed prototype.

Design of the Four-Electrode Conductivity Sensor

The intention of this part is to present and characterize a prototype for waterconductivity measurements based on a four-electrodes sensing unit which using in the aquaculture. The smart sensor also includes a temperature sense part to provide compensation of conductivity measurements caused by temperature variation.

Principle of measurement

For sensing water conductance, metal-solution interface modeling should be taken into account as shown in Fig. 1 [ 14 ] [ 15 ] . Assuming the behavior of electrolyte resembles a pure resistor and neglecting the polarization, the charge transfer back and forth the interface results in the reduction-oxidation reaction, and the double layer capacitive effects caused by charging of the electrode-solution at the interface. The two processes can be modeled as a resistive (R CT and Z W ) and a capacitive (C DL ) component in parallel. The charge transfer resistance R CT is highly nonlinear and depends on both the concentration of ions in the solution and the applied potential. The Warburg impedance Z W is due to the ion diffusion process in proximity to the interface. The double layer capacitance C DL depends on the material of the electrodes and on the ion concentration, with typical values in the range 10-40μF/cm 2 .

To minimize these factors, a four-electrode cell was projected and implemented. This type of cell is like a four-terminal precision resistor: Two electrodes are used to force a uniform time varying electric field and the other two measure the voltage.

Four-electrode water-conductivity sensors are widely used to reduce contact and interface interference in conductance measurements [START_REF] Crescentini | Design of integrated and autonomous conductivity-temperature-depth (CTD) sensors[END_REF] . The primary advantage is that the current and voltage electrodes of sensors are separated, eliminating the contribution of wiring impedance and contact resistances R S as illustrated in Fig. 2. While the alternating current running through two connections, C and D, a voltage drop can be measured across the impedance Z x by voltage sensing in A and B connections. Disregarding wiring resistances, sensed impedance can be determined by Ohm's law as the current is known. The approach, based on current-forcing and voltage-sensing, is particularly suited for the water-conductivity sensors which are used to eliminate polarization and fouling effects in aquaculture.

R S1 R S2 Z X B D I C A Fig. 2.
Impedance sensing by four terminal sensing technique.

Design of the hardware

The structure of intelligent water-conductivity sensor mainly contained excitation current source model, constant-current source, conductivity measurement system, temperature sensor, signal conditioning model, power source model, RS485 field bus, microprocessor and Sensors Electronic Data Sheets (TEDS), hardware structure is shown in Fig. 3. The alternating current via two excitation electrodes EI1 and EI2,hence magnetic field is generated in solution, a voltage drop can be measured across the measure electrodes EV1 and EV2. Alternating voltage transform to direct current by signal conditioning model. Standardized RS-485 interface is integrated with and possesses self-recognition capability provided by its TEDS. Power source model ensure each model fully functional at supply voltages.

RS485

A negative temperature coefficient thermistor precision achieved 0.1℃ is adopted to temperature sensing unit.

Temperature compensation

Temperature compensation is of important to water-conductivity measurement as well. Generally, the conductivity of common electrolytes increases with increasing temperature. In this paper, a valid temperature-compensated method by software is given out.

The measurements for different concentrations of KCl confirm the linear variation of the conductivity with temperature:

    t t ref         1 ref t (1)
where σ t is the conductivity at any temperature t (in degrees celcius), σ ref is the conductivity at the reference temperature t ref (in degree celcius), and α is the temperature coefficient of the solution at t ref .α can be calculated of every solution at the reference temperature t ref by measured the values of σ t , σ ref and t. A temperature value of 20 ℃ is chosen at the temperature reference in this paper.

Experiment

The basic experiments were conducted to evaluate the characteristics of the waterconductivity sensor. All solutions were prepared with distilled water and analytical grade chemicals. The KCl solutions of different electric conductivity were prepared. All experiments were carried out in the lab. In addition to distilled water, five KCl solutions of different concentrations are presented as standard solutions to calibrate the water-conductivity of the sensor. They are 4.6mS/cm, 10.4mS/cm, 20.7mS/cm, 32.1mS/cm and 52.6mS/cm.

After the calibration was completed, measurements are repeated 10 times in standard solutions of 4.10mS/cm and 9.20mS/cm every ten minutes to test the reproducibility of sensor. Besides, the experiments of accuracy analysis, stability test and effect of temperature compensation have be done by measure different conductivity KCl solutions.

Result and discussion

The results of experiments were listed to evaluate the characteristics of the waterconductivity sensor at this part. By these experimental data, the characteristics of conductivity curve, reproducibility, accuracy, stability and effect of temperature compensation are verified.

Water-conductivity sensor calibration

The curvilinear relation of out-put voltage values changing with different of conductivity at the reference temperature(20℃) is presented respectively in Fig 5 .  The conclusion that a better measurement effect the sensor has when the conductivity is lower than 32.1mS/cm, a low resolution at 32.1mS/cm to 52.6mS/cm the sensor has can be draw from the Fig. 5. It indicates that the low sensitivity the sensor has when the water-conductivity is higher than 32.1mS/cm.

Accuracy analysis

Measurement accuracy reflects the closeness between the measurement result and the true value of the measure. Table 1 shows the results of continuous monitoring of KCl standard solutions (0.5, 10.0, 20.7, 32.1 and 50.0mS/cm) with the sensor. As the table shows, the measurement accuracy is very high and the relative measurement error is within ±1.5% for all these samples.

Stability test

Generally stability refers to the ability of metrological characteristics of the measuring instrument does not change with time. Fig. 7 shows the results of measuring the water-conductivity of KCl standard solution (4.20mS/cm, 9.20mS/cm) using the sensor. Measurements of one half day at 5 min intervals were recorded. Results show no significant change and the variation is within ± 0.2mS/cm for each samples. The sensor gives a stable output value.

Effect of temperature compensation

The effect of temperature compensation in accordance with the method mentioned in this paper is shown in Fig.

Conclusion

The proposed prototype is an attractive solution for water quality measurements systems in fishery. The sensor is based on a novel four-electrode conductivity cell that eliminates errors caused by fouling and polarization. Main characteristics of the proposed prototype include an automatic temperature compensation of conductivity measurements, a low sensitivity to disturbances caused by electrolytic polarization, double layer. Measurement system's conditioning signal circuitry and digital signal processing assure an appropriate conductivity measuring range, good measurement reproducibility, accuracy and stability. Future works will focus on improving the resolution at the solution of high conductivity so that measurement range, in addition, a more simple and effective method of temperature compensation needs to be presented.

Fig. 1 .

 1 Fig. 1. Equivalent circuit for an electrochemical cell.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Hardware structure of the sensor.Excitation current source generates equal and opposite current by means of a dual, bidirectional and single-pole/double-throw (SPDT) CMOS analog switches. It is of paramount importance in water-conductivity sensing unit. Constant current source is used to avoid the fluctuations of output current due to the change of load variations. The system is proposed in this paper adopts the following circuit as shown in Fig.4.

Fig. 5 .

 5 Fig. 5. Curve between out-put voltage and KCl standard solutions.

Fig. 6 .

 6 Fig. 6. Reproducibility of measured water-conductivity.

Fig. 7 .

 7 Fig. 7. Experimental results of Stability.

8 .

 8 Measurements of one half day at 5 min intervals were recorded for KCl standard solution of 4.20 and 9.20mS/cm. As the Fig. shows, the effect of temperature compensation is relative effective at 0~30℃, however, the values of measured conductivity are absolutely lower than the actual value at 40 ℃.The data indicates that the method of temperature compensation have a certain error when the temperature above 30℃.

Fig. 8 .

 8 Fig. 8. Effect of temperature compensation.

Table 1 .

 1 the results of accuracy testing

	Wate					Measurements			Average	Absolute	Relative
	r									value	measurem	measure
	sampl e	1	2	3	4	5	6	7	8		ent error	ment error
	0.50	0.5	0.4	0.5	0.4	0.4	0.52	0.48	0.5	0.49	0.01	1.50%
		0	9	1	7	7
	10.00	9.9	9.9	9.9	9.9	10.	10.06	10.08	9.96	9.97	0.03	0.30%
		1	3	0	2	00
	20.70	20.	20.	20.	21.	20.	20.62	21.04	20.71	20.75	0.05	0.23%
		57	77	89	00	38
	32.10	31.	31.	32.	32.	32.	32.05	32.31	32.01	32.05	0.05	0.15%
		77	87	12	29	01
	50.00	50.	50.	50.	49.	50.	50.02	50.18	50.2	50.09	0.09	0.18%
		04	44	04	34	46

Acknowledgments

The authors would like to thank Development and Applications of sensor network applied to monitor bloom of blue-green algae in Taihu lake (2010ZX03006-006), and Beijing Natural Science Foundation " Integrations methods of digitalization technologies in intensive fish farming " (4092024) for their financial support.


Xiang Guo

Lihua Zheng
email: zhenglh@cau.edu.cn

Xiaofei An

Jia Wu

Minzan Li

Integration and Development of On-site Grain Yield Monitoring System Based on IPC

Keywords: IPC, grain yield monitoring system, CAN-bus, GPS, GPRS

Variable Rate Technology (VRT) is the goal of precision agriculture.

In ordet to realize VRT, it is necessary to measure yield accurately and build yield map in real time. A grain yield monitoring system based on IPC (Industrial Personal Computer) was integrated and developed to fit for China actual conditions and combines. The system consisted of a hardware part and a software part. The hardware part included IPC, CAN-bus module, GPS receiver, GPRS transmission module, and yield monitoring sensors. The CAN-bus module, GPS receiver and GPRS transmission module were integrated with IPC. The yield monitoring sensors included a dual-plate differential flow transducer, a grain temperature sensor, a grain humidity sensor, a ground speed transducer, a grain elevator speed sensor and a header height sensor, which were connected to the CAN-bus module. The software part included five main modules, device settings module, data receiving module, data delivering module, data processing module, and data analyzing module. Firstly, the system used CAN-bus technology to construct a sensors network so that the signals of the grain flow, grain temperature and humidity, ground speed, elevator speed and the header height could be sent to the CAN-bus via CAN converting module. Then, the IPC collected data packets on the CAN-bus and recorded and displayed the yield data after analyzing and calculating through yield models. At the same time, IPC collected geographic information from GPS receiver and stored it with the yield data from CAN-bus on this location together as one record. Finally, the system uploaded the collected data to the host server in real-time or packaged it via GPRS. The experiments showed satisfactory results.

Introduction

With the rapid growth of world population, the demand for food increases day by day. Precision agriculture technology has been showing powerful superiority [START_REF] Bao-Qian | Research advancement of precision agriculture[END_REF] . It is necessary to develop the precision agriculture mechanized equipment supporting for the implementation of precision agriculture. The harvester with the grain yield monitoring system can collect geographic information via GPS receiver, provides real-time production data intuitively and accurately by using yield map. It provides the necessary information supporting for decision-making of irrigation, fertilization, planting and pesticides spraying according to spatio-temporal variety, hence farming inputs will be much saved, the environment pollution will be reduced, the costs will be reduced and the land yields will be increased [START_REF] Feng-Hua | Reserch Progress of the Farming Informatiion Collections Key Technologies on Precision Agriculture[END_REF][START_REF] Li-Jin | Research on an Intelligent Mointor System of Grain Combine Harvester[END_REF] .

Precision agriculture has already made a considerable development since the late 1980s. In recent years, thousands of professional academic researches on Precision Agriculture have been carried out. Leading by the United States, Britain, Canada, Australia, Japan and other developed countries, the developing countries such as Brazil, Malaysia, and China have launched many research projects on precision agriculture technology, sucn as development of PA equipment and demonstration projects. On the other hand, up to now no commercial grain yield monitoring system was used to monitor grain on-site harvest in China [START_REF] Wu-Yun | The Development and Thinking of the Precision Agriculture Technology[END_REF] .

The combine with GPS positioning system and yield sensors has been produced by a couple of large international agricultural machinery manufacturing enterprises, and the information processing system has also been developed [START_REF] Zhan | Research advances and prospects of combine on precision agriculture in China[END_REF] . These systems were generally compatible with the GPS receiver and memory card to enable real-time yield monitor and generate yield map automatically [START_REF] Shu-Ren | Research and Development of the Grain Combin Harvester yield Monitor System[END_REF] . However, importing overseas system is higher cost, and meanwhile the yield monitoring software is just appropriate to the corresponding foreign harvesters so that it is difficult to match with the Chinese domestic harvester models. And it is so difficult for ordinary Chinese users to operate the yield monitoring system because the overseas system interface is all operating in English. There is an urgent need to develop a lower-cost, on-site monitoring system fitting for the conditions of domestic combine and needs of local farmers in China.

Integration and development of the grain yield monitoring system 2.1

Hardware structure

The hardware based on IPC integrated three modules, CAN-bus module, GPS module and GPRS module. The CAN-bus module connected with all sensors needed by the yield monitoring system, GPS module received position information in real time, and GPRS module communicated with the host server [START_REF] Qi | Research on an Intelligent Monitor for Yield Mapping System of Grain Combine Harvester[END_REF][START_REF] Zhi-Yuan | GPRS Communication Technology[END_REF] . The system structure is shown in Fig. 1.

Fig.1 System Structure

Hardware integration and development

The grain yield monitoring system was compose of the IPC, USB-CAN interface card, analog signal-CAN converter module, digital signal-CAN converter module, conditioning circuit, power conversion module, GPS receiver, GPRS module and yield monitoring sensors [START_REF] Mao-Hua | Precision Agriculture[END_REF] . The hardware integration scheme is shown in Fig. 2.

Fig. 3 shows the modules need to be integrated together, including IPC mainboard, GPS receiver, GPRS module and CAN-bus module. Fig. 4 shows the yield monitoring sensor group including the dual-plate differential flow transducer, the grain temperature sensor, grain humidity sensor, ground speed transducer, grain elevator speed sensor and header height sensor [START_REF] Wu Guan | Development of Grain Yield Monitor System with CAN-bus[END_REF] . The system integration schematic is shown in Fig. 5. [START_REF] Bo | Development of Advanced Sensing Technology of Grain Yield Monitor System[END_REF] Fig. 6 On-site winter wheat harvesting [START_REF] Wei-Dong | Studies on the GPS Data Processing and Absolute Positional Accuracy[END_REF] Firstly, via CAN-bus the system got the signal from the header height sensor to determine whether the system started to work. Secondly, yield relevant parameters were collected through CAN-bus including the grain flow coming from the dual-plate differential flow transducer, the grain temperature and humidity from temperature and humidity sensor, harvester speed from the ground speed transducer and rotate speed of elevator from the elevator speed sensor. And then the grain yield was calculated using the data collected from those sensors. Finally, on-site grain yield map was drawn using the yield and the real-time geographic location data getting from GPS receiver. Fig. 6 shows on-site winter wheat harvesting in Huantai County, Shandong

Province in North China on 13th of June, 2012.

Software development and implementation

The software system was designed running on the IPC. It was embedded stand-alone structure. The data outflowing from the CAN-bus and GPS flowed into the on-site grain yield monitoring application to be processed, analyzed, stored and uploaded to the yield server through GPRS module. Yield data were designed to be sent to the server every 30s by default or at anytime by clicking the corresponding function button. They were packaged as a file to be sent after harvesting finished. The data flow diagram of the system is shown in Fig. 7.

CAN-bus GPS Receiver

Grain Yield Monitoring System

GPRS Mould Yield Server

Yield Data Files Fig. 7 Data flow diagram of system software

System design

The system included five main functions: device settings, data receiving, data processing, data delivering and data analyzing. The usecase diagram of the system is shown in Fig. 8. Accordingly five modules were designed to fulfill four functions, as shown in Fig. 9.

用户

Fig. 9 Software system modules

CAN-bus data designing and analyzing

CAN-bus is a kind of bus with multi-master mode serial data communication. The short frame structure of CAN-bus does not occupy the bus for too long and the interference keeps lower. Thus it ensures real-time communication and fits for field work [START_REF] Jun-Bo | Analysis and Online Evaluation of CAN Message Real-time Performance[END_REF] . CAN-bus is easy to construct an underlying control network of open, digital, multi-point communications. Compared with traditional distributed control system, it has more advantages of all digital, distributed control, two-way transmission, openness, etc. [START_REF] Bing | Design of General Data Acquisition Equipment Software System Based on Filed-bus[END_REF] . The data frame issued by CAN converter module is shown in the Tab.1.

Tab. 819680A787A28000 Response Frame, "81" is a flag, "96" is lower byte of channel 3.

"80A7" is data from channel 4. "87A2" is data from channel 5. "8000" is data from channel 6.

C1800D

Response Frame, "C1" is a flag, "800D" is data from channel 7.

Assuming that the actual voltage was V m , the measured voltage was A data and K r was the coefficient in corresponding measurement range. The baseline value was set as 0x8000 according to the CAN module provider's specification [15] . When A data >0x8000, the actual voltage was positive and calculated using formula (1). When A data <0x8000, the actual voltage was negative and calculated using formula (2). The coefficient of K r was defined as shown in Tab.2.

(

) 1
(2)

Tab.

System development and implementation

The main flow chart of the system is shown in Fig. 10. The main interface is shown in Fig. 11.

Fig. 2 Fig. 3 Fig. 4 Fig. 5

 2345 Fig.2 Hardware integration scheme

Fig. 8

 8 Fig.8 Use case diagram of the system Device settings function set three devices (CAN-bus module, GPS receiver, GPRS module) up; Data receiving function collected and analyzed data from two devices (CAN-bus module, GPS receiver); Data processing function calculated and saved yield data and drew yield GIS map; Data delivering function sent yield data to the yield server; Data analyzing function translated binary yield data to text data.

  Fig.10 Main flow chart of the system Fig.11 The main interface

( 2 )

 2 GPS device settingA class (CserialPort) was developed to operate GPS device. The GPS device configuring made the GPS receiver ready to receive the GPS data. The flow chart is shown in Fig.13(b).

2

  Coefficient of K r

	Measuring Rage	Coefficient of K r	Measuring Rage	Coefficient of K r
	±150mV	0.15625	±2.5V	2.5
	±500mV	0.625	±5.0V	5
	±1.0V	1.25	±10.0V

ACKNOWLEDGEMENTS

This research was supported by National High-tech R&D Program of China (Contact Number: 2012AA101901), National Science and Technology Support Program (2012BAH29B04, 2011BAD21B01).

Configuring the GPRS device made the IPC connect with the server. DTUSet.dll, DTUSet.lib and dtucfgheader.h header files were added into the project and the API functions provided by them were used to make a serial port as the DTU configuration serial port in the IPC, and then the center of the domain name, server IP, server port, transmission Baud rate were set to configure the GPRS device. The flow chart of GPRS device configuration is shown in Fig. 13(c).

Data analyzing module

The received data were stored in .dat files in binary. The system translated the .dat file into a readable file and saved in .txt format so that it was convenient for farmers to read the data. The flow chart of data analyzing module is shown in Fig. 16.

Data delivering module

By default, a set of yield data was sent to the host computer per 30s via GPRS. It also could be sent by clicking button at any time, or sent in data package. The result on server side is shown in Fig. 17.

Yield GIS map drawing

In the data receiving interface (Fig. 15), the on-site yield GIS map was drawn in real time. In the map, GPS coordinates were transformed into the system interface coordinates and the depth of red color was used to represent the measured yield. The Flow chart of yield GIS map drawing is shown in Fig. 18.

Conclusions

According to the conditions and actual needs of the domestic harvesting machinery in China, an on-site grain yield monitoring system was developed based on the vehicle IPC. It could collect yield data from CAN-bus sensor network in real-time, acquire GPS data from GPS receiver and delivery the yielding data to the server via GPRS. The winter wheat yield model was embedded in the system. Moreover, it could provide on-site yield GIS map plotting on the IPC.The on-site grain yield monitoring system was running in the winter wheat field in north China. And the result showed that the system could meet on-site yield monitoring and yield visualization needs.


Xiaoshuai Pei

Lihua Zheng

Yong Zhao

Menglong Zhang

Minzan Li

Development and performance test for a new type of portable soil EC detector

Keywords: :Soil EC detector, Four-electrode method, Precision agriculture

The soil electrical conductivity (EC) refers to the capability for soil to conduct current. It is a comprehensive reflection of soil salinity and moisture. Therefore, acquiring soil EC rapidly and accurately can provide better guidance for farming production. Based on improving four-electrode method, a new portable soil EC detector with six electrodes was developed and its performance was tested. Inside two electrodes and outside two electrodes were used to measure soil EC near the surface and in deeper soil, respectively. And middle two electrodes were used to input a constant current to soil. The stability tests of the current source showed that the amplitude fluctuation was less than 3%.

Introduction

China is the largest agricultural producer and consumer in the world. With the rapid development of agriculture, large amounts of chemical fertilizers and pesticides were used to increase the crop yield. It has resulted in soil erosion, agricultural pollution and groundwater pollution and decreased soil productivity consequently. Precision agriculture, as a new farming approach, aimed at avoiding waste and excessive fertilizer spraying caused by blind input in farmland [1,2] . Precision agriculture technology is an information-based agricultural management system. Implementing spatial-temporal variable management could achieve true sense of the "intensive farming" based on information and advanced technology [START_REF] Li | Precision agriculture characteristics, current status and development[END_REF] . Soil researches has shown that soil electrical conductivity (EC) contains a wealth of information for analyzing soil nutrients and physicochemical properties [START_REF] Kitchen N R | Soil electrical conductivity as a crop productivity measure for claypan soils[END_REF] and can reflect the soil salinity, moisture, organic matter content, soil texture, structure and porosity, etc. Acquiring soil EC effectively is of great significance to determine the spatial-temporal distribution of soil parameters [START_REF] Sudduth | Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture[END_REF]. In this paper, we tried to improve the traditional current-voltage four-electrode method to better fit for in-situ measuring, and aimed at developing a in-situ soil EC detector with low price, easy operation, high measurement precision, integral control procedures and data processing procedures.

Materials and methods

Principle of soil EC detector development

Figure 1 is a typical structure of the current-voltage four-electrode approach, including two current electrodes (J and K) and two voltage electrodes (M and N). By loading constant amplitude current as excitation signal through two current electrodes, the soil EC can be calculated according to the voltage collected from two voltage electrodes. The earth is complex object with uncertain cross-sectional area and length. Researches showed that the earth EC could be measured according to formula (1) [START_REF] Sun | A mathematical model and its experimental study for a kind of measurement method of soil electric conductivity[END_REF].

1 ) , ( 2
1 1 1 1                       MN MN KN KM JN JM V b a k V I d d d d   (1)
where,  (with unit of S/m) is the value of EC ; I (with unit of A) is the current To measure the EC in deep soil layer, two electrodes were added on the basic four electrode theory, which is shown in Figure 2. Moreover, accurate circuits were designed to ensure constant alternating current could be loaded between two current electrodes, and the voltage of inside and outside electrodes could be collected accurately.

Constant current source circuit

Experiments and literatures suggested that the soil EC measuring accuracy mainly depended on the constant amplitude alternating current source when using current-voltage four-electrode method [7,8] . Figure 5 is the circuit diagram of stable alternating current source controlled by LM324. LM324 series are devices of the four op amp with differential input and have some advantages compared with standard op amp. With 3~32V supply, the quiescent current is only one fifth of the MC1741's. In digital systems, it can easily provide the necessary interface circuitry without extra supply. In Figure 5, the benchmark (pin 3 in LM324, the input signal)

of the AC source is Sinusoidal signal, which is the output signal from the sinusoidal signal generator ICL8038.

This circuit was with high output impedance and the output current could be controlled by programming digital potentiometer. It could be automatically adjusted with different impedance conditions and designed to fit for the soil EC with low regularity and wide changes, so that the system could reach high accuracy without changing the system parameters artificially.

Differential circuit

Unbalanced texture and structure of soil are likely to cause the instability of the signal. A subtraction circuit was designed to reduce signal instability, remove unreasonable signal, enhance useful signal and improve the accuracy. The differential circuit is shown as Figure 6. It was the combination amplifying circuit of inverting input and non-inverting input, and implemented the subtraction between Vin+ and Vin-. In the ideal conditions, two input voltages of the op amp were equal. It meant that there were virtual short and common-mode voltages between both ports of the op amp. When R 5 =R 6 and R 7 =R 8 =R 9 =R 10 , Vin can be calculated by formula (3).

Vin=1+2R 17 /R 16 [(Vin+)-(Vin-)] (3)
Because of common-mode voltage, the op amp with higher common-mode rejection ratio should be chosen and the bias voltage of the op amp should be taken into consideration. In the case of the above two factors and cost reason, LM358 and OP07 were used in this paper. This circuit was with high input impedance and low output impedance, which is suitable for the signal conditioning circuit.

Design and development of in-situ soil EC detector

The electrode and structure and structure of the detector, signal generating circuit, signal conditioning circuit, LCD module and storage module were designed and developed. In addition, the system was set aside many peripherals used for future extensions.

Hardware integration and development

The structure of the in-situ soil EC detector is shown in Figure 7. The instrument consisted of handle, straight pipe, electrodes, data acquisition controller and other components. After measuring the voltage of the electrodes, the detector calculated EC, and then displayed and stored data.

Software design and development

The overall software flow chart is shown in Figure 8.

Figure 1

 1 Figure 1 Principal diagram of current-voltage four-electrode method

J

  provided by the constant current source. V MN (with unit of V) is the voltage measured between the M and N electrode; k(a,b) (with unit of m) is the function with variables of d JM 、d JN 、d KM and d KN ; Besides, a=d JM =d KN , b=d MN .

Figure 2

 2 Figure 2 Schematic of improved current-voltage four-electrode method 2 Results and discussions2.1 Soil EC in-situ detecting system designIn-situ measurement system of soil EC was designed as shown in Figure3. It mainly includes signal generating circuit, output signal conditioning circuits and data acquisition circuit.

Figure 3 1

 31 Figure 3 System diagram

Figure 4

 4 Figure4is 300Hz sinusoidal signal generating circuit. C 15 and R 2 were used to implement low-pass filter, playing the role of cutting off direct current and conducting alternating current. Resistors of (R 14 +R x ) (R 15 +R 13 -R x ) were used to adjust the amplitude of current source of I 1 , I 2 and duty cycle square wave. Tests indicated that in order to minimize the total harmonic distortion of signal (TDH), it was necessary to keep (R 14 +Rx) and (R 15 +R 13 -R x ) equal. R 16 was used to adjust the shape of the sinusoidal signal. Tests showed that when R 16 =82 kΩ, TDH kept the minimum. C 3 and (R 14 +R x ) were used to adjust the frequency of output sinusoidal signal calculated by formula (2).

f=0. 3 / 3 ( 2 )Figure 4

 3324 Figure 4 Sinusoidal signal generating circuit

Figure 5

 5 Figure 5 Constant current source circuit

Figure 6

 6 Figure 6 Differential circuit

Figure 7

 7 Figure 7 Schematic of the in-situ soil EC detector

Figure 8

 8 Figure 8 Overall flow chart of the system software

ACKNOWLEDGEMENTS

This research was financially supported by National Science and Technology Support Program (2011BAD21B01) and NSFC program (61134011).


Haijiang Tai

Lihua Zeng

Shuangyin Liu

Longqin Xu

Ji Chen

Daoliang Li

Water Temperature Forecasting in Sea Cucumber Aquaculture Ponds by RBF Neural Network Model

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


Yapeng Zhou

Lin Liu

Yang Yang

Li Zhang

Hao Xu

Yigong Zhang

Zhiwei Li

Discussion on Calculation Method of Social Stability Price of Farmland Requisition Price: Taking Bazhou City as an Example

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Discussion on Calculation Method of Social Stability

Price of Farmland Requisition Price

--Taking Bazhou City as an Example

Yapeng Zhou1 Lin Liu [START_REF] Liming | Land Resource Science[END_REF][START_REF] Huifang | On Composition and Quantification of Farmland Price in China[END_REF] Yang Yang [START_REF] Xiaomei | On Choice of Strategic Objectives and Models of Food Security in China[END_REF] Li Zhang [START_REF] Xiaomei | On Choice of Strategic Objectives and Models of Food Security in China[END_REF] Hao Xu [START_REF] Xiaomei | On Choice of Strategic Objectives and Models of Food Security in China[END_REF] Yigong Zhang zhouyp@hebau.edu.cn, zhangyg63@hebau.edu.cn Abstract: Land is a kind of basic resources and of great importance to the survival and development of human beings. Due to the conflict between the limited amount of land and the unlimited development of human beings, land surely plays a significant role in national security and social development. The academic circle has introduced the social stability price to the pricing system of farmland, which reflects the social stability value of farmland. This paper analyses the composition of social stability function of farmland and takes Bazhou city as an example to discuss two calculation methods of social stability price of farmland, a direct method and an indirect method.

Keywords: Farmland; Social Stability Price; Calculation

The farmland price we refer to at present usually only involves its productivity price, which is the price of farmland as productive power or a means of production, the opposite side of value in use. And the social value of farmland, which lies in the fact that farmland provides farmers with survival guarantee and social welfare, and farmland is also of the function of national security and social stability as it provides food for the society, is ignored. At present, knowledge of its social stability function and social stability price is just limited to the aspect of food security and the elaboration on its definition and functions is not clear and complete [START_REF] Xiaomei | On Choice of Strategic Objectives and Models of Food Security in China[END_REF] .

Definition of the Social Stability Function and Price of Farmland

The social stability function of farmland means the function that farmland performs so that the state and the social development can enjoy a safe and stable state which is healthy, high-efficiency and not threatening and accepted by human beings.

The risk of this kind of stable state is predictable and accepted by human beings.

The social stability price of farmland means that the social stability function of farmland is monetized or realized in the market, which becomes the social stability price of farmland.

Price is the embodiment of value and comes from the realization of function. In order to figure out the social stability price of farmland, the social stability function of farmland should be analyzed first, so that its social stability price can be obtained [START_REF] Liming | Land Resource Science[END_REF].

The Composition, Characteristics and Influencing Factors of Social Stability Function of Farmland

2.1The Composition of Social Stability Function of

Farmland

Generally speaking, the function of land lies in the four aspects, namely production function, environment function, bearing function and space function [START_REF] Huifang | On Composition and Quantification of Farmland Price in China[END_REF] . As far as farmland is concerned, its social stability function also lies in the four aspects. First, due to its productivity, farmland can yield various kinds of crops which are necessary for the survival of human being, meet the requirement of human beings for food, and ensure the food security. Second, farmland is a constituent of the whole ecological environment and farmland is indispensable to ensure the structural integrity of the ecological environment and realization of its functions. Third, human beings carry out their activities on land and farmland as a constituent of land also serves the bearing function, which ensures the economic development of humans need some space and before they find a usable space in addition to the earth, land is the only choice of human beings (Diagram 1).

2.2The Characteristics of Social Stability Function of Farmland

Due to the characteristics of farmland and influence of the external environment on farmland, the social stability function of farmland is characterized by the following aspects: eternity, spatial differentiation, time variability, limited function, complicated influencing factors and various function ways.

The Influencing Factors of Social Stability Function of Farmland

Farmland an open complicated system, with many influencing factors of complicated functions. Our analysis reveals that the influencing factors that affect social stability function of farmland cover the following aspects (Table 1):

Calculation of Price of Social Stability Function of Farmland

The influence of various influencing factors on the function of farmland is reflected directly by price. There are two ways of price realization; one is direct calculation, which is rather difficult; the other one is indirect method, which evaluates the price in accordance with the substitution principle. The concrete ideas are as follows:

3.1Direct Calculation Method

This calculation method begins with the social stability function of farmland, evaluates the importance of social stability function of farmland to the whole society, works out the weight of the function and then finds out the influencing factors that influence the security function of farmland. Taking the economic conditions and scientific levels into account, quantify these influencing factors and the social stability price of farmland is obtained. The calculation equation of social stability price in a unit area is as follows:

S=S1 +S2 +S3 +S4 (1)
In this equation, S is the social stability price of farmland in a unit area.

S1 is the production safety price in a unit area.

S2 is the bearing safety price in a unit area.

S3 is the environment safety price in a unit area.

S4 is the space safety price in a unit area.

The price of each function is the function of the influencing factors. The influencing factors are already listed in the table above and they can be used in calculation. The equation for the function price in a unit area of each part is SI=ƒ(X1,X2,X3,…XI)

In this equation, SI is the function price in a unit area of the four parts in the social stability function of farmland

XI is the various factors of stability function price in each part

The present conditions show that the influencing factors of the last three kinds of stability function price are of complicated function and it is hard to calculate the price.

Therefore, production safety price is used to replace the stability price of the whole society temporarily.

Indirect Calculation Method

The calculation of the social stability price can be based on the substitution principle and use land reclamation fees as the quantized value of the social stability function, because the land reclamation fee is the compensation of value for land taken up. The charging of land reclamation fee is a economic means to ensure the dynamic balance of the total cultivated land in China and is the basis for food safety in the society. The determination of standards of land reclamation fees should first consider the land capital input of land with average quality levels, including tangible farmland water conservancy facilities and intangible soil economic fertility. For the tangible farmland water conservancy facilities, the quantity of value can be determined based on the replacement cost; for the intangible soil economic fertility, the formation cycle of soil fertility as well as the capital input each year within the cycle should be considered. According to the sum of capital input each year within the formation cycle of soil economic fertility, the quantity of value is determined by gains and losses of investment within the formation cycle of economic fertility. As far as soil fertility is concerned, due to difference in starting reclamation, hysteresis quality of reclamation benefit and gradualness of improving soil fertility, land reclamation fees are different. At present, for the wasteland which is easy to reclaim, it is predicted that in the first four years, there is profit, but not much. Generally, from the fifth year on, the profit begins to increase. Therefore, taking the input factors to form enough soil fertility and the losses of profit in the first four years, suppose the average input level of land reclamation is T yuan/mu, the yield level is P yuan/mu, the capitalization rate is r, the average interest rate of recent years, so the present value of input for reclaiming wasteland for five years is V1=T+T/(1+r)+T/ (1+r)2+T/(1+r)3+T/(1+r)4

The present value of loss in revenue in four years is V2=P/(1+r)+P/(1+r)2+P/(1+r)3+P/(1+r)4 (4)

The social stability price of farmland V=V1+V2. With the land development and management gathering momentum and increase of demand for land from the society, land reserve resources are decreasing day by day, land reclamation becomes more and more difficult and the land reclamation fees will increase day by day.

An Attempt to Calculate the Social Price of Farmland in Bazhou City

  development and social stability and progress. Finally, as the survival and

4. 1 A

 1 Brief Introduction to Bazhou City Bazhou city is located in the south-central part of Hebei province and the center of the triangle area formed by Beijing, Tianjin and Baoding. With the north latitude of 38°58′57″--39°13′10″ and east longitude of 116°15′13″--116°55′ 09″, Bazhou city is situated at the semiarid continental monsoon climate zone in temperate zone. Adjacent to Tianjin and Wuqing on the east, bordering on Xiongxian on the west, adjacent to Wenan on the south and bordering on Guan, Yongqing and Anci district of Langfang City on the north, it covers an area of 80027.25 square kilometers, among which 57348.41hectares of farmland, accounting for 71.7% of the total area.

  Here the production safety price of the first part is calculated. The concrete process is as follows: taking the practical situation of Hebei province into consideration, evaluate the function of the production capacity of farmland of Bazhou city in Hebei province and get the weight. Then consider the food safety in Hebei province and arable land reduction of Bazhou city within a certain planning period, get the food safety value that farmland in Bazhou city should have in the planning period, quantify it and obtain the production safety price of farmland in Bazhou city. According to the statistics of 2003, the grain out of Hebei province was 23.878 million ton and that of Bazhou city was 177.198 thousand ton. Calculate the production safety weight of Bazhou city: PS= grain output of the small area/grain output of the large area =grain output of Bazhou city/grain output of Hebei province =177198/2387.8 =0.0074 Based on the population base and the natural growth rate of Hebei Province in 2003, it is predicated that the population of Hebei will reach 70.2318 million in 2010.If 350 kg of grain per capita is determined to be the survival safety standards, 24581130647 kg will be needed. If 420 kg of grain per capita is the nutrition safety standards, 29497356776 kg will be needed. Based on the grain output of Hebei province, the food gap is 703130646.8 kg and 5619356776 kg respectively. Taking PS, the production safety weight of farmland of Bazhou city, into account, the selling price of wheat is converted into the production safety price of farmland and the total amount of production safety function value of farmland in Bazhou city is obtained.SZ1=quantity demanded for survival safety in Hebei province×production safety weight of Bazhou city×selling price of wheat =703130646.8×0.0074×0.7

Table 1

 1

Table of Influencing Factors of Social Stability Function of Farmland

		Aspects Influenced			Influencing Factors
			taken up by construction, industrial restructuring of
		Number	agriculture, taken up by people and lying waste, destroyed
			by natural disasters, etc.
	Social	Quality	water	resources,	lighting,	temperature,	nutrients,
	Stability		environment pollution, etc.
	Function
	of	Social	safety quantity demanded within planning period, investment
	Farmland	Economy	in agriculture, local living standards, construction of
			infrastructure, relevant policies, etc.
		Science and	irrigation techniques and facilities, seed resources,
		Technology	cultivation techniques, etc.

The paper is supported by the Hebei Provincial Department of education funding for a project, (project number is

2009451)

4.2.2Indirect Method

The equation above is adopted.

V1=T+T/(1+r)+T/(1+r)2+T/(1+r)3+T/(1+r)4 V2=P/(1+r)+P/(1+r)2+P/(1+r)3+P/(1+r)4 social stability price: V=V1+V2 According to a survey, the average input level each year into agricultural production in Bazhou city is 5250-9900 yuan per hectare and the output level is 15000 yuan per hectare. The annual input into land reclamation is 9900 yuan per hectare and the interest rate is 3%. Therefore, the present value of input into land reclamation is V1=9900+9900/(1+3%)+9900/(1+3%)2+9900/(1+3%)3+9900/(1+3%)4 =46699.2(yuan/hectare)

The present value of loss in revenue for 4 years is V2=15000/(1+3%)+15000/(1+3%)2+15000/(1+3%)3+15000/(1+3%)4 =55756.5(yuan/hectare) Therefore, V=V1+V2=102455.7(yuan/hectare))

Discussion

The result of the direct calculation has already deducted the amount of grain based on the production capacity of farmland and the production safety price of it is calculated, with the reduction of arable land taken into consideration. As the safety function of grain production of farmland is calculated, arable land is considered rather than other kinds of farmland. When the social stability price of farmland is calculated, the price of only one kind of crop-wheat, is calculated and the increase in grain yield due to the scientific progress and the increase in the input into social economy is not considered. In practice, the price of many kinds of crops should be used in the calculation and the influence of social progress and increase in input into social economy on the increase in grain yield should also be considered when the production safety price of farmland is determined. In addition, the calculation only covers the production safety price and does not involve the safety function price of the other three kinds due to differnt calculation method and data source.

Compare the result of the direct calculation and that of the indirect calculation, we can see that there is great difference in fees used for cultivating land and the safety price, as the direct calculation does not take the other three kinds of price into consideration.