Kunya Fu

Hongxin Cao
email: caohongxin@hotmail.com

Wenyu Zhang

Weixin Zhang

Daokuo Ge

Yan Liu

Chunhuan Feng

Weitao Chen

Wenyu Zhang

Weixin Zhang

Analysis of Changes in Agronomic Parameters and Disease Index of Rapeseed Leaf Leukoplakia Based on Spectra

Keywords: Rapeseed leukoplakia, Spectrum reflectance, Disease index, Agronomic parameters

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

Introduction

Rapeseed is one of very important oilseed crops in the world, and its plant area in normal year is about 18-30 million ha. The plant area of rapeseed in China is about 6-7 million ha, and its total yield is about 10-13 million tons, which ranks the fifth place in crop production in China [1]. However, the control for diseases and insect pests is the key to crop production with high yielding, good quality, better benefit, ecology, and safety, and the traditional control for diseases and insect pests depends on manned or machined application of pesticide, which has some obvious disadvantages such as bigger work intensity, high cost, and environmental problems, etc. [START_REF] Zhang | The Pollution of Agricultural Environment with Chemical Pesticide and its Control[END_REF][START_REF]China's Pesticide Pollution and Its Being Problems and Suggestions[END_REF][START_REF] Zhu | Controlling the Environmental Pollution of Chemical Pesticide, Carrying out Consecutions Protection[END_REF][START_REF] Tu | Thinking of Problems of Agriculture Chemicals and Circumstance, Reclaiming and Rice Cultivation[END_REF].

The spectrum technology is a new subject that detects changes in properties and shapes of object as non-touch manner through using physics theories, mathematics methods, and geography rules [START_REF] Wang | Review for space optical remote sensing technology[END_REF]. In recent years, studies on crop spectrum technology have made rapid progress. Some researchers pointed out that vegetation reflective spectrum can denote its health status, and some relevant physiological biochemistry indices, e.g. Xue et al. [7,8], Zhang et al.[9], Li et al. [10], and Hou et al. [11] developed the reflective spectrum-based models of plant nitrogen content for rice, wheat, cucumber, orange, and cotton, respectively; Tian et al. [12,13], Zhang et al.[14], Mao et al. [15], and Fu et al. [16] proposed the reflective spectrum-based quantitative models of plant water content for rice, wheat, rapeseed, lettuce, and alfalfa, respectively; and Xie et al. [17], Zheng et al. [18], Chen et al. [19], Zou et al. [20], and Li et al. [21] set up the reflective spectrum-based quantitative models of leaf SPAD value for rice, wheat, corn, tea plant, and apple. However, few studies have focused on the spectrum-based quantitative models for agronomic parameters of crop disease sample.

The objectives of this research were to find characteristic spectrum band using comparison of the reflective spectrum of health and disease leaf for rapeseed, to reveal the quantitative relationships between the characteristic spectrum and agronomic parameters of disease leaf for rapeseed, and to provide a basis for spectrum-based identifying of rapeseed leaf leukoplakia.

Materials and Methods

We conducted field experiments at the Jiangsu Academy of Agricultural Sciences farm, Nanjing, China (32° 03′ N) during the 2012 to 2013 and 2013 to 2014 growing seasons. The soil type of the experimental area is a hydragric anthrosol. Soil test results indicated the following: organic carbon, 13.8 g kg -1 ; whole nitrogen, 2.03 g kg -1 ; available phosphorus, 20.30 mg kg -1 ; available potassium, 139.00 mg kg -1 ; and pH, 7.31.

Materials

This study used 3 rapeseed cultivars breed by Institute of Economical Crops of Jiangsu Academy of Agricultural Sciences: (V1) Ningyou18 (normal), (V2) Ningyou16 (normal), and (V3) Ningza19 (hybrid).

Methods

Experimental conditions and design

The experiments on cultivars were a randomized complete design with 3 varieties (listed above) and 3 replications. The sowing date was Oct. 8 and the transplanting date was Nov. 9 for 2012 to 2013, and Oct. 3 and Nov. 1 for 2013 to 2014, respectively. The 5-leaf seedlings (30, and 33 seedling days for 2012 to 2013, and 2013 to 2014)were transplanted into 60-by 30-cm spacings in 7.98-by 3.50-m subplots. The fertilizer included 90 kg N ha -1 (as urea), 90 kg P 2 O 5 ha -1 , and 90 kg K 2 O ha -1 . The basal: overwinter: elongation fertilizer ratio was 5:3:2. The 15 kg borax ha -1 was sprayed on foliage after elongation for 2013 to 2014. Other management activities followed local production practices.

Measurements

Spectrum: we used CropScan MS16R Multispectral Radiometer with 16 wave bands (460, 550, 650, 710, 760, 810, 870, 1080, 1200, 1280, 1320, 1480, 1540, 1600, 1650, and 1700nm) to collect reflective spectrum of healthy and leukoplakia disease leaf at 31.1° view field angle under field canopy background and under black cloth.

(1) Reflectance spectrum of leaves in the field: we randomly selected 5 to 10 healthy, and leukoplakia disease leaf, and investigated their reflective spectrum during 10:00 to 12:00 AM under clearness or few cloud every 7 days for Mar. 15 to May. 3 in 2013, and for Apr. 9 to May.6 in 2014. At measuring, the spectrum sensor should be plumbed with ground, and hold out about 30 cm high over target leaf. The data of each leaf were measured in 5 repeats, and their average values were computed.

(2) Reflectance spectrum of leaves under black background: after finishing measurement in the field, the target leaves were taken from the plant, and lain on the black background to examine their reflective spectrum taking nature light as light source with the same method in (1).

Leaf water content: leaf fresh weights were weighed before reflectance spectrum of leaves was determined under black background, and after finishing measurement under black background, put in oven, and dried in 30 min. at 105℃, then at 80℃ until reaching a stable weight, where dry weight were weighed using a 0.001g electro-level. Leaf water content can be computed.

Leaf SPAD value: we used SPAD-502 Chlorophyll meter to measure SPAD value of upside, middle part, and basal for the healthy and the leukoplakia disease leaves, and their average values were computed.

Disease index (DI): we acquired leukoplakia disease leaves images using Nikon P5000 numeral camera under black background and 1 cm 2 reference, gained pixels and numbers of leukoplakia disease spot, and leaf area in Photoshop CS4.0, and the DI was computed.

All experiment data were analysed using Microsoft EXCEL.2003 and SPSS ver. 19.0.

Model validation

The models developed in this paper were validated by calculating the correlation (R) , with i= sample number, n = total number of measurements, n-1= n when n≥30, X Si = simulated value, and X Oi = observed value.

Results

Changes in reflectance spectrum of healthy and leukoplakia disease leaves in different growth periods for rapeseed

Reflectance spectrum of leaf in field

The field experiment data in 2013 showed that the changes in spectrum reflectance of rapeseed leaf in field were decreasing trend with growth period duration, and the biggest spectrum reflectance (around 810nm) from early anthesis to the 17 th day after end anthesis was reduced from 45% to 30%, in that the decline for spectrum reflectance of leukoplakia disease leaves was earlier than that of the healthy; The biggest spectrum reflectance from the 9 th day after early anthesis to the 9 th day after end anthesis was kept at around 35% for healthy leaves, and reduced at around 30% for leukoplakia disease leaves. Thus, this period was the optimum time for identifying rapeseed leukoplakia disease in field based on reflectance spectrum, and the average spectrum reflectance was about range of 30% to 35% for leukoplakia disease leaves; In total, the spectrum reflectance in near infrared band (760-1080nm) for healthy leaves was higher than that of the leukoplakia disease, while there was a reverse status in short-wave infrared band (1080-1700nm) except for around 1500nm moisture absorb peak, and there was no obvious difference in others; In addition, because of no obvious difference for color of leukoplakia disease and healthy leaves in field, there was no obvious difference for the spectrum reflectance in visible light band (460-760nm) for leukoplakia disease and healthy leaves (shown in Fig. 1 to Fig. 4).

Reflectance spectrum of leaf in black background

In order to eliminate the effects of soil environmental factors in field on reflectance spectrum of leaf, Observations of reflectance spectrum for leaf at various growth period made under black background (shown in Fig. 5 to Fig. 7), the results showed that the spectrum reflectance in visible light band (460-760nm) for healthy leaves less than that of the leukoplakia disease, and that of the reasons were that chlorophyll was destroyed when the leukoplakia disease occurred, absorption to light from coloring matter was reduced, and this made the spectrum reflectance in visible light band increase; In near infrared band (760-1080nm), because the rapeseed leaf cell structure was broken when the leukoplakia disease occurred, the number of cell layer was diminished, this made the light transmission increase, and the spectrum reflectance was weaken correspondingly; Although the spectrum reflectance in near infrared band for healthy leaves was slight decreasing with rapeseed growth period duration, its highest value was from 70% to around 65%, there was more obvious diminishing for that of the leukoplakia disease leaves, and in total, the distance of spectrum reflectance between the healthy and the leukoplakia disease leaves was gradually increased in advantages to leukoplakia disease identifying.

Correlation between the reflectance and the disease index (DI), and agronomic parameter

Correlation between the reflectance and the DI, and agronomic parameter

The correlation between the reflectance and the disease index (DI), and agronomic parameter for disease leaves was shown in Table 1. We saw that the correlation between the reflectance and the DI at 1200nm, 1480nm, and 1600nm was significant with p<0.05, that of between the reflectance and the disease leaf moisture content in short-wave infrared band 1320nm to1650 nm was significant with p<0.01, in which there was the highest negative correlation, -0.810, at 1540nm, that of between the reflectance and the disease leaf nitrogen content in band 810 nm to1320 nm, and 1540 nm to 1700 nm was significant with p<0.01, in which there was correlation larger than 0.70 at band 810 nm, 870 nm, and 1080 nm, and that of between the reflectance and the disease leaf chlorophyll SPAD value in band 1200 nm, 1280 nm, and 1540 nm was significant with p<0.01. Note:The asterisk * denote significant correlation at p<0.05, and the asterisk ** denote significant correlation at p<0.01; r (20, 0.05) =0.423, and r (20, 0.01) =0.537.

Table1. Correlation coefficients between reflectance and DI, agronomic parameters (n=22)

Changes in agronomic parameters with the DI

The field experiment data in 2013 showed that there were declines in the moisture content, the nitrogen content, and the SPAD value for rapeseed disease leaf (shown in Fig. 8 to Fig. 10) with the DI increase, which revealed that some leaf physiological indices were in response to the effects of leukoplakia disease on rapeseed leaf, of that there was better correlation between the SPAD value and DI.

However, we known that the SPAD value for rapeseed disease leaf increased with its nitrogen content increase through correlation analysis (Fig. 11), and the studies in literature [24-26] also indicated that there was higher correlation between the SPAD value and nitrogen content. Therefore, we considered that the SPAD value can be as one of index indicated severity degree of leukoplakia disease and leaf nitrogen content for rapeseed. In order to reflectance-based quantify some leaf agronomic parameters, it need to develop the regression models between the multi-spectrum variables and leaf agronomic parameters, such as the moisture content, the nitrogen content, and the SPAD value of disease leaf. The correlation analysis indicated that there was higher correlation of reflectance for various bands, and a serious issue with multicollinearity (Table 2). Thus, we found regression functions between the agronomic parameters and the reflectance using multiple stepwise regression and principal component analysis, of that the quantitative model for the disease leaf moisture content was taking the reflectance at 1540nm (Eq.1 in

Table 3), for the disease leaf nitrogen content taking the reflectance at 810nm (Eq.2 in Table 3), and for the disease leaf nitrogen content taking the reflectance at 1280nm, 1080nm, and 650nm as independent variable (Eq.3 in Table 3), respectively, and the variable inflate factor (VIF) of these regression functions were less than 10, which revealed that there were higher tolerance among reflectance in various bands.

The statistics test for the models was shown in Table 4. 1.00 -0.41 -0.41 -0.42 -0.42 -0.12 1.00 -0.47 -0.42 -0.44 -0.40 -0.09 0.92 1.00 1080 -0.45 -0.38 -0.40 -0.34 -0.05 0.82 0.98 1.00 1200 -0.69 -0.78 -0.75 -0.79 -0.65 0.79 0.67 0.56 1.00 1280 -0.73 -0.76 -0.76 -0.76 -0.54 0.85 0.86 0.81 0.93 1.00 1320 -0.72 -0.74 -0.75 -0.75 -0.52 0.80 0.87 0.84 0.87 0.99 1.00 and the SPAD value (1200nm, 1280nm, 1320nm, and 1540nm) for the disease leaf, made dimension reduction, and found principal components. Then, regression analysis was made selecting the first principal components as independent variable.

Because almost the parameters in regression equation with the highest decision coefficient had no significant in t-test (Table 7), and the decision coefficients were less than that of the stepwise regression, it was seen that principal components regression was not suitable for regression of multi-band combination and agronomic parameters.

Model Validation

The stepwise regression models developed in this paper were validated by using independent experiment data in 2013, and 2014, and the results showed that the average absolute difference (da), the ratio of da to the average observation (dap) of simulation and observation in moisture content were less than 5%, and that of the nitrogen content, and the SPAD value were larger than 20% except for da for the nitrogen content (Table 8 and Table 9). The 1:1 figure on simulation and observation in the moisture content, the nitrogen content, and the SPAD value are represented in Fig. 12-16. As illustrated by comparisons of 1:1 map, the model on moisture content performed reasonably well, though performance of precision could probably be improved by further analysis. Because there was large difference of the severity degree of disease leaves collected in various growth periods, of that the severity degree of partial disease leaves was less, whose leaf color had no difference for the normal and disease leaves basically, and its chlorophyll structure was not destroyed, the correlation between the reflectance in visible bands (determined by chlorophyll absorption) and the DI was not high, and there was a significant correlation only in near infrared band (1480nm), which indicated that there were large effects of leaf moisture content on disease leaf reflectance. This was in accordance with the low correlation between the reflectance in visible bands and the SPAD value.

There were serious multicollinearity among bands

Because there was serious multicollinearity among bands, the independent variable with higher correlation was exclude using the stepwise regression, the multiple linear regression equations between the DI, the moisture content, the nitrogen content, and the SPAD value and reflectance were set up.

The effects of leaf thickness on SPAD value were in accordance with the former studies

Due to low correlation between the SPAD value and reflectance in visible light bands, and high correlation at 760nm, and 810nm (Table 1), leaf cell structure was principal factor of effect on reflectance in above bands, which was in accordance with the former studies. Bauerle et al. [29] pointed out that although there was significant between the SPAD value measured and leaf transmission and absorption, there was no high correlation between the SPAD value and reflectance. Yang et al.

[30] also indicated that the leaf thickness was important factor of effect on SPAD forecast model. Thus, we thought about not only visible light band, but also near infrared band in the reflectance-based SPAD forecast.

In addition, the nitrogen is important component of cell structure, thus changes in the disease leaf nitrogen content can affect its cell structure, and these changes can be reflected in spectrum reflectance, further in correlation with SPAD value.

Conclusions

This study screened out the reflectance-based bands with large difference between the disease and the normal samples, found the identifying bands with the same character through comparison of the reflectance for canopy in field and the reflectance for leaf in black background, the optimum growth period for reflectance-based rapeseed leukoplakia identifying was form the 11th day after early anthesis to the 9th day after end anthesis, and 760-1080nm was sensitive band in field identifying.

The correlation between the DI, the moisture content, the nitrogen content, and the SPAD value and reflectance in many character bands were analysed, and the multiple linear regression equations between the DI, the moisture content, the nitrogen content, and the SPAD value and reflectance were set up. A

  the average absolute difference (d a ), the ratio of d a to the average observation (d ap ), the standard error of absolute difference (S d , like the root mean squared errors, RMSE (Evers, et al.,2005))[22,23], and 1:1 figure of simulated and observed properties, which were defined as:

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .

 1234 Fig.1. Reflectance spectrum of Brassica napus leaves under the background of field groups anthesis (4.2)

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig.5. Leaf reflectance under natural light in early anthesis (4.2)

Fig. 8 .Fig. 10 . 3 . 3

 81033 Fig.8. Relationship between moisture content and DI Fig.9. Relationship between nitrogen content and DI

Fig. 12 .Fig. 13 .Fig. 14

 121314 Fig.12. Comparison of observation and simulation in moisture content of the same disease

Fig. 15 4 Discussion 4 . 1

 15441 Fig.15 Comparison of observation and simulation in moisture content of different diseases

s

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Table 2 . Correlation coefficients between the reflectance for various bands

 2

	460	550	650	710	760	810	870 1080 1200 1280 1320 1480 1540 1600 1650 1700
	1.00
	0.96	1.00
	0.93	0.98	1.00
	0.86	0.95	0.97	1.00
	0.8	0.87	0.81	0.84

Table 6 . Coefficient of equations and t-test

 6

	Equation	Unstandardized coefficients	Parameter	t	Sig.
	Moisture content	-0.071	b	-2.451	0.024
		0.001	a	2.642	0.016
		2.251	c	3.675	0.002
	Nitrogen content	-0.096	b	-4.896	0.000
		1282.838	a	0.813	0.427
	3.3.1.3 Principal components analysis. We selected the higher correlation bands with
	moisture content (460nm, 550nm, 650nm, 710nm, 760nm, 1480nm, and 1600nm),
	nitrogen content (810nm, 870nm, 1080nm, 1280nm, 1540nm, 1650nm, and 1700nm),

Table 7 . Coefficient of principal components regression equation and t-test

 7

	Model	Unstandardized coefficients	Corresponding variable	t	Sig.
	Moisture content	-0.001	c	-1.768	0.095
		0.005	b	-1.233	0.234
		0.007	a	-0.665	0.515
		0.861	d	90.769	0.000
	Nitrogen content	-0.734	c	-2.342	0.032
		-0.093	b	-0.716	0.485
		0.069	a	0.531	0.603
		3.353	d	17.752	0.000
	SPAD	-2.866	c	-0.535	0.600
		-6.698	b	-1.874	0.078
		-3.619	a	-1.139	0.271
		31.400	d	9.906	0.000

Table 8 .Stepwise regression model test of the same disease samples

 8

				Parameters	N		da	dap(%)	r	t	Sig.
				SPAD	5		22.684	52.901	-0.512	7.247	0.002
			moisture content	10		3.477%	4.138	0.810	-1.137	0.269
			nitrogen content	10		3.424%	85.465	0.748	0.520	0.616
	content		95 100
	含水率观测值Moisture	observed(%)	80 85 90
			75
			75	80	85	90	95	100
			含水率模拟值Moisture content simulated
					(%)

Table 9 . Stepwise regression model test of different diseases samples

 9

	Parameters	N	da	dap(%)	r	t	Sig.
	SPAD	21	20.260	76.849.	0.775	-12.934	0.000
	moisture content	21	5.268%	6.083	0.537	0.281	0.781
	100
	95
	90
	85
	80
	75
	75 含水率模拟值Moisture content simulated 80 85 90 95 100
	(%)

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Funding Information

This work was supported by National Natural Science Foundation of China [31171455, 31471415 , 31201127], Jiangsu Province Agricultural Scientific Technology Innovation Fund [CX (14)2114]; Jiangsu Academy of Agricultural Sciences Basic Scientific Research Work Special Fund [ZX(15)2008].

-0.72 -0.81 -0.75 -0.79 -0.81 0.57 0.40 0.28 0.93 0.77 0.70 0.89 1.00 -0.75 -0.83 -0.79 -0.82 -0.74 0.71 0.63 0.55 0.98 0.92 0.89 0.97 0.94 1.00 -0.73 -0.79 -0.77 -0.79 -0.63 0.78 0.78 0.72 0.95 0.98 0.97 0.97 0.84 0.97 1.00 -0.58 -0.57 -0.59 -0.55 -0.31 0.78 0.91 0.92 0.71 0.91 0.94 0.76 0.49 0.72 0.87 1.00 [27,28] showed that there was larger difference when various models were fitted using same independent variable, thus, we fitted the reflectance-based equation on moisture content, nitrogen content for disease leaf chosen linearity (y=ax+b), logarithm (y=log a bx), quadratic (y=ax 2 +bx+c), and exponential equation (y=a bx ), and the decision coefficient was shown in Table 5. Of that the decision coefficients (R 2 ) of the reflectance-based quadratic equation on moisture content, and the exponential equation on nitrogen content were the largest, and the t-test on parameters in these two equations was shown in Table 6. We saw that all parameters in the quadratic equation on moisture content, Y=2.251-0.071R 760 + 0.001R 760 2 , had significant with p<0.05, and the parameter a in the exponential equation on nitrogen content had no significant in the t-test.


Xinping Fang
email: candyfxp@sohu.com

Research on Freshwater Fish Information Service Mode for Modern Production and Circulation in the Internet + Era

Keywords: Internet plus, freshwater fish, modern production and circulation, information service

In the traditional information service for freshwater fish, there existed several crucial problems involving in the information classification and content organization, which was not clear and specific to serve market and consultancy etc., information refreshing not timely, and information sharing difficult to take, and so on. These problems seriously restricted the development of the freshwater fish and the other relative industries. The "Internet + Strategy", which has been advocated and carried out in China since 2015, has promoted the formulation of new ideas for information service model to construct and enhance modern production and circulation of the freshwater fish. Based on the analysis of the core and characteristics of information service in the production and circulation for the modern freshwater fish, this study constructed a new information service model consisting of production, sale, transaction and circulation. In the development of the freshwater fish industry for the future, it is necessary and beneficial to accelerate the construction of the information service platform, integrate the information service as well as the improvement of information resources with the merchandise production and circulation, and strengthen the brand building of enterprises, especially the leading enterprises.

Introduction

The articles specific to the information service model of the freshwater fish are reported few until now, but the information service model is similar with the agriculture industry, which is well developed and could be referenced.

The information service models of the agriculture industry are well developed and widely reported.

In 2005, it was suggested by Zheng and Hu that there are three models: traditional model, web information service model and blended model. [START_REF] Dang Yong-Liang | Research of Agricultural Information Service Mode in Big Data Era[END_REF] pointed out that there are three types: web service platform, mobile newspaper SDI, and mobile Internet for the agriculture information service model [START_REF] Dang Yong-Liang | Research of Agricultural Information Service Mode in Big Data Era[END_REF]. Zhong and Wan (2013) stated that the agriculture information service model is a multiple model consisting of many systems such as network system, sound system, video, short message, remote video, etc [START_REF] Zhong Ju | Study on the Application of Multi-Pattern Mode in Agricultural Information Service in Jinan[END_REF]. [START_REF] Sun Yan | Agricultural Information Service Situation and Countermeasures in Henan[END_REF] explored the new models of integrating modern information technology with professional cooperative information service [START_REF] Sun Yan | Agricultural Information Service Situation and Countermeasures in Henan[END_REF]. it was suggested by [START_REF] Jiang | Practice and Exploration for the Service of Grass-root Agricultural Information--Taking the Construction of Agricultural Information Service in Langzhong City as A Perspective[END_REF] that there existed several types of information service model --"government + farmer", "government + rural cooperative organization + famer", "government + IT enterprise (communication enterprise) + farmer", "government + agriculture-related enterprise + rural cooperative organization + famer" and "rural cooperative organization + famer" [START_REF] Jiang | Practice and Exploration for the Service of Grass-root Agricultural Information--Taking the Construction of Agricultural Information Service in Langzhong City as A Perspective[END_REF]. [START_REF] Wang | Model Research of Agricultural Literature and Information Resources Serving the New Socialist Countryside Construction[END_REF] advocated the models of "government + farmer", "government + association + farmer", "government + communication enterprise + association + farmer", "government + enterprise + association + farmer", "agricultural leading enterprise + farmer", and so on [START_REF] Wang | Model Research of Agricultural Literature and Information Resources Serving the New Socialist Countryside Construction[END_REF].

In another hand, the information service models of the agricultural product and the extension of agriculture technology are well studied, too. Hu (1997) proposed three types of agricultural technology extension model including the base type, the industry type and the universal type [START_REF] Hu Cui-Qin | Patterns of Current Agricultural Technology Dissemination and Application[END_REF]. [START_REF] Zheng Qiu-Mei | Socialized Agricultural Technological Service System: Pattern Framework and Implementation Strategy[END_REF] believed that there existed five kinds of information service models according to the service suppliers: the government, the industrial organization, the regional organization, the exporting market and the domestic market [START_REF] Zheng Qiu-Mei | Socialized Agricultural Technological Service System: Pattern Framework and Implementation Strategy[END_REF]. Wu made a deep analysis on the problems in freshwater fish production and circulation chain in China and its possible reasons, and proposed the strategies to upgrade the freshwater fish circulation chain [START_REF] Wu Hui-Man | Research on equipment and technology integrating and mode optimizing of the freshwater-fish modern circulation[END_REF].

At present, the existing and running information service models for agriculture, agricultural products, and the freshwater fish all were constructed and improved before the strategy of "Internet +", and are marked by the traditional information concepts, which were government-oriented, mid-scale organization-oriented, enterprise-oriented. These information service models often failed to clearly and specifically classify and organize the necessary information, to refresh information timely, and share the information widely, and so on. In the era of "Internet +" and the competitive domestic and international market economy, it is urgent and necessary to refresh the old or develop new information service models to serve and keep up with the rapid development of modern production and circulation in freshwater fish in China.

The Core Elements and Features of the Information Service Model of Modern Production and Circulation in Freshwater Fish

All the procedures in the freshwater fish production and circulation, including production information, sale information, transaction information and circulation information, should be accomplished by leading enterprises of large-scale fishery industry. Besides, production enterprises should build an information platform about production, sale, transaction and circulation information so that all the information can be acquired on the platform. In this way, information can be achieved timely and accurately at any time and intermediate links can be reduced. As all the intermediate links can be displayed on the information platform, the direct link of "freshwater fish to consumer" can be achieved in that freshwater fish can be delivered from the place of production to consumers quickly and directly, hence the freshness of freshwater fish can be guaranteed.

The most fundamental requirements of freshwater fish production and circulation are rapidity and freshness, which means that the rapidity of circulation and the freshness of the product are the core goals of freshwater fish production and circulation procedure. While the Internet plus Era has made these two requirements become possible. To make the core goals of "freshwater fish to consumer" come true, the following two aspects need to be fulfilled at least: First, the network information platform must be built to present all the relevant information including production information in the production process, supply and price information of enterprises' sales, demand and preference information of consumers, payment and trade information of the transactions. In addition, the third party represented by government, industry associations and research institutes will also provide information of the industry, policies and researches to enrich the information platform and build up the big data net of freshwater fish modern production and circulation.

Second, all the intermediate procedures need to be fulfilled on the information platform so that product flow can be cut down greatly. The information flow in the platform will be clearer and more easily to access, at the same time, production enterprises and consumers can get all kinds of relevant information by web searching in real time. Therefore, all the intermediate procedures can be completed based on network information. Consumers can get production and sale information to decide whether to close a deal, if so, the transaction can be completed directly with trade and payment information.

Once the production enterprises gain the information, the products will be delivered immediately. The breeding base and retail investors can bring the products together to the point of sale and transportation based on the directions from the information platform, and the logistics service will deliver the products to the supermarkets, farmers' markets and exclusive shops immediately, then the fish will be butchered based on the demand information of consumers from the platform and delivered to consumers' home through cold-chain logistics within one kilometer. As a result, all the procedures including sale, transaction, payment, supply and demand can be completed online to achieve the goal

The Construction of the Information Service Model of Modern Production and Circulation in Freshwater Fish

The nucleus of freshwater fish modern production and circulation lies in the rapid transfer from production to consumers, including the production, sale and circulation procedures, alongside with the relevant information of freshwater fish production, quality, sale, transaction, circulation, demand and so on. The Internet plus Era has made the delivery and transportation of freshwater fish more convenient as all kinds of information including production, supply, demand, sale, transaction and circulation can be achieved immediately through the information platform, which means that consumers can decide whether to make the deal rapidly and complete the transaction and payment online. So the transaction process is remarkably smooth and coherent and other intermediate procedures can be cut down. The products can be transported from the production base to the dinning-tables of consumers rapidly through the most efficient circulations to guarantee the rapidity of transportation and the freshness of fish. Therefore, the following information service model can be built:

Production link

Every link of production calls for the establishment of an information monitoring system based on the Internet of Things and the collection and analysis of information via the Internet. In so doing, consumers can acquire all relevant information regarding product production and circulation as well as product quality by web searching, QR code scanning, or APP information inquiry. This is also part of information services that enterprises are supposed to provide in their production process.

3.2Transaction link

Transaction entails transaction in both retail outlets and e-tail outlets. Enterprises build up sales information networks via Internet information platforms. Enterprises provide information concerning production, supply, and price by means of APP or online shops, and consumers offer such demand information as consumer preferences and purchase habits. Enterprises can recommend right products to consumers based on their demand information while consumers can make purchase decisions on the basis of supply information and product information provided by enterprises. If consumers decide to purchase, they can instantly accomplish online payment and transaction on the Internet. After receiving instant transaction information, enterprises deliver fish products directly through logistics to ensure the efficiency of delivery. The entire process of transaction and marketing is done based on enterprises' information platforms. Both consumers and enterprises can convey information via network platforms, thus getting transaction done at one go and saving the time it takes to go through intermediate steps.

3.3Circulation link

Logistics is central to freshwater fish production and circulation. The difficulty of freshwater fish production and circulation lies in the failure to realize long-distance and long-time circulation. In the Internet plus era, all intermediate transaction steps are done on the Internet, except that only logistics cannot be completely replaced by the Internet. While offering cold-chain logistics, third-party logistics enterprises (represented by SF Express) are incapable of transporting freshwater fish. Future enterprises should establish a logistics system featured by self-owned logistics by learning from the logistics model of Heshishuichan, providing specialized services, and striving to innovatively achieve long-distance and long-time transportation of freshwater fish products. Meantime, during logistics and transportation, an Internet-based monitoring system should be set up to achieve real-time monitoring and guarantee information services during the process of logistics.

Suggestions

In the Internet plus era, the production and circulation model of freshwater fish changed completely. It becomes possible to sell freshwater fish quickly and alive, thus creating a direct link from enterprises to consumers. The paper constructed a modern information service model to serve the modern production and circulation in freshwater fish in China and suggested several important ideas related to improve the information service for the production and circulation.

Firstly, efforts should be exerted to accelerate the construction of information service platforms. The information platforms are the key to a modern information service for modern production and circulation of freshwater fish. According to the various scales of fishery enterprises, different types of enterprise platforms could be established:

① Large-enterprise platforms for large-scale fishery enterprises. With adequate human and financial resources and relatively comprehensive systems of production, circulation and marketing, large enterprises could take a lead in constructing the internet-based information service platforms integrated with marketing, production and circulation. ② Enterprise union(or alliances) platform for small or mid-scale fishery enterprises. Due to limited human and financial resources, small fishery enterprises could imitate large enterprises to establish flexible joint fishery information and marketing platforms in joint unions. At the same time, small enterprises could initiate collaboration in other fields including production and circulation to reduce cost and increase profits.

Second, the integration of freshwater fishery between the production and circulation and the information service resources should be promoted. The first aspect of integration is about the production resources. Individual producers could integrate their production resources with other producers or enterprises through cooperatives or enterprise production bases to improve product quality, monitoring product information, and reduce production cost. Small scale enterprises could cooperate as producer teams to form an industry alliance via collaboration to build production bases for scale and standardized production. The second aspect of integration is about the integration of information resources from marketing, production and circulation, resource purchase and product sales as well as post-sale information feedback to coordinate every procedure in a whole enterprise and the whole industry. Small-scale or mid-scale enterprises could coordinate their production and sales based on their established joint information platforms suggested in the last paragraph to compete with large enterprises.

Thirdly, the brand building of enterprises should be enhanced, especially leading enterprises. With the developed production bases and the scale and standardized production, product quality will be guaranteed and brands could be set up, and the strategy of branding could be applied for in the market.

Tongwei-labeled fish products of Tongwei fish are from the branded Tongwei Group, which is built in the long-term brand strategy. Therefore, while enterprises emphasize on quality during production, the marketing in product sale should be emphasized to take the brand strategy to build the long-term constant brand in the market.

  of linking production enterprises to consumers directly. The whole product flow chain covers production base, logistics sites, intermediate logistics, retail stores and consumers. Compared with traditional ways, some intermediate steps can be cut down thus the whole process is reduced greatly and the circulation efficiency is also improved significantly to ensure the rapidity and freshness of fresh fish. While all the intermediate procedures are based on the information monitoring of the Internet of Things around the clock so that consumers can get relevant information all time.

Fig. 1 .

 1 Fig. 1. Production flow and information flow of freshwater fish modern production and circulation in the Internet plus era

Fig. 2 .

 2 Fig. 2. Information service model of freshwater fish modern production and circulation in the Internet plus era


Qinglan Shi

Yunling Liu

Wen Zhang

Design of High-Frequency Based Measuring Sensor for Grain Moisture Content

Keywords: grain moisture measurement, high-frequency electromagnetic wave, dielectric constant, sensor

Accurate measurements of moisture content are indispensable for maintenance of a detecting sensor is designed in this paper to determine the grain moisture content by measuring dielectric constants. To optimize the performance of the designed sensor, electromagnetic waves with suitable frequencies are chosen first followed by deep studies on its transmission characteristic in grain media. Taking wheat as testing samples and applying a total of six frequencies, the network analyzer from Agilent Technologies E5061A is used to measure the dielectric constant and loss tangent of grains with different moisture content. The variation of dielectric constants against moisture under various frequencies is obtained based on which the grain moisture is deduced. According to high frequency transmission line theory, as the impedance of probes wrapped in wet media varies with dielectric constants and is mismatched with transmission lines, standing waves are generated by the composition of reflected waves and incident waves. The strength of reflections depends on probe and characteristic impedances. The relationship of moisture content and dielectric constant can be deduced by analyzing the dynamic variation of reflected and incident waves. Key hardware circuits have been designed involving the moisture detecting circuit, signal generator circuit, high frequency transmission lines, etc.Calibration experiments are carried out, compared with drying method, a cubic polynomial relationship of moisture and output voltage with R 2 = 0.995was obtained by regression analyses. The standard error of predicted results from regression analysis and measured results is 1.09%. It indicates that the proposed method has a high accuracy.

Introduction

The quantity of water in grains usually refers to "moisture content". Detecting and controlling the moisture content of grains play a key role throughout different stages of grain purchase, transportation, storage, processing, commerce, etc. It is essential to employ appropriate methods to measure moisture of grains for the quality maintenance purpose. Many measurement methods have been proposed at home and abroad with various characteristics and a wide scope of application. Generally, they can be divided into two main groups: direct method and indirect method. The former, directly detecting the absolute moisture content by weighting crushed grains before and after drying, is the classic and standard method of grain moisture measurement, such as drying method. Although the direct method is precise and has no effect on the nature of grains, it costs too much time and is not suitable for real-time and on-line measurement. On the other hand, indirect methods such as infrared and microwave methods [START_REF] Zhang | Measurement of grain moisture content using microwave attenuation at 10.5 GHz and moisture density[END_REF][START_REF] Jiang | Study on microwave measurement for grain moisture content in grain depot[END_REF]have high precision, good repeatability but high costs. In addition, physical parameters of media such as dielectric constant, conductance, dielectric loss, etc. may be used to measure grain moisture content [START_REF] Zhang | Detector testing of grain moisture based on resistance capacitance method[END_REF]and capacitance method [START_REF] Lu | Study on Grain's Moisture Detector Based on Capacitive Sensor[END_REF]. Kandala et al. measured grain moisture by filling grains between plates of parallel capacitors [START_REF] Kandala C K | Capacitance sensor for nondestructive measurement of moisture content in NUTS and grain[END_REF]. Mcintosh et al. proposed a sensor available in storerooms to detect grain moisture information [START_REF] Mcintosh R B | Fringing field capacitance sensor for measuring the moisture content of agricultural commodities[END_REF].

As highly depending on the working environment, the methods mentioned above only apply to the moisture measurement of bulk grains. For packaged grains, non-destructive measurement of grain moisture requires a simply and flexible means of elongate probes inserting into grain packages. Based on high frequency-based electromagnetic wave measurement methods, this paper proposes measures in the design of moisture sensors to enhance its measuring precision.

Relationship between dielectric constant and moisture content of wheat

The high frequency electromagnetic measurement method is to measure the dielectric constant of grains by high-frequency electromagnetic waves to indirectly determine grain moisture content [START_REF] You K Y | A small and slim coaxial probe for single rice grain moisture sensing[END_REF][START_REF] Kraszewski A W | Comparison of Density-independent expressions for moisture content determination in wheat at microwave frequencies[END_REF][START_REF] Kamil | Determination of dielectric properties of corn seeds from 1 to 100 MHz[END_REF].

Testing samples were collected according to the national standard GBT21305-2007 in China (Determination of moisture content of cereals and cereal products) to study the relation of dielectric constant and moisture content. The samples were harvested in June 2013 at Raoyang town in Heibei province (38º 15'N,115º 44'E). After harvest, the samples were stored in a dry and cool place for one year. Red durum wheat in a volume weight of 750g/L were dried under a constant temperature of 130º ±2℃ for 90min±5min and its moisture content was determined. Seven types of tested wheat samples with various moisture content values were then made by mixing water, as listed in Table 1. Dielectric constants of media with different moisture content varies under different frequencies [START_REF] Nelson | Microwave dielectric properties of insects and grain kernels[END_REF].

Nelson studied the frequency characteristics of dielectric constant of different grains [START_REF] Nelson S O | Dielectric spectroscopy of wheat from 10 MHz to 1.8 GHz[END_REF]. The results indicated that high frequency signals with higher dielectric constants, monotonic variations and less dielectric losses were preferred for moisture measurement. A frequency range of 50MHz~300MHz was recommended [START_REF] Stuart O N | Factors influencing the dielectric properties of agricultural and food products factors influencing the dielectric properties of agricultural and food products[END_REF]. The RF network analyzerAgilentE5061A was used in this study. The dielectric constants of seven samples were measured under six frequencies of 51MHz, 100MHz, 150MHz, 200MHz, 250MHz and 300MHz, respectively. The relationship between dielectric constant and moisture content is plotted in Figure 1. The relations of real part and imaginary part of permittivity and wheat moisture content are shown in Figures 1(a) and (b), respectively. It can be seen that the dielectric constant monotonically increases with moisture content. As a dielectric substance, water molecules have strong polar characteristics. The increase in the dielectric constant of dielectric media is due to the increase in its moisture content.

From Figure 6, it can be seen that the measured results are very close for samples with lower moisture content. However it is not the case for saturated samples. Signals in a medium frequency of 100MHz were taken in the test. The Least Square Method was used to obtain a cubic polynomial relationship between the real part of permittivity and moisture content as follows: ε , = -778.5ϑ wheat 3 + 548.7ϑ wheat 2 -63.66ϑ wheat + 3.709(1)

The coefficient of determination of the real part of permittivity and the fitting curve was calculated as

9988 . 0 2 1  R .
The relationship between the imaginary part of permittivity and moisture content can be expressed in an exponential function as:

ε ,, = 0.063e 14.80ϑ wheat (2)
The coefficient of determination of formula (2) R² = 0.890 was derived in this case.

The two different coefficients of determination in Equations ( 1) and ( 2) indicate that the real part of permittivity is more related to grain moisture content than the imaginary part of permittivity. In other words, grain moisture content is mainly dependent on the real part of permittivity. Therefore, the moisture of grains can be determined from the real part of permittivity [START_REF] Trabelsi | New density-independent calibration function for microwave sensing of moisture content in particulate materials[END_REF]. The dielectric constant in the following sections denotes the real part of permittivity. The high frequency method [START_REF] Guoa | Frequency, moisture and temperature-dependent dielectric properties of chickpea flour[END_REF]is proposed based on this principle. 3 Design of moisture measurement sensor system based on high frequency method

General design

The block diagram of the sensor system of high frequency-based grain moisture measurements is illustrated in Figure 2. A high frequency signal is first produced and transferred to probes inserted into tested grains connected through high frequency transmission lines. The dielectric constant measured is larger than 1 (1 for air) since there is water in grains. It varies in a range of 1~20 for different moisture content. If the probe in tested grains is regarded as the output load of high frequency signals, the load impedance varies with grain moisture content. In this way, the feedback on the signal magnitude through output load changes. The moisture content of grains can be determined according to variation in the signal magnitude.

Signal generator

According to the frequency characteristics of wheat dielectric constant mentioned above, high frequency signals of 100MHz were used in the test.

Design of the transmission line circuit

The moisture sensing circuit was designed based on the transmission line theory. From Figure 3, it can be seen that high a frequency signal generated from high-frequency oscillation sources was transferred to sensor probes by coaxial cables. As each point has its own distributed parameters, the equivalent circuit of the transmission line is depicted in Figure 5. A high frequency cable of 50Ω was used for coaxial cables. The two points of the transmission line are represented by A and B. The impedance of probes wrapped in grains is denoted by Z L .

-

+ U(z,t) i(z,t) ZL Rdz Ldz Gdz Cdz A B
As wrapped in grains, the impedance of probes changes with its surrounding media which leads to the mismatch of terminal impedance and the signal reflex. The composition of incident waves and reflected waves on transmission lines generates standing waves [16,17] . If the cable length is taken as one quarter of the signal wavelength ( i.e. 0.75m) and lossless transmission line is assumed, the standing wave ratio can be expressed [START_REF] Ball | Characteristic impedance of unbalanced TDR probes[END_REF] as

max min 1 1 u u     
 , where max u and min u are the peak and trough of A u and B u , respectively. The peak voltage of the point A can be calculated as:

) 1 ( A    A U (3)
The peak voltage of the point B can be given as

) 1 ( B    A U (4)
Whereρ is the reflection coefficient defined as

c L c L Z Z Z Z     (5)
Therefore, the voltage difference between the points A and B is

c L c AB Z Z A U     Z - Z 2A 2 L  (6)
Where c Z is the characteristic impedance, A is the voltage constant; the resistance of the cable is taken as 50Ω.

In order to simplify the circuit and enhance its reliability, the electric cable of 0.75m was replaced by a 100μH inductor. The equivalent circuit is shown in Figure 5.

Fig.3.High-frequency signal generator circuit

Fig.4Distributed parameters of high frequency transmission lines

The high frequency signal output from signal generators was transmitted to transmission lines by R17 and R18. A π-type filter composed of capacitors C16, C17, C19, C20, was used to filter harmonic components. The 0.75m cable was replaced by a 100μH inductorL2 with smaller size and better stability. The voltages at the two ends of the inductor L2 are denoted by VA and VB, respectively.

Design of sensor probes

As shown in Equation 5, the voltage difference between A and B depends on the impedance of probes.

A three-needle type of stainless steel probe was used in this study. This multi-pin probe can be approximated as parallel transmission lines. Its impedance varies between coaxial cables and parallel lines [START_REF] Zegelin S | Improved field probes for soil water content and electrical conductivity measurement using time domain reflectometry[END_REF][START_REF] Sun | Theoretical and experimental approach to calculation of the impedance of soil probe[END_REF]. The probes proposed in references [18,19] were used for water content measurement soil.

For the same porous media of grains as soil, the impedance is related to the dielectric constant of tested media. The impedance function of soil water measurements was also applied to this study. The function is shown as follows [20-21] .

C l f ctg Z j Z L    2 0   (7)
where 0 Z is the characteristics impedance of probe in air; f is the frequency of high frequency signal source (100MHz);  is the dielectric constant of tested grains; l is the length of probes (162mm); C is the velocity of light, i.e. 3 × 10 8 m • s -1 .

Design of differential amplifier circuit for moisture detection

) 8 ( f 2 Z 50 f 2 Z 50 K 0 0 1         l ctg j l ctg j U out   
Where K 1 is a constant which is always as 2~2.5V, this output signal includes the dielectric constant information related to grain moisture. From the relation of dielectric constant and moisture content in Equation 1, a nonlinear function of output voltages and moisture content can be established.

The coefficient of the nonlinear function was determined by calibration experiments.

Calibration Experiments

The moisture measurements and calibration experiments were carried out on tested samples based on the proposed moisture sensing circuit. Table 2 lists the measured output voltages of the seven samples with different moisture content. Stable readings of voltage meters for each sample were recorded by inserting probes into the tested grains. Repeat the test for five times and take the average value as the output voltage. The variation of measured voltages listed in Table 2 against grain moisture content is plotted in Figure 7. For the mathematical model derived from the regression analysis , its cross correlation coefficient of predicted and measured results is calculated as

    ) 10 ( 995 . 0 1 1 R 2 2 2         n i i n i i        ) 11 ( 0.01089 1 2     n i i i n RMSE   
The standard error of predicted results from regression analysis and measured results is 1.09%. where n is the total number of samples in the regression analysis and n=7 in this case; i  is the moisture content of the ith sample; (2)The relationship between the moisture and probe impedance can be determined the relation of moisture and dielectric constant as well as high frequency transmission line theory. A key grain moisture sensing circuit has been designed to measure grain moisture.

(3) Calibration experiments are carried out on the sensor circuit using drying method. A cubic polynomial relationship of moisture and output voltage with R 2 = 0.995was obtained by regression analyses. The standard error of predicted results from regression analysis and measured results is 1.09%. It indicates that the proposed method has a high accuracy.

It is worth noting that the measuring error of the designed sensor meet the precision demand for wheat.

While for other grains, no calibration experiments are conducted and further work should be done.

Fig. 1 .

 1 Fig.1. Relation between the dielectric constant and wheat moisture content

Figure 4 Fig. 2 .

 42 Fig.2. The block diagram of high frequency sensing circuit system

Figure 6

 6 Figure 6 shows the differential amplifier circuit of grain moisture detection. There are dynamic standing waves at AB , uu which were transmitted to RF input of LTC5507 chips and transformed into DC voltage. After filter, the DC voltage was sent to the differential amplifier circuit. The AD623, serving as an operational amplifier, is powered by a single supply +5v. Programmable adjustment can be made on its gain by changing resistances. Resistances with error smaller than 0.1~1% were used to obtain precise output voltage gains of AD623. Meanwhile, resistances with low temperature coefficient are preferred to enhance the stability of voltage gains and avoid high gain drift. As the output voltage of AD623 relative to the reference terminal, to obtain a high common mode rejection ratio, an earth REF pins were applied. Transient voltage suppressor SMBJ5.0A was used to prevent the over high instantaneous voltage at the output terminal.

Fig. 5 .

 5 Fig.5.Equivalent circuit of high frequency transmission lines

i

  is the average value of measurements of all samples; content calculated from the output voltage of the ith sample according to the regression equation;The result of 2 0.995 R  indicates a good relativity between regression function and measured data.

Fig. 7 . 5 Conclusions( 1 )

 751 Fig.7. Relation of output voltage of moisture sensing circuit and wheat moisture content

Table 1 .The moisture

 1

		content of wheat samples under investigation (bulk density 750g/L)
	Samples	S1	S2	S3	S4	S15	S6	S7
	Moisture content (%)	4.52	12.15	14.65	19.94	24.20	32.11	36.54

Table 2

 2 The output voltageof moisture sensing circuit for wheat with different moisture content

			Fig.6.Differential amplifierfor moisturedetection
			1st	2nd	3rd	4th	5th
	Sampl	Moisture	measurement/	measuremen	measuremen	measuremen	measuremen	Average/
	e	content	V	t /V	t /V	t /V	t /V	V
	1	4.52%	0.68	0.69	0.71	0.66	0.7	0.69
	2	9.78%	0.9	0.93	0.91	0.9	0.9	0.91
	3	12.15%	1.02	1.03	1.04	0.98	1.02	1.02
	4	14.65%	1.24	1.17	1.23	1.18	1.18	1.2
	5	17.15%	1.3	1.31	1.29	1.29	1.23	1.28
	6	19.94%	1.33	1.35	1.3	1.34	1.35	1.33


Chong Niu
email: niuchong0503@163.com

Han Li

Yuguang Niu
email: cygniu@sina.com

Zengchan Zhou
email: dzengchan@sina.com

Yunlong Bu
email: buyunlong2001@163.com

Wengang Zheng
email: fzhengwg@nercita.org.cn

Segmentation of Cotton Leaves Based on Improved Watershed Algorithm

Keywords: machine vision, image segmentation, lifting wavelet, watershed algorithm

Crop leaf segmentation was one important research content in agricultural machine vision applications. In order to study and solve the segmentation problem of occlusive leaves, an improved watershed algorithm was proposed in this paper. Firstly, the color threshold component (G-R)/(G+R) was used to extract the green component of the cotton leaf image and remove the shadow and invalid background. Then the lifting wavelet algorithm and Canny operator were applied to extract the edge of the pre-processed image to extract cotton leaf region and enhance the leaf edge. Finally, the image of the leaf was labeled with morphological methods to improve the traditional watershed algorithm. By comparing the cotton leaf area segmented using the proposed algorithm with the manually extracted cotton leaf area, successful rates for all the images were higher than 97%. The results not only demonstrated the effectiveness of the algorithm, but also laid the foundation for the construction of cotton growth monitoring system.

Introduction

Crop growth information is the basis of precise management of crop production, which plays a decisive role in the management of growth, quality and yield of crops.

Leaf information is a direct reflection of crop growth status, and it is a research focus to extract leaf region effectively from crop image in the present research [START_REF] Ye | Crop feature extraction from images with probabilistic superpixel Markov random field[END_REF][START_REF] Pachidis | Real time feature extraction and Standard Cutting Models fitting in grape leaves[END_REF]. Cotton is strategic materials relating to the national economy and people's livelihood, and leaf is also an important organ for photosynthesis of cotton. The size of leaf area has a direct effect on the yield of cotton in a certain extent. Therefore, establishing a convenient and accurate method for obtaining leaf area is of positive significance to guide cotton production time activity and develop high yield, high quality and high efficiency cultivation technique measures [START_REF] Trooien | Measurement and Simulation of Potato Leaf Area Using Image Processing.I.Model Development[END_REF][START_REF] Chien | Non-Destructive Growth Measurement of Selected Vegetable Seedings Using Orthogonal Images[END_REF]. In the process of agricultural automation, machine vision technology has become an indispensable part. This technology has been used in many fields of agricultural automation to mine the data from crop images, such as crop water stress [START_REF] Leinonen | Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress[END_REF][START_REF] Meron | Foliage temperature extraction from thermal imagery for crop water stress determination[END_REF] and detection of crop diseases and insect pests [START_REF] Phadikaer | Rice diseases classification using feature selection and rule generation techniques[END_REF], etc. In recent years, many researchers have developed leaf separation algorithms to separate leaves from the crop images by machine vision [START_REF] Jo˜ao | Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm[END_REF][START_REF] Lee | Recognition of Partially Occluded Plant Leaves Using a Modified Watershed Algorithm[END_REF], and these algorithms have been applied to crop identification, weed control, and some other fields. However, there are different degrees of over segmentation and under segmentation in the extraction of leaves with shadow and overlapping. Therefore, in order to meet the requirements of the practical application, it is needed to develop an effective algorithm to extract the area of overlapping and shaded leaves. The objectives targets of this study paper were to extract the single cotton leaf quickly and accurately in cotton images obtained in natural light condition, and to compute the leaf area size to provide data reference for crop growth monitoring [START_REF] Peper | Evaluation of four methods for estimating leaf area of isolated trees[END_REF].

The extraction of cotton leaf area based on machine vision technology will generally use the image segmentation. The basic idea of the threshold method is to calculate one or more gray thresholds based on the gray level of the image, and the gray value of each pixel in the image is compared with the threshold value, the pixels are classified according to the comparison result [START_REF] Thorp | Color image segmentation approach to monitor flowering in lesquerella[END_REF][START_REF] Xia | Plant leaf detection using modified active shape models[END_REF][START_REF] Kavitha | Implementation of Gray-level Clustering Algorithm for Image Segmentation[END_REF]. The difficulty of using the threshold methodthis algorithm is to determine calculate the optimal segmentation threshold value and the segmentation effect is uneasy to grasp. In image segmentation, image edge information is often used. The edge is a collection of two different regions of the boundary line of the image, which is a reflection of the discontinuity of the local feature of the image and the change of image features, such as gray, color, texture, and so on [START_REF] Yang | Research and analysis of Image edge detection algorithm Based on the MATLAB[END_REF][START_REF] Asano | A unified scheme for detecting fundamental curves in binary edge images[END_REF][START_REF] Yu | A new edge detection approach based on image context analysis[END_REF]. However, the traditional edge detection is not ideal for the detection of overlapping edges. Lifting wavelet transform not only has good time-frequency local characteristic and multi-resolution analysis characteristic and avoids the loss of information due to the limitation of the computer accuracy when dealing with the image. Canny operator can detect the real weak edge, and it is the optimal edge detection operator. Researchers have used the combination of them to get a good edge detection effect [START_REF] Han | Cotton leaf image edge detection using Mean-shift algorithm and lifting wavelet transform[END_REF], but they did not have a further image segmentation.

Watershed algorithm [START_REF] Meyer | Topographic distance and watershed lines[END_REF] is a morphological segmentation algorithm, which is based on the gradient of the image. It has a good response to tThe weak edge can be disposed effectively with this method, but the noise in the image can cause over segmentation of the watershed algorithm. Thus So the traditional watershed segmentation algorithm is not suitable fit to forbe used direct applicationly. Therefore, many researchers have proposed the improvement methods for the traditional watershed algorithm [START_REF] Lee | Recognition of Partially Occluded Plant Leaves Using a Modified Watershed Algorithm[END_REF] [START_REF] Orbert | Watershed segmentation of binary images using distance transformations[END_REF].

Major difficulty of the extraction of single leaf area in natural light is to handle the cotton leaf images with occlusion and shadows. This paper presented a A method based on lifted wavelet and improved watershed method was presented to extract cotton single leaf in this paper.

Experiments and Methods

Experimental Material

In laboratory conditions, images were collected in Beijing Academy of Agricultural and Forestry Sciences. In order to simulate the natural conditions of the day, the cotton plants were placed in the laboratory by the window to receive natural light irradiation, the laboratory temperature was 21℃, humidity was 39%. A CMOS camera(Nikon Inc.,J1,Japan) with a resolution of 3872×2592 was used to get the RGB image, which was saved as a JPG format. According to the number of cotton leaves, cotton images were divided into two groups, images with 2 leaves and images with 4 leaves. According to whether there was occlusion or shadow between cotton leaves in the cotton image, the cotton images was further divided into four kinds. In this paper, Matlab (R2010b) and photoshop 7 software were applied to process the image. In order to improve the processing speed, the cotton RGB image was reduced to one fifth of the original image size, which was 510×775.

Experimental Methods

Overall description of the algorithm

Firstly, the green area of cotton in an RGB image was extracted based on color threshold and filtered the region that were not interested in the image, including most of the shadows, background and cotton stalk, in this way the cotton leaf area could be get. Then lifting wavelet and Canny operator for respectively accomplished the image gray enhancement and edge detection so that image edge could be effectively and accurately extracted from occlusive leaves, based on the operations above, watershed algorithm could achieve better segmentation effect. Finally, in order to solve the problem of the over segmentation caused by directly using the watershed transform, the leaf area image was converted to HSV space and morphological marking of the foreground and background was processed in the image to improve the watershed algorithm to reach a better effect of segmentation.

Extraction of leaf area in the image based on the color threshold, lifting wavelet transform and Canny edge detection operator

Color threshold (G-R)/(G+R) applied to highlight the green component in the cotton image, the threshold value of the component was automatically calculated by the maximum difference method. Wavelet transform could enhance the gray image, but in practical applications, due to the limitation of computer calculation precision, the image would produce information loss after wavelet transform, lifting wavelet overcome the shortcomings. Canny operator was the most effective edge detection operator of edge function. After applying the lifting wavelet and Canny operator, the gray level of the image could be effectively enhanced. Finally, the leaf region was extracted from the enhanced edge, which allowed us to use the watershed algorithm obtain a good segmentation effect.

Cotton image segmentation based on improved watershed algorithm

Principle of watershed algorithm

The ideological source of the watershed algorithm [START_REF] Meyer | Topographic distance and watershed lines[END_REF] is in topography, the topography image is seen as a natural landscape covered by water, the gray value of each pixel in the image indicates the altitude of the point, each of its local minimum and its influence area is called catchment basin, two adjacent catchment basin boundary is the watershed. The main process of the algorithm is to find the catchment basin and the algorithm is described as follows.

} ) , ( | ) , {( ] [ n t s f t s n T   (2.1) where, ) , ( y x f is image, ] [n T represents a collection of coordinate ] , [ t s ,the points in the collection are located below the level of the n y x f  ) , ( . ) ( ) ( ) ( n n T M C M C i i   (2.
n i i n M C C 1 ) ( ] 1 max [    (2.4) where, ] 1 [max C
is the collection of all the catchment basin, min and max represents the minimum value and the maximum value of ) , ( y x f

, as the water level in whole number is increasing from

1 min n   to 1 max  n
, the image of the terrain will diffuse through the water.

From analyses above, the result is that every connected

component in ] 1 [  n C is a connected component of ] [n T . Set ] 1 [min ] 1 [min    T C
in search of the dividing line, then process the recursive call to obtain ] [n

C according to the ] 1 [  n C
.

The watershed algorithm can achieve better results when the targets is connected each other, however, the watershed algorithm is highly sensitive to the change of the image, and the image contains noise and other factors often lead to over segmentation, so the contour of the hope is not covered by a large number of irrelevant contours.

The direct application of the watershed segmentation algorithm is not good, so it is necessary to do the pre-processing of the input image.

Image pre-processing before using watershed algorithm

Morphological marker and watershed algorithm are combined to segment the cotton leaf and get the accurate segmentation results. Foreground markers are the dot pixels that are attached to each object and they will create a maximum of each object and find out the target region by these maximums, laying the foundation for the correct segmentation of the image. Background markers are pixels that are not belonging to any object. The method of threshold is used to distinguish the background and the target region to segment images accurately.

The gray scale dilation of image f with structure element b is called fb 

} ) , ( | ) , ( ) , ( min{ ) , )( ( b D y x y x b y y x x f y x b f             (2.
b f f    ) ( b  (2.7)
Morphological opening operation completely removes the object region which didn't contain structural elements, smooths the object contour, disconnects the narrow connection and removes the tiny protruding part.

The morphological closing operation of image f with structure element b is

called b f  and described as follows. b b f b f     ) ( (2.8)
Morphological closing operation will connect the narrow the gap to form slender curved mouth, and filling the hole with smaller structure elements.

After morphological opening and closing operation, the image is reconstructed by morphological reconstruction to clean modify the image and create find a maximum value of the unit within each object.

After the operation leaf region of gray image becomes smooth, then the revised local extreme value and the image labeled were combined with the watershed algorithm to find the catchment basin and watershed ridge line to realize the algorithm of image segmentation correctly.

Image processing based on improved watershed algorithm

For the sake ofIn order to objectively evaluate evaluating the effect of the algorithmmethod, the effect of automatic segmentation and manual segmentation was compared. After the use of watershed algorithm on the image of cotton leaf, region labeling method was applied for every cotton leaf, then the area of each leaf was found by using the region property. Photoshop7 software was applied to extract a single leaf and obtain the pixel area. By comparing the two area of the corresponding single leaf, the accuracy of our experimental algorithm could be verified.

Results and Discussion

Single Sample Experimental Results Exhibition

To demonstrate the performance of key steps of the proposed algorithm, an image taken for a four-leaf cotton with partial occlusion was processed as an example. The color threshold of the original image was combined with the lifting wavelet and Canny operator to extract the region of the leaf, the extraction effect was shown in Table 1 compares automatic segmentation with manual segmentation results.

The extracted single cotton leaf area was numbered in a clockwise direction. It could be seen that the algorithm in this paper could successfully segment cotton leaves under various conditions and the average correct rate of leaf segmentation was higher than 98%.

  as the two value image given by formula 2of a point, this point is located in the catchment basin associated with the local minimum in the representsis the collection of the catchment basin of the flooded part at the stage n .



  erosion of image f with structure element b is called b f and described as follows.

  operation of image f with structure element b is called b f  and described as follows.

  b

Fig. 1 ,

 1 Fig.1, Fig.1-a is original image, Fig.1-b extracts the leaf region based on the color threshold, lifting wavelet and Canny operator.

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .Fig. 4 .Fig. 5 .Fig. 6 .

 1234456 Fig.1. Extraction of leaf area based on color threshold,lifting wavelet and Canny operator

Table 1 : Leaf area results of cotton image segmentation

 1

	Cotton image	Leaf number	Automatic area calculation of leaf	Manual leaf area calculation of	Correct rate
	I	1 2	25283 28474	25006 27753	98.89% 97.3%
	Ⅱ	1 2	23886 30158	23439 30044	98.09% 99.62%
		1	15783	15952	98.94%
	Ⅲ	2 3	25149 17850	25251 17999	99.6% 99.17%
		4	35463	35357	99.6%
		1	6961	6912	99.29%
	Ⅳ	2 3	23371 7481	23204 7426	99.28% 99.26%
		4	19127	18983	99.24%
		1	9389	9502	98.81%
	Ⅴ	2 3	26652 8103	26945 8057	98.91% 99.43%
		4	19049	19101	99.73%
		1	9933	10064	98.7%
	Ⅵ	2 3	26046 6557	26217 6625	99.35% 98.96%
		4	14406	14557	98.96%

Acknowledgment

This research study was supported by Beijing Academy of Agriculture and Forestry postdoctoral scientific research funds (2014003), and The Beijing municipal science and technology plan (D151100003715002).

Conclusions

In order to realize automatic monitoring of the cotton growth status, it is necessary to segment and extract the cotton leaf area. In this process, the segmentation of the cotton leaves with partial occlusion is a key step. This paper presented an algorithm to segment cotton leaf images based on lifting wavelet and improved watershed method.

Firstly, the image was segmented by applying color threshold (G-R) / Then the region of the leaf and the edge was extracted by the lifting wavelet and Canny edge detection. Through using morphological method, the foreground and background of the image were marked to improve the traditional watershed segmentation algorithm. The leaf extraction accuracy was greatly prompted and the image was segmented successfully. Experimental results on six cases of cotton images showed that the algorithm in this paper was able to successfully deal with the presence of shadow and occlusion in the cotton images, and the single leaf was successfully segmented. By comparing with the results of manually obtained leaf area, the correct segmentation rate of single leaf area was higher than 98%, which had met the requirements for the single leaf area extraction in practical application. This approach could be used as a preliminary step to build a monitoring system and monitor the growth status of other natural objects, such as wheat, corn, or any other crops or vegetable .