Changqi Ouyang

Daoliang Li

Jianlun Wang

Shuting Wang

Yu Han

Keywords:

In this paper, a synthesis segmentation algorithm is designed for the real-time online diseased strawberry images in greenhouse. First, preprocess images to eliminate the impact of uneven illumination through the "top-hat" transform, and remove noise interference by median filtering. After comprehensively applying the methods of gray morphology, logical operation, OTSU and mean shift segmentation, we can obtain the complete strawberry fruit area of the image. Normalize the extracted eigenvalues, and use eigenvectors of part of the samples for training the BP neural network and support vector machine, the remaining samples were tested in two kinds of disease strawberry recognition model. Results show that support vector machines have a higher recognition rate than the BP neural network.

Introduction

Strawberry fruit image segmentation is used widely in strawberry picking and fruit grading.In strawberry picking , the strawberry fruit is mature normal strawberry, it's easy to segment the fruit image because the color of the fruit area is almost the same.

In strawberry fruit grading , the strawberry fruit has already been picked, and it's not difficult for image segmentation because the background is relatively consistent. The strawberry image segmentation algorithm discussed in this paper aims to offer the objects to the real-time online identification of the three common strawberry diseases. [START_REF] Zhou Tianjuan | Comparison of two algorithms based on mathematical morphology for segmentaion of touching strawberry fruits[END_REF] ※ Ouyang Changqi ,the first author [START_REF] Zhiyong | A new method of segmentation of strawberry image[END_REF] Corresponding author, Email: ouyangchangqi@126.com Therefore, this algorithm is for the image segmentation of the complete strawberry fruit without picking it. As the color distribution of diseased strawberry fruit is inconsistent, and fruit background is quite complicated, it needs to combine kinds of image processing methods to obtain the complete segmentation of the strawberry fruit according to the characteristics of the diseased fruit area.

Pattern recognition is an intelligent activity, including analysis and judgment. The analysis process is to determine the division of pattern class characteristics and expression method; judgment is reflected in the characteristics of the unknown object, and its judgment belongs to a certain class. It aims to use a computer to achieve the ability to identify the class, which is the contrast of two different levels of identification ability. Pattern recognition system includes the sample acquisition, preprocessing, feature extraction, classification decisions and classifier design.

Material and equipment

The objects studied in this paper are the strawberries of the three common diseases in greenhouse,as shown in Fig2-1.These three diseases are powdery mildew, shrinkage and uneven ripening. The characteristic of powdery mildew is that the fruit surface is coarse and with a layer of white powder. The characteristic of shinkage is that the fruit atrophies and is deformed. The characteristic of uneven ripening is that the fruit's color is uneven, but its surface is smooth.

Image Preprocessing

As a result of noise, uneven illumination, and the transform of analog and digital signals of the CCD camera, the online images captured in the greenhouse under natural light are blurred in details ,such as its contours. Therefore, before image segmentation, the image preprocessing must be taken to eliminate the impact.

Image denoising

To ensure that the edge of the strawberry fruit is not vague while image denoising, the median filtering is applied to remove image noise, in detail, each channel of the RGB image is removed noise through median filtering respectively, and then the denoised image can be obtained through the integration of the three denoised channels .

Uneven illumination effect elimination

Strawberry images captured under natural light are greatly affected by uneven illumination. If this impact is not eliminated, the object may be segmented into disperse areas. In this paper, the "top-hat" transform is applied to eliminate the impact. Conduct the logical AND operation between the template complementation and

Image Segmentation

The extraction and transform of the red and green area templates

Strawberry fruit area color clustering

Because K-means cluster and fuzzy C-means cluster both need to set the cluster number initially, which is unknown for the image containing complicated background, it is infeasible to apply these two methods to segment the fruit image.

The mean shift algorithm is an adaptive probability density gradient ascent method of searching for the peak. It is as follows:1)Select the window size and initial position ;2)Calculate the gravity center of the window ;3)Move the center of the window to the center of gravity ;4)Repeat Step2 and Step 3 until the center of the window "convergence", that is, the moving distance of the window must be less than a certain threshold.Each pixel in the image can be set as the initial point, after performed the mean shift algorithm respectively, the initial points converged to the same point are considered as the same class. Therefore, the mean shift algorithm can be applied to image cluster segmentation.

Based on the above discussion, the mean shift segmentation algorithm is used in image segmentation, which make pixels of the strawberry fruit area cluster into the same class in color space, namely strawberry fruit regional pixels are all of the same value.

Target area extraction

After

c)Data preprocessing

The physical meaning of the color, texture and shape characteristic value is different, not comparable, and their range changes, so there is a big numerical difference among them. Data normalization is necessary, or it will lead to the classifier learning slows down or even does not converge. In this study, each dimensional feature data were normalized preprocessed, used in a normalized mapping shown as follows:

In which x is the corresponding eigenvalue of a certain characteristic properties,x max , x min shows the largest and the smallest eigenvalue of the properties.

While classifier training is completed, the input data for the test needs to be normalized in order to enter the classifier.

Conclusion

Image segmentation of the strawberry fruit of the three common diseases consists of two main parts: the first part is image pre-processing, including the application of median filtering to remove the noise from image acquisition, and the application of top-hat transform to eliminate the effects of uneven illumination; the second part is image segmentation, using gray morphology, OTSU algorithm, the mean shift segmentation algorithm to segment the fruit from the image. These experimental results indicate that image processing algorithm can obtain effective segmentation results to the online images of the three common kinds of diseased strawberries and nomal ones under natural illumination.

This paper uses digital image processing, pattern recognition technology to extract the integrated fruit of disease strawberries. Based on parameters of color characteristic, texture features and shape features, use BP neural network and support vector machine to make classification and recognition for disease strawberry. The experimental results show that support vector machine has a higher recognition rate than the BP neural network so that support vector machine is used as the classifier for disease strawberry.

Fig 2 - 1

 21 Fig 2-1 Three common diseases of strawberry

Figure 4 -

 4 Figure 4-1 (a) shows the preprocessed strawberry image. The red area template can be got by 2R-G-B, as shown in Figure 4-1 (b), where R, G and B were the three channels of the color image in RGB space. Due to the uneven surface of the strawberry fruit, the fruit area contains many holes, which needs to be filled in order to get the closed template. Then, execute the OTSU operation, the initial fruit template can be obtained as shown in Figure 4-1 (c).

Figure 4 -

 4 Figure 4-1 (a) to remove the fruit area in Figure4-1(a) while keeping the image outside of the template unchanged. The result is shown in Figure 4-2 (a). The green area template can be obtained by 2G-R-B, as shown in Figure 4-2 (b).Fig 4-2(a) canbe removed the green zone in similar way, and backfilled with the red template, then the result can be got as shown in Fig4-2(c). Now, the green area around the fruit is all black, and the fruit region is almost all white. The purpose of this series of operations

  Fig 4-1 Initial fruit template

  the operation of converting the image into grayscale and filling holses, execute the "open" operation on the image with circular structural element of a radius of 2 to remove the adhesion between the target and background. The experiments showed that: when the split threshold is 0.8, the grayscale threshold segmentation can obtain good results as shown in Figure 4-3; and then extract the largest connected area of the image to obtain strawberry fruit template. Combine this template and the preprocessed image by logical AND operation, then the image of the complete strawberry fruit area can be obtained.

Fig 4 - 3 SegmentationFig5- 1

 431 Fig 4-3 Threshold segmentation image

Figure 6 - 1 ,Fig6- 1 Fig6- 3

 6113 Figure 6-1, Figure 6-2, Figure 6-3, and Figure 6-4 are the segmentation results of the above strawberry image segmentation algorithm, which corresponds to normal strawberry, powdery mildew of strawberry, shrinkage of fruit disease strawberries and uneven ripening disease strawberries respectively. Each subgraph corresponds to the result after the corresponding step of the above segmentation algorithm. The algorithm can get effective segmentation results under natural illumination.

7. 2

 2 BP neural network training and prediction According to Kolmogorov theorem, using a three-layer BP neural network( 29 × 59 × 4)for the classifier, in which the hidden layer activation function is the non-symmetric Sigmoid function: ,the output layer activation function is a linear function f (x) =x, and set a target error e = 0.01. Use the training data to train the BP neural network, to make the BP neural network can predict the nonlinear function output. Use trained BP neural network to predict the test data, and use the predicted output and actual output of the BP neural network to analyze the classification ability of BP neural network. 7.3 SVM training and prediction Implementation of SVM in this experiment uses the libsvm toolbox, the kernel function uses the radial basis kernel function. The training data is used to train SVM classifiers to get the SVM classification model. Use the trained SVM classifier to make the label prediction of test data, and analyze the Classification ability of SVM with predicted SVM output and actual output.


Lin Yuan

Jingcheng Zhang

Jinling Zhao

Shuhong Cai

Jihua Wang

Selection of leaf orientation insensitive bands for yellow rust detection

Keywords: yellow rust, winter wheat, frontside and backside, fisher linear discrimination analysis (FLDA)

The disease detection by means of hyperspectral reflectance is influenced by the spectral differences between frontside (adaxial surface) and backside (abaxial surface) of a leaf inevitably. Taking yellow rust as an example, this study investigated the spectral differences between frontside and backside of healthy and diseased wheat leaves at grain filling stage using large size samples. We attempted to detect yellow rust with reflectance that was sensitive to the disease and insensitive to the orientation of leaves. The spectral difference between frontside and backside of leaves was analyzed by band ratioing and a pairwise t-test. The bands that were insensitive to the orientation of leaves were identified with a thresholding method. Then, with the aid of an independent t-test analysis, we recognized the bands that were sensitive to the disease. The overlapped bands were applied for developing models that quantifying disease severity by fisher linear discrimination analysis (FLDA). The results suggested that the bands within 606-697nm and 740-1000nm were suitable for disease detection yet insensitive to the orientation of leaves. Based on these bands, the model accuracies reached 71% for FLDA. These bands can be used as a basis for further selection of appropriate bands to detect yellow rust at canopy level.

Introduction

Winter wheat (Triticum aestivum L.) is one of the most important crops in China. Yellow rust disease which is induced by Puccinia striiformis f. sp. Tritici, is one of the most destructive diseases that has a severe impact on both yield and grain quality of winter wheat [START_REF] Li | Integrated Management of Wheat Pests[END_REF][START_REF] Strange | Plant Disease: A threat to global food security[END_REF]. Currently, scouting by human being is the widely used method for crop diseases inspection, which is not only time consuming and labor intensive, but also expensive and subjective. As the only way in obtaining the disease distribution information spatially, remote sensing is a promising alternative to traditional methods in disease monitoring [START_REF] Muhammed | Hyperspectral crop reflectance data for characterizing and estimating fungal disease severity in wheat[END_REF]. A number of studies were conducted in understanding the spectral characteristics of yellow rust [START_REF] Bravo | Early disease detection in wheat fields using spectral reflectance[END_REF][START_REF] Huang | Inversion of the severity of winter wheat yellow rust using proper hyperspectral index[END_REF][START_REF] Huang | Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging[END_REF][START_REF] Zhang | Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat[END_REF]. The wavelengths at 446-725 nm and 1380-1600 nm were identified as sensitive bands for disease detection [START_REF] Huang | Spectral reflectance feature of winter wheat single leaf infected with stripe rust and severity level inversion[END_REF]; Based on the spectral reflectance at 680 nm, 725 nm and 750 nm, Moshou et al. [START_REF] Moshou | Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks[END_REF][START_REF] Moshou | Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps[END_REF] successfully distinguished infected wheat leaves from healthy ones at an early infection stage. It should be noted that in most of the studies that were conducted at leaf level, the leaf spectra were measured only for the frontside of leaves. However, in fact, due to the curling of leaves, the frontside and backside of leaves are presenting in a mixed pattern in the nadir view at canopy level. The spectral difference between the frontside and backside of leaves may complicate the spectral response at canopy level and even lead to the failure of disease detection. Therefore, it is necessary to understand the spectral difference between frontside and backside of healthy and diseased leaves, and search for those bands that were sensitive to the disease yet insensitive to the orientation of leaves. However, the spectral difference of frontside and backside of leaves is rarely reported for healthy or diseased wheat plants. Our objectives were: [START_REF] Li | Integrated Management of Wheat Pests[END_REF] to analyze the spectral difference between frontside and backside of leaves at grain filling stage, and to identify bands that were insensitive to the orientation of leaves, (2) to identify those bands that were sensitive to the occurrence of disease, and (3) to develop model for disease detection based on those identified bands.

Materials and methods

Study area and yellow rust inoculation

The experiment was conducted at Beijing Xiaotangshan Precision Agriculture Experimental Base, in Changping district, Beijing (40°10.6' N, 116°26.3' E) on the 2010-2011 growing season. The cultivar of winter wheat was 'Jingdong 9843', which was moderately susceptible to yellow rust. The soil at this site is a silt-clay loam. The average topsoil nutrient status (0-0.30 m depth) was as follows: organic matter 1.42-1.48%, total nitrogen 0.08-0.10%, alkali-hydrolysis nitrogen 58.6-68.0 mg kg -1 , available phosphorus 20.1-55.4 mg kg -1 , and rapidly available potassium 117.6-129.1 mg kg -1 . The experimental field received 200 kg ha-1 nitrogen and 450 m3 ha -1 water, which was a recommended rate for this cultivar. Spray method was used in inoculating yellow rust spores to wheat plants. Two different concentrations of spore solutions were applied to generate various infection levels.

Inspection of disease severity

The disease severity of each sample was determined by visual estimation of the cover percentage of pustules on the leaf [START_REF] Graeff | Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticumaestivum L.) by means of leaf reflectance measurements[END_REF][START_REF] Luedeling | Remote sensing of spider mite damage in California peach orchards[END_REF]. To minimize the error induced by investigator, the diseased leaves were inspected by one investigator. In this study, the disease severity of leaves was determined on the basis of the proportion of infected region on the leaf (0-100%). Apart from the healthy leaves, all diseased leaves were grouped into 3 severity classes: slight for proportion within 5-25%, moderate for proportion within 25-60%, and heavy for proportion over 60%. Since those leaves with an infected proportion less than 5% were actually difficult to be visually separated from healthy ones, they are classified as healthy leaves.

Leaf sampling and spectral measurement

The leaf sampling and measurement were conducted on May 23 (grain filling stage) in the year of 2011. The spectra of frontside and backside were measured for each leaf. The leaves were cut from the plants in the fields with scissors. After that, the samples were immediately packed with ice bags and transported to a nearby indoor laboratory to be measured. A total of 91 leaf samples were measured, including 26 healthy leaves and 65 diseased leaves. All samples were separated for model calibration (60%) and validation (40%) randomly.

Leaf spectral measurements were made by a FieldSpec® UV/VNIR spectroradiometer (ASD Inc., Boulder, Colorado, USA) over the 350-2500 nm wavelengths, coupling with an ASD Leaf Clip. The spectrum of a white Spectralon reference panel (99% reflectance) was measured once for every 10 leaf measurements. Ten readings were recorded and then averaged to obtain a spectral measurement for each leaf.

Comparison between frontside and backside of leaves

Apart from comparing the frontside and backside of leaves by their original spectra, the band ratioing and pairwise t-test were adopted for the spectral comparison. The band ratioing was used to emphasize the spectral difference between frontside and backside of leaves, whereas the pairwise t-test provided a more explicit way to quantify the spectral differences between two sides.

Band selection and disease severity estimation

The significance level of spectral differences between healthy and diseased leaves was quantified by an independent t-test analysis for each band, whereas the significance level of spectral differences between the frontside and backside of leaves was quantified by a pairwise t-test. To eliminate the impact from the leaf orientation, only those bands that were sensitive to disease yet insensitive to the orientation of leaves were chosen. Such band selection was facilitated by an overlapping procedure based on the results from the independent t-test and pairwise t-test as illustrated above. The efficiency of these overlapped bands was then tested by comparing the disease severity estimates generated by a Fisher linear discriminate analysis (FLDA) with the measured value [START_REF] Mclachlan | Discriminant analysis and statistical pattern recognition[END_REF]. Five measures, overall accuracy (OAA), average accuracy (AA), producer's accuracy, user's accuracy, and kappa coefficient were calculated from confusion matrix to evaluate the accuracies of the discriminate model.

Results

Spectral differences between frontside and backside of leaves

The curves of raw reflectance, band ratios between frontside and backside, and corresponding p-values of pairwise t-test were summarized in Fig. 1. From the ratio curves, it is obvious that the spectral differences between the frontside and backside of healthy leaves showed a similar pattern with diseased leaves. The spectral reflectance of backside was higher than the frontside in most of the bands, except for the bands at 646-690 nm. For the results of pairwise t-test, a threshold of p-value<0.05 was used to identify those insensitive bands to the orientation of leaves.

The identified bands differed greatly in their positions between healthy and diseased samples. The bands within 741-923 nm were found to be orientation insensitive for healthy leaves, whereas the bands within 606-697 nm and 740-1000 nm were identified as orientation insensitive for diseased leaves. It should be noted that most of these bands were located at some specific positions that were responsible for the absorption of chlorophyll and water, and cellular structure.

Selection of effective wavelengths and disease severity determination

As shown in Fig. 2, the spectral difference for most bands reached the significant level except for several bands at the beginning of the spectrum. This phenomenon indicated that the disease can induce a clear spectral response within a wide spectral region from visible to shortwave near infrared regions. Combining with the bands that were selected in section 3.1, it is possible to generate bands that were sensitive the disease yet insensitive to leaf orientation through an overlapping procedure. As shown in Fig. 3, only the bands at 606-697 nm and 740-1000 nm were retained. These overlapped bands are theoretically suitable for disease detection since they are able to suppress the disturbance from the leaf orientation when monitoring yellow rust disease. Based on these bands, the FLDA model was established based on the calibration dataset (Table 1). The model accuracies were satisfactory in general, with OAA of 0.71 and kappa coefficient of 0.61 for calibration samples, and OAA of 0.70 and kappa coefficient of 0.59 for validation samples (Table 1).

Discussion and Conclusion

In this study, based on the leaf spectra data with a large sample size, the reflectance of frontside and backside were found to be with significant difference at several bands. This finding was in good agreement with Zhou and Wang (2002)'s study, which compared the reflectance of frontside and backside of rice leaves under different nitrogen treatments [START_REF] Zhou | Comparison of adaxial and abaxial surface reflectance under different nitrogen level[END_REF]. Differences in surface reflectance were attributed to the dorsiventral morphology of wheat leaves. This might related with the larger portion of sclerenchyma, vascular bundle and bulliform cells on the upper leaf side of graminaceae [START_REF] Takahashi | A system for imaging transverse distribution of scattered light and chlorophyll fluorescence in intact rice leaves[END_REF][START_REF] Kirby | Botany of the wheat plant[END_REF]. The absolute difference of reflectance between the two surfaces was about 4% for the healthy leaves, and 3% for the diseased leaves, which means that the occurrence of disease diminished the spectral difference between frontside and backside of leaves. It was found that the reflectance of yellow rust pustules was higher than healthy positions at visible spectral region [START_REF] Devadas | Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves[END_REF]. The appearance of those pustules at both surfaces for diseased leaves may whiten the spectral difference between the frontside and backside for healthy positions, which thereby lead to a reduction of spectral difference for those diseased leaves. The spectral difference between frontside and backside was relatively weak, comparing with the spectral signals that were induced by disease. With a spectral overlapping procedure, the spectral reflectance at 606-697 nm and 740-1000 nm bands were selected for the detection of yellow rust since it can eliminate the impact from the orientation of leaves. However, it should be noted that the present study was conducted at leaf scale, which did not consider more complicated situations that might existed at canopy scale. For example some leaves may present as partially frontside and partially backside. To account for this situation, the spectral imaging techniques and some radiometric transferring models, such as PROSPECT+SAIL should be incorporated for mechanism study and model development.

Fig. 1 .

 1 Fig. 1. Curves of original reflectance, band ratios between frontside and backside and their corresponding p-values based on pairwise t-test (a, b) curves of original reflectance; (c, d) curves of band ratio; (e, f) curves of p-value. "H" indicates the healthy leaf samples; "D" indicates the diseased leaf samples. (+) indicates the frontside of leaves, (-) indicates the backside of leaves

Fig. 2 .

 2 Fig. 2. Curves of the p-values of independent t-test between disease severity and band reflectance (+) indicates the frontside of leaves, (-) indicates the backside of leaves, (+&-) indicates the pooled of both.

Table 1 .

 1 Confusion matrices created based on the FLDA models

		350 350	600 600	850 850	1100 1100	Wavelength(nm) 1350 1600 1350 1600	1850 1850	2100 2100	2350 2350
	Insensitive (+&-) 1.00
		0.00
	Sensitive (D&H) 1.00	350		850			1350			1850	2350
		0.00
		1.00
	Overlapping 0.00	350		850			1350			1850	2350
			350		850			1350			1850	2350
					Fig. 3. Overlapping band selection
	"Insensitive(+&-)" represent bands that are insensitive to the orientation; whereas
	"Sensitive(D&H)" represent bands that are sensitive to the yellow rust.
					Reference
				Healthy	Slight Moderat	Heavy	Sum	U.'s a. (%) OAA	AA	Kappa
		Healthy	25	4	e 1		0	30	83.33	0.71	0.73	0.61
		Slight	5	23	5		0	33	69.70
	Calibration	Moderate	2	5	12		1	20	60.00
		Heavy	0	2	6		17	25	68.00
		Sum		32	34	24		18	108
		P.'s a. (%) 78.13	67.65	50.00	94.44
		Healthy	16	4	0		0	20	80.00	0.70	0.73	0.60
		Slight	2	16	4		0	22	72.73
	Validation	Moderate	2	4	8		0	14	57.14
		Heavy	0	0	6		12	18	66.67
		Sum	20	24	18		12	74
		P.'s a. (%) 80.00	66.67	44.44		100
	OAA is overall accuracy; AA is average accuracy; P.'s a. represents producer's accuracy; U.'s a.
	represents user's accuracy

Acknowledgments. This work was subsidized by National Natural Science Foundation of China (41071276, 41101395), Beijing Natural Science Foundation (4122032). The authors are grateful to Mr. Weiguo Li, and Mrs. Hong Chang for data collection.


Yujian Yang

Mingchuan Ji

Self-Organizing Map Analysis on peanut yield and agronomy characteristics

Keywords:

The model system between peanut yield and agronomy characteristics which is nonlinear, irreversible and dissipative. The objective in the study was the peanut cultivated in the different ecological regions in Shandong province, aimed to establish the new non-nonlinear model based on Self-Organizing Maps (SOM) to improve the cultivation information of peanut growth process. In the article, applying SOM network achieved the cluster between peanut yield and agronomy characteristics about 4 variables, involved in plant height, branches, full pods and peanut yield ratio. MATLAB 7 software is used to classify 60 samplings of peanut yield and agronomy characteristics. It is concluded that the SOM network can respond the complicated information classification among each peanut yield, during the analysis, the results also showed SOM method is the most suitable for peanut yield and characteristics classification, especially analysis of clusters on basis of peanut agronomy parameters,so the study can be applied on agronomy characteristics and peanut yield of the different ecological regions in Shandong province.

Introduction

As the important oil and economic plants in China, peanut is widely planted in Shandong province, where is the core area of production and export of the superior peanut cultivars (Wan Shubo, et al., 2007, 2009) [START_REF] Shubo | Peanut Quality[END_REF][START_REF] Shubo | The high Quality of synergism Cultivation Theory and Technology of Peanut[M[END_REF] . An important subject between peanut yield and all kinds of agronomy characteristics has been reported in recent years. The quantitative relationship focused in yield and its correlated traits (Zhang Haiyan, et al., 2006) [START_REF] Zhang Haiyan | Analysis on the Relationship between Yield and Correlated Quantitative Character of Soybean[END_REF] . The important study was mainly developed by the some statistical methods, such as correlation analysis and regression analysis, which carried on the variation of genetic factors and environmental conditions of peanuts (Wu Zhengfeng, 2008) [START_REF] Zhengfeng | Analysis of characteristics and stability of peanut yield in different ecological regions of Shandong Province Chinese[END_REF] .The capability of Self-Organizing Maps (SOM) to visualize highdimensional data is well known, an important SOM subject between peanut yield and agronomy characteristics has been rarely reported at home and abroad. In recent years, the SOM is widely applied in many fields, such as model establishment, imaging application, text clustering. There is the rarely reports of clustering results considering peanut yield and agronomy characteristics with the support of SOM, so the main objective in the article is achievement of effective clusters about peanut yield. Correspondingly, the organization of the paper is as follows: a first section describes the basic SOM algorithm with a deeper insight of its capabilities. An approach, based on SOM visualization for peanut yield and agronomy characteristics is suggested in the next section for 60 samples. The last section discusses the obtained results, followed by the conclusion.

2

Material and Methodology

Materials and experimental designing

Materials and data were collected from multiple sites in 9 different ecological regions about 7 peanut varieties in Shandong province in 2007. Experimental sites with ecological regions includes Laixi city, Jiaozhou city, Changyi city, Qixia city, Haiyang city, Donggang District, Lanshan District, Dongchangfu District and Ningyang country in Shandong province. 7 peanut varieties were selected in the paper with obvious representative, they are respectively: "LuHua 11","LuHua 12","LuHua 14","Huayu 20","Weihua8", especially,"Huayu22" ,"Huayu23","Huayu22" for the traditional big export-oriented peanut, "Huayu23" for the traditional small exportoriented peanut, the others is high yield peanuts. In the experimental designing, the local plant mode was selected for spring peanuts or the summer peanuts. Each site is the same planting specifications, for spring peanut, ridge is 80-85cm, wide is 50cm, the seeding approach was hill-drop, row spacing is 30cm, the hill spacing is 17cm, 9,800 plants were planted per 666.7 m2, interplanting cultivation mode of peanut was selected to be naked before 20d, 10,000 holes were dug per 666.7 m 2 [START_REF] Yujian | The grey correlation degree analysis on yield of peanut and traits in warm temperature regions[END_REF] . In the process of data measurement, 4 traits variables of peanut(plant height, branches, full pods and peanut yield ratio) was acquired involved in 60 samples. In order to achieve the SOM results, we determined the cluster numbers of peanut yield according to SOM algorithm, so we developed the preprocessing classification, there are 3 kinds of productions including 3000-4000 Kg/per ha, 4000-5000Kg/per ha and >5000 Kg/per ha. Use the SOM for clustering data correlated with the agronomy characteristics of input data (plant height, branches, full pods and peanut yield ratio), which provides a topology preserving mapping from the high dimensional space to map units. Map units, or neurons, usually form a two-dimensional lattice and thus the mapping is a mapping from high dimensional space onto a plane. In the literature [START_REF] Kohonen | The self-organizing map[END_REF][START_REF] Vesanto | Clustering of the Self-Organizing Map[END_REF] , the effective characteristics of SOM method was determined by the sample distribution characteristics.

2.2

Use of the Self-Organizing Map algorithm

Self-Organizing Map algorithm

The Self-Organizing Map has been proven useful in many applications, it belongs to the category of competitive learning networks, so it is also called SOM, which is put forward by T. Kohonen(1981) [START_REF] Kohonen | The self-organizing map[END_REF] . It is a neural network that maps signals from a high-dimensional space to a one-or two-dimensional discrete lattice of neuron units, or is named after cluster algorithm on basis of neural network. SOM embodied the similarities in samples, achieved the transformation from high-dimensional data to low-dimensional data. In many perspectives, spatial structure and function of neurons of SOM is very important, the traditional model of neural network didn't consider the spatial structure characteristics, but SOM not only considering the structure, but also achievement of the similar input data responded the best matching units without supervision and prior knowledge [START_REF] Qindi | Self-organizing feature map classification and ordination of Larix principisrupprechtii forest in Pangquangou Nature Reserve 9. Zhang Yancheng, etc. Soil Classification Based on SelfOrganizing Feature Mapping Neural Networks[END_REF]9,10] .

SOM has the better status of non-linear clustering, it was also used to find correlations between the data by labeling the neurons of the SOM using the training set and finding the best-matching-units for every example. In theory, each neuron stores a weight. The map preserves topological relationships between inputs in a way that neighboring inputs in the input space are mapped to neighboring neurons in the map space. And by grouping units that respond to similar stimuli together. Nerve cells, neurons, in the cortex of the brain seem to be clustered by their function. During the computation, by defining the Hebbian learning rule is to determine the learning rate and update the relationship for best-matching units on the map. In essence the learning rule of the SOM defines the model as a collection of competitive units that are related through the neighborhood function. In practice, the units are placed on a regular low dimensional grid and the neighborhood is defined as a monotonically decreasing function on the distance of the units on the map lattice, thus creating a latent space, which has the dimension of the map grid and flexibility determined by the neighborhood function, and embedding when the dimension of the map grid matches the dimension of the input data manifold. In general, gaussian function is selected in the neighborhood function [START_REF] Kohonen | Self-Organizing Maps[END_REF] .

SOM, based on unsupervised learning, or high-dimensional observations projected to the two-dimensional coordinate system, which means that no human intervention is needed during the learning and that little needs to be known about the characteristics of the input data. In a word, Self-Organizing Maps is a categorization method, a neural network technique and the unsupervised characteristics.

The main computing steps of SOM, including construction of data sets, data preprocessing, initialization and training of input data and visualization and analysis of the correlated results. In the elaborate SOM computing process, important parameters should be also considered as the significant contents, such as Unified distance matrix (U-matrix), importance degree in the trained self-organizing map(D). Especially, U-matrix, the parameter representation of the Self-Organizing Map visualizes the distances between the neurons, the distance between the adjacent neuons is calculated and presented with different colorings between the adjacent nodes, for each node in the map, compute the average of the distances between its weight vector and those of its immediate neighbors. In the results, the different colors between the neurons presents the different distance significance and responds the cluster differences, average distance is a measure of a node's similarity between it and its neighbors, this can be a helpful presentation when one tries to find clusters in the input data without having any a priori information about the clusters. For the parameter D, it responds the important degree of the agronomy characteristics impact on peanut yield in the article [START_REF] Vesanto | SOM Toolbox for Matlab[END_REF][START_REF] Suri Guga | Application of self-organizing map to quantitative analysis of mountain meadow in the Songshan Nature Reserve of Beijing, China[END_REF] .

Data processing and data analysis

First, the data has to be brought into Matlab using construction of data, second, we used the function som_normalize for data preprocessing data to perform a linear scaling to unit variance. The function som_make is the basic function to use when creating and training a SOM, it is a convenient tool that combines the data of creating, initialization and training a SOM. After the data set is ready, the data set is loaded into Matlab7 and normalized, the variance normalization is used, a SOM is trained. Since the data set had labels, the map is also labeled using som_autolabel. There are a variety of methods to visualize the SOM, the basic tool is the function som_show, it can be used to show the U-matrix and the component planes of the SOM. After this, the SOM is visualized using som_show [START_REF] Vesanto | Self-organizing map in Matlab: the SOM Toolbox[END_REF]15] .

Here is the usage of the Matlab Toolbox to make and visualize a SOM of a data set about peanut yield and agronomy characteristics. This data set consists of four measurements from 60 samples: the first classification of peanut yield has 18 samplings, the second classification of peanut yield has 13 samplings and the third classification of peanut yield has 29 samplings. The data is in an ASCII file, the first line contains the names of the variables, the corresponding sequences, including plant height characteristics, branches characteristics, full pods characteristics, peanut yield ratio characteristics and peanut yield classification. Each of the following lines gives one data sample beginning with numerical variables and followed by labels [START_REF] Vesanto | Self-organizing map in Matlab: the SOM Toolbox[END_REF] .

Results and Analysis

We developed the cluster analysis based Matlab7 SOM Tool. the PCA-projection of both data and the map grid was required by the program computerization, in general, the projection also presented the four variables and the subspecies information from the SOM. Projection of the sample data set to the subspace spanned by its three eigenvectors with greatest eigenvalues. The SOM grid has been projected to the same subspace, and visualizes all four variables of the SOM plus the subspecies information using coordinates, neighboring map units are connected with lines, labels associated with map units are also shown. U-Matrix visualization provides a simple way to visualize cluster boundaries on the map, the U-matrix visualizes distances between neighboring map units, and thus shows the cluster structure of the map, high values of the U-matrix indicate a cluster border, uniform areas of low values indicate clusters themselves. For each node in the map, compute the average of the distances between its weight vector and those of its immediate neighbors, average distance is a measure of a node's similarity between it and its neighbors. Each component plane shows the values of one variable in each map unit. On top of these visualizations, additional information can be shown: labels, data histograms and trajectories.

As illustrated in Figure 1, Gradient distribution of peanut characteristics in the trained SOM ordination diagram. U-matrix on top left, then component planes( including plant height characteristics;branches characteristics;full pods characteristics; peanut yield ratio characteristics), and map unit labels on bottom right. The six figures are linked by positions, in each figure, the hexagon in a certain position corresponds to the same map unit, additional hexagons exist between all pairs of neighboring map units. The map unit in top right corner has low values for plant height, branches and full pods, and relatively high value for peanut yield ratio. The label associated with the map unit is "p1" and from U-matrix it can be seen that the unit is very close to its neighbors. From the U-matrix it is easy to see that the top two rows of the SOM form a very clear cluster. By looking at the labels, it is immediately seen that this corresponds to the peanut yield (p1) subspecies, the two other subspecies peanut yield (p2) and peanut yield (p3) form the other cluster. The Umatrix shows no clear separation between them, but from the labels it seems that they correspond to two different parts of the cluster. From the component planes it can be seen that the branches characteristics and full pods characteristics are very closely related to each other in the certain degree.

Figure 2 visualizes all four variables of the SOM plus the subspecies information, 4 variables barcharts ( including plant height characteristics ; branches characteristics;full pods characteristics; peanut yield ratio characteristics) in the topography maps. The distribution of 3 types peanut yield in the topography maps. In every topography map, the weight of each variable is shown in the figure. The four variables shown with the different barcharts in each map unit and in the background color indicates the subspecies.

In this paper, SOM is used to classify 60 samplings of peanut data of the different ecological regions in Shandong province, the kind of yield classification method promotes the comparative analysis for the different ecological regions in Shandong province for the yield prospective. The results showed that SOM is an excellent tool in the visualization of high dimensional data about peanut yield and agronomy characteristics and as such it is most suitable for peanut traits and yield classification, especially analysis of clusters on basis of peanut agronomy parameters.

It is concluded that the SOM network can respond the complicated information among each peanut yield. The effect of classification is good and SOM considered the complicated characteristics of peanut agronomy parameters. And it can be applied on peanut characteristics and yield. Although SOM cluster applied in peanut yield and agronomy characteristics is a preliminary trail in the study, the SOM has the obvious characteristics from the high dimensional data to low dimensional data for peanut growth process, with the development of spatial analysis technology, combining with GIS data, the spatial evaluation on peanut agronomy characteristics and yield should be explored on the precision scale to clarify the spatial variability and spatio-temporal characteristics of complicated parameters under soil-peanut system in the next step.

Figure1

  Figure1 Visualization of the SOM of peanut yield and agronomy parameters data Figure 2 SOM ordination diagram of characteristics


Jiaogen Zhou

Xu Chen
email: chenxu@saas.sh.cn

Jingyin Zhao

Dongsheng Wang

Modeling and Simulating of Spatial Spread of Cross-Boundary Crop Diseases

Keywords: complex network, spatial model, crop epidemics

Understanding of the timing, orientation and transmission mode of crop disease spread is very important in prevention and treatment of crop diseases, especially for crossborder crop diseases. In this paper, we introduced the complex network approach to model trans-boundary spread of diseases. In the model, the process of crop disease spread is characterized as a network, where patches of crop planting are abstracted into nodes, and the diseases spread from a node to neighboring nodes. The edge between two nodes is connected, only if the Euclidean distance between two nodes does not exceed the radius of disease spread. We obtained wheat-growing distribution data of Beijing in 2007 by image classification, and further abstracted patches of wheat growing into network nodes, and finally constructed the disease spread network to analyze its topology characteristics. The experimental results show the heavy influence of spatial constraint on disease spread.

Introduction

Occurrence and spread of crop diseases is of great harm to crop yield and quality. Forecasting of occurrence and spread of crop diseases is very significant for diseases control. Existing forecasting methods can be divided into two categories: 1) linear or nonlinear statistical models; 2) multi-spectral remote sensing prediction.

In the linear or nonlinear statistical models, there is a close association between the occurrence and spread of crop pests and diseases with environmental factors of growth and reproduction habitat (such as light, heat, water, terrain, etc.).So, based on history data extensively collected and surveyed on the disease bio-statistical information (such as spores), disease occur strength data as well as the habitat factor data during disease occurrence, a statistical model underlying relationship between disease bio-statistical data or intensity data occurs and environmental factors can be constructed to predict the range, intensity or time of future disease occurrence [START_REF] Nutter | Quantifying alfalfa yield losses caused by foliar diseases in Iowa,Ohio,[END_REF]. For example, environmental data of average temperature, precipitation and relative humidity are used to determine spreading areas of wheat scab epidemic in the irrigation areas, and ground meteorological data, upper air circulation and North Pacific sea surface temperature field factor can be effectively used in the Shanghai area of wheat scab occurrence degree forecast [START_REF] Guan | Relationships between defoliation, spot pathosystem[END_REF]. Statistical regression model is relatively simple and accurate, and still it is time-consuming and laborious, due to the requirement of fine and enough information of disease, environmental factors and their relationships.

In environmental conditions and physiological cycle, the reflectance spectrum of the crop follows certain distribution law. However, when subjected to disease invasion, the change of crop physiology and structure will result in the change if its reflection spectrum [START_REF] Crooks | SAR observations of dry land moisture-towards monitoring outbreak areas of the brown locust in South Africa .Proceedings of IEEE International Symposium on Geosciences and Remote Sensing[END_REF].Variation of its reflectance spectra during the disease occurrence can be used as indicators to predict or identify crop diseases For example, it demonstrates that crop spectral changes before and after the study onset are used to effectively identify the corn borer pest, wheat embroidery disease and the cotton wilt disease. Overall, the multi-spectral remote sensing prediction methods can accurately monitor the scope and area of crop pests and diseases, but not be able to predict earlier occurrence of crop diseases. After the occurrence of crop diseases, the changes of its physiological and ecological structure and reflectance spectra are not synchronized, but the slow responds. In fact, the predicted results of the multi-spectral remote sensing prediction methods show the late extent of disease occurrence, but not for early prediction in advance.

In essence, statistical models or multi-spectral remote sensing prediction methods the region to be predicted as an isolated, static unit of non-interaction with neighboring areas, and cannot give a reasonable explanation of driving mechanism that the spatial spread of disease runs from infected areas spread to neighboring areas.

In this paper, we first briefly survey the advantage and disadvantage of two traditional methods of disease occurrence forecasting, and focus on the natural essence of the cross-boundary diseases, and propose a complex network-based model to simulate the spatial spread of a new disease from a early source area of its out breaking to adjacent ones, and finally throughout the whole region. Our contribution is that the experimental results show the influence of spatial constraint on disease spread using our proposed model.

The following of the paper is organized as follows. Section 2 gives an emphasis of the spatial transition of cross-boundary diseases and propose a complex networkbased model to simulate its spatial spread when a new and unknown disease outbreaks. Section 3 presents experimental results on real data, and finally, Section 4 makes a conclusion of the paper.

Model hypothesis

Agricultural cross-border disease usually first occurs in a planting area (the source of the disease area), and then gradually spread to the surrounding adjacent planting areas, finally throughout the region. There is a significant difference between cross-border agricultural diseases and conventional ones, which disease transmission only from the source of the disease area spread to nearby areas that are not infected. Additionally, considering that radius of disease spread is limited, whether the disease occurrence in non-infected areas appears or not depends on its spatial distance to the source of the disease. So for any new and unknown disease, a hypothesis is proposed, that is :(1) its propagation path is only from the pathogens areas to the adjacent non-epidemic areas, and whether the outbreak of the new epidemics in crops of adjacent nonepidemic areas depends on spatial spread radium of the epidemic; (2) crops are certainly infected within a radius of the epidemic spread, regardless of environmental factors, while not outside the radius;(3)subjectively, epidemic spread is not allowed to occur between adjacent non-epidemic areas to reduce the complexity of the network.

Based on this, the spatial spread of trans-boundary diseases is considered as network evolution problem, the complex network theory method is used to solve the problem. Given study area, crop plaques are abstracted into a graph node, the onset of disease from one planting unit spread to neighboring units, even edge between two nodes, in order to build a crop epidemic communication network. To further introduce the process of construction of epidemic spread network in detail, an example of disease spread is given. Figure 1 indicates a crop planting area consisting of A~G seven polygon blocks. In the blocks, different/same crops grow, and are susceptible to the infection of a new epidemic. Supposing a new epidemic first appears in the A block, and then the epidemic spreads to the B block close to A and within the spread radium, finally in all other blocks. By the limit of the model assumption, epidemic spread doesn't appear between the blocks of F and G, which are non-epidemic areas. The epidemic spread is abstracted to the network, which its nodes stands for the blocks and directed edge for the interaction of two nodes, and then a simple network consisting of seven nodes and six edges appears (shown in figure 2).

Evaluation index

There is a great influence on network topology on the spread of the disease [START_REF] Vittoria | Epidemic modeling in metapopulation sysyems with hetergeneous coupling pattern: Theory and simulations[END_REF][START_REF] Ball | Epidemics with two levels of mixing[END_REF][START_REF] Barthelemy | Dynamical patterns of epidemic outbreaks in complex heterogeous networks[END_REF].Investigation of network topology characteristics helps to understand the disease propagation. The network structures, which meet random, uniform, small world or free scale distribution law are generated to investigate the robustness and vulnerability of different types of network on disease spread [START_REF] Barthelemy | Dynamical patterns of epidemic outbreaks in complex heterogeous networks[END_REF][START_REF] Cooper | Delaying the international spread of pandemic influenza[END_REF][START_REF] Colizza | Reaction-diffusion process and metapopulation models in heterogeous networks[END_REF]. Network degree distribution exponent, clustering coefficient, node / edge betweenness, average path length are used to indicate the network topology characteristic [START_REF] Newman | Random graphs with arbitrary degree distributions and their applications[END_REF][START_REF] Bodin | A network approach for analyzing spatially structured populations in fragmented landscape[END_REF][START_REF] Albert | Statistical mechanics of comlex network[END_REF]. Here two indexes of clustering coefficient and average path length are considered as the indicators of epidemic spread.

A clustering coefficient is a measure of degree to which nodes in a graph tend to cluster together. Average path length is a concept in network topology that is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network. Giving a disease spread network consisting of a set of vertices V and a set of edges E between them. The degree k i for a vertex V i is defined as the number of its immediately connected vertexes, E i as the number of its immediately connected edges and local clustering coefficient (C i ) for V i is then given by the proportion of links between the vertices within its neighborhood divided by the number of links that could possibly exist between them. So for all vertexes, the average clustering coefficient (CC) can be obtained from the equation ( 2). Let d (v i , vj) denote the shortest path between v i and v j . The average path length calculated by the equation (3).

) 1 (   i i i i k k E C    n C C i i       j i j i v v d n n APL , ) , ( ) 1 ( 1 

Experiment

Data Preparation

In this paper, all algorithms are encoded using vb.net language, and run inWin7 operation platform. The Study area of Beijing is located at latitude 39 º 28 '~ 41 º 05', longitude 115 º 24 '~ 117 º 30' with Plains of southeast, western and northern mountains, has a temperate continental climate.

In the study area, there are major land use types of grain field, woodland, orchard, and residents and facilities for agricultural land (vegetable).The land use of wheat data is abstracted from classified agricultural land in Beijing, and used to construct to the spread network of diseases, and simulate the response of diseases spread network of environmental elements, spatial elements and scale changes.

Classification of the land use of wheat in the suburb of Beijing are with the following data: the remote sensing data (4 m spatial-resolution panchromatic image of Landsat TM in March, 2007 and 100m spatial-resolution Beijing-1 satellite image in March, 2007), basic GIS data (1:10000 basic farmland of 1996, 1:100000 land use data, as well as 1:10000 Beijing administration, roads, water systems, residential and other data).To identify the land use type of wheat n the suburb of Beijing through remote image classification, data preprocessing is first performed. The process of data preprocessing includes projection transformation and convert geographic coordinates, geometric correction and image fusion. Projective transformation and conversion of geographical coordinates are done for all data, to convert to a variety of reference system to the uniform Beijing local coordinate system.

The Image geometric correction process is as follows: first crudely correct Beijing-1 satellite image and TM image geometry using1:50000 topographic map of the Beijing, and then further precisely correct them with truth ground GPS control points, in order to ensure the geometric correction accuracy of better than 1 pixel. The Beijing-1 satellite panchromatic image is high clear, but subject to the limitations of the single-band image, and so it is very difficult to distinguish its approximate surface features. TM image has multi-spectral bands characteristics of surface features, but the low resolution. Therefore, after fusion of the Beijing-1 satellite image and TM image, a high-resolution and multispectral image is obtained.

Based on the reference image (the fusion image), visual interpretation of surface features begins. In interpretation process, the following data of 1:1000000 basic farmland data of 1996, 1:100000 land use data of 2004 as well as 1:10000 Beijing administration, roads, water systems, residential and other data are taken as the priori knowledge. Clearly, the land use types of non-concern, such as cities, villages, water, roads, etc., are directly excluded, and but the type of wheat is outlined and specified attributes. The land use types not easy to determine are further confirmed through aerial photos and field investigation. Finally, the land use data of wheat (shown in figure 3) is used to map into the network which is characteristic of spatial spread of disease.

Fig. 3. The land use type of wheat

Topology characteristics of disease spread

The land use of wheat extracted from remote sensing image of Beijing is mapped to an epidemic spread network. To test the influence of topology structure on epidemic spread, a new epidemic first randomly outbreak in a node, and then gradually transfers from the node to others. Considering epidemic spread radium heavily affect network structure, a variety of epidemic spread radium are set to generate the corresponding networks. For every generated network, its clustering coefficient and average path length are calculated (shown figure 4(a-b)). The results show that the network is not connected with the small radius of disease spread, and disease spread occurs only in a small amount of local connected subnets. With increasing of disease transmission radius, the network's clustering coefficient and average path length gradually show the topology features of a small world network that means when spread radium exceeds a certain range, spatial segmentation is no longer a factor to inhibit epidemic spread. So an interesting conclusion can inferred from the above results, which spatial isolation can deter epidemic spread with small spread radium, while with the radium more than a certain range, the epidemic will quickly spread the whole region.

Conclusion

In this paper, we have introduced and discussed the problem of a crossing boundary crop disease spread, and construct a complex network-based model to simulate crop disease spread. We took wheat-growing distribution data of Beijing in 2007 by image classification, and further abstracted patches of wheat growing into network nodes, and finally constructed the disease spread network to analyze its topology characteristics. The results demonstrate that the network gradually shows the topology features of a small world network with epidemic spread radium increasing. Based on that, an interesting conclusion can be inferred that spatial segmentation may be a reasonable treatment in control of a new epidemic spread with a small spread radium, while when the radium exceeds a certain threshold, spatial segmentation will not so work that the epidemic will quickly spread all over.

Fig. 1 .

 1 Fig. 1. The process of crop disease occurrence and spread. Polygons means different neighboring planting plaques

Fig. 2 .

 2 Fig. 2.Directed graph mapped into spatial spread of crop disease. the circles stand for nodes, while the arrows for the edges

Fig. 4 .

 4 Fig. 4. The results of avarage path length (APL) and clustering coefficient of disease spread network, respectively.

modeling of cross-boundary disease spreadIn previous section, the goodness and limits of two traditional methods are briefly surveyed. This section will focus on the characteristics of cross-boundary disease transmit ion , and further give a hypothesis to model and simulate the process of its spatial spread when a new and unknown disease outbreak.

Acknowledgment

The authors are very grateful to those who collected the data of remote sensing images and GIS used in the paper.

The authors were supported financially by Shanghai Natural Science Foundation (No.11ZR1432700)


Minghua Shang

Guoying Tian

Leilei Qin

Jia Zhao

Huaijun Ruan

Fengyun Wang

Greenhouse Wireless Monitoring System Based on the ZigBee 1

Keywords: Greenhouse, Monitoring, WSN, ZigBee

Designed a WSN-based wireless monitoring system for greenhouse environment. The overall structure of the system is introduced. The design and realization of the monitoring node, the gateway node and the upper computer system are respectively described. Through the practical application in the greenhouse, it is proved that the system comprehensive performance is significantly better than the traditional greenhouse monitoring system.

Introduction

The traditional greenhouse monitoring system is mainly based on the cable communication mode which has a series of questions such as complex wiring, difficult maintenance, inflexible deployment for sensor nodes and so on. These questions limit the popularization and application of greenhouse monitoring system to some extent. With the rapid development of modern information technology, there are many kinds of wireless communication technology such as WiFi,bluetooth,ZigBee and so on.WiFi and bluetooth etc cannot be widely used in greenhouse monitoring field because of its high cost and large power consumption etc shortcomings. As a brand new information acquisition and processing technology, the wireless sensor network based on ZigBee has a big scale, small volume, low cost, AD hoc network etc characteristics which has wide application prospects in agricultural environment monitoring field.

In view of the problems and shortcomings occurred in the current monitoring system of greenhouse environment, a wireless monitoring system for greenhouse is designed in this article which has designed low power consumption, low cost, flexible networking, friendly interface, convenient on-site and remote management. It is successfully applied.

Overall Design

System Requirements Analysis

The environment in greenhouse has many features such as large temperature difference between daytime and nighttime, large air humidity, bad gas exchangeability, poor light intensity, strong soil acidity and so on. There are more crop species planted in greenhouse with dynamic change. The monitoring area is large and monitoring parameters are too many. In addition, the farmer is also very sensitive to whole cost and reliability of monitoring system. Through investigation and analysis, most of the current acquisition demand for greenhouse monitoring environmental parameter is mainly focused on six factors i.e. air temperature and humidity, soil temperature and humidity, light intensity, concentration of CO 2 . In addition, a few greenhouses also need to collect EC value, pH value of nutrient solution and outdoor weather factors and other information. The quantity of sensor nodes can increase or decrease at random and the nodes can change its position without affecting the normal operation of the system according to crop growth, species replacement or greenhouse space structure change etc. The system is with intuitive interface, comprehensive analysis easy to use and low application cost etc.

Overall Structure

In accordance with the characteristics of greenhouse monitoring system and the above mentioned function demand, this paper integrates the wireless sensor network technology, ZigBee technology and embedded technology to design the wireless sensor network monitoring system based on ZigBee for greenhouse. The whole system hierarchy is shown in figure 1. The system consists of monitoring node, gateway node and a host computer three layers. Monitoring node includes sensor node and actuator node deployed in greenhouse monitoring region which is automatically establish unified wireless sensor network through the ZigBee protocol. Each sensor node collects the real-time greenhouse environment data to the gateway node by multi-hop routing mode, the actuator nodes receives the real-time control orders from gateway node to control the fan etc actuating mechanism. The gateway node realizes the local communication through the serial port or remote communication through the Ethernet and GPRS etc modes which provide the sending support for monitoring data and control data. Host computer system provides a user interface to realize the interactive management operation between the user and system.

Sensors

Monitoring Node Design

Hardware Design

Monitoring nodes are the base of greenhouse monitoring system which is the basic unit to information perception, executive control and network function of wireless sensor network. According to different division of labor, monitoring nodes are divided into the sensor node and actuator node which are introduced as follows.

1)Sensor nodes

The hardware design core of sensor node is microprocessor chip. The microprocessor of node finishes data acquisition, data processing, wireless communications etc functions in collaboration with the wireless transceiver module. The hardware structure diagram of wireless sensor node is shown in figure 2. The hardware design of wireless sensor node mainly considers the low cost, low power consumption, stability and reliability etc factors. Its main features are as follows:  enhanced high speed 8051 kernel;  support the latest ZigBee 2007PRO protocol;  support 2v-3.6v power supply range;  3 power management mode: the wake up mode 0.2mA, sleep mode 1uA, interrupt mode 0.4uA, with ultra low power consumption characteristics;  high density integrated circuit. The node design based on the CC2530 only needs few peripheral circuit to realize data acquisition and transmission which greatly improves the reliability and reduce the power consumption of the system. b)Sensor In the selection of sensor, it is requied that the sensor has higher precision and lower power consumption. It adopts 5 sensors in this design. Their technical parameters are as follows:

 SHT11 digital temperature and humidity sensor, detecting current 0.5mA, standby current 0.3uA, temperature accuracy ±0.5℃, humidity accuracy ± 3.5%RH, I2C bus interface.  ISL29010 digital light intensity sensor, detecting current 0.25mA, standby current 0.1uA, measurement accuracy ±50Lux, I2C bus interface.  H550 digital CO 2 sensor, working current 15mA, accuracy ±30ppm, I2C bus interface.  SLST1-5 digital soil temperature sensor, measuring current 1.5mA, standby current 1uA, measurement accuracy ±0.5℃, single bus interface.

 FDS100 simulation model of soil moisture sensor, working current 15mA, accuracy ≤3%, analog output signal.

2)Actuator nodes

The actuator node carries out switch control for indoor fan, sunshade etc equipment according to the control command from the host computer. The actuator node includes a drive circuit, but not includes the sensor circuit. The hardware structures of actuator node and sensor node are approximately same. The drive circuit of actuator node is mainly used to control solenoid valve associated with the actuator etc switch equipment which can output multichannel high-low level control signal. Data communication is of master-slave mode.

Node Software Design

The chip program of sensor node is based on the Z-Stack protocol. Its development environment is IAR 7.51A. Z-Stack is Zigbee protocol stack launched by TI company in April, 2007. It has been generally accepted and widely applied within the industry because of full support for Zigbee2006 and Zigbee PRO feature set and conforming to the latest intelligent energy standard. The protocol stack provides a protocol stack scheduler named operating system abstraction layer (OSAL). For developers, except being able to see the scheduling procedure, the specific implementation details of any other protocol stack operation are encapsulated in the library code. For the specific application development, the corresponding operation is finished by calling the API function interface of protocol stack, such as network device initialization, network configuration, network starting, collected data transmitting, control command receiving and so on to realize ad hoc network of wireless monitoring nodes distributed in several greenhouses. In addition, the node software designs flexible, convenient, dynamic configurable regular data collection, regular sleep and wake-up etc functions in order to further reduce the node power consumption.

Gateway Node Design

Gateway Node Hardware Design

The gateway node is the key device to realize the protocol conversion between wireless sensor network and external communication network. It not only has the function of data transmission, but also has equipment management function. When design the gateway node, follow the concept of modular design that the gateway system is divided into data collection module, processing/storage module, access module and power supply module.

The design is based on S3C2416 core board and establishes the hardware platform of gateway node of wireless sensor network. The hardware structure of gateway node is shown in figure 3.

1)Data Collection Module

It is the coordinator node in the wireless sensor network to achieve the data collection and aggregation of greenhouse environment. In this design, the interface type between data collection module and processing/storage module is UART which communicate through serial.

2)Processing/Storage Module

It is the core module of gateway node. S3C2416 core board integrates Samsung S3C2416XH-40 processor based on ARM926EJ core which main frequency is 400MHz. In additio it also integrates 512MB DDR2 SDRAM and 128MB Nand Flash and provides abandunt peripheral equipment interfaces so as to furthest reduce the cost of the system development which is very suitable for high cost-performance and low power requirements of embedded equipment.

3)Access Module

It mainly adopts ethernet mode to connect the gateway to the external network. The core board integrates local high-speed ethernet chip LAN9220 of SMSC company. It realizes the Ethernet data transmission under the support of operating system. Network transformer uses HR601680 which main function is to match the impedance, enhance signal and realize voltage isolation etc. In addition, GPRS, as an optional way, uses MC37I module of Siemens company.

4)Power Supply Module

The module is responsible for the power supply of gateway node. The power module designed here has the function of hot plug and voltage conversion. The possible power supply mode comprises utility power, solar power and storage battery.

Software Platform Design of Gateway

Embedded Linux is a small operating system designed in accordance with the requirements for embedded operating system which consists of a kernel and some system modules customized as per requirements. Its kernel is very small and has the characteristic of multi-task and multi-process which is very suitable for transplantating to embedded systems. This paper transplants Linux2.6 kernel and associated drive on S3C2416 target platform and uses the open-source LwIP protocol stack instead of the TCP/IP protocol stack of Linux system and then designs the program on application layer of gateway node on the base of embedded Linux and LwIP. It mainly realizes two functions i.e. gateway node configuration through Web server and connecting Modbus serial communication link to ethernet through Modbus/TCP protocol.

1)Web Server Function Design

In the gateway configuration mode, the gateway node serves as the Web server, while the client is any one computer that connects to gateway RJ45 interface through the crosswire.

After gateway reset starting, operating system will start the Web service. The client sends out a request of HTTP GET method to the gateway through the browser. After receiving the request, the gateway judges the method field of message. If it is the GET method, it is the first request and returns the configuration information of Web pages and gateway embeded in the Flash out of the chip back to the client. After parameters configuration, the user clicks SUBMIT and the client sends out POST request to the gateway. The gateway erases the original configuration information in the Flash out of the chip and then writes the new information so as to ensure that the gateway configuration is not lost after resetting and the configuration information will take effect after gateway restarting.

2)Modbus/TCP Protocol Conversion Function Design

After reset starting, the gateway firstly performs a series of initialization and finally starts the Modbus server to realize the transmission between Modbus/TCP frame and Modbus RTU frame in the serial link. When a client inquires, it firstly sends out a connection request to 502 port of gateway, the gateway executes the interrupt service program to awake Modbus server in a wait state and creates the TCP connection, the client then sends a Modbus/TCP request frame and waits for a response. The gateway analyzes the frame to generate a query frame of Modbus RTU format to the serial link. If it receives RTU response frame in serial link, the frame is encapsulated into a Modbus/TCP reply frame which is sent to the Ethernet client and disconnect.

Design of Upper Computer System

The design is under VS.NET development environment and based on the SQL Server database and C# language to write the monitoring management software of greenhouse environmental information to complete the sensor node management and data management of greenhouse environment. Its main function is as follows:

1

)Real Time Monitoring

The user can view the latest environment parameters as well as the status of fan, water pump etc control equipments in greenhouse field and can control and adjust in the current interface which is convenient for users to operate.

2)Historical Data User can inquire monitoring data through many modes can also make a period of history data into curve to reflect the changes in greenhouse environment more intuitively.

3)Device Control It includes two modes i.e. automatic control and manual control. In manual mode, the user can remotely control the switches of fan etc equipments. In the automatic mode, the system can automatically adjust the switches of fan etc equipments according to the environmental monitoring parameters.

4)Alarm Management

The user can define multilevel alarm conditions and can view the detailed information of set alarm. In an alarm condition, the user can specify the operation when alarming e.g. start alarm and turn on the switch of fan etc devices and send an alert notification etc.

5)Node Management

It includes the display and configuration of node ID, node location, sensor types and parameters, sampling period, running state, update time etc attributes. The user can master the working status of all monitoring node in field and discover equipment failure in time.

System Application

Node Deployment Solution

The system designed in this paper is applied in 1# greenhouse of Ji'nan Modern Agricultural Science and Technology Demonstration Park. It is placed 12 nodes in the vegetable cultivation area in the greenhouse where includes 10 sensor nodes and 2 actuator nodes. In addition, one gateway node is arranged in management area in the greenhouse. The air temperature and humidity sensor, the light intensity sensor, CO2 sensor and the corresponding sensor nodes are integrated into one whole body, and the soil temperature and humidity sensors are respectively connected to sensor node by a cable and the other end is inserted into the soil about 8cm, cable length 1.5~2m. Each sensor node is placed on a monitoring node position through the fixed supporting rod or rope hanging upside down mode and the height from the ground is generally about 1.2m. The sensor node uses one 1# batteries and the actuator nodes and gateway nodes use DC power supply.

System application

The average communication distance between the nodes is about 20m, the nearest distance between monitoring node and gateway node is about 100m. Through installation and operation, after starting of gateway nodes, the average time required for node binding and self organizing network establishing is less than 1min. The sampling frequency setting scheme of sensor node is 2min for air temperature and humidity, 10min for soil temperature and humidity, 3min for light intensity and 30min for CO2 concentration. After completing data acquisition and transmission, each node will automatically enter sleep state until the next sampling cycle wakes up. Through actual test, the system can support the dynamic adjustment of sensor nodes. When add, remove the nodes or temporarily change the position of the nodes, the operation of whole wireless sensor network will not be affected. The host computer system can receive and display temperature and humidity, light intensity and CO 2 concentration etc environmental data from the sensor nodes in real time and can display real-time operating state of each node. When the collected environmental parameters exceed the alarm threshold, if the control mode is of automatic control, it will automatically start the corresponding mechanism according to the alarm processing rules to realize the automatic control of greenhouse environment.

Fig. 1 .

 1 Fig.1. Framework of the greenhouse wireless monitoring system

Fig. 2 .

 2 Fig. 2. The hardware structure of the sensor nodes a)CC2530Considering the cost and performance etc factors, CC2530 is selected. It integrates the microprocessor module and a wireless transceiver module in one single chip. CC2530 is introduced by United States TI companies for IEEE802.15.4 and ZigBee application. At present, it is also one of the most outstanding microprocessor among many ZigBee equipment product.Its main features are as follows:  enhanced high speed 8051 kernel;  support the latest ZigBee 2007PRO protocol;  support 2v-3.6v power supply range;  3 power management mode: the wake up mode 0.2mA, sleep mode 1uA, interrupt mode 0.4uA, with ultra low power consumption characteristics;  high density integrated circuit. The node design based on the CC2530 only needs few peripheral circuit to realize data acquisition and transmission which greatly improves the reliability and reduce the power consumption of the system. b)Sensor In the selection of sensor, it is requied that the sensor has higher precision and lower power consumption. It adopts 5 sensors in this design. Their technical parameters are as follows: SHT11 digital temperature and humidity sensor, detecting current 0.5mA, standby current 0.3uA, temperature accuracy ±0.5℃, humidity accuracy ± 3.5%RH, I2C bus interface.  ISL29010 digital light intensity sensor, detecting current 0.25mA, standby current 0.1uA, measurement accuracy ±50Lux, I2C bus interface.  H550 digital CO 2 sensor, working current 15mA, accuracy ±30ppm, I2C bus interface.  SLST1-5 digital soil temperature sensor, measuring current 1.5mA, standby current 1uA, measurement accuracy ±0.5℃, single bus interface.

Fig. 3 .

 3 Fig.3. The hardware structure of the gareway nodes

Supported by: Science and Technology Development Plan of Shandong Province, China (2011GGC02035)