Shizhou Du

Qinhong Liao

Chengfu Cao

Yuqiang Qiao
email: dyuqiangqiao@163.com

Wei Li

Xiangqian Zhang
email: fxiangqian111@163.com

Huan Chen

Zhu Zhao

Analysis of Differences in Wheat Infected with Powdery Mildew Based on Fluorescence Imaging System

Keywords: Fluorescence imaging system, Powdery mildew, Rapid light-response curve, Difference

This study aimed to investigate the variation characteristics of rapid light-response curves of wheat leaves infected with powdery mildew. According to the heterogeneity between two selection patterns of area of interest (AOI), determination of fluorescence induction parameters and fitting of rapid light-response curves were conducted based on fluorescence imaging system in wheat powdery mildew experimental plots. The results showed that relative electron transport rate rETR was reduced with the increase of disease severity level; rETR of the rectangle selection pattern was relatively low. Specifically, the reduction in rETR is mainly influenced by the decrease of absorption coefficient Abs. Among fitting parameters of rapid light-response curves, the potential and the maximum relative electron transport rate, initial slope, light suppression parameter and semi-saturation intensity were reduced with the increase of disease severity level; the heterogeneity of fitting parameters between two selection patterns reflected the "critical state" of leaf fluorescence characteristics. Infected leaves at severe level (80%) had relatively low light-harvesting capacity and tolerance to strong light, which easily caused light inhibition. According to the lateral heterogeneity analysis of photosynthesis of wheat leaves infected with powdery mildew, there was relatively high heterogeneity between fluorescence parameters of wheat infected leaves, especially in leaves with lesions on the surface.

Introduction

Wheat powdery mildew (Blumeria graminis f. sp. tritici) is a major disease in worldwide wheat production, which is caused by infection of Erysiphe qraminis D.C. f. sp. tritici marchal. After infection of Erysiphe qraminis D.C. f. sp. tritici marchal., greatly propagated pathogenic bacteria disrupt the moisture transport in wheat leaves and cause the destruction of chlorophyll. With the disease progression, wheat leaves show yellow spots or patchy lesions under severe situations, powder mildew layer is attached to the surface, leading to leaf chlorosis and yellowing or even death [START_REF] Morgounov | Global incidence of wheat rusts and powdery mildew during 1969-2010 and durability of resistance of winter wheat variety Bezostaya 1[END_REF][START_REF] Liu | Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed data[END_REF]. Modulated pulse fluorescence analyzer (Imaging-Pam Mini-version: 24 × 32 mm) has a relatively large detection window to acquire fluorescence parameters within a range of 24 × 32 mm [START_REF] Pedro | Assessment of photosynthesis tolerance to herbicides, heat and high illumination by fluorescence imaging[END_REF][START_REF] Pedro | Assessing photosynthesis by fluorescence imaging[END_REF]. In addition, fluorescence imaging system can clearly distinguish leaf lesion area and non-lesion area due to its visual advantage. Powdery mildew lesion areas on wheat leaves are covered by conidiospores of pathogenic bacteria. Due to the differences in the thickness and status of covering layer, chlorophyll fluorescence characteristics of the lesion areas are relatively complex [START_REF] Elisa | Applications of chlorophyll fluorescence imaging technique in horticultural research: A review[END_REF]. Currently, few studies have been reported on the advantages of fluorescence imaging system combining with disease stress. Therefore, in this study, two selection methods of area of interest (AOI) were adopted as data sources which respectively represent the conventional optical fiber probe mode and visualization window mode [START_REF] Lucia | Effects of ozone exposure or fungal pathogen on white lupin leaves as determined by imaging of chlorophyll a fluorescence[END_REF], characteristics of the fluorescence differences in rapid light-response curve between non-lesion area and mixing area of wheat leaves infected with powdery mildew were investigated, to reflect the "critical state" of fluorescence characteristics of infected leaves and explore the dynamic changes in light response of wheat leaves under stress of different levels of powdery mildew, which provided theoretical basis for the in-depth research of fluorescence characteristics of wheat diseases.

2

Experiments and Methods

Design of field trials

During March-June 2013, the experiment was carried out in National Precision Agriculture Experimental Base of Xiaotangshan Town, Changping District, Beijing City, China. Wheat variety Jingshuang 16 was selected as experimental material, which is highly susceptible to powdery mildew. A total of three treatments were designed. After pathogen inoculation on April 5, 2013, wheat powdery mildew was controlled using Triazolone with effective amounts of 240, 120 and 30g/hm 2 at booting stage (April 30, 2013) to develop different incidence levels; infected wheat leaves in control group were treated with water. The planting density in experimental plots was 3 000 000 seedlings/hm 2 , with a row spacing of 0.2 m. The rest of the managements were in accordance with the field practices.

Leaf selection

Wheat heading stage (May 10, 2013) is the outbreak period of wheat powdery mildew in experimental plots. Wheat leaves infected with different incidence levels of powdery mildew (top second leaf) in various experimental plots were collected as experimental samples. Based on the percentage of the coverage area of lesion hyphal layers on infected leaves accounting for the total area of leaves, disease severity levels (SL) of wheat leaves were classified into four levels, including mild level (10%), moderate level (50%), severe level (80%) and control level (0%, with no symptom in the whole plant), respectively. Ten leaves at each level were collected.

Fluorescence data acquisition

Based on the pulse amplitude modulation (PAM) techniques, a modulated pulse fluorescence analyzer (Imaging-Pam Mini-version: 24 × 32 mm, Heinz Walz GmbH, Effeltrich, Germany) was used to detect the wheat leaves with disease severity levels according to the chlorophyll fluorescence induction kinetic curve and rapid light response curve [START_REF] Olaf | The use of chlorophyll fluorescence nomenclature in plant stress physiology[END_REF].

Rapid light-response curve fitting

PSⅡ relative electron transport rate was calculated in accordance with the formula: rETR = (Fm'-F) / Fm'• PAR• 0.5• Abs. Specifically, PAR indicated the photosynthetic active radiation; 0.5 was the approximate PSⅡ distribution coefficient of light energy; Abs indicated the absorption coefficient. Rapid light-response curves (RLCs) are variation curves of relative electronic transport rate (rETR) with photosynthetic active radiation (PAR). Rapid light-response curve was fit with the least square method using SPSS19.0 software and drawn using Origin 8.0 software.

Fluorescence parameter selection

Modulated pulse fluorescence analyzer has the advantage of image visualization, which provides a variety of data selection patterns of area of interest (AOI). Therefore, lesion areas on wheat leaves infected with powdery mildew could be clearly distinguished. In this study, four sample points were selected in the non-lesion area based on a circle pattern, with a radius of 1 mm, detected values of the four sample points were averaged; four points were selected in the mixing area based on a rectangle pattern, with the length and width of 8 × 28 mm.

Results and Discussion

Changes in rapid light-response curves of wheat leaves infected with powdery mildew based on different selection patterns

The relative electron transport rate rETR based on different selection patterns rapidly increased with the increase of photosynthetic active radiation PAR, while rETR increased slowly and tended to decline when PAR reached 600-1 000 µmol/m 2 •s . As shown in Fig. 1, the control (0%) remained relatively high relative electron transport rate rETR; with the increase of severity level, rETR was gradually reduced. Differences in rETR between two selection patterns were gradually enhanced with the increase of severity level, indicating the negative response of the expansion of leaf lesion area to rETR value. At the same severity level, the relative electron transport rate based on a rectangle pattern was lower; for instance, at severe level (80%), rETR based on a rectangle pattern was only a half of control (0%).

Changes in characteristic parameters of rapid light-response curves

As shown in Fig. 2, fitting parameters Pm, α, β and Ik were constantly reduced with the increase of severity level, which were all higher with a circle sampling pattern in non-lesion area; changes in fitting parameters with the disease severity level slightly varied between two selection patterns.

Variation coefficients of the maximum potential relative electron transport rate Pm in non-lesion area and mixing area were 24.7% and 43.2%, respectively. Pm in mixing area at severe level (80%) was only 33.1% of normal leaves. Initial slope α reflected the level of light-harvesting capacity of leaves, and the variation coefficients of two selection patterns were 17.6% and 24.9%, respectively. Light suppression parameter β reflected the slope of the decline curve, and the variation coefficients of two selection patterns were 22.6% and 43.8%, respectively. After powdery mildew infection, β value in mixing area declined greatly and varied slightly between infected leaves, suggesting that the dynamic regulation ability of PS Ⅱ reaction center under strong light was reduced after infection, resulting in insufficient capacity of heat dissipation of excess light energy. Semi-saturation light intensity Ik reflected the capacity of leaves to bear strong light, and the variation coefficients of two selection patterns were 8.4% and 19.4%, respectively. Ik value in mixing area varied slightly between mild level (10%) and moderate level (50%); Ik value was greatly reduced at severe level (80%), indicating low light-harvesting capacity of leaves and relatively low tolerance to strong light, which could easily cause light inhibition or even light damage.

Fig. 2. Changes in characteristic parameters of rapid light-response curves

Heterogeneity of chlorophyll fluorescence imaging at different severity levels

Conventional fluorescence modulation equipments mostly use optical fiber as signal conductor, such as PAM-2100/2500 and Mini-PAM, which can only be adopted to detect partial photosynthetic activity of leaves. Different parts of leaves have different tissue structures and chlorophyll contents. Therefore, fluorescence characteristics in different parts of the same leaf have lateral heterogeneity, but using fiber optic probes to obtain fluorescence characteristics of a point is difficult to reflect photosynthetic characteristics of an entire leaf. Based on plant chlorophyll fluorescence imaging techniques, the lateral heterogeneity of photosynthesis of wheat leaves infected with powdery mildew was analyzed, which was represented by variation coefficient (CV). Fluorescence imaging monitoring system can even applied to detect the invisible damages in early period, thus revealing the stress state and demonstrating the damage mechanisms.

In healthy wheat leaves (Fig. 3), Abs of leaf veins was slightly reduced, while other fluorescence parameters showed relatively high consistency, with variation coefficients lower than 2%, suggesting the homogeneity of healthy wheat leaves.

Conclusions

In this study, based on the fluorescence imaging system, two selection patterns were adopted to characterize the heterogeneity of rapid light-response curves of wheat leaves infected with powdery mildew, thereby approximately reflecting the "critical state" of fluorescence characteristics of leaves. Rapid light-response curves of non-lesion area of infected leaves vary due to the different severity levels, suggesting that there are differences in PS Ⅱ system between different severity levels, which may be related with the blocked PS Ⅱ electron transport caused by the destruction of moisture and chloroplast in leaves, thus leading to the reductions in actual photochemical quantum yield Y (Ⅱ) and relative electron transport rate rETR [START_REF] Kuckenberg | Detection and differentiation of nitrogen-deficiency, powdery mildew and leaf rust at wheat leaf and canopy level by laser-induced chlorophyll fluorescence[END_REF].

The rectangle selection pattern of wheat leaves infected with powdery mildew covers lesion area and non-lesion area, and the physical damage and inhibition degree become serious, leading to differences in fitting parameters of two selection patterns, which is consistent with the characteristics of other crops under disease stress [START_REF] Stephen | Chlorophyll fluorescence imaging of plant-pathogen interactions[END_REF].

Fluorescence parameters of all the pixels in the line (with width of 3) of infected leaves at moderate level (50%) were shown in Fig. 5. The results showed that: under the same light intensity, the actual photochemical quantum yield Y(Ⅱ) of leaves is reduced with the increase of photosynthetic active radiation PAR, especially in the lesion area. Relative electron transport rate rETR is an expression way of photosynthetic rate. Results confirm that rETR has an ideal linear relationship with photosynthetic oxygen evolution rate or CO 2 assimilation rate before light saturation. During the detection process, the light adaptation time under different PAR gradients is very short (10 s), resulting in low interference effects on the natural photosynthetic state of samples [START_REF] Deng | A comparative study in photosynthesis heterogeneity of Viola baoshanensis and V. yedoensis[END_REF] . Under the same light intensity, rETR is mainly affect by actual quantum yield Y( Ⅱ ) and absorption coefficient Abs. The heterogeneity of Y( Ⅱ ) (CV = 1.45%-6.37%) and Abs (CV = 9.17%) under different light conditions is shown in Fig. 5, which suggests that absorption coefficient Abs of wheat leaves infected with powdery mildew has relatively great effects on rETR, reflecting the heterogeneity of rETR between two selection patterns, which is mainly due to the differences in red band absorption ability of powdery mildew spores on leaf surface and mesophyll cells, and this conclusion has been verified by leaf spectrum detection [START_REF] Huang | Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging[END_REF].

Fig. 1 .

 1 Fig. 1. Rapid light-response curves of wheat leaves infected with powdery mildew based on different selection patterns (left: selected based on a circle pattern; right: selected based on a rectangle pattern)

Fig. 3 .

 3 Fig. 3. Heterogeneity of imaging fluorescence parameters of healthy wheat leaves (linear)Fluorescence parameters of wheat leaves infected with powdery mildew (SL=80%) showed significant heterogeneity. To be specific, fluorescence parameters Fv/Fm (CV=5.3%) and PS/50 (CV=7.8%) had relatively low heterogeneity; the variation coefficients of other fluorescence parameters Abs, Y(Ⅱ), NPQ/4 and qL were 9.32%, 15.12%, 21.37% and 25.43%, respectively. As shown in Fig.4, fluorescence

Fig. 4 .

 4 Fig. 4. Heterogeneity of imaging fluorescence parameters of wheat leaves infected with powdery mildew (linear) Heterogeneity of imaging fluorescence parameters of plant leaves infected with wheat powdery mildew pathogen was mainly produced by the combined action of powdery mildew cleistothecium on leaf surface and damaged photosynthetic systems. Fluorescence techniques provide lossless probes for revealing plant physiology, and the fluorescence imaging system can accurately characterize the physiological characteristics of plant leaves infected with wheat powdery mildew pathogen.

Fig. 5 .

 5 Fig. 5. Heterogeneity analysis of infected leaves based on fluorescence parameters Y (Ⅱ) and Abs (SL = 50%)

Acknowledgements:

Funds for this research was provided by Outstanding Youth Fund of President of Anhui Academy of Agricultural Sciences (13B0203); National Science and Technology Support Program of China (2011BAD16B06, 2012BAD04B09).


Liting Liu

Fu

Qiao Deng

Chunhong Liu

Liting Fu

Research of Fractal Compression Algorithm Taking Details in Consideration in Agriculture Plant Disease and Insect Pests Image

Keywords: plant disease and insect pests image, detail, fractal encoding, image compression

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

Introduction

China is an agricultural country with the frequency of crop disasters. The great harm of crops diseases and insect pests is an important restriction factor of agricultural product quality improvement. On the other hand, the scientific and technological cultural quality of farmers is generally low, and they are urgent to be guided by experts who have knowledge of crop diseases and insect pests. But experts can't have the energy and time to the field for the majority of farmers to guide, they can only get the image through remote. However, agriculture plant disease and insect pests image is rich in detail and it contains a lot of information. So it need much transmission bandwidth and storage. At present, because the cable communication cable is unable used to cover the majority of farmland, the wireless wide area network technology can be used to transmit the field images. The wireless communication network is narrow of bandwidth, easy of jitter, low of transmission efficiency, so it is not conducive to transmit a large amount of data.Therefore, it is very important to develop an algorithm of high compression ratio, good quality in transmitting agricultural images [START_REF] Salar&n | A new modified fast fractal image compression algorithm[END_REF].

There are many classification methods for image compression, the most commonly used one is lossless compression and lossy compression. Lossless compression is a technique of image compression and decompression without any loss of data. Image obtained by decompressing the image is completely equal to the original image, but the compression ratio is about 2:1, which is a very low ratio [START_REF] Fisher | Fractal image compression: Theory and Applications[END_REF]. Lossy compression can be reconstructed only by the approximation of the original one, the reconstructed image is similar to the original one, but it is not the exact copy. Lossy compression can get higher ratio compare to lossless compression [START_REF] Lin | An edge property-based neighborhood regions search strategy for fractal image compression[END_REF].

In decades of image compression research, great progress has been made with image compression technique, and a series of international compression standards were formed, such as JPEG based on DCT and JPEG2000 based on wavelet transform that have been widely used [START_REF] Shin | The effectiveness of image features based on fractal image coding for image annotation[END_REF]. Although these methods have achieved a higher compression ratio, however, compared with a rapid development of multimedia technology, a development of image compression is relatively backward. Moreover, these traditional compression algorithms still have some problems, such as low compression ratio, the block effect, etc. In recent years, in order to overcome the shortcomings of traditional image compression, experts and scholars proposed many new compression and broke the limitation of traditional entropy coding, such as fractal image compression [START_REF] Seeli | A study on fractal image compression using soft computing techniques[END_REF].

Fractal compression method is a new method of compression in the 90's of last century. It uses the similarity of image itself to achieve compression, so we can achieve a high compression ratio . In 1990, A.E.Jacquin, one of Barnsley's student, using local iteration function theory, put forward a full automatic fractal image compression based on block and make fractal image compression from manual coding to automatic coding become a reality [START_REF] Jacquin | Image coding based on a fractal theory of iterated contractive image transformations[END_REF].

When small scale for sub block was used in Jacquin algorithm encoding, lost details were small and encoding time is short, but the compression rate is low.while the use of large scale for sub block encoding is the opposite. To improve the efficiency of Jacquin algorithm, quartering method, adaptive search method and other fractal generation algorithms have been proposed in recent years. But when encoding image of rich details, compression rate of these algorithms is almost same with the original one, and the encoding time is too long [START_REF] George | Speeding up fractal image compression using DCT descriptors[END_REF]. Therefore, this paper proposes a fractal image algorithm based on image details to shorten compression time and improve the efficiency of encoding [START_REF] Peixin | Image compression researchbased on fractal theory[END_REF].

Jacquin Algorithm

In corresponding decompression process, fractal compress is used as operator to calculate fixed point, which is suitable for the situation of one compression, multiple decompression. The basic compression algorithm is shown in Fig. 1. First, original image was divided into blocks of equal size, which was called range block (R) or sub block, then image region matching for each range block R was found, called domain block (D) or parent block. The length of parent block is generally 2 times of the sub block [START_REF] Fei | Fast encoding algorithm for fractal image compression[END_REF]. Then it shrinks to the same size to match the range blocks, then rotates and transforms symmetricly to get 8 transformed domain blocks. Comparing transformation of domain blocks with every range block to search the best match of the domain block. Compression coefficients and offset were calculated, so that the error of R and sD+oI is the smallest, I is a unit vector whose size is the same as D. After calculating the whole image, the encoding parameters are obtained [START_REF] Wujia | Fractal encoding compression method based on detail block of image[END_REF].

Fig.1. Regional matching image

Jacquin compression algorithm solves the image segmentation problem of Barnsley compression algorithm, by taking use of local self-similarity to construct affine transformation, which is the first practical image segmentation method. However, the searching volume of sub block is very large and searching time is very long. For example, an original image A, if there are 22 NN  pixels, by using Jacquin's compression algorithm, can be divided into sub block of 22 NN  . Sub blocks were not relevant, the number of sub blocks were:

2 22
(2 ) 22

NN NR RR n     (1)
If A is divided into parent block of 22 DD  , D>R, the number of parent blocks is: (2 2 1) (2 2 1)

N D N D
     . In Jacquin algorithm, 8 kinds of transformations, such as rotation and reflection, are performed to a parent block. So 错误! 未 找 到 引 用 源 。 8 (2 2 1) (2 2 1)

N D N D      
times searching for computation is needed to complete encoding. The magnitude of searching is about 错误!未找到引用源。。For example, a gray image of 512  512 pixels is divided into sub blocks of 8  8 and parent blocks of 16  16。Each sub block is compared with (512-16+1)  (512-16+1) =247009 parent blocks, and each time there are 8 kinds of operations such as rotation, reflection, in the comparison with parent blocks.So each sub block is compared 8  247009=1976072 times. All sub blocks are compared 4096  1976072=8093990912 times [11].错误!未找到引用源。 Thus, the main reason for the slow speed of the fractal image encoding is that time searching for the best parent block consume too much. Therefore, researching for a fast and effective method of encoding is an important direction of fractal encoding [START_REF] Wee | A novel fast fractal super resolution technique[END_REF].

The above discussion shows that, using Jacquin algorithm, for each sub block 错 误!未找到引用源。,the number of parent block i D needed to search is:

8 (2 2 1) (2 2 1) N D N D K        (2
) Fixed network algorithm is that position of the parent block is fixed on the image grid to reduce the number of K. There is no need to search for each possible parent block but to search for the parent block on the grid. So the number of searching times can be reduced not to produce much impact on the compression quality [START_REF] Huang | A noval method for embedding gray image watermarking into orthogonal fractal compression image[END_REF]. The grid interval is l , which is distributed in image A, 错误!未找到引用源。.The upper left corner of parent block is located on the grid . This number of parent block is:

1 2 1 KK l  (3)
When 错误!未找到引用源。, the number of parent blocks in the parent block library are 1/4 of the original number. But this method is not combined with the characteristics of different images respectively, and it is not adaptive. Therefore, there is great significant to find a kind of image compression algorithm with short encoding time, high image quality and adaptability.

错误!未找到引用 源。

Fractal compression algorithm taking details in consideration

A sub block segmented from an image can be divided into two categories. One is rich in detail, the other does not contain details [START_REF] Guangchao | An improved fractal image compression algorithm[END_REF]. These two kinds of sub blocks have their own features in the encoding. Sub blocks are rich in detail, which have high complexity and much time to encode. While non-detailed sub blocks have low complexity and high degree of similarity. According to the differences, different methods can be used to encode. The detailed sub blocks have much impact on the quality of the image, so the detailed sub block need to be fully encoded. Non-detailed sub blocks have a high degree of similarity, so that we can only encode a small part of detailed sub blocks to reduce encoding time and encoding space.

In this paper, fractal image algorithm based on details is proposed. Firstly, the edge of the image was detected which is the main component of the image details. Sobel algorithm was used to detect the edge of the image. The operator of Sobel algorithm is:

( , )= ( 1, 1) 2 ( 1, ) ( 1, 1) ( 1, 1) 2 ( 1, ) ( 1, 1) + ( 1, 1) 2 ( , 1) ( 1, 1) ( 1, 1) 2 ( , 1) ( 1, 1) D x y f x y f x y f x y f x y f x y f x y f x y f x y f x y f x y f x y f x y                               (4)
To filter out the details which can't be resolved by human eyes, we treat them as non-detailed part, a threshold value need to be set. The formula is:

错误!未找到引用源。 ' ( ) ( ) T () 0 ( ) T D x, y D x, y D x, y D x, y       ( ) ( ) (5)
T is the threshold value, 错误!未找到引用源。is the gradient after setting the threshold value. The selection of T is based on adaptive method and the value is 1/5 of the max gradient value. After the details of the image was detected, the encoding detailed sub blocks were selected. The selection method is to calculate the sum of gradient of each pixel in the sub block and calculate whether the sub block contains the details. The mathematical expression is:错误!未找到引用源。 ij s is the sum of internal pixel's gradient value of sub block whose coordinate is (i,j).

If ij s is not 0, then the sub-block is considered containing details. After analyzing all the sub blocks, we start encoding from the first sub block. First sub blocks containing details were encoded, then a representative of non-detailed sub block for encoding was selected. The method to select detailed sub block is to calculate the variance of each pixel value and average gray value in each sub block and select the block which has mini variance as a non-detailed block encoding.

First, calculate the average gray value ij Us ( ) 错误!未找到引用源。of sub blocks whose coordinate is (i,j):

11 2 00 1 ( ) ( ) MM ij ij nm U s s m,n M     (7)
Then calculate the variance 错误!未找到引用源。according to its gray mean value:

11 2 00 ( ) ( ( ( )) MM ij ij ij nm D s s m,n U s       (8)
错误!未找到引用源。 Because there are only difference 错误!未找到引用源。 on the gray level between other no-encoded sub blocks and the sub blocks of mini variance, we can get the encoding parameter ' p , '

g of other no-encoded sub blocks according to the parameters 错误!未找到引用源。that sub block D had calculated:

'( ) '( ) g ( )

p i, j p g i, j N i, j        (9)

Experiment and analysis of results

To verify the effect of proposed algorithm, encoding and decoding test were carried for Jacquin algorithm and the proposed algorithm, the computer is Intel(R) Core(TM) i3-2120 3.30GHz. The size of test image is 256  256 pixels, which is shown in Fig. 2, the image is a leaf of northern leaf blight of corn. The sub block size is 4  4, and is divided into 4096 sub blocks and 1024 parent blocks.

In the experiment, the number of detailed sub blocks,which is obtained by the edge detection algorithm is 1287, about 31.42% of the total blocks. Non-detailed blocks is 2809, about 68.91% of the total blocks. There is a sub block has to encode of the non-detailed blocks, so the actual encoding number is 1288.In this paper, the actual encoding number is 31.45% of Jacquin algorithm. The result of Sobel operators is shown in Fig. 3, The distribution of details is shown in this image. The result of 10 times decoding image using Jacquin algorithm is shown in Fig. 4. The result of 10 times decoding image using algorithm of this paper is shown in Fig. 5. The comparison of proposed algorithm with the original Jacquin algorithm is shown in Table 1, the PSNR (peak signal-to-noise ratio) is calculated as follows: 错误!未找到引用源。 () f x, y is an image to be evaluated ,and 0 () f x, y is a referencing image. The image size is M  N.

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

 2345 Fig.2. Original image Fig.3. The detailed image after calculating by Sobel operator 迭代后图像 迭代后图像

Table 1 .

 1 The comparison of proposed algorithm and Jacquin algorithm

Acknowled

This paper was financially supported by the National International Cooperation Special Program(Grant No. 2013DFA11320).

The PSNR got by using Jacquin algorithm is slightly greater than the proposed one, which is shown in Table 1. While the encoding blocks of the proposed algorithm is much less than Jacquin's, so time of the proposed algorithm is less compare to Jacquin algorithm .The decoding time is both short. The number of encoded blocks reduces to 31.45% and the encoding time reduces to 32.9% of the original one.This algorithm is better than Jacquin algorithm.

Conclusions

Fractal image algorithm has much potential of increasing compression ratio, and decoding time is very short. Therefore, in image and multimedia, the fractal image compression algorithm is promising in application. But the process time of the fractal image algorithm is too long to be done, which greatly limits the application of this method.

In this paper, the fractal image compression method based on details of the image was proposed. So the number of sub block encoding is significantly less than the existing method in the image which is rich in detail. Encoding time of proposed method is greatly shorter than the original method, and it improved the compression effect of encoding.


Xin Zhao

Yang Jiao

Lianjun Yu

Chuanhong Zhang

Design and Implementation of TD-LTE-based Real-time Monitoring System for Greenhouse Environment Temperature

Keywords: TD-LTE, Precision agriculture, Data collection, long-distance monitoring

With the characteristics of fast transferring speed and wide coverage of TD-LTE network, the research designs and implements the TD-LTE-based Real-time Monitoring System for Greenhouse Environment Temperature. The TD-LTE network, which is connected with the data server of the Internet, can transmit the real-time temperature data that sensor collects to the data server. The B/S mode-based data server can realize data query, analysis and other functions. This monitoring system solves the problems that previous monitoring systems are slow and can not be transmitted at a distance, and it can also meet the need of the real-time data acquisition that precision agriculture needs.

Introduction

Compared with the commonly used wired network, wireless network is a new type of network formation. It is no longer dependent on the network cables which the traditional wired network depends on, eliminating the need for network cabling, effectively saving the cost, being used more and more conveniently. In the past, most wireless networks construct network with the help of WIFI, ZigBee, Bluetooth and so on. This method can implement the wireless transmission, but the transmission distance is near. If you want to transfer to a far distance, you need get access to the Internet. TD-LTE fully Time Division Long Term Evolution,also simply LTE-TDD, is one of the duplex technologies used in mobile communication technology. It is also one of the international standards led by China. TD-LTE can directly get access to the Internet, which makes up for the shortcomings of the traditional wireless network and greatly improves the efficiency of the wireless network. [START_REF] Wei | Analysis on Key Technologies and Development Trend of LTE[END_REF][START_REF] Liao | Discussion on TD-LTE:The Only TDD International Standard of LTE[END_REF][START_REF] Tongxu | An Overview of LTE and its Development[END_REF][START_REF] Hu Hai-Ming | Analysis of fourth generation mobile telecommunications technology[END_REF] The greenhouse, also called greenhouse, can keep heat. In the season that is not suitable for plant growth, it can ensure the normal growth of plants, and ensure the output. The greenhouse is an important part of modern agriculture. [START_REF] Weigang | The Data Acquisition System for the Greenhouse[END_REF] In the modern precision agriculture, the control of the growth environment of crops in the greenhouse has been changed from the traditional personal management mode to the modern, centralized management mode. Highly centralized management can effectively reduce the management cost, can make timely and accurate judgment in the shortest time, ensure the normal operation of the greenhouse, and ensure the normal production of greenhouse crops. [START_REF] Li Zhong-Hua | Current situation and thinking of development of protected agriculture in China[END_REF][START_REF] Qichang | Current situation and development strategy of agricultural research in China[END_REF][START_REF] Hu | Analysis on the Current Situation and Development of Modern Facilities Agriculture [J[END_REF][START_REF] Ma | Parameter Estimation and Growth Variation Analysis in Six Capsicum Cultivars with the Functional-structural Model GreenLab[C[END_REF] Greenhouse temperature is the basis of greenhouse planting and the coverage of centralized management is large, and the wireless network, which the traditional greenhouse temperature collection system used, is ZigBee mode, while ZigBee itself does not have the ability to get access to Internet, so the traditional greenhouse temperature monitoring system can only work in a small range, which is a problem for centralized management. [START_REF] Shi-Hui | The research on the intelligent greenhouse monitoring system based on wireless sensor network[END_REF][START_REF] Heqing | Intelligent Greenhouse Irrigation Subsystem Design and Implementation Based on ZigBeeTechnology [J][END_REF][START_REF] Lei | Development of a wireless safety monitoring system for laboratories based on Zigbee[END_REF] Based on TD-LTE network and embedded system, the greenhouse environment temperature monitoring system, which is comprehensive covering network, provides the wireless transmission mode that is not limited by the distance, and the efficient and fast data transmission service..

Overall Design System

The system consists of a wireless remote temperature acquisition system and an online data server based on WEB. The core of the greenhouse environment temperature acquisition system is embedded system, which uses the temperature sensor to collect the real-time data of the greenhouse environment temperature, and transmits the collected data to the data server on Internet through TD-LTE network. Database server is based on WEB technology, which achieve real-time monitoring, analysis and management of long-distance data. The overall structure of the system is shown in figure 1:

TD-LTE Terminal1 TD-LTE terminal2
Base station

Internet

Data center TD-LTE terminal3 It is also one of the international standards led by China. It is a followup evolution technology scheme of TD-SCDMA, and its network transmission speed is dozens of times as fast as TD-SCDMA. TD-LTE can be directly connected to the Internet, and functions with the Internet serve, which greatly improving the efficiency of wireless network. HUAWEI B593S is the production of TD-LTE produced by HUAWEI. The device supports TD-LTE through the built-in DHCP function, and also down is compatible with TD-SCDMA and EDGE networks, which can achieve a comprehensive coverage of the network. In the network access mode, the device supports 802.11 and RJ45, and 802.11 supports the connection of multi-users, so the system mainly used RJ45 mode.

Embedded

The hardware of this system includes the embedded microprocessor and peripheral equipment. The system mainly consists of CPU chip, FLASH chip, SDRAM chip, DM9000 network card, sensor and other hardware components. And the CPU, which is the core of the whole hardware system, uses the ARM series embedded processor $3C2440A by Sanxing. SDRAM and FLASH chips are the system storage module. FLASH stores boot code, kernel, and file of system (including the applications), and SDRAM provides the running space for the system and applications and stores temporary data when system working.

Operating System

Linux is an operating system that can run on a variety of different hardware platforms.

The software platform of the smallest embedded Linux system consists of three parts-the boot process, the kernel image and the file system. When the system is powered, firstly the guidance program is implemented, then the kernel is called and booted by it in order to realize the detection and drive of hardware system, finally mount the file system and run the applications. Linux kernel is well-structured and integrated with the most parts of the mainstream of the hardware driver. With the help of the Linux kernel, the whole system is more stable. And Linux has the perfect network function, which can make our system get good network compatibility. The Linux core of the current standard is too large to the resource-constrained embedded system. So due to the features that embedded software and hardware can be cut, we need to abandon our unused modules in the kernel, leaving only the functions needed by the system.

Data Communication

In order to guarantee the fast, safe and efficient data communications between the acquisition system and the server, it is necessary to carry on a series of conventions for the communication heat preservation format between each other. The system uses the data preservation format as "verification code || node number|| date || temperature 1 || temperature 2 || temperature 3 ##", and the" || "is the data delimited identifier, and" ##" is the data end identifier. In the acquisition frequency of temperature data, the system can be accurate to 1 second, but, in fact, such a high accuracy is not necessary, so the system's acquisition time is set to 1 minute. Internet server intercepts properly the relevant data that the acquisition system sends and stores them in the database.

In order to guarantee the continuity and effectiveness of the data and prevent network outages and other emergencies, the system carries on the real-time transmission of data to the server, then backups them and stores the data to the embedded system. And at 0 o'clock everyday, the aggregated data will be sent to the server and be verified with server-side data so as to ensure the continuity and effectiveness of the data.

Server

The operating system of the server uses Linux operating system because Linux is the open source operating system. It has obvious advantages in terms of security. At the aspect of software cost, the windows system is charged and running software is also charged, so the cost is higher, while the Linux operating system is lower than the windows operating system and most running software are free software. At the aspect of centralized management, windows needs a large number of servers, while using Linux can effectively reduce the cost. Because Linux is selected as the operating system, Mysql is chosen to be the database. Mysql is also an open source database and it has the advantages, such as small size, fast speed and it can effectively guarantee the security of data storage.

Data Reception

Data transmission is through the Internet network, using the "standard" data communication format, so the server side intercepts the transmission data in terms of the calibrated data format. This program uses JAVA to write and the database uses Mysql to carry on the data storage. When the program is started, the process will automatically monitor the port to transmit data. Upon receipt of the data, the first data packet header data is verified to determine whether the data is sent to the client data, if verified on, then split the received data according to the agreed data format and stored them in the corresponding database.

Data Monitoring

The data monitoring platform features the real-time display for the greenhouse temperature data and analyzes the historical data. The monitoring platform is developed with the help of B/S structure model. The advantage of this model is that the application core of the system is focused on the server, which simplifies the development, maintenance and usage of the system. In the client side, there does not need carry out a separate application development, the client can interact with the server information through the browser. The client is also not limited to the windows operating system, but both Linux and MAC can be used. The server uses Linux as the operating system. Database uses MySQL as the database, uses JAVA to carry out the relevant WEB page development and uses apache as the middleware. The network construction can effectively ensure the stable running of the system and the interaction between the user and the database. Through the browser to get access to the WEB page, the client can monitor the real-time temperature of the remote greenhouse and can view, analyze and export the historical data.

System Implementation

Data Acquisition

When the embedded system is running, it will automatically take 1 minute to collect the temperature data in the greenhouse, as shown in Figure 2:

Fig. 2. Collect data

In addition to real-time transmission of data, the system also files historically data. At 0 o'clock daily, a full day's data will be uploaded to the server so as to ensure the continuity and effectiveness of data, as shown in Figure 3:

Fig. 3. Collect data

Data monitoring

In the data monitoring, the system not only displays the current temperature, but also display the recent 5 temperature state below so as to facilitate the observation of data trends, as shown in Figure 4:

Historical Data

The system has a historical data filtering function and meanwhile supporting to export to excel, as shown in Figure 5:

Conclusions

The system uses the embedded system and TD-LTE technology to implement the automatic collection of greenhouse environment temperature, and the collected data is transmitted to the data server side on Internet. Administrators can get access to the data server through the client's browser, and access contents information includes the current state of the greenhouse as well as browsing and analyzing the historical data. After testing, the system works stably and the transmission distance is more far, which provides a stable foundation for the modern agriculture centralized management. At the same time, the usage of the Linux operating platform has significantly reduced the cost of production, and produced certain economic benefits compared to the traditional windows platform.

Fig. 1 . 3 Temperature

 13 Fig. 1. Conventional and spectra after pretreatment

Fig. 4 .

 4 Fig.4. Data monitoring

Fig. 5 .

 5 Fig. 5. Historical Data

Acknowledgment

Funds for this research was provided by National 863 subjects (2012AA10A506-4, 2013AA103005-04), Jilin province science and technology development projects(20110217), China Postdoctoral Science Foundation the 54th surface funded(2013M541308), Jilin University Young Teachers Innovation Project (450060491471).


Peng-Fei Zhao

Tian-En Chen

Wei Wang
email: cwangw@nercita.org.cn

Fang-Yi Chen

Research on the Agricultural Skills Training Based on the Motion-Sensing Technology of the Leap Motion

Keywords: Leap Motion, motion-sensing, agricultural, skills training

With the increasing development of virtual reality technology, the motion-sensing technology used in agricultural skills training more widely, it plays a great role in promoting the agricultural production, scientific research and teaching. To break the space-time constraints, in order to train the farmers to know well the high precision agricultural skills, and increase the user's immersive and interactive, we propose a training method based on motion-sensing technology. This article in view of the grape vines binding technology, puts forward a kind of agricultural skills training methods based on the leap motion technology. Through maya bone modeling technology to realize 3-D simulation of grape vines, the system completes the interactive simulation of grape vines binding based on leap motion technology.

The experimental results show that the system can be a very good simulation of the grape vines binding process. System is stable, reliable and strong commonality, it can be used for simulating different plants vine binding, and the system innovative interactive, it can increases the user experience.

Introduction

Without using complex control equipment , the motion-sensing technology allow people use the body movements and digital equipment interact with the environment, and in accordance with people's movements to complete a variety of commands. Motion-sensing technology used in agricultural skills training, can solve many problems, for example, the experimental equipment that is insufficient, long time consuming problem. So, the technology can improve teaching efficiency and quality [START_REF] Meng | The Research of Motion Control in Virtual Human[J][END_REF]. Training system based on the motion-sensing technology can demonstrate farming activities of seeding simulation, crops such as fruit picking, crop cultivation simulation. This article presents an interactive design training method for virtual agriculture grape park based leap motion motion-sensing technology. First, build a three-dimensional grapes park foundation template library and grape dimensional model, and then track user gestures, and identify the user crawling vines, enhance vines, binding vines gestures, etc. When a series of binding vine action is completed, by setting a good UI interface, the user is prompted to re-tie the vines or success to bind vines.

Virtual 3D model

Traditional agricultural skills training let the farmers learn the technology in indoor classroom, then the farmers go to work in the filed, the process is often greatly limited by the space-time, weather factors, material, etc. Using the virtual farm training, the farmers can avoid damaging the crops because of mistake of farming. In agricultural training using a virtual farms, farmers can change the farmland environment and cultivation measures, directly observe changes in crop growth and yield, quality, so as to deepen understanding of the new technology, new varieties and master degree. The traditional agricultural science and technology promotion mode can not achieve this effect.

Virtual farm plant modeling refers to the use of physical or mathematical method, the mathematical model obtained to describe the system needs to simulation, it is essential for the digital simulation steps [START_REF] Hai-Lin | Application of virtual agriculture technology[END_REF]. Three-dimensional modeling technology is mainly divided into computer modeling and software modeling. Due to computer modeling is composed of surface, the technology require higher performance computer, and many difficulties exist in the process of model building. So, the system adopts the Maya software modeling.

Maya Modeling Technology

Maya software is a leader in the field of three-dimensional digital animation and visual effects. The Maya function is flexible operation, highly realistic rendering, and maya integrates the animation with digital effects technology. Moreover, it integrates 3D modeling, animation, special effects with the rendering plate that provides a variety of tools for making model and animation need [START_REF] Ya-Ping | Design of Characters Walking Movement Based on Maya Technology[END_REF]. From the actual physical simulation to character animation, and the particle system complex, maya can almost making all of CG(Computer Graphics) forms.

Types of modeling in Maya software is divided into NURBS modeling, Polygons modeling, Subdivision modeling. The establishment of bone is the process of using joint and bone building level connection structure. The bones are the joint structural level, it can be animated and positioned the deformable objects. The user can make objects become joints and bone sub objects, and use the skeleton to control the movement of objects.

Grapes vines modeling

In this system, according to the laws of the grape phyllotaxis, we build the model of grape leaves through the Maya software.In this method, through key nodes and nurbs curve, we build the structure of branches. [4]. The construction of the grapes 3D model steps are as follows:

First, through the digital imaging equipment to capture static grape appearance, and the photo processing for texture mapping real. Second, consult the grape vine growth morphology, in accordance with the law, construct three-dimensional grape contour model.

Third, according to the laws of the grape phyllotaxis, build a virtual 3 d model of grape leaves.

Fourth, according to the grape inflorescence rule and the growth cycle of the fruit, build relevant three-dimensional form in different periods the grapes.

Fifth, in the virtual model of grape vines, to join the bone invasion, simulate the grapes and vine deformation.

As shown in Fig. 1, Fig. 1-a according to the growth rule of nature, extract the grape contour skeleton. Fig. 1-b through the optimization design of pipelines, the creation of fruit and three dimensional profile.Fig. 1-c through texture mapping and the optimized model, 3D virtual simulation of grape. Motion-sensing interaction devices provide the user with high accuracy and low cost requirements, the user can easily distinguish different gestures, and the user can define a gesture to grasp virtual objects, or make a pull or folding finger movements. Kinect is the use of peripheral body sense device most widely, it can complete real-time capture and tracking of human body in real-time. But kinect recognition accuracy is about 4mm, which requires the user need to have larger movement range [START_REF] Jing-Chao | Kinect based Agricultural virtual teaching[J][END_REF]. And 30 frames per second capture ability make equipment cannot distinguish the rapid movements of players, delay is very bad. Because the weakness of precision and delay temporarily ,the kinect is unable to solve on the wrist precision farming activity.

Leap Motion motion-sensing Technology

The sensing orange of Leap Motion accuracy can reach one percent mm, so it can let a person directly control computer through the fingers, including image scaling, moving, rotating and instruction operation, precise control, etc [6]. In this space that your 10 finger movements can be tracking in real-time, error in the 1/100 mm. The leap motion is superior to Kinect in interactive speed and accuracy that can guarantee the user successfully completed more accurate, more precise farming activities. As shown in Fig. 2, Leap motion captured five fingers outstretched palm movement [START_REF]Leap Motion[END_REF].

Custom gesture model

Through the Maya modeling software, we can creat the 3-D hands model, as shown in Fig. 3. In the system , the model is imported into the system, and we create the respond events of the hands [START_REF] Hong | Research of Interactive Virtual Agriculture Simulation Platform Based on Unity3d[J][END_REF]. In response to an event trigger, the system can complete the simulation of gestures. As shown in Fig. 4,Fig. 4-a, in the real world, the system capture the left hand stretched out, and in the system, the response effect of virtual left.As shown in Fig. 4-b, in the system, the effect of virtual left hand clenched. As shown in Fig. 5, Fig. 5-a in the real world, the system capture the right hand stretched out, and in the system, the response effect of virtual right hand.As shown in Fig. 5-b, in the system, the effect of virtual right hand clenched.

Inverse Kinematics

Inverse dynamics is a method to calculate the root nodes information through the modeling nodes information. In a nutshell, IK (Inverse Kinematics) algorithm confirms the location information of end, according to the position information of the end nodes derivate the position information of skeletal class N chain of the father [START_REF] Zhen-Long | The research based on the 3D inverse dynamics of IK algorithm[END_REF].Through this method specifies the role of "position", namely the first rendered roles into bone joint structure, the so-called position expressed as the angle formed therefrom, including the state vector θ and the direction vector and the position of the node. For the IK algorithm, the basic formula is:

θ = f -1 (X)
Among them, X is usually the end location information, and use this algorithm to calculate to meet the conditions of the θ [START_REF] Tolani | Real time inverse kinematics techniques for anthropomorphic limbs[END_REF]. As shown in Fig. 6, we add the joint points in the vine branches and use IK inverse dynamics, the users can hold the vines users and the vines around the key points for spatial arbitrary direction of movement.

Fig. 6. Joint point graphic

System working process

After the user login, the system will automatically peripheral initialization, and check whether the normal peripheral peripheral, if not connected system or the driver is not installed, then the system prompt error and exit the training system. If the connection is normal, the system will pop up a simple introduction to the gesture operation, as shown in Fig. 7. Next, the user hands could be detected in the Leap Motion space, with the left hand grabbing vine, right hand to pick up tying vine machine, and then the vines binding in the right position. The system will be based on the vine binding position to judge whether the user is tied to vines success. Tied cane is successful, then the system exit; If bind cane not successful, he system will prompt the user for training again. System work process is shown in figure. User login, such as the system peripherals are connected properly, then start tying the rattan movement simulation. As shown in Fig. 8, the user's left hand appears in the Leap Motion detection range, there will be left hand in the virtual system, and prompts the user use the left hand grab the vine. As shown in Fig. 9, users grab the vines, according to the arrow tips and improve near to a grape vines upward. After users use the left hand complete a series of actions and keep the state invariant. When users right hand appear in the Leap Motion range of visibility, the system appears virtual right hand with tied rattan machine. At a specific location, the vines machine will be fixed on the grape vine, as shown in Fig. 10. When bound nodes meet the requirements, system prompts tied rattan successfully, and exit the system, as shown in Fig. 11. As shown in Fig. 12, it is the system sketch. The frame rate of the system is 54 frames / sec. As shown in Fig. 13, it is the sketch that how to operate the system. According to the above results,compared with the reference [START_REF] Jing-Chao | Kinect based Agricultural virtual teaching[J][END_REF],Leap Motion can capture users movements, such as, finger movements. Moreover, the system runs smoothly and the FPS can reache 54 frames /sec, so that the system consumes less IT resources. Finally, the cost of the system hardware is low, and the operation is simple, the method is suitable for all kinds of agricultural training. So , it is feasible to carry on the agricultural training that using Leap Motion to capture the finger movements.

Conclusions

Compared with the teaching system of motion-sensing technology based on current, the system aimed at the agricultural field demand, the method can meet high precision farming gesture operation, and achieve fast response, low delay capture effect. Through the teaching system, whenever and wherever, the farmers can be better familiar with the technology of tying vines, and break the limit of space-time to grasp the essentials of tying vines. Achieve the demand of science and technology, science and technology education peasants farming, realize the modernization of agriculture.

The system can make full use of human gestures convenience, make the interactive design and experience of agricultural park is more natural, more real, and can meet the agriculture park aided design, virtual experience farming operations and science education application requirements.

Fig. 1 .

 1 Fig.1. The construction of the grapes 3D model

Fig. 2 .

 2 Fig.2. Hand simulation

Fig. 3 .

 3 Fig. 3. Hands gesture model

Fig. 4 .

 4 Fig.4. Left hand status

Fig. 5 .

 5 Fig.5. Right hand status

Fig. 7 .

 7 Fig.7. Operation method

Fig. 8 . 9 .

 89 Fig.8. Virtual left hand Fig.9. Grasp the vine

Fig. 10 .

 10 Fig.10. Hand tied the vine Fig.11. Complete grasp vine

Fig. 12 .

 12 Fig.12. System sketch

Acknowledgment

Funds for this research was provided by the Technology Innovation Ability Construction Projects of Beijing Academy of Agriculture and Forestry Sciences(KJCX20140416) ,and The Beijing Municipal Science and Technology Projects (D141100004914003). The research work also has been done under the help of the team of the Information engineering.


Jian Ma
email: amajian@caas.cn

Principal Component Analysis Method-Based Research On Agricultural Science And Technology Website Evaluation

Keywords: agricultural science and technology website, principal component analysis method, website evaluation Agricultural science

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

technology departments and relevant units on the Internet to show their own image, also a concentrated expression of service ability and service level and service features, At the same time,also as a important carrier of serviceing "three rural" and promoting agricultural informationization. From the current point of view, although the agricultural science and technology website is rich in resources, but the quality is uneven, how to better improve the website construction is a problem urgent need to solve. A Scientific evaluation method, can make the website administrator to complete understanding of the operation of the site, consummate the existing problems, improve the quality of the website. On the basis of the research of the peer, the principal component analysis method is used,exploring a new method of comprehensive evaluating websites .

 1 overview of website evaluation method

At present, there are no unified standards and methods for the evaluation of the website. The author consulted a large number of literature data, summarized the following two kinds methods of the evaluation of the website :

 Foundation item: Intelligent search technology and method research of website platform (1) web link analysis At present, Link analysis and web impact factor measure related to it are widely used to evaluate the websites. Link analysis through the number of sites are linked to reflect the quality of the site. The evaluation is on the basis that a web site is linked another website is approval and use of this website , and the content of two website is related; the more number of external links of a web site, Explain Its influence is greater. The network impact factor measure is based on the link analysis, reflect the influence of the web site by the size of web impact factor.

Although this method is applied to a wide range of applications, but it also has shortcomings: it only start from the link analysis of the website, not comprehensive evaluation of a website, the authority is to be verified. At the same time, the data used to analysis mostly get from Alta Vista or Google and other search engines, which makes the results depends too much on the search engine, but due to the drawbacks of itself, search engine may not included all external links and internal links of a website.

(2) analytic hierarchy process Analytic hierarchy process divide the decision problem according to total target, sub target, evaluation criteria until the specific input sequential scheme is decomposed into different levels of the hierarchy and layer by layer analysis, ultimately,get the importance weights that the lowest level factor for the highest level factor.

Analytic hierarchy process can be used to evaluate the site, but the relative importance of each factor in the same level must be evaluated in the construction of evaluation matrix. This will have error inevitably due to the subjective behavior.

Based on the above research, In this paper, we give the evaluation index and calculation method of agricultural science and technology website, and use the principal component analysis method to analyze the evaluation index. Finally, the model is used to obtain the comprehensive ranking of 18 agricultural science and technology websites, and the relevant suggestions are given according to the evaluation results.

principal component analysis method for the comprehensive evaluation of the principles and methods

Principal component analysis is also called the principal weight analysis, which is an important method to study how to transform the multi index problem into a less comprehensive index. Because there is a certain degree of correlation between multiple variables, people naturally want to extract information from these indicators as quickly as possible through linear combination. Principal component analysis can change the problem of high dimensional space into a low dimensional space to deal with, make the problem become more simple, intuitive, and the comprehensive index of these less interaction and provide most of the information of the original index.

In practical application, the specific steps of principal component analysis are:

(1) standardization of raw data (2) set up the correlation coefficient matrix of variable (3) obtain the eigenvalues and eigenvectors of the correlation matrix.

(4) the number of principal components is determined by the cumulative variance contribution rate, and the principal components are extracted.

(5) weighted synthesis of principal components,get comprehensive evaluation

evaluation index and method of agricultural science and technology website

The evaluation of agricultural science and technology website mainly investigation from the content of the website, website design and user operation three major aspects. Each of the major aspects of the index selection consider of agricultural science and technology website features. The evaluation model is shown in Figure 1: The evaluation method of the index is too dependent on the subjective score, so the evaluation of the importance of the indicators in the evaluation of the importance of a strong subjectivity. At the same time, simply rely on the objective data can not be a comprehensive reflection of agricultural science and technology website. So we make full use of the advantages of existing systems and professional personnel, the index can be described with the objective data as an objective data, can only rely on artificial score index scoring more than re synthesis method is implemented. The index data is more scientific. Specific practices are as follows:

(1) website content Web content is good or bad is a valuable key of a agricultural science and technology website. For the content of the site we were from the following four aspects to Investigate: Comprehensiveness. Comprehensiveness is the breadth of agricultural science and technology website contains content. Web site can be used to quantitatively expressed by the number of web pages. The number of sites webpages can be estimated approximately using the search engine included page numbers. Practical application. Whether the choice of the content of the site is in line with the "three rural" needs, whether it is suitable for the specific user base, which is suitable for the "three rural" information. This indicator cannot be described by objective data, and it is determined by expert scoring method. Authority. Authority is the impact of agricultural science and technology sites and the extent of the popularity of the site. This index can be described by using the website of agricultural science and technology anti link number, which the number of links from other sites to this site. The more the number of a website's anti link, the greater the influence, the greater the authority.

The quality of Webpage. Web quality evaluation can be from the user point of view. Bounce Bounce RateBounce RateBounce RaterBounce Rateate is an important index to measure the quality of web pages. The Bounce rate is the percentage of the total number of visits from a particular portal to visit a site, which only access to one page on the number of visits to the total number of visits. When the site's bounce rate is high, the quality of the page is very poor, do not attract users.

(2) website design Good website design should have reasonable structure, the page is simple and beautiful, easy to use.

For the website design we investigate from the following three aspects: Navigation function. Navigation function for the user to use the entire site is essential. Design good navigation function can make the user more convenient, more quick browse information. This indicator is determined by expert scoring method. layout is reasonable, Logo is beautiful, etc.. This indicator is determined by expert scoring method.

Connectivity. Effective connectivity mainly investigate the link to the page, if the webpage have broken link or dead link. This indicator can be used to described with broken link rate, broken link rate is the number of all the broken links of the website divided by the number of all links of the website.

(3) user operation User operation is to measure the website good or bad from the users. For the user operation we investigate from the following three aspects: ① Friendly degree. Here is not to examine the degree of friendship from web design, but examine the entire web server response time, and page download time. Obviously, the response time of the server and the web page download times shorter, the higher the site the user-friendliness. Therefore, there are two indicators measure friendliness: server response time, page download time.

② User visits. User visits Is the number of user visits the site. we can use the average daily IP traffic number to describe user visits , visitors every page view can be described by Daily average page views.

data sources

Data sources of this paper are the following four:

(1) network technology resource monitoring, analysis, evaluation system.

The system long-term monitoring 18 agricultural science and technology website in Table 2, the system provided data included the number of pages, the number of anti-link, download time and the response time four indicators.

(2) artificial: This paper invited 5 of the author's colleagues, 5 ordinary users to form a group of experts. 5 of the author's colleagues engage in agricultural science and Technology Information

Research for many years, while the other 5 are ordinary users that often used agricultural science and technology website.

Respectively, usability, navigation, page layout and search functions independently scoring four indicators, score interval [0,1], 1 on behalf of full marks, 0 on behalf of 0 . For each item of data to remove one of the highest and lowest scores, then averaging the remaining data is the index score.

(3) Alexa:Alexa is a site that specialized publishing website ranking . Alexa every day collect more than 1000GB of the information, not only give billions of web site links, but also rank for each of the sites. It can be said, Alexa is currently has the largest number of URL, released ranking information the most detailed site.

Alexa provides the data for: average daily IP access, the average daily amount of page view browsing and bounce rate (4) Chinaz (Chinese webmaster station): is a specializes in providing information for Chinese site, technology, resources and services of the website, website existing millions of users.

Chinaz provides data for the number of broken links of the site and the total number of links to the site.

data extraction

Extracting data from the four data sources mentioned in 4.2, after finishing get the relevant index data of 18 agricultural science and technology site as shown in Table 3: Note: the number column is one one corresponding to the number in Table 2.

comprehensive ranking of agricultural science and technology website based on principal component analysis

In this paper, using SPSS13.0 execute the principal component analysis, and the specific steps are:

(1) standardization of raw data

In principle component analysis method, the standard method is Normal standardization, and for the practical application, according to the difference of the index, divided the bigger the better and the smaller the better. So we take the following standard method:

With m evaluation object, n evaluation index. All data constitute a m*n order matrix X= (X1, X2,...

Xn).

Where Xi is a m*1 dimensional vector. Let max (Xi) is the maximum value of Xi, min (Xi) is the minimum value of Xi. Max (Xi) = (max (Xi), max (Xi),... Max (Xi)) T is a m*1 dimension vector, Min (Xi) = (min (Xi), min (Xi),... Min (Xi)) T is the m*1 dimension vector, For the bigger the better type index, the formula is:

i i * i X Min(X ) X = max( ) min( ) i i X X   (4.1)
For the smaller the better type index, the formula is:

i i * i Max(X ) X X = max( ) min( ) i i X X   (4.2)
In this paper, n=12, m=18. The bigger the better type included web number, practical, the number of anti link, navigation, page design, average daily IP access, the average daily amount of page view browsing and search functions eight indexs. The smaller the better type included bounce rate, broken link rate, download time and response time four.

(2) Use SPSS 13.0 for factor analysis Get the standardized data input SPSS data editing window,the 12 indicators were named X1~X12.

Select Analyze->Data Reduction->Factor menu item in the SPSS windows, Tune out factor analysis main interface and move the variable X1~X12 into the variables box, click the OK button, execute factor analysis process. Get the characteristic roots and variance contribution rate table shown in Figure 4 and factor loading matrix as shown in Table 5: By multiplying the matrix of the original data with the eigenvector matrix, can obtained the 5 principal components Y1-Y5. Then the 5 main components weighted comprehensive, you can get the comprehensive score of the agricultural science and technology website, Specific data shown in Table 7.

The formula for calculating the comprehensive score is

1 2 5 1 2 5 5 5 5 i i i i=1 i=1 i=1 Y= Y Y Y             L (4.1) Y is a composite score, i Y is the main component i, i
 is the characteristic root of the main components i.

analysis and suggestion

We compare the ranking of this paper with the Alexa ranking, and the results are shown in Table 8: From table 8 we can get the following results:

(1) We can see,in this paper the first top seven sites is consistent with Alexa ranking, but the first and the second ranking is not consistent. This is because in Alexa ranking,its information flow rank is predominant factor, the impact of other parameters is very small. At the same time also can be seen,when the site information flow reach a certain degree, the greater the information flow, the better the site of the index to maintain , the higher the overall ranking.

(2) The websites ranked in 8-18, this article ranking and Alexa have a larger discrepancy, so we can see, Alexa for the websites comprehensive ranking is not high not has a very high reference value.

(3) Overall, the ranking of the national agricultural science and technology websites is higher than the local agricultural science and technology websites. But there are exceptions, such as nine hundred million network, so the nine hundred million network need to be further improved. Single from the national agricultural science and technology websites: Golden agriculture network, nong bo network and Chinese Academy of Agricultural Sciences network, the comprehensive ranking is higher, the utilization of its website is also higher.

Figure 1

 1 Figure 1 evaluation index model of agricultural science and technology website

③

  search function. search function for users to find resources is very important, well-designed site should have a comprehensive in-station search function, which should have advanced search and fuzzy queries. The index score is determined by experts. Detailed indicators and evaluation of the site's content as shown in

Zhongshan

  From the local agricultural science and technology websites: Beijing, Shandong, Hunan stay ahead, It explained that the Ranking of Agricultural Science and Technology websites are relevant with the local information level and agriculture level. Guangdong, Zhejiang although the level of economic development is higher, the level of agricultural information needs to be further improved. In this paper, through construction agricultural science and technology websites evaluation model,Using principal component analysis to analysis, For 18 agricultural science and technology site ranking, And based on ranking results comparison with Alexa ranking, giving agricultural science and technology websites analysis and evaluation.Through this type of evaluation,can play an important promote role to agricultural science and technology websites healthy development and agricultural Information Development. Reference 1. Sha Yong Zhong, Ou Yang Xia Evaluation of China Provincial websites influence_website link analysis and web impact factor measurement [J]. Beijing: Information and Documentation work, 2004 21. Li Chunping, Yang Yimin, Ge Yingyu compare of principal component analysis and analytic hierarchy process in quantitative evaluation of comprehensive index [J]. Nanjing: Journal of Nanjing University of Finances and Economics, 2005

Table 1 :

 1 Table 1 Evaluation System of agricultural science and technology website The evaluation object of this paper is 18 agricultural science and technology information website, which has 7 national agricultural science and technology website, 11 provincial agricultural science and technology website, as shown in table 2:

	Level indicators Secondary		Evaluation Content
		indicators
	website content Comprehensiveness. Include the number of pages
		Practical application	Experts rate, standard: meet the needs of
					agricultural information
		Authority		Number of anti-link
		The	quality	of	Bounce rate
		Webpage
	website design	Navigation function		Experts rate, standard: ease of navigation

Table 3

 3

		index data of 18 agricultural science and technology website
	nu	Inc	Prac	Anti	Boun	Navi	Page	Bro	Down	Res	Dai	daily	Sear
	mb	Lude	Tica	link	Ce	Gati	Desi	Ken	Load	Pon	ly	page	Ch
	er	Page	Bili		rate	On	gn	Link	time	Se	IP	view	Func
		Num	ty			Func		rate	(s)	time	Acc	bro	tion
		ber				tion				(s)	ess	wse
	1	937	0.85 2540	33.30% 0.9	0.92 0.146 1.18	0.02	360	2160	0.83
	2	2140	0.73 1470	81%	0.7	0.51 0.026 0.619	0.44	780	1170	0.82
	3	2520	0.9	1420	78%	0.88 0.9	0.102 0.494	0.27	300	300	0.65
	4	20700 0.73 2480	60%	0.9	0.85 0.037 0.445	0.22	300	300	0.87
	5	4990	0.8	2680	37.50% 0.85 0.8	0.4	0.864	0.01	780	4680	0.76
	6	2470	0.85 1830	77.80% 0.78 0.85 0.048 4.909	0.23	420	420	0.25
	7	1060	0.87 1190	12.50% 0.79 0.74 0.021 0.623	0.06	1500	180000 0.98
	8	16400 0.92 723000 50.90% 0.81 0.92 0.09	1.034	0.04	9600	37440	0.84
	9	1040	0.78 349	50%	0.85 0.74 0.225 0.784	2.77	180	230	0.83
	10 4360	0.72 691	50%	0.86 0.8	0.032 6.599	0.07	250	270	0.2
	11 6800	0.88 20100	34.10% 0.97 0.9	0.076 0.541	0.01	6000	23400	0.8
	12 477	0.72 353	67%	0.85 0.85 0.017 0.848	2.87	240	280	0.7
	13 116	0.68 47	84%	0.6	0.64 0.07	1.338	3.89	180	200	0.82
	14 728	0.54 503	60%	0.78 0.82 0.164 3.611	0.1	120	120	0.75
	15 7580	0.71 79500	35.30% 0.85 0.9	0.1	2.127	0.04	64200 46680	0.8
	16 7080	0.68 18500	57.90% 0.88 0.83 0.04	2.539	0.05	660	990	0.75
	17 873	0.57 2360	54%	0.95 0.8	0.069 12.244 0.08	160	220	0.85

Table 4

 4

characteristic root and variance contribution rate table

In Table

4

in total column is each factor corresponding characteristic root , In this case extract five common factors; %of Variance column is the variance contribution of each factor; Cumulative % column is the cumulative variance contribution rate of each factor, As can be seen, the first five factors can explain 75.177% of the variance.

Table 5

 5 factor load matrix (3) using factor analysis result execute principal component analysis Get Table 5's data input SPSS Data Editor window, Column named a1 ~ a5. Then click on the menu item Transform-> Compute, Recall Compute variable dialog, Enter z1 = a1 / SQRT (3.008) in the dialog box, Click the OK button, you can get a first feature vector. Similarly, it can be calculated for all the feature vectors, which results are shown in Table6:

	Table 6 feature vector matrix
		z1	z2	z3	z4	z5
	x1	0.336	-0.051	0.489	0.073	-0.165
	x2	0.303	0.266	0.118	-0.32	0.532
	x3	0.279	0.097	0.516	0.115	-0.074
	x4	0.398	0.14	-0.47	0.001	0.044
	x5	0.338	-0.406	-0.202	-0.163	-0.041
	x6	0.394	-0.327	0.104	-0.153	-0.035
	x7	-0.069	-0.037	0.212	0.621	0.425
	x8	0.129	0.51	0.192	-0.331	0.018
	x9	0.366	-0.264	-0.14	0.114	0.192
	x10	0.231	0.008	0.019	0.362	-0.444
	x11	0.244	0.355	-0.283	0.431	0.214
	x12	0.145	0.411	-0.16	0.069	-0.467

Table 7

 7 Agricultural Science and Technology site principal component scores and order

		Y1	Y2	Y3	Y4	Y5	score order
	China Agricultural
	Science and Technology Information Network	1.958	0.16	-0.088 -0.093 0.539	0.729	4
	Hunan Agricultural Science
	and Technology Information Port	1.441	-0.02	0.08	0.154	0.353	0.556

Table 8

 8 Comparison of the ranking of this paper with Alexa

	website name

Liu Yicheng, Li Hua evaluation method and evaluation index system of agricultural website[J]. Beijing: agricultural network information, 2010


Shuwen Jiang
email: jiangsw@nercita.org.cn

Tian'en Chen

Jing Dong
email: cdongj@nercita.org.cn

Application And Implementation Of Private Cloud In Agriculture Sensory Data Platform

Keywords: Internet of Things, private cloud, big data, hadoop, HDFS

With the explosive development of the Internet of things technology in recent years, the Internet of things technology is also used more and more widely in modern agricultural production.For mass sensor data was produced by the Internet of things in agricultural production, While big data bring many benefits and unprecedented challenges to users. the Internet of things in agriculture production produces some complexity problem which are mass sensor data's Scale,sensor data's heterogeneity and mass sensor data's operation , distribution of sensor , high concurrency of data is written etc.In the presence of these problemes, this paper put forward a kind solution of agricultural private cloud sensor data Platform ,which is named "Sensor PrivateClouds Platform" (SPCP).The Private cloud platform including following modules, All of these are distributed sensor data caching module based on cluster of memercached and Nginx load (SensorCache); heterogeneous data adapter of sensor module (SensorAdpter), distributed computing storage module based on hadoop'HDFS (SensorStorage), efficient query module of sensor data warehouse based on the Hive (SensorStore), management module of sensor metadata (SensorManager), parallel sensor data analysis module (SensorNum) based on the map-reduce of the hadoop, cloud service of sensor data module (SensorPublish) based on webservice.The experimental results show that SPCP have had the abilities which are mass sensor data storage, cleaning of heterogeneous sensor data,realtime query and processing of mass sensor data.These abilities provides a feasible solution for the heterogeneous data storage and mass sensor data's query in the Internet of things of agriculture production.

Introduction

For improving the efficiency of agricultural production, the Internet of things technology also has been widely used in modern agriculture, its main purpose is to collect plenty of sensor data in agricultural production, through the analysis of the sensor data for the agricultural production and utilization to further improve the efficiency of agricultural production.However, in the face of characteristic that are wide area of the production, poor environmental conditions, weak signal of communications in agricultural production, collection and utilization of the sensor data is faced with many challenges.For IOT to compared with Internet, facing the core issues is the storage and query of mass heterogeneous sensor data, processing a large number of sensor's intelligent analysis and work together, complex events such as automatic detection and effective coping. [START_REF] Zhao Zhuo-Feng | A Real-Time Processing System for Massive Sensing Data[END_REF] These technology research of problems are relatively limited. Through the application of sensor data in agricultural production, agricultural Internet of things can be found the following problems in the sensory data is:

The big sensor data. Internet of things of agriculture usually contain vast amounts of sensors nodes. Most of these sensors such as GPS sensors, temperature sensors, humidity sensors, these sensors are usually deployed in many diffrernt parks or more agricultural production environment in the form of group sensor network. [START_REF] Ghemawat | The Google file system[END_REF] And each sensor can timing acquire the latest sensor data, the sensor data gathering to the storage of sensor data network node.the storage nodes is more than store the recent seneor data and in most cases also need to store a historical data such as 1 year all the historical sensor data value for meeting the needs of the complicated data's processing and analysis. As you can imagine the above data is huge, ordinary server storage for huge amounts of sensory data storage, transmission, query and analysis will be an unprecedented challenge. heterogeneity of sensoy data. The sensor data collection network nodes may include different kinds of sensors such as the environment parameter sensors, geographic information sensor, geological, meteorological sensors, video sensor and so on in a large agricultural production environment.While each kind of sensor also includes many specific sensors such as environment parameters of sensors can be subdivided into soil temperature sensor, a soil moisture sensor, co2 sensor, light intensity sensor, air temperature sensor, humidity sensor and oxygen sensors. These sensors are not only the structure and function is different, and the format of the sensor data according to the design of the sensor are also different.This will cause the heterogeneity of sensory data. [START_REF] Dean | MapReduce:Simplified data processing on large cluster[END_REF] This heterogeneity has greatly enhanced the difficulty of software development and data processing.According to the above problem, this paper proposes a agricultural seneor data Platform based on private cloud which named " Sensor PrivateClouds Platform "(SPCP).

Overall design of private cloud in agricultural sensor data platform

SPCP platform mainly includes the design of Sensor-Cache,which is deploy on a cluster server of MemerCached and Nginx,while is designed as a distributed cache component;Sensor-Adpter which is a adapte component of multi-source heterogeneous sensor data;Sensor-Storage is designed as a distributed storage server based on Hadoop Distributed File System, Sensor-Store is a kind of warehouse which contains sensor meta data, Sensor-Store can efficiently query and compute sensor data via the Hive;Sensor-Manager realize a management system of sensor basic information;Sensor-Num is a component of distributed computing based on the mapreduce of the hadoop;Sensor-Publish is designed as a WebService which realize sensor data's service.

Overall architecture of the SPCP is four levels,First Level is interface level,It contain SensorCache and SensorAdpter.First level mainly complete sensor data's receiving and transforming.Second level is Applications level.It contains SensorPublish and SensorNum.Second level mainly provide private cloud service of analysis and publish.Third Level is manage level which contains SensorManager,it mainly complete management of sensor data and cloud module.Fourth level is storage level which contains SensorStorage and SensorStore.It mainly complete storage of sensor data in private cloud platform. SensorStorage and SensorStore mainly implements the data storage, operation, and query of huge mass data. [START_REF]Apache Hive[END_REF]The platform used the hadoop distributed file system, in order to HBase as non-relational data storage, HIVE for processing and query of mass sensor data.building sensor data management system on top of hadoop platform structures, the user can manage data access of sensor data node, real-time monitoring, history query and other functions through the web mode. Overall architecture of sensor data network platform is shown below . Sensor data platform use the hadoop distributed file system for data storage and query, now the hadoop platforms use HBase column storage methods for storage, use Hive data warehouse for data query operation, but the experiment proved that real-time latency situation is obvious when the currently used Hive query mass data. it is the problems the entire sensor data platform is facing at present. according to the above problem, [START_REF] Pike | Interpreting the data:Parallel analysis with sawzall[END_REF]this paper studies a kind of the cache cluster architecture on top of sensor data platform, cache cluster cache sensor data for a period of time and use the Mysql relational database for sensor data's disaster backup in a period of time.This cache cluster can achieve real-time effect in mass data query.the structure of the cache cluster diagram is shown below.

Fig. 2. the overall architecture of the cache cluster diagram

The design of the Memcached and Mysql cluster

The design of Memcached and Mysql cluster is a kind of method based on querying optimization and sensor data backup of Memcached and Mysql cluster. The cluster is used Memcached cluster as sensor data cache service that does not have direct access sensor data from Mysql and liberating the real database. The Mysql so only is used for the history the sensor data's backup, so as to reduce load when the hadoop query mass data. Cluster's cache is make full use of multiple servers network memory, CPU and server memory, its basic process of data access as shown in figure 1, When the cache cluster is started, the cache cluster first query out the all sensor data for one day from hadoop data platform by using Hive, and stored the sensor data in the corresponding Memcached server through the distributed algorithm, so that in the next trip, user can obtained the sensor data from the corresponding Memcached server. When number of sensor data reach the specified value,the cache server automatically delete useless cache based on the LRU algorithm, the frequency of accessing database will have obvious drop even zero,it is good for mass data query service. even database mainly perform sensor data disaster backup, the low configuration database also can easily complete. [START_REF] Thusoo | Hive-A petabyte scale data warehouse using Hadoop data engineering[END_REF] Sensor data's high concurrent writing is divided into three steps, the first step happens in the cache strategy layer, sensor data will be sent by wireless sensor networks into Memcached nodes via the first layer of the load polling, Memcached node will cache the sensor data as gateway node for the key, sensor data for the value. The second is concurrent sensor data archived Mysql is used to backup history data.Finally, in the time of few users, the cache cluster enable the cache data write to hadoop's sensor data service platform in a distributed file system.The cache cluster meet mass sensor data storage, also has reached the efficient querying requirement of real-time mass sensor data. sensor data platform Sensor data management system is deployed on a sensor data cluster as a management system.It was mainly used in data access of the wireless sensor network, management of nodes, real-time monitoring of sensor data, query of historical sensor data and simple mass sensor data processing,etc.

Sensor data management system is build with J2EE enterprise architecture .The server used the Spring MVC framework, front end used jquery + backbone.Js + HTML5.Sensor data management system is set up in the hadoop data platform between hadoop cluster and cache strategy cluster, cache cluster mainly complete the sensor data query service as query of real-time and query of short-term history sensor data. Query and analysis of mass sensor data are processed in the hadoop distributed file system. [START_REF] Olston | pig Latin:A not-so-foreign language for data processing[END_REF] Sensor data management system provides the user for a kind of cloud services of sensor data as entrance door of private cloud.The graph is sensor data management system's operating interface.

The design of SensorStorage and SensorStor in sensor data platform

Agricultural sensor data's structure is single and mass.Using relational database for persistence in the early stages that does not take into the characteristics of sensor data's large scale and distributed.With the development of IOT technology, relational database processed data and have a bottleneck.the general solution is that copy tables or distribute storages on different server's partition, but the cost of installation and operation is very high. through distributed storage system(such as HDFS)technology, the server can dynamically change storage nodes by elasticity features ,while the existing storage way of sensor data will not be changed sensor data is distributed on server cluster. [START_REF] Chen | Clustering-based and consistent hashing-aware data placement algorithm[END_REF] Hadoop sensor data storage platform is designed by hadoop open source framework which is used by the hadoop's HDFS system.The platform used of natural database HBase in hadoop, and make a distributed storage of mass sensor data.Its advantage lies in the dynamic increase and decrease of distributed cluster, backuping redundancy and efficient distributed computing.

The sensor data platform adopts 6 server cluster, including one namenode, four datanodes, one manager zookeeper.Sensor data platform obtained history sensor data from the cache cluster and transfer to HBase.Using simple analysis service interface of mass data via the HIVE.Fig. 3 for architecture diagram of hadoop sensor data platform.

The design of NoSql's sensor data

Sensor data's storage contains two NoSql relationship tables , the tables respectively contain the table of sensor's meta information and table of sensors original data. The table of sensor's meta information is mainly designed to save sensor's meta basic information. Among the table, SensorId is designed for a column family as a row with the field named sensorInfo. sensorInfo store sensor's meta information by key-value pairs such as sensor-name, sensor-coordinates, sensor-info for a column.Which contains (sensorInfo: id, sensorInfo: net-IP, sensorInfo: net-port, sensorInfo: region servers, sensorInfo: avaCapacity, sensorInfo:location). The table of sensors' original data mainly store original sensors' data of sensor.The table has a raw which contains sensorId and reverse timestamp, field "data" is designed for a family of column which contains(data: temperature, data: carbon-dioxide,data:soil-temperature, data: humidity, data: soil-humidity, data: light). [START_REF] Murtyj | ProgrammingAmazon Web Service:S3,EC2,SQS,FPS,and SimpleDB[END_REF] How big data of sensors can be efficiently read is the key technology in DSM platform. A primary key of "sensorid" is used for a row's identification which describe the table of sensor information.while row key used a id which combines sensor's identification and timestamp for the table of sensor original data. so that we can query real-time data from sensor original data via timestamp. As shown in the figure below.

Conclusions

This paper analyzes the problems of big sensor data's acquisition, mass storage and big data analysis in the background of development and application of the IOT and cloud computing technology, with the IOT and cloud computing were wildly applied in the modern agricultural production, these problems brought about the bottleneck is worthy of our attention and solve.To solve the problems existing in the storage of big data, this paper designed a architecture of SPCP, build a wireless sensor network clustering, cache policy cluster and sensor data storage based on hadoop platform.Wireless sensor network realize sensor data acquisition in high concurrency and load balancing, the cache cluster realize efficient real-time query and data backup, data storage platform based on hadoop has realized the huge mass data distributed storage and parallel computing.Experiments show that using SPCP is good at accessing concurrent data, high efficient query and calculation, mass data storage, solved a series of problems brought by the big data.

Fig. 1 .

 1 Fig.1.Overall architecture of sensor data web platform

Fig. 3 .

 3 Fig.3.data management system of hadoop sensor data platform.

Fig. 4 .

 4 Fig.4.architecture diagram of hadoop sensor data platform.

Table 1 .

 1 design of sensor data table

Acknowledgements

The research work was supported in part by Beijing Natural Science Foundation of China (key project) -Research of adaptive the key technology and application model in agricultural cloud service (4151001) and The Beijing science and technology plan project(D141100004914003).Also with the help of the information engineering department to complete this paper.