Gaili Gao

Hefei Zhao

Jun Liu

Daoliang Li

Important Parameters Optimization for Opening Cotton Device

Keywords: cotton, foreign fiber, opening roller, feed roller

A new opening cotton device used in detecting and eliminating foreign fibers of cotton was presented in this paper. It is the first time to propose that the multi-variable nonlinear constrained optimization algorithm should be applied to obtain parameter values of the tooth profile of the opening roller and feed rollers as well as their diameter and rotating speed values. This approach was used to simultaneously consider the impact of all the performance parameters, and the important performance parameters may be weighted. As a matter of experience, the cotton fiber and foreign fibers were damaged small by the tooth profile of the opening roller and feed rollers. The foreign fibers could be easily detected and removed for they would not be torn.

Introduction

Foreign fibers of cotton mainly include cloth strips, hemp ropes, the polypropylene fibers, plastic films, color yarns, feathers, hairs, and so on. In spinning process, the foreign fibers are not only eliminated difficultly, but also pulled, torn into shreds easily, and become tinier foreign fiber. They may have a strong impact on the strength of the yarn, and are not easy to be dyed [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF][START_REF] Kang | Development and application of lint foreign fiber cleaning equipment in cotton production line[END_REF]. In recent years, the domestic and foreign researchers have applied different techniques to identify various foreign fibers of raw cotton based on their characteristics of geometry, physics and spectroscopy. These techniques are machine vision at visible light and ultraviolet light wave, infrared spectroscopy, X-ray image, and so on [START_REF] Kang | Development and application of lint foreign fiber cleaning equipment in cotton production line[END_REF][START_REF] Li | Design of a sophisticated foreign fiber separator[END_REF][START_REF] Guo | Progress on detecting technique and sorter of raw cotton foreign matters[END_REF][START_REF] Zhang | Research on detecting heterogeneous fibre from cotton based on linear CCD camera[END_REF][START_REF] Li | Key technology in detecting and eliminating isomerism fibre in cotton[END_REF]. Different kinds of sorter have been developed. However, some foreign fibers can only be detected and eliminated by the sorters. Two main factors may lead the foreign fibers in cotton not to be detected or eliminated. The first factor is that the polypropylene fiber which has the same color with cotton is hard to detect, and still exists in the yarn. The second factor is that the foreign fibers are increased dramatically after the cotton is opened, and it is hard to detect and eliminate the foreign fibers.

At present, many researchers focus on the opening roller in rotor spinning. [START_REF] Duru | Determining an optimum opening roller speed for spinning polyester/waste blend rotor yarns[END_REF] suggested the opening roller speed should be high [START_REF] Duru | Determining an optimum opening roller speed for spinning polyester/waste blend rotor yarns[END_REF]. [START_REF] Mwasiagi | Study of rotor spun Basofil/cotton blended yarn quality characteristics during optimisation of processing parameters[END_REF] also proposed that the opening roller speed be 6500 rpm [START_REF] Mwasiagi | Study of rotor spun Basofil/cotton blended yarn quality characteristics during optimisation of processing parameters[END_REF]. However, [START_REF] Murugan | Fibre rupture phenomenon in rotor spinning[END_REF] and [START_REF] Ulku | Effects of opening roller speed on the fiber and yarn properties in open-end friction spinning[END_REF] tested that fiber rupture had direct relationship with the opening roller speed and a high level of fiber damage caused by the opening roller occurred at high speed [START_REF] Murugan | Fibre rupture phenomenon in rotor spinning[END_REF][START_REF] Ulku | Effects of opening roller speed on the fiber and yarn properties in open-end friction spinning[END_REF]. So the opening roller immediately influences the performance of the opened textile fiber, and then influences textile quality. The opening roller referred on rotor spinning is not suitable for the device of detecting and eliminating the cotton foreign fibers. Because the opening roller combs the cotton fiber too tiny, and cotton fiber is extremely injured. For this reason, a new opening cotton device used in detecting and eliminating the foreign fibers was proposed in this paper.

Model of opening cotton device

A model of an opening cotton device for detecting and eliminating foreign fibers is shown in Fig. 1. The device mainly comprises a pressing roller, a conveying belt, two feed rollers, an opening roller and a cover plate. The pressing roller is set above the conveying belt. The two feed rollers are installed at one end of the conveyer belt. One of the two feed rollers is on the top of another. The opening roller is set under the cover plate, and near the two feed rollers.

When the opening cotton device is working cotton that is put on the conveying belt and compressed by the pressing roller is carried to the two feed rollers. The two feed rollers that rotate in opposite direction pick up the cotton and transfer it to the opening roller that rotates counterclockwise. The point and side of its saw teeth insert the cotton from the bottom to the middle and top gradually and cut up the cotton. When the cotton goes through separation zone between the opening roller and the cover plate cotton fiber is separated from the cotton. At the moment, the foreign fibers in the cotton are exposed in the surface of the cotton and can be detected effectively. 3 Important parameters optimization for opening cotton device

Building the objective function

There are two requests to be contented for conveying cotton in the opening cotton device: [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF] The two feed rollers may provide enough cotton for the opening roller. [START_REF] Kang | Development and application of lint foreign fiber cleaning equipment in cotton production line[END_REF] In order to prevent the cotton plug, the feed rollers cannot feed too much cotton to the opening roller. For these reasons, the absolute value of d-value of the cotton volume that is carried by the feed rollers and picked up by the opening roller in the unit time is as optimizing objective function. The objective function may be expressed as follows:

2 2 1 1 f C f C f   . ( 1
)
Where

  C C

. The cotton volume that is picked up by the opening roller in the unit time is the product of the total tooth number on the opening roller, rotational speed of the opening roller, the area of a single tooth profile and the width of base part of rack. The cotton volume may be described as follows:

1 1 1 1 1 1 2 25.4 f d lz n S w   . ( 2
)
Where

1
d is diameter of the opening roller ( mm ), l is length of the opening roller ( mm ), The tooth profile of the opening roller is line rack with a minus angle  .

Parameters of the tooth profile are shown in Fig. 2. Area of the tooth profile may be expressed as follows:

ELG MLK DEF BCD AJGC S S S S S S      1 . (3)
Where

) cot (cot 2 ) ( 1 1 2 1 1 1 1       h a p h S AJGC . ( 4
) 2 2 tan 2 1 1 1 2 1 R R S BCD     . ( 5
) 2 2 tan 2 1 2 1 R R S DEF     . ( 6
)
2

) ( 2 cot 2 2 1 1 2 2 R R S MLK           . ( 7
)   . 2 tan 2 tan cot cot ) sin( 2 sin sin 2 1 1 1 1 1 1 1 1 1                            R h a p S ELG (8)
MK ( mm ).
Substituting equations ( 4), ( 5), ( 6), ( 7) and (8) into equation (3) and rearranging terms gives:

    2 11 1 1 1 1 1 1 1 cot cot tan tan 2 2 2 h S h p a R                 2 2 12 2 11 12 cot 2 2 2 R R R                2 11 1 1 1 1 1 1 1 sin sin cot cot tan tan 2sin 2 2 p a h R                    . ( 9
)
Substituting equation ( 9) into equation (2) yields:

    2 1 1 1 1 cot cot tan tan 25.4 2 2 2 d lz n w h f h p a R                  22 2 1 1 1 2 12 () cot 2 2 2 RR                  2 11 1 1 1 1 1 1 1 sin sin cot cot tan tan 2sin 2 2 p a h R                         . ( 10
)
The cotton volume that is carried by feed rollers in the unit time is the product of the total tooth number on the two feed rollers, rotational speed of the two feed rollers, the area of a single tooth profile and the width of base part of rack. The cotton volume may be described as follows:

2 2 2 2 2 2 2 4 . 25 2 w S n z l d f   . ( 11
)
Where Area of the tooth profile may be expressed as follows:

DEI BCD AHEC S S S S    2 . ( 12
)
Where

          2 2 2 2 2 2 cot 2 tan 2 a p R R h S AHEC   . ( 13
) 2 2 tan 2 2 2 2 R R S BCD     . (14) 2 ) ( 2 cot 2 2 2 2 R R S DEI       . ( 15
)
Where 2

h is tooth height of the feed rollers ( mm ),

2
p is tooth pitch of the feed rollers ( mm ),  is tooth rack angle of the feed rollers ( rad ), R is the radius of arc BDI ( mm ).

Substituting equations ( 13), ( 14) and (15) into equation ( 12) and rearranging terms gives:

  2 2 2 2 2 2 2 2 2 2 tan cot 2 2 2 2 2 hh S R R p a R                      .
(16) Substituting equation ( 16) into equation ( 11) yields:

2 2 2 2 2 2 2 2 2 2 tan cot 25.4 2 2 2 d lz n w h f R R                     2 2 2 2 22 22 h p a R           . (17)
Substituting equations ( 10) and (17) into equation ( 1), the objective function is:

    2 1 1 1 1 1 1 1 1 1 1 1 1 2 cot cot tan tan 25.4 2 2 2 d lz n w h f h p a R                    2 2 12 2 11 12 cot 2 2 2 R R R                2 11 1 1 1 1 1 1 1 sin sin cot cot tan tan 2sin 2 2 p a h R                         2 2 2 2 2 2 2 2 2 tan cot 25.4 2 2 2 d lz n w h RR                     2 2 2 2 22 22 h p a R           . ( 18
)

Choosing optimized parameters and constraint conditions

The magnitude of the cotton volume that is picked up by the opening roller or carried by the feed rollers in the unit time is mainly determined by the tooth profile, tooth density, length, diameter and rotational speed. The shape of the tooth profile is relevant to the tooth pitch, the tooth height, the tooth top width, the working angle, the tooth rack angle, the minus angle and the arc radius. By experience, the working angle 1  and 2  are 75 degree. The magnitude of the length l is proportional to the quantity of cotton that is carried by the device in the unit time, thus the magnitude of length l should be determined by the quantity of opening cotton. According to analysis above, the optimized parameters are

1 d , 2 d , 1 n , 2 n , 1 z , 2 z , 1 p , 2 p , 1 h , 2 h , 1  , 2  ,  ,
      12 f x f x f x . ( 19
)
According to experimental data and experience formulas [START_REF] Fei | The technological characteristics, manufacture and use for carding[END_REF], the constraint conditions of the optimized parameters are given by: (1) In this paper, a new opening cotton device used in detecting and eliminating foreign fibers of cotton was presented. The cotton fiber and foreign fibers were damaged small by the new opening cotton device. The foreign fibers could be easily detected and removed for they would not be torn.

(2) The multi-variable nonlinear constrained optimization algorithm was selected to obtain parameter values of the tooth profile of the opening roller and feed rollers as well as their diameter and rotating speed values. (3) It is the first time to propose that the multi-variable nonlinear constrained optimization algorithm should be applied to evaluate the design parameter values of the opening cotton device. This approach was used to simultaneously consider the impact of all performance parameters, and the important performance parameters may be weighted. This could get more reasonable parameter values.

Fig. 1 .

 1 Fig. 1. Model of an opening cotton device.1-pressing roller; 2-conveying belt; 3-feed rollers; 4-opening roller; 5-cover plate.

1 f 2 f 2 C

 122 is the cotton volume that is picked up by the opening roller in the unit time ( is the cotton volume that is carried by the two feed rollers in the unit time ( are weight coefficient. For the influence on transferring and opening cotton, important degree of C1 and C2 is the same, so

1 z 1 S 2 mm ), 1 w

 1121 is tooth density of the opening roller,teeth 2 /(25.4 ) mm , 1 n is rotational speed of the opening roller ( / rs ), is area of a single tooth profile ( is width of base part of rack ( mm ).

Fig. 2 .

 2 Fig.2. Profile of the opening roller.

2 d 2 z 2 n 2 S 2 mm ), 2 w

 222222 is diameter of the feed rollers ( mm ), l is length of the feed rollers ( mm ), namely the length of the opening roller, is tooth density of the feed rollers,teeth 2 /(25.4 ) mm , is rotational speed of the feed rollers ( / rs ), is area of a single tooth profile ( is width of base part of rack ( mm ).The tooth profile of the feed rollers is line rack. Parameters of the tooth profile are shown in Fig.3.

Fig. 3 .

 3 Fig.3. Profile of the feed rollers.

2 a 2 

 22 is the tooth top width of the feed rollers ( mm ), is working angle of the feed rollers ( rad ),

2

 2

2 R

 2 and R . The parameters are converted to the form that may be used in the optimizing function. The corresponding relationship is:And the objective function may be written in the form

  Where 1 h is tooth height of the opening roller ( mm ), 1 p is tooth pitch of the opening roller ( mm ), 1 a is the tooth top width of the opening roller ( mm ), 1

					 is
	working angle of the opening roller (	rad ), 1  is tooth rack angle of the opening
	roller (	rad ),  is minus angle (	rad ), 1 R is the radius of arc	BF ( mm ), 2 R is
	the radius of arc

Results and discussions

Objective function ) (x f is nonlinear. Constraint conditions are linear. It is the more parameters and nonlinear constraint optimization. In this paper, we adopt "fmincon" optimization function from MATLAB software [START_REF] Edward | principle and application of MATLAB(Second Edition)[END_REF][START_REF] Su | MATLAB engineering mathematics[END_REF]. The optimal values of the parameters of the opening roller and feed rollers are given in the following table.

Optimal values of parameters of the opening roller and feed rollers

Names of parameters

Optimal values of parameters

Diameter of the opening roller


Yutao Wu

Wenzhu Yang

Zhenbo Li

Daoliang Li

Fast Segmentation of Foreign Fiber Image

Keywords: Cotton, Foreign fibers, Fast segmentation, image processing, image block, background extraction, Ostu method

Abstract. In the textile industry, different types of foreign fibers may be mixed in cotton, and the foreign fibers seriously affect the quality of cotton products. The step of image segmentation is of vital importance in the process of the foreign fibers identification, which is, in the same way, the foundation for cotton foreign fiber automated inspection. This paper presents a new approach for fast segmentation of foreign fiber images. This approach includes four main steps, i.e., image transformation, image block, image background extraction, image enhancement and segmentation. In the first step, we transform the captured color images into gray-scale images, and invert the color of the transformed images. In the second step, the proportion relationship between target image and background was analyzed, and then the whole foreign fibers image was divided into several blocks based on the analysis results. In the third step, the background of foreign fiber image was extracted by image corrosion and gray-level correction. In the final step, the histogram of the gray-scale image was analyzed, and a piecewise linear transform model was proposed to enhance the image blocks based on the analysis results, and then the image blocks were segmented by Otsu's method. The experiment results indicate that the proposed method can segment the foreign fiber image directly and precisely, and the speed of image processing is much faster than that of the conventional methods.

Introduction

The foreign fibers in cotton refer to those non-cotton fibers and dyed fibers, such as hairs, binding ropes, plastic films, candy wrappers, and polypropylene twines, etc. Foreign fibers mixed with cotton during picking, storing, drying, transporting, purchasing and processing, are difficult to remove in spinning process, and can cause yarn breakage, even reducing the efficiency. Every low content of foreign fibers in cotton, especially in lint, will seriously affect the quality of the final cotton textile products, as they may debase the strength of the yarn, and are not easy to be dyed [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF].

Since the price of the cotton for sale is affected by the content of foreign fibers in it, the cotton farmers and traders are willing to keep the foreign fibers away to obtain a high price. This will lead to great economic loss for the cotton textile enterprises. Two main factors may lead to high level of foreign fiber content in cotton. One is the inappropriate picking technique. The foreign fibers are generally removed manually using human visual inspection, or mechanically using automated visual inspection *Corresponding author, Tel: +86-10-62736764, Fax: +86-10-62737741, Email: dliangl@cau.edu.cn [START_REF] Lieberman | Determining gravimetric bark content in cotton with machine vision[END_REF]Su etal., 2006). When most western countries are using machines to pick up cotton automatically, Chinese cotton farmers in most regions are still picking cotton manually and putting them in polypropylene bags. Currently, foreign fibers are generally removed by hand picking methods using human visual inspection in most Chinese enterprises, which is time consuming and inefficient. So a machine vision system for online measurement of the content of foreign fibers in cotton is now being studied. High quality image acquisition, fast image processing, effective feature extraction, accurate object classification and precise content measurement are key factors in the implementation of the system (Li, et al., 2010).

The recognition of foreign fibers of targets is the key machine vision technology, in which image segmentation is an important step. Image segmentation is a process of partitioning an image into multiple regions and typically used to locate objects and boundaries (lines, curves, etc.). The aim of image segmentation is to partition the image into meaningful connected components to extract the features of the objects [START_REF] Zhang | Image segmentation evaluation: a survey of unsupervised methods[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. The popular approaches for image The methods mentioned above may perform well in their specific circumstances, but it does not apply to segment images of cotton foreign fibers because of the low contrast. Due to the uneven thickness of the layers of cotton and foreign fibers of different colors and shapes, it is hard to attain a satisfying result by using the above conventional image segmentation methods. Therefore, we need to use the image pre-processing methods to eliminate the low image contrast and gray uniform of the background image. In the practice of image processing, image preprocessing refers to processing work in advance of the feature extraction, segmentation and matching of the image input. And the main aim of image preprocessing is to eliminate the irrelevant information in the origin image, regain the valuable and authentic information, and strengthen the detestability of relevant information, as well as to simplify date to the maximum, therefore to improve the reliability of feature extraction, segmentation, matching and recognition. In another words, image preprocessing provide better information base image for the image segmentation and makes it easy to image segmentation. In this article, a novel method based on image preprocessing and adaptive thresholding method is proposed to segment such low-contrast images.

In our research, the proportion relationship between target image and background was firstly analyzed, and then the whole foreign fibers image was divided into several blocks based on the analysis results. Thereafter,improve the contrast ratio by means of the background subtraction of each image block. In the next step, a piecewise linear enhancement model was established based on the results of the histogram analysis, and applied to enhance the contrast of the image further. Finally, the best thresholding was determined by the Otsu's method.

Materials and methods

Materials preparation

The foreign fibers used in this research were collected from cotton mills including feathers, hair, hemp rope, plastic films, polypropylene twine, colored thread, cloth piece etc. as shown in Fig. 1. Adequate pure lint with no foreign fibers was also prepared for making the lint layer. The experiment selected a sufficient amount of lint cotton which does not include foreign fibers.

Image acquisition

The Image Acquisition System has two cameras, two light sources, one shaft encoder, one synchronizing amplifier, two image acquisition boards and a computer, as shown in Figure 2. Colorful images are captured by a Canadian DALSA high-speed 3CCD color line scan camera under high-brightness LED lightning. The typical foreign fibers are concluded from the research on China's textile enterprises. To make the experiment easier, the foreign-fiber samples were dropping onto the surface of the pure lint one by one while the lint was feeding into the opening machine. After the lint with foreign fibers being opened, a continuous cotton layer is formed,400mm wide and 2 mm thick, Totally 40 color images with 4000×500 in resolution were then obtained.

By observing the images obtained, it was easy to find that the opened foreign fibers appeared in three typical forms, as shown in Fig. 3a-c, (1) sheet, such as plastic films, papers, etc., (2) wirelike, such as hair, color thread, etc., and (3) villiform, such as hemp rope, chemical fibers, etc. As the original color image is too large but the target image is small, if the original image is directly inserted, it will result in an unclear target image, and affect the reader's vision. So same images in this paper are cut target images

Image segmentation

In the online detection of foreign fibers in lint using the machine-vision-based system, the speed and accuracy of algorithm are the key factors. Therefore, in order to ensure segmentation accuracy, the fastest segmentation algorithm should be chosen. Thresholding is a traditional method,which is widely used due to its computational simplicity, high speed and easy implementation. Ostu's Method, as an adaptive threshold method to confirm the threshold value, possesses the advantages of simple algorithm, high speed and so on. Moreover, it makes the biggest segmentation between-group variance implies getting the slightest chance to make errors, which means the Ostu's Method has the optimum segmentation threshold value so that the application of Ostu's method become widespread. However, due to the its problems existing in segmentation processing of images with heterogeneous background, small area proportion between object and background, or subtle difference of the gray rate between object and background, Otsu's Method has big variance in threshold value and therefore is unable to get segmented image with high quality. Nevertheless, cotton fiber image exactly belongs to the category small object image, which also has the characteristics such as small variance of gray rate and heterogeneous gray rate. Consequently, this paper employs preprocessing such as image block, image background extraction, gray-level correction, image enhancement, etc, to solve the problems

Image blocking

The segmentation result binarized by Otsu's method of an image is very satisfying for a good separation of figure and background. But, under the Otsu's method, only when object figure is larger than 25% of the whole image, the segmentation property is approximately optimum; when the area of moving object figure becomes smaller, the property of algorithm declines rapidly, which leads to smaller object figure and larger threshold value variance. Thus, to guarantee the segmentation accuracy and high-qualified segmentation result we divide the foreign fiber figure into several parts and combine them in sequence after image processing respectively, , in order to resolve the problem of low ratio of the object figure and background area.

In the analysis of the foreign fiber image, we finds that whole foreign fiber image is 4000*500 large, with the object figure only occupying 5% or even less of the whole image. The most part of foreign fiber object figure occupies only 0.5% to 3% of the whole image. In order to increase the ratio of object figure and the background, we horizontally divide the origin foreign fiber image equally into 8 parts, each 500*500.

Image background extraction

Previously stated, the contrast ratio of foreign fiber image is low, while the gray rate difference between the object and background is also very small. On the other hand, due to the uneven thickness of the layers of cotton, the gray rate of background of foreign fiber image is heterogeneous. Meanwhile, the segmentation effects of Otsu's method on the low-ratio image or heterogeneous gray rate image is unsatisfying. Cotton foreign fiber image is just the case mentioned above. In order to get qualified segmentation effect, we need to enhance the image contrast ration by the use of the method of background subtraction and to eliminate the impact from the heterogeneous image background.

Background subtraction is to, by means of mathematical morphology or image gray-level correction and other methods, get the image background without the object, then subtracted from the origin image so as to reach the aim to highlight the object and reduce the influence of image background. This paper processes the foreign fiber image by image corrosion in mathematical morphology to obtain the image background.

( , ) f x y is the tight gradation function of 2 () LR , ( , ) g i j is the gradation function for structural element.

The definition of corrosion for gradation function ( , ) f x y is:

Corrosion definition:     ( , ) ( , ) min , ( ,
) ij f g x y f x i y j g i j      (1)
To make calculation convenient, the gray structure elements often take 0, that is ( , ) 0 g i j  . The structural element employed in the paper is round structural element with the radius of 3. After the corrosion of foreign fiber image, we get a first-stage image background.

Image gray-level correction

We can get background figure with comparative high quality by applying the above methods to the two kinds of foreign fiber image---wirelik and villiform, but we confront problems with the sheet foreign fiber image. The reason behind this is that the sheet foreign fiber image has larger object figure area, which incurs to the object figure remnants after the corrosion by radius 3 structural element. After the analyzing background figure of this type, we find that sheet foreign fiber image has greater gray-level and has more obvious difference with the cotton background. Therefore, we can improve the problems of this type via image gray-level correction to get highly-qualified background figure.

This paper utilized fixed threshold value 0.6 as a basic point to conduct image gray-level correction on image, that is, to replace the gray-level of all pixels that above 0.6 by certain needed gray-level value.

The paper makes that certain value as 0 for convenience. Then we get a more satisfying background figure through image gray-level correction. Subsequently,subtracting origin foreign fiber image with the obtained background figure, we get the foreign fiber image with the background extracted.

Image enhancement model

It is not difficulty to get the conclusion from the analysis result of the gray-level histogram of background-subtracted foreign fiber image that the gray-level of the background-subtracted foreign fiber image still exists in the low level part, as shown in Figure 4. This makes it difficult to perform the image segmentation to obtain the region of interesting. To solve this problem, we need to increase image contrast ration by image enhancement, so as to provide better image information basis for subsequent image segmentation. From Figure 6 we can see that the gray-level values of most pixels are below 0.08, which belongs to background figure, whereas pixels with gray-level value above 0.1 belongs to other parts of foreign figure image. And the pixels whose gray-level values are among 0.08-0.1 belong to the co-existing part of background and foreign fiber, that is, the gray edge of object and background, which exerts the most significant influences on subsequent image segmentation.

Thus, a piecewise linear transform model was proposed, and the contrast of the image can be remarkably enhanced by this model, especially in the range from 0.08 to 0.1

.A piecewise transform model splits the distribution range of image pixels into two or more pieces, and performs transformation to each piece respectively to enhance the region of interesting. In our research, the main objective is to segment the foreign fiber objects out of the background, so a three-piece nonlinear model was proposed and described as follows.

Denote the original gray level in image position (i, j) to be GO (i, j), and the corresponding enhanced gray level to be GE (i, j). The gray-scale range need to focus on the image enhancement is [Ll, Lh].

The three-piece linear transform model for image enhancement is defined as

(2)
According to the histogram analysis, we set Ll=0.078 and Lh=0.1. The line of this three-piece enhancement model is shown in Fig. 5.

Image segmentation

Speed and accuracy of an algorithm are key factors for the online visual inspection system (Golnabi and Asadpour, 2007). Hence, in addition to ensuring the segmentation accuracy, algorithms with faster speed are more attractive. The edge-based segmentation algorithm is effective when processing images with sharp edge and high contrast [START_REF] Zhang | Image Engineering(II)Image Analysis[END_REF]. There are very little foreign fibers in lint, and the foreign fibers will be smashed by the opening machine before image acquisition. Therefore, the images of foreign fibers are of low contrast and are not suitable for using the edge-based segmentation algorithm. The special segmentation methods are also not fit for processing the foreign-fiber images due to their complexity and time consuming feature. Thresholding, a region-based segmentation technique, is then the preferred technique in segmenting the foreign-fiber images for its simplicity, speediness and stability.

The identification of cotton foreign fibers is carried out through real-time monitoring. However, different circumstances will have different light intensity. Therefore, the target segmentation for foreign fibers can not use a fixed thresholding value. The optimal threshold can be determined through a variety of ways, including histogram bimodal method (also known as the mode method), Otsu method and the iterative threshold method, etc. The Otsu's method [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF], which is also called maximum between-group variance method, is a kind of adaptive thresholding method which has the optimal threshold according to the statistics. The image is divided into two parts, namely, background and object, according to the gray character of the image. Computerizing between-group variance of background and object separated by every threshold value, we could find that when the between-group variance is the largest, the correspondent variance is optimum. The larger the variance is, the greater the difference of the two parts is. Therefore, the segmentation that has the largest between-group variance manifests the slightest chance of segmentation mistake, i.e., has the best threshold value in the sense of statistics. In this case, the dynamic thresholding was automatically selected by Ostu's method.

Results and discussion

Seven typical foreign fibers, namely, plastic film, feather, polypropylene twine, hair, color thread, hemp rope and cloth piece, were used in the experiments. Ten samples were prepared for each type of foreign fibers. That is to say, there are totally 70 foreign-fiber samples. Adequate pure lint without foreign fibers was also prepared for making the lint layer. In our experiments, the pure lint with one type of foreign fiber dropped onto the surface at each time interval firstly was fed into the opening machine and then made into uniform thin layer, with 400mm in width and 2mm in thickness. The foreign fibers in the lint layer were presented in three forms, namely sheet, wirelike and villiform.

All the results of this paper were processed by the computer with the programming tools developed in Matlab7.0. Operating environment consists of: Inter Core2. PC-frequency: 2.60GHz, 2G Memory. and

Windows XP was selected as the operation system.

Analysis of image background subtraction

Foreign fiber image has low contrast ration and heterogeneous gray-level background, which brings a lot of troubles to the subsequent image processing. Therefore, we resort to the method background subtraction to solve to problem. Owing to the fact that foreign fiber image includes three types, i.e., sheet, wirelike, villiform, and the latter two are abundant in all foreign fibers, whereas the sheet fibers have a comparative low percentage. In addition, the connected area occupied by wirelike and villiform is so small that the foreign fiber image could be removed by erosion of smaller structural elements to get better background. On the other hand, because connected area occupied by the foreign fiber image of sheet type is comparatively large, so we need to use the large structural elements to corrode image.

If we do so, the difference between the background figure subtracted and the original image will be too great to eliminate the influence of heterogeneity of background image. We can see from the gray-level analysis that the foreign fiber of sheet type mainly includes three subcategories as plastic film, feather and cloth piece, which all have deeper image gray-level and greater difference from the background figure. According to this, we could eliminate the remains of the object figure in the background by the Image gray-level correction, in order to get better background figure.

As mentioned above, the structural element employed In the process of image corrosion is round structural element with the radius of 3. After the corrosion of foreign fiber image, we get a first-stage image background as shown in Figure 6.

Analysis of image segmentation results

In the paper <

A Fast Segmentation Method for High-resolution Color Images of Cotton Foreign Fibers> [START_REF] Zhang | A Fast Segmentation Method for High-resolution Color Images of Cotton Foreign Fibers[END_REF], the author presents a fast approach for segmenting images of foreign fibers in cotton.。 Firstly, color images were captured, and the edge of color images were detected by edge detection method which is based on improved mathematical morphology. Then the color images were converted into a gradient map, the law of experience values was analyzed, and the best thresholding of the gradient map was chosen by selecting the best experience value iteratively. The proposed method can segment the high-resolution color images of cotton foreign fibers directly and precisely, and the speed of image processing is double more than traditional methods (referred to as zhang's algorithms hereinafter).

In our research, more than 2500 sample images were tested and compared using the Otsu's algorithm, Canny algorithm, the conventional watershed algorithm and zhang's algorithm. The image segmentation results were shown as figure 10. We can obviously see from the above figure the direct use of Ostu, Canny and the conventional watershed algorithms to segment image can not result in the clear segmentation of the object figure to meet our requirement. While Zhang's algorithms could get better segmentation result, the algorithm of this paper reserves more foreign fiber image details that make the segmentation result much clearer and higher segmentation accuracy.

Analysis of image calculating speed

In our experiments of image segmentation, we have drew a comparison of the calculating speed of five methods, namely, Otsu's algorithms, Canny algorithms, the conventional watershed algorithms, zhang's algorithms and the algorithm of this paper.The results have shown that the average time for the five kinds of segmentation method of processing foreign fibers are: 0.26 s, 5.84 s, 9.23 s, 3.92s and 2.38s.Table 1 shows the average time of the five kinds of segmentation algorithm.

Tab. The results shown in Table 4 indicate that, the calculating speed of Canny algorithm, the conventional watershed algorithm and Zhang's algorithm for segmentation of images for foreign fibers are significantly slower than the one presented in this paper. Moreover, seen from the segmentation result, it is obvious that this algorithm has great advantages over other algorithm in segmentation accuracy.

Exclusive use of Ostu algorithm without any other pre-processing to segment the image, though fast in speed, could not provide us with the result we expect.

Thus, the results of above analysis showed that we can acquire better segmentation results, whether in the case of wirelike, the villiform or the sheet foreign fibers. And the calculating speed in this stage has been improved Significantly,compared with other algorithms.

Conclusion

Owing to the fact that Ostu algorithm is faced with big problems in segmenting the image that has low contrast ratio between object figure and background, small area of object figure or heterogeneous gray-level of background image---which type that foreign fiber image is unfortunately belongs to, we seek for solution by a series of pre-processing methods such as image block, background subtraction, gray balance, etc, to get better segmentation result.

The experiment result demonstrates that the algorithm of this paper far outweigh other algorithms in the past both in accuracy or speed.

The mandate of speed is a key factor for the online visual inspection system. Hence, in addition to ensuring the segmentation accuracy, algorithms of faster speed is now being studied.

  segmentation are: histogram-based methods, edge-based methods, region-based methods, model based methods and watershed methods (H. Zhang, J. E. Frittsb et al.,2008;Y. Jiang and Z.-H. Zhou et al.,2004). Fast and precise segmentation has always been of great concern of people. Various image segmentation methods are reported in the literature (Bakker et al., 2008; Kim et al., 2003; Pichel et al., 2006) some of which are used in Automated Visual Inspection system in agriculture (Chen et al., 2002; Jiang et al., 2008). In recent years, researchers have developed more efficient, but also more complicated methods for segmentation. For example, Kainuller et al. (2007) used a statistical shape model combined with a constrained free-form model. Schmidt et al. (2007) presented a system that allows defining a set of rules, based on which abdominal organs are segmented (including the liver) using simple functions (like region-growing, or morphological operators). Liapis et al. (2004) proposed a wavelet-based algorithm for image segmentation based on color and texture properties. Furukawa et al. (2007) used a maximum posterior probability estimation for rough liver extraction subsequently refined with a level-set method. Seghers et al. (2007) presented an active shape model method, in which multiple local shape models are used. Susomboon et al. (2007) used intensity-based partition, texture-based classification, probability model, and thresholding to segment the liver.

Fig. 1

 1 Fig. 1 Foreign fiber samples

Fig. 2

 2 Fig. 2 The image acquisition system

Fig. 4 .Fig. 5 .

 45 Fig.4.Histograms of hair image

Fig. 6 .

 6 Fig. 6.Acquired background image examples: (a) background of plastic film, (b) background of hair, and (c) background of hemp rope.It is safe to say from Fig.6we can get background figure with comparative high quality by applying the above methods to the two kinds of foreign fiber image---wirelik and villiform, but we confront problems with the sheet foreign fiber image. Thus, we could eliminate the remains of the object figure in the background by the Image gray-level correction, This paper utilized fixed threshold value 0.6 as a basic point to conduct image gray-level correction on image, that is, to replace the gray-level of all pixels that above 0.6 by 0. Then we get a more satisfying background figure through image gray-level correction. Subsequently, subtracting origin foreign fiber image with the obtained background figure, we get the foreign fiber image with the background extracted, as shown in Figure7.

Fig. 7 (

 7 Fig. 7(a) Acquired background image of plastic film after Image gray-level correction,

Fig. 8 (

 8 Fig. 8 (a) original image of plastic film (b) Image of plastic film after background subtraction when the threshold is 0.5 (c) Image of plastic film after background subtraction when the threshold is 0.6 (d) Image of plastic film after background subtraction when the threshold is 0.7

Fig. 10 (

 10 Fig.10 (a) original image of hemp rope (b)Ostu's algorithm (c) canny algorithm (d) the conventional watershed algorithms (e) zhang's algorithms (f)algorithm of this paper


Cong Wang

Daoliang Li

Lingxian Zhang

Qisheng Ding

Zetian Fu

A Portable Measurement Instrument for the Measurement of Water Body Chlorophyll-a in the Support of Fluorescence Detection

Keywords: Chlorophyll-a, Relative fluorescence intensity, Dual-optical structure, Fluorescent signal

Chlorophyll-a was regarded as the important indicator to describe the marine primary production because the chlorophyll a content of phytoplankton in the ocean is related to its photosynthesis production.The concentration of chlorophyll a is also the major parameter to evaluate marine water quality, organic pollution and detect the fishing ground, and the temporal and spatial variation of chlorophyll a contains the basic information of sea areas. Based on the spectral characteristics of chlorophyll fluorescence, this document recommends a new dual optical detecting instrument for the measurement of chlorophyll-a concentration , the microcontroller MSP430F149 as the key control module,by controlling the ultra-high brightness LED which wavelength is 450nm to excite chlorophyll a to produce the fluorescent signal about 680nm , at the same time this LED is used as reference light , the dual-optical structure exclude the light fluctuations due to the impact of test results. At last, we get the relationship between relative fluorescence intensity and chlorophyll-a concentration with the spectrophotometer, we find the system has the good linear consistency when measures the low concentrations of chlorophyll-a.

Introduction

In recent years, China's Taihu, Wuhu lake, Songhua river and other water environment have emerged water crisis, due to the continued outbreak of blue-green algae classes in the water, making the phytoplankton in water sharply increase, the water oxygen exhaust, the water body is showing a large area of hypoxia and so large number of aquatic life is dying. As the rapid growth of algae, green algae accumulate on the surface of the water, causing water problems, water body cannot exchange air with the outside world, day by day become foul, to the people's production, life and health have brought great harm.

The evaluation of eutrophication can be achieved by tracking and monitoring the chlorophyll content in water, all of which chlorophyll a is highest concentrations of chlorophyll, chlorophyll a determination can thus trace the lake's eutrophication [START_REF] Daying | Study on in-situ measuring set for chlorophyll-a in the seawater[END_REF][START_REF] Mingcui | Evaluation method and classification standard on lake eutrophication[END_REF] .

In the mid-1970s, Soviet oceanographers first use pulse xenon lamp as the light source, narrow band interference filter and pulse detection technology for the development of pulsed underwater fluorometer. From the late 1970s to the early 1980s, many developed countries had been studying multi-parameter, multi-purpose drag fluorometer /conductivity, temperature, depth (CTD) sensor system development [START_REF] Ruth | A Device for the Determination of the Microsecond Component of the in vivo Chlorophyll Fluorescence Induction Kinetics[END_REF] . In 1997, K.Wild-Allen simultaneously measured water color and chlorophyll concentration by using the sea buoy quipped with a color sensor and fluorometer [START_REF] Wild-Allen | Observations of diffuse upwelling irradiance and chlorophyll in case Ⅰ waters near the Canary Islands(Spain)[END_REF] . R. Barbini established laboratory of the fluorescent sensors which supplied for large-scale walking route monitoring from different conditions of seawater, monitoring object included the concentration of phytoplankton, yellow substance, turbidity, biomass productivity and organic pollutants [START_REF] Barbini | Fluorescence Determination of Pollutants and Natural Components in Seawater from the on Board Mobile Lidar Apparatus[END_REF] . In the late 1990s, TriOS companies (German) used super-efficient blue LEDs (wavelength 470nm) and the internal reference diode control energy output, selected photodiode as a monitor, successfully developed a portable microFlu-chl-type sensors which can be directly related to the different instrument to read continuous data and collect data , measurement range of 0~100 μg/L, a resolution of 0.02 μg/L, the accuracy for the optical reading of 3% (http://www.trios.de/). Such as these foreign chlorophyll fluorometer, there are many, but they are expensive, and their operation is more cumbersome. Laboratory methods of measuring the concentration of chlorophyll-a include high-performance liquid chromatography (HPLC) and spectrophotometry,they all need to extract chlorophyll from phytoplankton, not only the workload, and there is a lot of confounding factors in the whole process, the final result is likely to get the wrong data. Remote sensing methods need not extract, is an online measurement method, but only for a large area of water, and aerial work demanding, complex test results [START_REF] Yunwu | Satellite remote sensing in the ocean observing application[END_REF] .

This paper presents a new site available for determination of chlorophyll fluorescence, high sensitivity, fast, real-time and good benefits, can be used for lakes, rivers, oceans and other water eutrophication on-site inspection 7,8,9,10 .

MATERIALS AND METHODS

Fluorescence detection principle

When chlorophyll a is excited by the wavelength of 450nm light around, it will send around a wavelength of 680nm fluorescence [START_REF] Ruth | A Device for the Determination of the Microsecond Component of the in vivo Chlorophyll Fluorescence Induction Kinetics[END_REF][START_REF] Guozhen | Fluorescence analysis[END_REF][START_REF] Brechet | A novel blue LED based scanning hand-held fluorometer for detection of terrestrial algae on solid surfaces[END_REF] , the fluorescence emission intensity:

  0 1 cb f I kQI e     (1)
Where: k is the instrument constant; Q is the fluorescence efficiency of material; 0 I is the light intensity of excitation source; c is the concentration of substance;

b is the optical path difference of sample;  is the molar absorption coefficient.

As long as the excitation light intensity is stable, the fluorescence intensity is only related with the concentration of chlorophyll a, chlorophyll-a concentration c and the relationship between the fluorescence intensity F:

  lg c A B D F    (2)
Where:

  0 2.3lg A kQI b   ; 2.3 B b   ; 0 D kQI 
. Therefore, the chlorophyll-a concentration can be detected by the detection of the received fluorescence intensity.

When the solution is very dilute, the total excitation energy is absorbed less than 5%, or 0.05 cb   , In Eq.(1) ,by power series expansion, since the second term is negligible, we get Eq.(3):

0 f I kQI cb   (3)
In Eq.( 3), when a fluorescent substance concentration is very dilute and the related excitation source is stable, fluorescence intensity is proportional to the concentration of the solution. However, when the concentration

Dual beam and dual-channel measuring

For the dilute solution, we can get Eq.( 5):

0 f I I kQ cb   (5)
Which can calculate the relative fluorescence intensity values to get the value of chlorophyll concentration. Therefore, the use of dual beam and dual-channel mode, by division and other operations to complete the normalization process, can effectively eliminate the excitation light intensity fluctuations on the measurement results, making the measurements more accurate.

Signal conditioning modules

In Fig. 1 In Fig. 1, the two photodiode parameters are the same, so Eq.( 6).

0 1 2 2 1 1 1 2 A B f I I I I U k U k A MU U    (6)
Where:

2 1 1 M k k A  .
The M value can be regarded as a constant when calculate the Eq.( 6).

Microprocessor Module

In order to reduce the energy consumption of the measuring device, this paper chooses TI's ultra low power MCU MSP430F149 as a core processor [START_REF] Lierda | MSP430F13X/F14X Chinese data sheet[END_REF] (Fig. 2), MSP430F149 embeds analog to digital conversion chip that can convert analog signals which come from signal conditioning module into digital signals, at the same time with the external power management module , microprocessor module also can supply energy for signal conditioning module. By using MSP430F149

internal timer A, we can control the duty cycle and frequency of the blue LEDs flashes, the system LEDs blinking frequency is 10HZ, the ratio is 50% [START_REF] Jian | Atmospheric S02 concentration analyzer based on ultra-violet fluorescence[END_REF] .

MSP430F149 single-chip system via the JTAG interface to debug and download program. Moreover, in order to make the measurement devices more intelligent, able to achieve record and store the channel parameters and calibration parameters table , we design the spreadsheets (TEDS) in the flash of MSP430F149. The system uses RS485 bus output(Fig. 2).

Software peak detector design

In laboratory experiments, we detect the chlorophyll-a fluorescence signal waveform(Fig. 3).

Fig. 3. Simulation of the Detection Waveform

This device which consists of interference filters ,largely reduces the external impact of background light and emit blue light when obtains the fluorescence signal, and part of the design of the signal conditioning module blocking circuit, band pass filter and 50HZ notch filter, as the implementation of these circuits make the extraction of the fluorescent signal more pure. After analyzing of chlorophyll-a fluorescence signal waveform ( Fig. 3), the paper designs a software peak detection method, by controlling the microcontroller A/D converter chip sampling delay, to achieve in a half-cycle-interval sampled 255 times, whichever maximum value is stored. We define the average of the 10 consecutive maximum as the original voltage / 1 U of the fluorescence signal, software peak detection method is also applicable to the original value of the reference signal acquisition, the original definition of the reference signal voltage is / 2 U . The final calculation of the relative fluorescence intensity can be expressed as

  / / 0 1 2 1 2 1 cb f I I MU U MU U kQ e       or   / / 1 2 1 cb U U kQ e M    
, that is the results in theory which the system should get, so, we can see different concentrations of the measurement results should meet the negative exponential curve. Also we can get / /

RESULT AND DISCUSSION

Preparation of chlorophyll a standard solution

In the preparation of chlorophyll a standard solution, we mainly extract chlorophyll-a by use of laboratory extraction method. First, select skeletonema costatum which was in the exponential phase of growth , weight class by its small to large is divided into A, B and C , respectively, filter through a diameter of 0.45 acetate membrane, the membrane is then placed in 100mL centrifuge tube, add 50 ml of 90% concentration of acetone, the tube extraction at 4℃ refrigerator 2 hours, then to 4000 r/min centrifugation for 5 minutes, last, collect the supernatant with 50 ml brown flask in constant volume, and this solution as stock solution. When laboratory measurements, we can dilute the stock solution according to the needs of the required concentration. Through analysis of the nature of chlorophyll, we find the chemical properties of chlorophyll was unstable, so the prepared acetone solution of chlorophyll-a needs to be placed in dark place and the effective time for the solution is less 24 hours.

Evaluate of Instrument performance and Standard Curve

The chlorophyll fluorescence measuring instrument measuring range is 0~25 μg/L, and the measuring minimum limit is 0.03 μg/L. Instrument in actual measurement, the value of the relative fluorescence intensity is very small, not easy to device calibration and instrument performance analysis, so we select / / 1 2 UU instead of 0 f I I , the method has no effect on the calibration of instruments, and the rest of the photos or tables are processed in the same way. In the experiment of the instrument repeatability, we choose the prepared acetone solutions of the chlorophyll-a A and B as the experimental fluid, every 5 min, respectively, measure repeatedly the two solutions five times, the measurement results as shown in Table 1. According to the definition of repeatability, we could calculate the standard deviation 0.0024 and 0.0021 by Bessel's formula [START_REF] Taylor | An Introduction to Error Analysis[END_REF] , that proves the instrument has good repeatability. .In Fig. 6, the relative fluorescence intensity and concentration is in a linear relationship, which meets the theoretical results of the analysis, so the system design is feasible. According to national standard methods, we detect the concentration of the five levels of chlorophyll dilute solution with multi-wavelength spectrophotometer method [START_REF]State Environmental Protection Administration《Water and wastewater monitoring and analysis methods》Editorial Board. Water and wastewater monitoring and analysis methods (Fourth Edition)[END_REF] , the measuring results is 0 μg/L, 3.05 μg/L, 6.32 μg/L, 12.53 μg/L and 19.82 μg/L from small to large order. As the Fig. 7 shown, fluorescence intensity converted into concentration by the equation:

14.2 0.01 yx  (7) Where: x is the relative fluorescence intensity; y is the chlorophyll-a concentration.

The linear regression coefficient is 0.997,that is, the measuring device in case of low value has a good linear consistency.

Conclusion

In this article, we carefully study the fluorescence detection method for measuring the chlorophyll-a content and chlorophyll-a fluorescence characteristics, combined with domestic and international issues related to research, design a low-power, low noise, high speed chlorophyll-a content measuring device.

(1) The system chooses MSP430F149 microcontroller as the core of the detection control unit, its rich pin function and the built-in functional unit simplify the system's design, while improve the system's stability and anti-jamming capability.

The system uses ultra-high brightness, a wavelength of about 460nm blue LEDs as

(2) the excitation source, while the use of single-chip PWM output control, and other functional units select low-power devices for designing, effectively reduce the system power consumption, system overall average power consumption is only 0.03W.

(3) Due to the light fluctuations, test results may not be accurate, so, in order to exclude the impact, the system applies the structure of dual optical detection. When debugging is complete, we select different concentrations of chlorophyll acetone solution for measurement, analysis the relationship between the relative fluorescence intensity and concentrations of chlorophyll-a, and then complete the calibration solution of chlorophyll with the spectrophotometer, the system repeatability tests and linearity tests. As can be seen from the experimental data, the system has the good linear consistency when measures the low concentrations of chlorophyll-a.

,

  concentration is not proportional, but when the concentration increases to a certain extent, to further increase the fluorescence intensity of concentration no longer enhanced. Because at this time, At this point, no fluorescence intensity changes with concentration changes.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Fluorescence signal detection system

1 A 2 kI 1 U 2 U

 1212 , fluorescence signal and reference signal via photosensitive device, the light signals into current signals, MSP430 MCU's internal analog-digital conversion chip cannot be directly on the current signal processing. System is involved in the signal conditioning circuitry, signal conditioning circuitry in addition to the current signal into a voltage signal, but also the role of a filter-amplifier, it can largely filter out the background and circuit noise from fluorescent signal and reference signal, and its amplification meets the MSP430 microcontroller chip sampling within the A/D requirements.Fluorescence signal conditioning modules involving two amplifier circuit, completes the first stage current-voltage conversion, the conversion factor is denoted as 1 k , also completes the second level voltage amplification, magnification is recorded as . The reference light signal conditioning module is relatively simple, according to the reference light intensity with the strong characteristics, using only one amplifier, the current voltage conversion circuit, the conversion factor is denoted as is light current from the fluorescence signal ; B I is light current from the reference optical signal; is output voltage from the fluorescence signal conditioning circuit; is output voltage from the reference optical signal conditioning circuit.

Fig. 4 .Fig. 4 .

 44 Fig.4.

Tab. 1 .

 1 Fig.5shows the measured relative fluorescence intensity close to the negative index, and theoretical analysis of the relative fluorescence intensity:

Fig. 5 .

 5 Fig. 5. Detecting results of the relative fluorescence intensity

Fig. 6 .

 6 Fig. 6. Detecting results of the relative fluorescence intensity in weak acetone solution

Fig. 7 .

 7 Fig. 7. Testing results using spectrophotometer

Acknowledgements. This work was supported by the programs "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).


Baoshi Jin

Xiaodan Ma

Zhongwen Huang

Yuhu Zuo
email: zuoyhu@163.com

PhD Zuo Yuhu

The Key Information Technology of Soybean Disease Diagnosis

Keywords: Soybean, Leaf Diseases, Image Processing, Neural network, Diagnose I. 1

Combining image processing and artificial neural network technology, a new diagnose method of soybean leaf diseases has been proposed, which identified the methods of division algorithm and eigenvalue computation, meanwhile, established a three-level neural network model to identify the diseased spot areas. According to the characteristics of soybean leaf disease, a new diagnose method has been formed, through extraction of geometric features, color and texture feature, which provided reference to Remote Intelligent diagnosis of soybean leaf disease.

agricultural research content of digital agricultural information detection, the disease type, extent and damage control methods can be passed to agricultural producers synchronously after the disease images have been processed, segmented and recognized using image processing technology, through which, the diseases can be diagnosed automatically, comprising to traditional diagnose method, the method based on image processing technology has many advantages such as simple, rapid, real-time, objective, accuracy etc.

For the most of plants, the diseases can be reflected through its lamina at a great degree, so it is possible to get affected condition through researching laminas, and the identification of disease spots and its associated feature information become the basis for judging Diseases. As early as 1995, Tao [START_REF] Tao | Machine vision for color inspection of potatoes and apples[END_REF] studied on color differences between apples and potatoes using machine vision, Zhou-Long [2]detected the image edge and Shen Zuo-rui [START_REF] Zuo-Rui | Use of math-morphological features in insect taxonomy[END_REF] distinguished the geometrical characteristic among images using fuzzy enhancement and mathematics morphology separately.

Since the mid-20th century, 80, the image processing technology has been widely used in agricultural engineering studies. after retrieving a large number of crop diseases diagnosis technology [START_REF] Jingao | Agricultural Plant Pathology[END_REF][START_REF] Junchen | Advances in Research on Computer-Vision Diagnosis of Crop Diseases[END_REF][START_REF] Georgieva | Theoretical substantiation of model of system for evaluation a state of vine plants and taking a decision for plant protection activities[END_REF][START_REF] Yibin | Machine vision technology in the agricultural harvest and process automation application[END_REF][START_REF] Shanwen | Image processing technology in environmental applications[END_REF][START_REF] Panigrahi | Background Segmentation and Dimensional measurement of Corn Germplasm[END_REF][START_REF] Yutaka | Automatic Diagnosis of Plant Disease[END_REF][START_REF] Yutaka | Construction of the Automatic Diagnosis System of Plant Disease Using Genetic Programming Which Paid Its Attention to Variety[END_REF][START_REF] Sanyala | Pattern recognition method to detect two diseases in rice plants[END_REF], we found that, Ma Xiaodan [START_REF] Xiaodan | Investigation and recognition on diseased spots of soybean leaf based on neural network[END_REF] etc has achieved automatic recognition of soybean leaf diseased spots using neural network technology, CaoMin [START_REF] Cao | Detecting Research of Soybean's Plant Nitrogen Based on image processing Technology[END_REF] has achieved determination of soybean nitrogen using image processing technology, Mari Oide [START_REF] Oide | Discrimination of soybean leaflet shape by neural networks with image input[END_REF]has recogonized 364 leaf shapes of 38 soybean varieties using Hopfield pattern recognition which did not require any shape feature extraction, the neural network determined right and wrong. in summary, soybean leaf disease diagnosis technology based on image processing and neural network have not been reported yet. This article will focus on technology based on image processing and neural network to diagnose the soybean leaf diseases, in the field environment, through the machine vision to obtain image of leaf disease samples, to achieve automatic identification of disease , computer image processing and pattern recognition technology are proposed to diagnose the soybean leaf diseases, furthermore, has become the basement for remote intelligent diagnosis of soybean diseases.

II Materials and methods

Image Acquisition

The samples of soybean leaf diseases have been collected in open environment of field, which used non-destructive collection method, in natural light, soybean leaves have been loaded in home-made loading template, and used DSLR-α350 SONY digital cameras wih 14.2 million effective pixels to collect the images which would be stored into soybean leaf disease image database with JPEG format .

Pre-processing of plant disease image

Image of plant disease leaf could be affected by various noise sources in the generation and transmission which would lead to quality degradation. It is impossible to describe statistical model using random process, thus, this article used a local smoothing method to eliminate image noises ,which named median filter approach.

A median filter window may be square(m×m), rectangular(m×n) or cross-shaped etc.

Firstly,Median filter will sort the gray value of template pixel, while n is odd number, the gray value of pixel Located in the middle is called the median number of these n numbers.,while n is even number, the gray value of pixel Located in the middle of the average of the two values is called the median number, the expression is as follows:

( ) ( ) , , A g i j median f i j =     (1)
Where, the median represents the middle value of the window, ( )

, A f i j
represents the gray value of pixel.

Recognition of diseased spots

BP network is one of the most widely used class of models, among of which the error is back propagation. From the topology point of view, BP network is a typical hierarchical network structure, is divided into input layer, hidden layer and output layer, between layers generally use the whole interconnect, the figure 1 is a three-layer BP neural network, i, j, k are input node,output node and hidden node separately. ,the each output of hidden layer nodes can be expressed as:

pj ji pi i I w O = ∑ (2)
Where,

For the attraction of disease pathogen, the color of diseased spots area has often been influenced in a certain degree, which different with healthy area of lamina obviously, thus, the color characteristics of diseased area and healthy area would be used as input values, concretely involves R , G , B values of each pixel;In the course of the study, choosing ten pixels as training samples, of which five samples of diseased spots used as input value need to be extracted and another five samples of healthy area as input values should be wiped out, and then, the effective network weights could be got though a series complicated evolution process including copy, cross and variation over and over again, and saved to weight file at last.

The values of R , G and B component in each pixel were listed out in table 1 ,of which the color values of sample points one to five represented that of diseased spots, and anther five sample points six to ten represented that of background area.

Feature calculation

1) Geometric feature [16] The characteristic parameters of the lesion is very important to determine the diseases, This region labeling and contour tracking has been used to calculate the geometric characteristics of lesion area.

Tab. 2) Color feature [17] While diagnosis of crop diseases, the HSI model has great advantages, HSI model can reduce the light intensity to determine the impact of color, therefore, this study extract the color feature in HSI color space, therefore, RGB space need to converted to HSI color space.

Tab.2Values of Color Feature

No. 3) Texture Feature [18] The image of disease leaf includes not only geometric information, color information, but also contains the texture information,fiom which many valuable information can be abtained.

H S

because different types of disease pathogens can produce different diseases, the lesion area showes different texture, thus,the texture features can provide an important basis for identification of disease.

Tab. Ⅲ

Result and Discussion

From figure 1, we see that the color of soybean brown spot is obviously different to that of healthy area, so it is feasible to use color characteristic as input value of the network, and the most of the diseased spots shown in figure 4 have been extracted, especially the bigger ones with distinct characteristic, so the algorithm accuracy can reach to more than 90%, furthermore, the main influenced factor lay in some aspects bellow:

1) The first factor is the number of samples, how many samples should be chosen depends on the complexity of the lamina itself, that is to say, the more complex types of diseased lamina, the more samples should be chosen.

2)The second factor is the resolution of the digital camera, which influenced the extraction accuracy of diseased spots.

3)The third factor is the light intensity which led to the loss of the image color, so reducing the interference of human factor and environmental factors should be taken into account when collecting the lamina images.

Ⅳ Conclusion

Based on the colorimetric mechanism, the soybean brown spots have been recognized successfully through training the artificial neural network, the method proposed in this paper overcomes the shortcomings of traditional threshold method.

The experiments result shows that the precondition for accuracy reached to 100% is choosing enough samples seriously and combining with image processing technology.

In the paper , we have studied the key technology of Intelligent diagnosis of crop diseases. Firstly, proposed the image acquisition method, secondly, determined the preprocessing algorithms of diseased leaves image and the segmentation algorithm of soybean leaf lesion, at last , pre-research the Identification model of soybean leaf disease from the geometrical features, color and texture characteristic respectively, which provide the basement for intelligent diagnosis of diseased soybean leaf .

This study which can be applied not only in recognizing of soybean diseased spots, but also in that of other farm crop, such as rice, maize have been popularized in practical life and production, meanwhile , provides a practical scientific method for agriculture researchers and solid technology basement for species identification, characteristic recognition and diseased diagnosis.

Fig. 1

 1 Fig.1 the image of diseased leaf

Fig 1

 1 Fig 1 Three-Layer BP network structure diagram The input layer receives external input mode, and has been transmitted by the input node to the nodes of hidden layer. The hidden layer is a layer of internal processing units of BP network and pattern recognition has mainly achieved by the

  weights between input layer and hidden layer nodes, pi O is the output value of input node, the output of hidden layer is expressed as Output layer produces the final classification result ,that is the output mode, the output node of input layer is as follows: between the node j of hidden layer and node k of output layer, pj Ois the output value of node j of hidden layer. The output of node k is as follows :

Fig 2

 2 Fig 2 Soybean leaf diseased spot Image before and after recognized

  1 Values of Geometric Feature

	No.	S	L	c	e	E	s
	1	278	62	0.909 13.827 0.844	0.576
	2	1311	139	0.853 14.738 0.950	0.638
	……
		……	……	……	……	……	……

The project supported by General bureau of state farms of Heilongjiang Province(HNK11A-06-02-02)


Shuangyin Liu

Yan Haijiang Tai

Longqin Xu

Daoliang Li

Prediction of Dissolved Oxygen Content in Aquaculture of Hyriopsis Cumingii Using Elman Neural Network

Keywords: prediction, dissolved oxygen, Elman neural network, Hyriopsis Cumingii

Abstract. Hyriopsis Cumingii is Chinese major fresh water pearl mussel, widely distributed in the southern provinces of China's large and medium-sized freshwater lakes. In the management of Hyriopsis Cumingii ponds, dissolved oxygen (DO) is the key point to measure, predict and control. In this study, we analyzes the important factors for predicting dissolved oxygen of Hyriopsis Cumingii ponds, and finally chooses solar radiation(SR), water temperature(WT), wind speed(WS), PH and oxygen(DO) as six input parameters. In this paper, Elman neural networks were used to predict and forecast quantitative characteristics of water. As the dissolved oxygen in the outdoor pond is low controllability and scalability, this paper proposes a predicting model for dissolved oxygen. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The Elman NN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest.

Experimental results show that: Elman neural network predicting model with good fitting ability, generalization ability, and high prediction accuracy, can better predict the changes of dissolved oxygen.

Introduction

Hyriopsis Cumingii is Chinese major fresh water pearl mussel, widely distributed in the southern provinces of China's large and medium-sized freshwater lakes. In recent years, aquaculture farmers to improve economic benefit in the production process, the use of livestock and poultry faeces or chemical fertilizer, lead to the water ecological unbalance, nitrogen and phosphorus excess water flooding, phytoplankton, nutrient runoffs, the serious influence of fresh-water cultured pearl's sustainable development. Lacking of dissolved oxygen water quality exacerbate in the ponds in summer easily causes freshwater mussel disease (G.F. [START_REF] Zhang | Advances of the studies on diseases of Hyriopsis cumingii and its control[END_REF]. Therefore, real-time monitoring and forecasting of Hyriopsis Cumingii ponds' water quality has becoming a priority issues, specifically dissolved oxygen prediction. Aquaculture water quality is a complex system. It affected by many factors such as environment, drugs, climate and bait etc. In this field, to prevent breeding water quality deterioration, forecasting DO value accurately has been quite an imperative subject at present. Prediction of water quality at home and abroad focused mainly in rivers, lakes, reservoirs, estuaries and other large waters using the gray system theory, polynomial regression, mathematical statistics, time series models and the neural network, etc (Xiaoyi Wang,2011; Yan-ping GAO,2008; LEE JHW,2003, LiangPeng Wang,2010; Xiao-ping Wang,2007; Wei-ren [START_REF] Shi | Water quality parameter forecast based on grey neural network modeling[END_REF][START_REF] Sun | Study of GM (1,1) model and its application in forecast of the water quality[END_REF]. Yu first made a prediction model for water quality based on improved BP neural network (Ch.X.Yu, 2008); Palani et.al. developed a neural network mode to forecast dissolved oxygen in seawater [START_REF] Palani | Development of a neural network for dissolved oxygen in seawater[END_REF]; Li established the BP and AR of the short-term forecasting model (F.F. Li, 2010); Zhang had adopted Elnman neural network model to forecast the SO2 in the atmosphere pollution index (Qi [START_REF] Zhang | Prediction ofData from Pollution SourcesBased on Elman NeuralNetwork[END_REF]; Wang Ruimei predicted dissolved oxygen using the fuzzy-BP neural network (Ruimei Wang, 2010).However, limited water quality data and the high cost of water quality monitoring often pose serious problems for process-based modeling approaches. ANN provide a particularly good option, because they are computationally very fast and require many fewer input parameters and input conditions than deterministic models. ANN does, however, require a large pool of representative data for training. The objective of this study is to investigate whether it is possible to predict the values of water quality variables measured by a water quality monitoring program; this task is quite important for enabling selective monitoring of water quality variables. Elman regression network is a kind of typical dynamic neural network, it in feedforward neural network on the foundation of basic structure, through the internal storage condition caused it to have the function of dynamic features mapping, so that the system has the ability to adapt to the time-varying characteristic, is a current of the most widely used type feedback neural network. This paper, we adopting Elman dynamic neural network to establish 1 hour dissolved oxygen predicting model in the Hyriopsis Cumingii ponds. This paper aims to find an effective way for prediction of in the Hyriopsis Cumingii ponds. The remaining parts of this paper are organized as follow. Section 2 covers some related work including data collection and reprocessing methods the Elman neural network model, Elman NN parameter selection and model performance evaluation. the experimental results are shown and evaluated in section 3. Finally, Section 4 presents our conclusions.

Relate work

In this section, we survey some related research techniques in the area of data collection, Elman NN architecture, Elman NN parameter selection, data pre-processing, model performance evaluation

water quality data collection

Ecological environment data of the Hyriopsis Cumingii aquaculture ponds are obtained by Digital Wireless Monitoring System of Aquaculture Water Quality (DWMS-AWQ) for real-time. It has been equipped at China Agricultural University-DuChang Aquaculture Digital System Research Center in DuChang city, Jiangxi province, China. The DWMS-AWQ consists of four parts: the data collection layer, the data transmission layer, the information processing layer and the application layer. The data collection layer directly connects to the intelligent sensor through RS485 connector and sends the data of intelligent sensor to routing node by wireless network. Then the routing node sends the data to the on-site monitoring center through a wireless network and to the remote monitoring center by GPRS.

In Hyriopsis Cumingii ponds, we installed two water quality data collection nodes101 and 102 collecting water level, water temperature, PH and DO at water level 1m and 2m, respectively, one meteorological data collection nodes 103 (collecting rainfall, temperature, humidity, wind speed, wind direction, solar radiation), routing node 1 and the GPRS module(Fig. 1). An artificial neural network (ANN) is a new generation of information processing system, which can model the ability of biological neural network by interconnecting many simple neurons. The neural network consists of layers of parallel processing elements called neurons; it is a simplified, simulation and abstract of human brain. They have the similarities in two main aspects: to acquire knowledge through learning from the external environment, and to store obtained knowledge use of the internal neurons. It is one of the well-known prediction models as it has excellent ability of mapping complex and highly nonlinear input-output patterns without the knowledge of the actual model structure. The feature of dealing with nonlinear problems makes ANN popular in many fields, such as financial signals prediction, language processing, biology, biochemical simulation and so on. There are many types of artificial neural networks. The Elman network is one kind of globally feed-forward locally recurrent network model proposed by Elman (J. [START_REF] Elman | Finding structure in time[END_REF]). It has a set of context nodes to store the internal states. Elman neural network is a kind of internal feedback, can store and use the last moments of the input and output information, and has a strong computing power. Elman neural network consists of four layers: the input layer (signaling effect), the hidden layer, the context layer. As shown in Fig. 2. There are adjustable weights connecting each two neighboring layers. Generally, it is considered as a special kind of feed-forward neural network with additional memory neurons and local feedback (P. S. Sastry, G. Santharam and K.P. Unnikrishnan, 1994). The self-connections of the context nodes in the Elman network make it also sensitive to the history of input data which is very useful in dynamic system modeling (J. [START_REF] Elman | Finding structure in time[END_REF]. The basic structure of a multilayer feed-forward network model can be determined as consist of an input layer, one or more hidden layer and an output layer. The input layer neurons receive input patterns from the external environment and propagate them on to the first hidden layer neurons. In this layer any data processing is not carried out. Input values distributed from each of the input layer neurons are multiplied by each of the adjustable connection weights linking the input layer neurons to hidden layer neurons. At each neuron in the hidden layer weighted input values are summed and a bias value is added. Then combined input value is passed through a nonlinear transfer function like sigmoid to obtain the output value of the neuron. This output value is an input for the neurons situated in the following layer. Finally, the output layer neurons produce the output value of the network model (Fatih Ozcan. Cengiz D. Atiş. Okan Karahan. Erdal Uncuoğlu, 2009).

Fig. 2. Elman neural network architecture

Set x is the n-dimensional hidden layer node vectors, y is the m-dimensional output node vectors, u is the r-dimensional input vector, x c is the n-dimensional feedback state vector; ω 1 is the weight matrix of the context layer to the hidden layer, ω 2 is the weight matrix of the input layer to the hidden layer, ω 3 is the weight matrix of the hidden layer to the output layer; x(t), x c (t) and y(t) are output of the hidden layer, context layer and output layer at time t, respectively. α(0≤α<1) is the self-connecting feedback gain factor, here set 0; f(x) is the sigmoid function, such as the Formula 1. Elman neural network mathematical model shows in the following Eq.( 2), ( 3), [START_REF] Gao | Prediction model and realization for water quality in an intensive aquaculture based on LM BP neural network[END_REF]. Suppose the t th step of real output of system is y d (t),desired output is y(t). The objective function of Elman neural network can be expressed as Eq. ( 5).

( ) 1/( 1)

x f x e   (1) )) ( ( ) ( 3 t x g t y   (2) )) 1 ( ) ( ( ) ( 2 1    t u t x f t x c   (3) ( ) ( 1) ( 1) cc x t x t x t      (4)
)

) ( ) ( ( )) ( ) ( ( 2
1 ) ( t y t y t y t y t E d T d    (5)

Elman NN parameter selection

Hidden layers and nodes

Determining the number of hidden layers and nodes is a usually a trial and error task in ANN modeling. A rule of thumb for selecting the number of hidden nodes relies on the fact that the number of samples in the training set should at least be greater than the number of synaptic weights [START_REF] Tarassenko | A Guide to Neural Computing Applications[END_REF]. As a guide, the number of hidden nodes M should not be less than the maximum of I/3 and the number of output nodes O, where I is the number of input nodes. The optimum value of M is determined by trial and error. For neural network training, the appropriate number of nodes in hidden layer is very important. In dealing with more complex problems, use of too few hidden units, the network can not remember all of the input-output model, the training of divergence; if too many neurons, the network will not only lengthen the training time dramatically, may be "overfitting "phenomenon, and will greatly reduce the generalization. Therefore, the model must be taken into account the training speed and generalization ability, choose the best number of hidden layer neurons. Let q for the output layer nodes, n nodes for the input layer, p hidden nodes for the common choice of several nodes in one hidden layer of valuation as follows:

(1) Maximum number of nodes in hidden layer p= q (n+1)

(2) Maximum number of nodes in hidden layer p=q×3 (3) Input nodes is larger than the output layer node p=(q×n)1/2 (4) p n q a    ,Where a is constant between [START_REF] Zhang | Advances of the studies on diseases of Hyriopsis cumingii and its control[END_REF][START_REF] Ch | Water Quality Early Warning System for Intensive Aquaculture of Paralichthys[END_REF] In this study, a trial and error procedure for hidden node selection was carried out by gradually varying the number of nodes in the hidden layer.

Learning rate (eta) and momentum (alpha)

The models were calibrated using the back-propagation algorithm, as it has already been used successfully for the prediction of coagulant doses (van Leeuwen et al.,1999) and other water resources and environmental variables (Maier and Dandy, 2000) and, unlike second order optimisation algorithms, has the ability to escape local minima in the error surface (White, 1989). Optimal values of the parameters controlling the size of the steps taken in weight space as part of the back-propgagation algorithm (i.e. learning rate and momentum values) were found by trial and error. There is no specific rule for the selection of values for these parameters. However, the training process is started by adopting one set of values (i.e. η = 0.2, range 0 to 1, and α = 0.5, range 0 to 0.9) and then adjusting these values as necessary (i.e. if the error reduction of the network is too slow or begins oscillating). The learning process in this study was controlled by the method of internal validation. Roughly 20% of calibration data was withheld and used to test the error at the end of each epoch. The weights were updated at the end of each epoch. The number of epochs with the smallest internal validation error indicates which weights to select. The ANN with the best performance when applied to the validation set was selected.

Initial weights

The weights of a network that is to be trained by BP must be initialized to some small, non-zero random values. If a network stops training before reaching an acceptable solution because a local minimum has been found, a change in the number of hidden nodes or learning parameters will often fix the problem; alternatively, we can simply start over with a different set of initial weights. When a network reaches an acceptable solution, there is no guarantee that it has reached the global minimum. The synaptic weights of the networks were initialized with normally-distributed random numbers in the range of -1to1.

Selection of input variables

According to the principles of ecological, there are three main sources of dissolved oxygen in the outdoor pond: production of phytoplankton photosynthesis, atmospheric oxygen dissolved in water, and increased by oxygen machine. Atmospheric oxygen dissolved in water is mainly affected by temperature, wind speed. But the gas in water diffusion is proceeding slowly, if not all kinds of mixed water masses (as well as horizontal flow fluctuations and vertical flow), this process limited to the surface. In the fertilizer ponds, the production of phytoplankton photosynthesis is the main source of oxygen; atmospheric dissolved oxygen played a secondary role. Aquatic animals consume a large amount of oxygen when breathing. Aquatic plants in photosynthesis during the day also carry out respiration, but respiration rate much lower than the intensity of photosynthesis. At night, respiration of plants (especially algae) for water gas has a great influence. In addition, bacterial respiration is the oxygen consumption of the most important factor, both in the bottom water layers and bacterial decomposition of organic matter. When the dissolved oxygen super saturation in surface water, it may also occur oxygen escaping. Wind disturbance on the surface of the water can accelerate the process of escaping. Therefore, the factors affecting pond dissolved oxygen is mainly reflected in the impact factors of photosynthesis. Solar radiation intensity, water temperature, air temperature are the most important factors affecting photosynthesis. Cooperation with the intensity of light with the intensity of solar radiation, water temperature, air temperature increases, reaching saturation values will decline. Phytoplankton photosynthesis, biological respiration and decomposition of organic matter led to changes in pH value. In summer, when water phytoplankton blooms, they tend to happen: the night at the deep water layer, in the biological process of respiration, due to the accumulation of carbon dioxide, pH value will decrease. High fishing pond, high aquatic life, strong biological photosynthesis and respiration more strongly, the variation of pH value is large [START_REF] Zhang | Advances of the studies on diseases of Hyriopsis cumingii and its control[END_REF]. This shows that pH is an important sign of reflecting the dissolved oxygen content.So in this paper we choose solar radiation(SR), air temperature(AT), water temperature(WT), wind speed(WS), PH and oxygen(DO) six parameters as Elman neural network input parameters.

Data pre-processing

In order to improve the training speed and accuracy, all data are normalized in this paper. Standardized function used in this paper is as follows Eq. ( 6).

min max min min max ) )( ( ' x x x x y y x     (6)
Where x denote the original data point here, xmin and xmax are the minimum and maximum values in the data set, respectively. Here, we set ymax is 1, and ymin is -1. In this way, all the input signals are changed to the interval [-1, 1].

Model performance evaluation

A model trained on the training set can be evaluated by comparing its predictions to the measured values in the overfitting test set. These values are calibrated by systematically adjusting various model parameters. The performances of the models are evaluated using the root mean square error (RMSE) [Eq. [START_REF] Wang | Prediction of water quality index in Qiantang River based on BP neural network model[END_REF]], the mean absolute error (MAE) [Eq. ( 8)], the Nash-Sutcliffe coefficient of efficiency (R2) [(Eq. ( 9)], and the correlation coefficient (r).

Scatter plots and time series plots are used for visual comparison of the observed and predicted values. R2 values of zero, one, and negative indicate that the observed mean is as good a predictor as the model, a perfect fit, and a better predictor than the model, respectively. Depending on sensitivity of water quality parameters and the mismatch between the forecasted water quality variable and that measured; an expert can decide whether the predictability of the Elman NN model is accurate enough to make important decisions regarding data usage.

observed predicted RMSE X X N   (7) 1 || predicted observed MAE X X N   (8) 2 0 1 ( ) F R F  (9) 2 () observed predicted F X X   (10) 2 0 () observed mean observed F X X    ( 11
)
where N is the total number of observations in the data set.

Results and discussion

The Elman NN model was developed to simulate every hour DO concentrations in the Hyriopsis Cumingii freshwater pond with training of variable learning rate momentum gradient descent algorithm. The quality of a neural network, is different with the traditional least square fitting evaluation (mainly based on residuals, goodness of fit, etc.), not reflect in the data fitting on the existing capacity, but a later predictive ability (the ability of generalization). Predictive ability of the network (also known as generalization ability) and training capacity (also called approximation ability, learning ability) is contradictory. In general, training is poor, the forecasting ability poor, and to some extent, with the ability to improve training, improve forecasting ability. However, this trend has a limit, when reached this limit, with the training capability, predictability declines and that the so-called "over-fit" phenomenon. At this point, samples of learning too many details, but the law does not reflect the sample contains. In this paper we use the methods of data classification and disrupting to improve Elman neural network generalization and avoid over-fitting phenomenon. We selected first The Elman NN model of DO in freshwater pond was successful in simulating patterns in measured DO concentrations that result from seasonal temperature variations, periodic blooms of phytoplankton (production of DO), and point-source discharges of oxygen-consuming substances like ammonia and BOD. In tropical ponds where there are frequent phytoplankton blooms to increase DO levels, DO concentrations fluctuate and supersaturate during these blooms. The DO models simulate the dynamics of the measured concentration and are within the range of the measured values. Elman NN models were successful in predicting patterns in the hourly DO data, and they can be applied on daily, weekly, monthly, and seasonal time scales. The Elman NN model"s performance was good, but the outdoor environment is complex and the measuring system has the larger error. More data sampling is thus required, and future work should recalibrate and revalidate the models to generalize our conclusions. The DO predicting model for freshwater will be further fine-tuned for higher accuracy by more accurate data. The result of the Elman NN DO predicting model was shown in Fig. 3(hidden layer neurons:8).

3.Summary and conclusion

Elman NN model was developed to predict dissolved oxygen in Hyriopsis Cumingii pond waters both temporally and spatially using continuous measurements of water quality variables. In spite of largely unknown factors controlling pond freshwater quality variation and the limited data set size, a relatively good correlation was observed between the measured and predicted values. The Elman NN modeling technique"s application for dynamic pond freshwater quality prediction was presented in this study. The Elman NN model has tremendous potential as a forecasting tool. An Elman NN prediction capability was tested and found to be faster than that from a process-based model with minimal input requirements.

In particular, we observed that the Elman NN methods were able to ""learn" the mechanism of convective transport of water quality variables quite well. Based on the ""best performance" in water quality parameter forecasting, we observed that Elman NN method with 7 or 8 hidden layer nodes is the better architecture for DO models, but the accuracy is not better for DO models. Also, the front part and last part of the forecast test data has a marked disturbance, which is Elman neural network inherent problem. The limitations of this study include its limited data set. The available data size is relatively small, reasonably good results were obtained for the water quality prediction of unseen validation dataset at locations separate from the training dataset stations. If more data become available, the proposed approach should provide better predictions3.

Fig. 1 .

 1 Fig. 1. Hyriopsis Cumingii from breeding ecological environment data acquisition nodes

Fig. 3 .

 3 Fig. 3. The test curve of Elman prediction model

Table 1 .

 1 The performance for different number of hidden layer neurons

	4 days of 96 sets of data as the

Acknowledgements.

This research is financially supported by the National Key Technology R&D Program in the 12th Five year Plan of china (2011BAD21B01), Beijing Natural Science Foundation (4092024), National Major Science and Technology Project of China (2010ZX03006-006) and 948 Project of Ministry of Agriculture of the people"s Republic of China (2010-Z13).