Juan Yang
email: yangjuan@saas.sh.cn.

Jingyin Zhao

Hailong Yu

Yunsheng Wang

Ruijuan Wang

Lihua Tang

Mathematical study of the effects of temperature and humidity on the morphological development of Pleurotus eryngiiafruit body

Keywords: Fruit body, morphology, air temperature, relative humidity, mathematical model, Pleurotus eryngii

The king oyster mushroom (Pleurotus eryngii) is one of the most popular mushrooms in Asia, Europe and North America. Its growth is influenced by environmental conditions, particularly air temperature and humidity. Using traditional methods of cultivation in plastic greenhouses, P. eryngii can be grown only in spring and autumn. However, mass cultivation using modern commercial methods is unrestricted by season, but precise environmental control is crucial, as the process is costly and a high energy consumption.

Through experimentation and analysis of the effects of variations in air temperature and relative air humidity on morphological development, a mathematical model for these effects was developed. This model can be used to predict the optimal ranges of air temperature and humidity for various indices of morphological development of the fruiting body, and to guide commercial production. Using the model, standard values of morphological development, optimal environmental control ranges for each day of the fruiting body growth period were calculated. These values provide a reference suitable for application to actual production.

Introduction

Mushrooms are known for their nutritional and medicinal value, and the diversity of their bioactive components [START_REF] Ng | A review of research on the protein-bound polysaccharide (polysaccharopeptide, PSP) from the mushroom Coriolus versicolor (Basidiomycetes: Polyporaceae)[END_REF] . These organisms have long been valued as highly tasty and nutritious foods by many societies throughout the world. Among the edible mushrooms, the king oyster mushroom (Pleurotus eryngii) is one of the most popular in Asia, Europe and North America [START_REF] Rodriguez Estrada | Yield, size and bacterial blotch resistance of Pleurotus eryngii grown on cottonseed hulls/oak sawdust supplemented with manganese, copper and whole ground soybean[END_REF] . P. eryngii has several bioactive components, such as β-glucan [START_REF] Carbonero | A β-glucan from the fruit bodies of edible mushrooms Pleurotus eryngii and Pleurotus ostreatoroseus[END_REF] and ribonuclease [START_REF] Ng | A novel ribonuclease from fruiting bodies of the common edible mushroom Pleurotus eryngii[END_REF] , which have been isolated from the fruiting bodies of edible mushrooms. However, the fresh fruiting bodies are mainly used as a food sources. In Japan, production increased from 60 t in 1995 to over 29,000 t in 2003 [START_REF] Yamanaka | Cultivation of new mushroom species in East Asia[END_REF] . Even greater increases in production have occurred in China, where commercial production began in the late 1990s. Chinese growers produced an estimated 7300 t in 2001, and 114,100 t in 2003 [START_REF] Chang | Witnessing the development of the mushroom industry in China[END_REF][START_REF] Tan | Cultivation of Pleurotus spp. in China[END_REF] . In the US, commercial production began in 2000 and has reached 85 t by 2004 [START_REF] Royse | Consumption and production of recently domesticated edible fungi in the United States with a projection of their potential[END_REF] .

As a commodity, the ideal morphology of the P. eryngii fruit body is generally columniform. The cap is smaller than that of the natural state, and can even be smaller than the stipe. Air environment seems to be very important for induction of fruiting body formation. The appropriate air environment is not only crucial for production, but also influences the ideal morphology. When air temperature and humidity are lower than optimal, the fruiting body does not growth, and can even wilt and die.

Conversely, when air temperature and humidity are higher than optimal, the fruiting body grows quickly, but a higher proportion of anamorphic fruiting body.

Most studies focusing on the relationship between fungal growth and air environment have been qualitative, because of the large range of the environmental control variables in conditions of traditional cultivation in plastic greenhouses. In modern methods of mass commercial cultivation of P. eryngii, air environment is monitored through air temperature and relative humidity probes placed in the cultivation room. Air temperature and humidity are then adjusted in real time through air conditioning, humidifiers and fans controlled by a dedicated computer.

Environmental control is thus very precise. Accurate environmental control can reduce the overall costs through increased yield.

The objective of this research, therefore, was to analyze the effect of different air temperatures and relative humidity on the morphological growth of the P. eryngii fruiting body, through experimental methods. A mathematical model for the effects of air temperature and humidity on growth was then established. Using the model, the appropriate environmental ranges for morphological growth of P. eryngii in commercial production were obtained. Finally, the model could make useful predictions about air environment variables to guide environmental control in the commercial production of P. eryngii.

Materials and methods

Experiments

In this study, the air climate control in the growing rooms was designed and manufactured by Patron AEM; temperature, humidity and CO 2 concentrations in the growing rooms could be controlled very precisely and efficiently. There were two treatments. The first was with air temperatures of 14°C , 15°C , 16°C , 17°C and 18°C .

The second was with relative humidity of 89%, 91%, 93%, 95% and 97%. The intensity of illumination in the growing room was 500-1000Lx. The experiment was a 2 (supplement) × 5 (treatment) design with three replicates per treatment.

Morphological growth indicators were measured daily (every 24 h), with 8 bottles for each experiment. Each value is the mean of 24 measured results (3 replicates × 8 bottles).

The morphological growth is characterized by three indicators: first, cap diameter, which reflects the size of the circular cap of fruiting body; second, stipe diameter, which is measured at the base and reflects the approximate size of the columniform stipe of the fruiting body; and third, stipe height, which is measured from the base of the stipe to the cap. Each day, two or three of the bigger fruit bodies in each cultivated bottle of eight examples were measured by vernier caliper.

Model validation

To validate the model the three morphological growth indicators described above were computed with the model and then compared with field data. The following three indices were computed under two situations to assess the closeness of the estimated data to the measured values: correlation coefficient (R), bias (Bias) [START_REF] Kobayashi | Comparing simulated and measured values using mean squared deviation and its components[END_REF] , root mean square error (RMSE) [START_REF] Moreno-Sotomayora | Improvements in the simulation of kernel number and grain yield in CERES-Wheat[END_REF] , and the percent root mean square error (%RMSE) [START_REF] Jansen | Calibration of process-oriented models[END_REF] .

   i i SIM OBS n Bias 1 2 1 (
)

n i i i OBS SIM RMSE n     ) 1 /( 100 %    i OBS n RMSE RMSE
OBS i and SIM i are the measured and simulated value, respectively, for the ith data point of n observations.

Results

During the growth period of the fruiting body, the three morphological growth indices of P. eryngii showed significant changes over different days (Table 1). We found that the optimal air temperature and humidity facilitated cap expansion, stipe heightening and thickening. For any single growth indicator, the optimal air temperature and humidity was different on each day (Table 2). Multiple statistical comparisons (Table 2) showed that over five days of growth, the optimal air temperature was 16°C , 17°C and 18°C in the first three days, and 17°C or 18°C , and 16°C for the last two days. Variation in air relative humidity over a range of 89-97% did not significantly affect morphological growth of the fruiting body in these experiments (Table 2), but comparatively, the optimum relative humidity was 97%, followed by 95% and 93%. *The relative humidity/temperature was not considered in the air temperature/relative humidity treatment, and the effect of air temperature/relative humidity was analyzed on morphological growth.

Time/d Indices

*The former three air temperature and relative humidity conditions that showed no differences between them were listed in the table.

There is complexity and uncertainty in biological growth and P. eryngii is no exception. The optimal air temperature is independent of optimal relative humidity.

For example, the optimum air temperature for stipe heightening is 18°C , but at that temperature, 93% is not the only optimum relative humidity. Therefore, it is necessary to further analyze and confirm the optimal air environment.

The optimal air temperature for cap expansion is 17°C (Table 3) under relative humidity conditions of 93-97% analyzed above. The optimal relative humidity for cap expansion is dependent on temperature (Table 3): 97% at 16°C and 18°C , and 91-97% at 17°C . So the optimal air temperature and relative humidity conditions for cap expansion are 17°C and 97% respectively.

Under better relative humidity conditions of 93-97%, the optimal air temperature for stipe heightening is 18°C at 93% and 97% relative humidity, and is 17°C and 18°C at 95% relative humidity. The optimal relative humidity for stipe heightening is also dependent on temperature (Table 3): 97% at 16°C ; 91%, 95% and 97% at 17°C ; 93% and 97% at 18°C . So the optimal air temperature and relative humidity conditions for stipe heightening is 18°C and 97% relative humidity.

Under better relative humidity conditions of 93-97%, the optimal air temperature for stipe thickening are 16°C and 17°C at 95% and 97%, 16°C and 18°C at 93% relative humidity. The optimal relative humidity for stipe thickening is also dependent on temperature (Table 3): 93% and 97% at 16°C ; 91%-95% at 17°C ; 97% at 18°C . So the optimal air temperature and relative humidity conditions for stipe thickening is 16°C and 93% relative humidity. #1 the optimum air temperature in temperature treatment under specific air relative humidity condition; #2 the optimum air relative humidity in humidity treatment under specific air temperature condition. The air relative humidity/air temperature conditions that no difference for the morphology growth were marked by the symbol "√".

Through comprehensive analysis of the data, we believe that the optimal air temperature and relative humidity conditions for morphological development of the fruiting body in the growing room is 16-18°C at a relative humidity of 93% and above. On different days, the optimal air temperature and relative humidity conditions for morphological development of the fruiting body are slightly different. In the appropriate range, lower air temperature and relative humidity are better for stipe thickening, higher air temperature and relative humidity are better for stipe heightening. 21 21 were estimated through the nonlinear least squares method using Statistica 6.0. Because of the small effect of humidity observed experimentally on morphological development of the fruiting body, humidity was not considered in the parameters estimated. As there were substantial differences in the three growth indicators and temperature on different days of the fruiting body growth period, the parameters were estimated separately for each day (see Table 4) over five days. environmental factor for the production of P. eryngii. Therefore the mathematical relationship between air humidity and morphological development was also simulated.

1 1 1 1 JB a T b T c t L u T k t             (2) 2 31 31 31 JB D a T b T c      (3)
From the results, the optimal air temperature for P. eryngii is 16°C and above. At temperatures of 16°C and higher, the morphological development of the fruiting body has the following mathematical relationship with relative humidity: u 2 and k 2 were estimated using the same method as for the parameters in Eqs. ( 1) -( 3).

2 12 12 12 JG D a H b H c      (4) 2 2 JB L u H k    (5) 2 32 32 32 JB D a H b H c      (
As there were substantial differences of morphological development indices on different days of fruiting body growth period, the parameters were estimated separately for each day (see Table 5) over five days.

Validation of the morphological development simulation model

The morphological development simulation model was validated by assessing the correlation coefficient (R), bias (Bias), root mean square error (RMSE) and the percent root mean square error (%RMSE) for the measured and estimated values. The detailed results are shown in Table 6.

From the mathematical model, the effect of air temperature gave the following results: R, Bias, RMSE and %RMSE were 88.6%, 0.325 cm, 0.335 and 13.42%, respectively, for cap diameter (Eq.1); 93.7%, 0.226 cm, 0.305 and 8.40%, respectively, for stipe height (Eq.2); and 92.6%, 0.173 cm, 0.213 and 10.09%, respectively, for stipe diameter (Eq.3).

The effect of relative humidity resulted in the following:R, Bias, RMSE and %RMSE were 85.4%, 0.350 cm, 0.396 and 17.42%, respectively, for cap diameter (Eq.4); 91.7%, 0.297 cm, 0.392 and 9.84%, respectively, for stipe height (Eq.5); 93.4%, 0.237 cm, 0.276 and 19.97%, respectively, for stipe diameter (Eq.6).

The estimated morphological development indices are correlated well with the measured values. This is also shown from Figures 123.

JB JB opt LJB JB b b a c SL b b a c SL t a a T SL k t u                     2 2 ( ) 1
where T opt-DJG is the optimal air temperature for cap diameter, T opt-LJB is the optimal air temperature for stipe height, T opt-DJB is the optimal air temperature for stipe diameter. T opt is the optimal air temperature for the fruiting body, which is the intersection of T opt-DJG , T opt-LJB and T opt-DJB . SD JG , SL JB and SD JB are standard values of cap diameter, stipe height and stipe diameter on different days, and is taken from the average value of the indices in this study (see Table 7).

where H opt-DJG is the optimal relative humidity for cap diameter, H opt-LJB is the optimal relative humidity for stipe height, H opt-DJB is the optimal relative humidity for stipe diameter. H opt is the optimal relative humidity for the fruiting body, which is the intersection of H opt-DJG , H opt-LJB and H opt-DJB .

Simulated optimal environmental conditions for the fruiting body growth period

Using equations ( 7) -( 14), the optimal environmental control standards can be computed for each day of the fruiting body growth period (Table 7).

Conclusion

When compared with traditional cultivation in plastic greenhouses, modern methods of commercial mass cultivation of fungi has the advantage of uniform growth, high efficiency and is unrestricted by season. However, production is costly and has high energy consumption. Therefore, optimal environmental control is crucial.

Higher fungi are considered difficult to cultivate in the laboratory without complex growth medium [START_REF] Baza1a | Transport of radiocesium in mycelium and its translocation to fruitbodies of a saprophytic macromycete[END_REF] because of the limited growing space in a laboratory.

Environmental control needs to be very precise in modern commercial production of fungi, and the application of the optimal climate control parameters is crucial for ideal fruiting body development, and therefore these study results have great practical significance. Using the simulation models morphological development rate and optimal climate control values can be obtained to guide the actual environmental control settings in the commercial production of P. eryngii.

In general, fungi are very difficult to study through experimental means alone because of the complexity of their natural growth habitat (e.g., soils) and the microscopic scale of growth (e.g., tip vesicle translocation and hyphal tip extension) [START_REF] Boswell | A mathematical approach to studying fungal mycelia[END_REF] . Some research focus on shape is on the cell [START_REF] Braaksma | Morphometric analysis of ageing mushrooms (Agaricus bisporus) during postharvest development[END_REF] .Mathematical modeling provides a complementary, powerful and efficient method of investigation. The aim of mathematical modeling is to reduce a complex (biological) system into a simpler (mathematical) system that can be analyzed in far more detail and from which key properties can be identified [START_REF] Boswell | A mathematical approach to studying fungal mycelia[END_REF] .

Temporal effects were considered in the model. The parameters in the model varied for each day. The relationship between the indices of morphological development and air environment in this paper was represented by a quadratic equation with one variable, reflecting the experimental results which showed that the estimated optimal air temperature and relative humidity are not the highest values of appropriate air temperature and relative humidity. The calculated optimal air temperature and relative humidity values predicted by the model are different for each day. For this reason, environmental parameters should be varied daily in the commercial production of P. eryngii, and not set at temperatures of 16-18°C or 93% above relative humidity, as analyzed to be optimal by the experiments. This also illustrates the practical value of the model.

We conclude that our model described in this article provides a powerful tool to predict the morphological development rate according to air environment, and to guide real-time adjustment of air environment for commercial production of P.

eryngii.

1

 1 Effects of air temperature on morphology development (ETMD) sub-modelFrom the experimental results, the relationships between air temperature and cap diameter, or stipe diameter were single-peak curves; the relationship between air temperature and height of the stipe was a single-peak curve on the first day, and linear on the other days. Mathematically, the effects of air temperature on morphological development of the fruiting body can be presented as follows:

Fig. 1 Fig. 2 Fig. 3

 123 Fig. 1 Measured vs. simulated diameter of the cap

4. 3 . 2

 32 The appropriate relative humidity range sub-model for morphological development From equations (4) -(6), we obtained the following formulas:

Table 1 .

 1 The values of morphological growth indices of P. eryngii on each day.

		1	2	3	4	5
	Cap diameter	0.729 a	1.164 b	1.890 c	2.922 d	4.041 e
	Stipe height	1.540 a	2.415 b	3.608 c	4.774 d	5.825 e
	Stipe diameter	1.170 a	1.433 b	1.881 c	2.347 d	2.639 e

*The value of each index was each day's mean growth for both treatments. Different letters indicate statistically different values (ANOVA/LSD) (P<0.05).

Table 2 .

 2 The multiple comparisons result showing the influence of different air temperatures and relative humidity levels on morphological growth of the fruiting body of P. eryngii on each day.

		Time/d	Cap diameter	Stipe height	Stipe diameter
	Air	1	18°C ,17°C ,16°C	16°C ,17°C ,18°C	16°C ,18°C ,17°C
	temperature	2	17°C ,16°C ,18°C	16°C ,17°C ,18°C	17°C ,18°C ,16°C
	treatment	3	17°C ,18°C	18°C ,16°C	16°C ,18°C ,17°C
		4	17°C	18°C	16°C ,18°C
		5	17°C	18°C	16°C
	Air relative	1	97%,95%,91%	93%,89%,95%	97%,95%,89%
	humidity	2	97%,95%,91%	93%,97%,91%	93%,91%,97%
	treatment	3	97%,89%,93%	93%,97%,89%	93%,89%,91%
		4	93%,97%,89%	97%,93%,89%	97%,93%,89%
		5	97%,91%	97%,93%,91%	97%,93%

Table 3 .

 3 The results of multiple comparisons of influence of different air relative humidity/temperature treatment on fruiting body growth under specific air temperature/relative

	humidity conditions.
	#1	16°C	17°C	18°C	#2	91% 93% 95% 97%
	97%	√	√		18°C	√

Table 4 .

 4 Estimated parameters in Eqs. (1) -(3)

	Time/d	a	11	b	11	c 11	a	21	b	21 /u	1	c 21 /k 1	a	31	b	31	c 31
	1	-0.02	0.76	-5.84	-0.03	0.87		-5.69	0.02	-0.56	4.69
	2	-0.03	1.10	-8.74			0.27		-1.93	-0.02	0.80	-6.02
	3	-0.03	1.25	-9.81			0.42		-3.02	0.02	-0.40	3.68
	4	-0.04	1.49	-10.73			0.47		-2.64	0.02	-0.50	4.78
	5	-0.22	7.11	-52.11			0.46		-1.57	0.06	-1.78	16.08

Table 5 .

 5 Estimated parameters in Eqs. (4) -(6)

	Time/d	a 12	b 12	c 12	a 32	b 32	c 32	u 2	k 2
	1	-0.004	0.66	-29.74	-0.001	0.10	-3.26	0.01	0.44
	2	-0.01	1.78	-81.63	-0.002	0.40	-17.12	0.09	-5.65
	3	-0.0003	0.05	-0.69	-0.002	0.43	-17.81	0.10	-4.74
	4	-0.005	0.94	-40.78	-0.001	0.10	-2.39	0.12	-6.23
	5	-0.02	3.68	-166.35	0.004	-0.69	34.34	0.09	-2.41

Table 6 .

 6 The values of validation indices for the morphological development simulation model

				Model validation indexes
	Model	Index	R 2	Bias /cm	RMSE	%RMSE
	ETMD sub-model	Cap diameter Stipe height Stipe diameter	88.6% 93.7% 92.6%	0.325 0.226 0.173	0.335 0.305 0.213	13.42% 8.40% 10.09%
	EHMD sub-model	Cap diameter Stipe height Stipe diameter	85.4% 91.7% 93.4%	0.350 0.297 0.237	0.396 0.392 0.276	17.42% 9.84% 19.97%

Table 7 .

 7 The standard values of morphological development and the simulated optimal environment conditions in the fruiting body growth period

	Time /d	SD JG /cm	SL /cm JB	SD JB /cm	Optimal temperature range /°C	Optimal humidity range /%
	1	0.729	1.540	1.170	[15.4,16.9]	[87,96]
	2	1.164	2.415	1.433	[15.9,19.1]	[89,95.4]
	3	1.890	3.608	1.881	[15.8,16.4]	[87,96]
	4	2.922	4.774	2.347	[15.8,16.4]	[91,96]
	5	4.041	5.825	2.639	[14.5,17.2]	[91,94]

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. 30800765) and the Project of the Science and Technology Commission of Shanghai Municipality, China (Nos. 08DZ2210600 and 08QA14058).

Carbohydr. Polym.


Weigang Kuang

Wancai Liu

Zhanhong Ma

Haiguang Wang
email: wanghaiguang@cau.edu.cn

Development of a Web-based Prediction System for Wheat Stripe Rust

Keywords: wheat stripe rust, web-based system, prediction, regression model, WebGIS

A web-based prediction system for wheat stripe rust was developed based on B/S (Browser/Server) mode in this study. Some existing prediction models of wheat stripe rust were collected, analyzed and then stored in SQL Server 2005 database according to certain rules. All these models could be called through this web-based system and used to predict wheat stripe rust. Meanwhile, Using multiple regression analysis principle, prediction regression model could be built based on the input historical data of wheat stripe rust through the network programming via this system, and significance tests of prediction factors could be conducted to obtain optimal prediction model and the built model could be stored into the model database for further prediction of this disease. Using WebGIS technologies, the prediction results of wheat stripe rust could be displayed in different colors in the web map according to the prediction values of disease prevalence. The web-based prediction system for wheat stripe rust developed in this study provided a convenient and fast way for the prediction of wheat stripe rust.

Introduction

Wheat (Triticum aestivum) with large plating area, is one of the major food crops in China. Pest damage to wheat yield and quality is very serious. Wheat stripe rust (or wheat yellow rust), caused by Puccinia striiiformis f. sp. tritici, is the most important wheat disease in China [START_REF] Li | Wheat Rusts in China[END_REF]. It is an important airborne plant disease. Severe epidemics of wheat stripe rust occurred in 1950, 1964, 1990 and 2002 in China and caused server yield losses resulting in significant economic losses [START_REF] Li | Wheat Rusts in China[END_REF], [START_REF] Wan | Wheat Stripe Rust Epidemic and Virulence of Puccinia striiformis f. sp. tritici in China in 2002[END_REF]. Therefore, timely and accurate prediction of wheat stripe rust has an important significance for taking timely and effective disease control measures and safe production of wheat.

Many methods used to predict wheat stripe rust have been reported, such as regression analysis [START_REF] Zeng | Interregional Spread of Wheat Yellow Rust in China (in Chinese)[END_REF], [START_REF] Yang | Dinamic Forecasting of Stripe Rust of Winter Wheat (in Chinese)[END_REF], [START_REF] Hu | Studies on the Prediction of Wheat Stripe Rust Epidemics in Hanzhong District of Shaanxi Province (in Chinese)[END_REF], [START_REF] Fan | Forecast Model for Prevalent Stripe Rust in Winter Wheat in Shanxi Province (in Chinese)[END_REF], [START_REF] Fan | Prediction Model on Wheat Stripe Rust in Gangu Country (in Chinese)[END_REF], discrimination analysis [START_REF] Chen | Forecasting Wheat Stripe Rust by Discrimination Analysis (in Chinese)[END_REF], [START_REF] Chen | Preliminary Research on the Regional Relationship of Epidemic of Puccinia striiformis in China (in Chinese)[END_REF], [START_REF] Yun | Forecast of Wheat Stripe Rust by Upper-air Wind (in Chinese)[END_REF], Markov forecast method [START_REF] Qiang | Markov Forecast of Wheat Stripe Rust in Qinghai in 1998[END_REF], principal component analysis [START_REF] Yuan | Application of Principal Component Analysis of Wheat Stripe Rust (in Chinese)[END_REF], grey model forecast method [START_REF] Pu | On Periodic Epidemic Regular Pattern and Prediction of Wheat Stripe Rust in Gansu Province (in Chinese)[END_REF], [START_REF] Liu | The Prediction Methodology of Wheat Stripe Rust Using Combination Model Based on GM (1, l) (in Chinese)[END_REF], neural networks [START_REF] Hu | Prediction of Wheat Stripe Rust in Hanzhong Area by BP Neural Network (in Chinese)[END_REF], [START_REF] Zhang | Research on Chaotic Characteristics of the Disaster Rate of Crops and Its GA-BPNN Forecasting Model (in Chinese)[END_REF], [START_REF] Jin | Long-term Meteorological Prediction of Country Wide Wheat Stripe Rust by Genetic Neural Network (in Chinese)[END_REF], [START_REF] Wang | Prediction of Wheat Stripe Rust Based on Neural Networks[END_REF] and support vector machine [START_REF] Wang | Prediction of Wheat Stripe Rust Based on Support Vector Machine[END_REF]. Generally, prediction functions of wheat stripe rust by using these methods were realized on single PC. A few of single computer versions of wheat stripe rust prediction systems have been developed [START_REF] Xiao | A Prototype Expert System for Long-term Prediction of Stripe Rust Epidemics (in Chinese)[END_REF], [START_REF] Zeng | Simulation Experiments on the Pandemic Dynamic of Wheat Stripe Rust in China by Means of a Simulation Model PANCTRIN (in Chinese)[END_REF], such as PANCRIN that could be used to make simulation experiments on the pandemic of wheat stripe rust in China [START_REF] Zeng | Simulation Experiments on the Pandemic Dynamic of Wheat Stripe Rust in China by Means of a Simulation Model PANCTRIN (in Chinese)[END_REF].

WebGIS technology is an emerging technology used in plant protection. It is convenient to process geographic information based on Internet by using WebGIS technology. It plays an important role in pest monitoring and forecasting by making full use of the processing capabilities of geographic information system (GIS) for spatial data and combining GIS with network technologies effectively. Based on longterm study of Sudden Oak Death in California coastal region, Kelly and Tuxen constructed a warning and monitoring website for Sudden Oak Death in California called the "OakMapper" by using WebGIS technology [START_REF] Kelly | WebGIS for Monitoring "Sudden Oak Death" in Coastal California[END_REF]. The information of the website is quarried and browsed in the web map. Users can get the latest information of disease occurrence and learn some knowledge related disease prevention by visiting the website. PhytoPRE+2000, an Internet based version of decision support system for potato late blight was developed for more efficient services in monitoring and management of this disease in Swiss [START_REF] Forrer | Monitoring of Potato Late Blight in Switzerland and Development of PhytoPRE+ 2000, an Internet Based Decision Support System[END_REF]. A potato late blight monitoring and warning system "china-blight" (www.china-blight.net) was constructed based on B/S (Browser/Server) internet structure by combining information technology with the principles of plant disease epidemiology [START_REF] Hu | Construction and Implementation of China-light: a Monitoring and Warning System on Potato Late Blight in China (in Chinese)[END_REF]. The system could provide the information on the infection risk of late blight pathogen in different districts of China in the coming 48 hours and also could provide decision support for the chemical control of this disease. With the development of information technologies, the Internet based or WebGIS based plant disease prediction systems play a more and more important role in monitoring and warning of plant diseases.

To the authors' knowledge, there is not any report about web-based wheat stripe rust prediction system. In this study, a prediction system for wheat stripe rust in China was developed using WebGIS technologies. Prediction of wheat stripe rust could be conducted using the existing prediction models or prediction regression models built by using this system based on the input historical data by users. The prediction of this disease by using the information and network technologies was achieved. A convenient and fast way was provided for the prediction of wheat stripe rust and the web-based services also could be provided for integrated control of this disease.

Development Platform of This System

The web-based prediction system for wheat stripe rust was developed based on the .NET platform. C# and JavaScript network programming language were used as the system development languages. Internet Information Server (IIS) was used as the web server and ArcGIS Server 9.3 of ESRI Company was used as the map server. The data information was stored in SQL Server 2005 database. The Microsoft Visual Studio 2008 was used as the development tool to develop the web-based prediction system for wheat stripe rust.

Overall Structure of This System

The web-based prediction system for wheat stripe rust was developed based on B/S mode which was a three-layer model structure including web application service layer, logic layer and database layer. The system was composed of the client side and the background (as shown in Fig. 1). Using the historical disease prediction models and the models built by using the system based on the input historical data, the prediction of wheat stripe rust could be implemented and the prediction results could be displayed in different colors in the web map at the client side according to disease prevalence. The background included user management module and model management module that could be used to manage the users and to manage the database, respectively.

Function Realization of the Web-based Prediction System

The disease prediction function of this web-based system was realized using two methods. One was to realize the function by using the historical models. The existing prediction models were collected and stored in the background database. The models could be queried from the database and then the suitable model could be selected to predict wheat stripe rust. Another method was to realize the prediction function by using the models built based on the input historical data of wheat stripe rust. The function structure chart of the web-based prediction system for wheat stripe rust was shown in Fig. 2.

Building Model Database of Wheat Stripe Rust

Historical regression prediction models of wheat stripe rust were collected through reviewing relevant literature. The models were stored into model database mainly according to provinces where the disease occurred, counties where the disease occurred, model types, coefficient value of impact factor, the values of constant term of the models, use instructions of the models, and so on. The model database was composed of prediction models and original data table. The models were stored in the model table including the fields, i.e. fid, fdisesse, fprov, fcity, fmodel, fx1, fx2, fx3, fx4, fx5, fx6, fchang and fexplain (as shown in Table 1). The input historical data of wheat stripe rust used to build regression model was stored in the original data table.

Building regression model

Province, Shanxi Province and so on, respectively, were collected for the web-based prediction system (as shown in Table 2). Users can query the models of corresponding regions by inputting information at the client side, and then use the corresponding model to carry out the prediction of wheat stripe rust. x 1 is the amount of pathogen in autumn, x 2 is the amount of pathogen in spring, x 3 is the average temperature in April, x 4 is the precipitation in April, x 5 is the proportion of susceptible variety area, and y is disease prevalence.

[5]

Shanxi Province y = -0.2766 + 0.0249x 1 + 0.0104x 2 + 0.0132x 3

x 1 is the precipitation in the middle ten days of April to the first ten days of May, x 2 is the precipitation in the middle ten days of May and the last tens of May, x 3 is the average relative humidity in May, and y is disease prevalence.

[6]

Gangu

County in Gansu Province y=-2.07472 + 0.242533x 1 + 0.06744x 2 -0.0091x 3 + 0.011865x 4 + 0.090152x 5

x 1 is the average temperature in March, x 2 is the precipitation in March, x 3 is the precipitation in April, x 4 is the precipitation in May, x 5 is the susceptible variety area, and y is disease prevalence.

[7]

Nonglan in Gansu Province y = -0.547 + 0.025 x 1 + 0.03 x 2 + 0.29 x 3 + 0.081 x 4

x 1 is the percentage of diseased fields in autumn in the preceding year, x 2 is the proportion of susceptible variety area, x 3 is the average temperature in January in Huixian, x 4 is the average maximum temperature in March in Wudu, and y is disease prevalence in Spring in Nonglan. [START_REF] Xiao | Prediction Model on Stripe Rust Influence Extent of Winter Wheat in Longnan Mountain Area (in Chinese)[END_REF] Jincheng in Shanxi Province

y = 3.7644 + 0.0070x 1 - 0.0097x 2 + 0.0273x 3 + 0.1079x 4 - 0.2145x 5
x 1 is the precipitation in the last ten-day period of July in the preceding year, x 2 is sunlight in the first ten days of November in the preceding year, x 3 is sunlight in the first ten days of March in the preceding year, x 4 is the average temperature in the middle ten days of April, x 5 is the precipitation in the last ten-day period of April, and y is disease prevalence. [START_REF] Cheng | Meteorological Forecast Models of Wheat Pests in Jincheng City[END_REF] Pingliang in Gansu Province y = -0.1736 + 0.2051x 1 + 0.4345x 2 + 0.0089x 3

x 1 is disease prevalence in spring, x 2 is the amount of oversummering pathogen, x 3 is the total precipitation in September and October, and y is disease prevalence on autumn wheat seedlings.

[27]

Realization of Automatically Modeling Using the System

The quantity of collected regression prediction model of wheat stripe rust is limited, and most of the models could only be used locally. With the changes in the preconditions, the accuracy of the models should be further improved. Therefore, the model construction module was designed in the web-based prediction system for wheat stripe rust so that the prediction models could be built by using the system. Users can establish regression prediction models using the principle of multiple regression with the help of network programming based on the input historical data of wheat stripe rust. F test on the independent variables at the level of 0.05 or 0.01 could be conducted by using the system to help users to get optimization model.

Regression analysis is a statistical method used to determine the quantitative relationship between two or more variables. Monadic linear regression is to use one of the major factors as independent variable to explain the change of the dependent variable. Multiple regression is to use two or more factors as independent variables to explain the change of the dependent variable. Least square method is used to calculate parameters during regression analysis.

K linear regression using N groups of observations can be solved through the following matrix (as shown in Equation ( 1)). (1) in which, vector y is observation variable vector, matrix x is the matrix of observation variables, vector B is regression parameter vector, vector u is random error vector. The simplified form of Equation ( 1) could be expressed as Equation [START_REF] Wan | Wheat Stripe Rust Epidemic and Virulence of Puccinia striiformis f. sp. tritici in China in 2002[END_REF].

U XB Y   (2)
According to the principle of least square method, the estimate value of the minimum parameter vector B could be expressed as Equation (3).

) ' ( ) ' ( 1 Y X X X B   (3)
Automatically modeling using the system could be realized in accordance with the following steps. Prediction models of wheat stripe rust could only be built by using regression analysis method via this system. Firstly, the targeted region where disease prediction result would be used should be determined. Secondly, model type should be chosen according to the related data. Five kinds of regression models could be built via this system including binary linear equation, ternary linear equation, first-order linear equation with four independent variables, first-order linear equation with five independent variables, and first-order linear equation with six independent variables. After choosing the proper model and clicking the OK button, a system interface as shown in Fig. 3 would be displayed in the client browser. Then the prediction factor values could be input into the system. After inputting all the historical data into the system and clicking the building model button. The system would deal with the data by using regression analysis method. Finally, the prediction model would be built just as shown in Fig. 4.

After building the prediction model by using regression analysis method via the web-based prediction system for wheat stripe rust, F tests on the independent variables at the level of 0.05 or 0.01 could be conducted to select the model with more accurate by using the system and the optimization model could be obtained. Then the relevant information about the model including the applicable area and the instructions of variables should be input into the system through system interface so that the model could be used in correct way later. The model could be stored into the model database and be used as the same as the historical regression prediction models of wheat stripe rust.

Realization of Prediction Function Based on WebGIS

Using the web-based prediction system for wheat stripe rust, the relevant regional prediction model can be queried through the user interface of the system. According to the instruction of the selected model, after the corresponding information being input into the system, the model could be used to predict wheat stripe rust. In order to make the prediction results more intuitive, WebGIS technologies were combined with the network technologies in this system and the results could be displayed in different colors in the web map at the client side according to disease prevalence.

Fig. 4.

The prediction model built by using the web-based prediction system for wheat stripe rust.

Spatial database was established to store national geographic information. The maps used in this system were administrative electronic maps of China which were acquired from the National Geographic Information System in China. The map scale was 1:400000000. The e00 format of the maps was converted to the Shape format by using ArcCatalog software firstly. The ArcGIS 9.3 was used to deal with the vector maps. And the map information was released by using ArcCatalog software.

In the web-based prediction system, the maps were divided into three layers; the first was the county and city boundary layer, the second was the province boundary layer and the third was Chinese boundary layer. Disease prevalence of wheat stripe rust was divided into five classes represented by 1, 2, 3, 4 and 5, respectively. The greater the grade of disease is, the more severe the disease prevalence is. Class 1, 2, 3, 4 and 5 are displayed in green, purple, blue, yellow and red, respectively. This function of the system could be demonstrated by the prediction model of wheat stripe rust in Gangu County that was built by Fan et al. [START_REF] Fan | Prediction Model on Wheat Stripe Rust in Gangu Country (in Chinese)[END_REF]. Firstly the background model that was suitable for Gansu County was called in accordance with the rules of the system. Then the model could be used to predict the disease prevalence of wheat stripe rust in Gangu County. The prediction result was shown in Fig. 5.

Maintenance of Model Database

Due to the changes of environments conditions, tillage practices and so on, the accuracies of the prediction models would decrease. In order to improve the model database continually, the prediction model can be added or modified via system background. Thus the validation and verification of the prediction models could be ensured. After inputting the correct user's name and password, the user can enter the system background. The kinds of regression prediction models stored in background were divided into 5 types just like described above. When the user selects the model type that he or she would like to add, the number of variables is determined. Then the user can fill in the form in the system interface with relevant information according to the instruction of the system. In order to ensure that the model could be correctly used later, the variables and using method of the model should be described in the system. After all information is input, the user could click the "Add" button and the model would be stored into the model database. By using the query function, the added models could be selected to predict wheat stripe rust.

Conclusion and Discussion

A web-based prediction system for wheat stripe rust was developed using network information technologies in this study. The existing prediction models stored in the model database of the system could be used to predict wheat stripe rust. Moreover, regression models could be built via the system by using C# network programming technologies based on the principle of multiple regression analysis. The optimized models could be stored into the model database and users could select the suitable model among the database to predict wheat stripe rust in certain region. Thus the waste of information resources and the repeat development of prediction models could be avoided. This system built in this study provided a convenient and fast way for the prediction of wheat stripe rust.

In general, the prediction of wheat stripe rust was conducted on single PC. The web-based prediction of wheat stripe rust could not be carried out before in China. However, the web-based prediction of other plant diseases has been implemented and plays an import role in practical application in China [START_REF] Hu | Construction and Implementation of China-light: a Monitoring and Warning System on Potato Late Blight in China (in Chinese)[END_REF]. Based on B/S structure, the system built in this study could display the prediction results in different colors by means of the powerful spatial information processing ability and graphical display function of WebGIS technologies. By combining GIS maps with the prediction models, the system makes the prediction results of wheat stripe rust more intuitive. The prediction of wheat stripe rust could be conducted by using the models built based on the related data via the system and the historical regression prediction models stored in the model database. This web-based prediction could make the prediction of wheat stripe rust more efficient and more convenient and then could provide better services for control decision making.

The development of the web-based prediction system for wheat stripe rust was a meaningful attempt in informationization and automatization of monitoring and early warning on wheat stripe rust. But the functions of spatial analysis and prediction of this system still need further improvement. There are many kinds of methods to construct the prediction models of wheat stripe rust. Each kind of method has its advantages and disadvantages. Only the historical regression prediction models were collected and stored in the model database of this system. And the models that could be built by using this system were also regression models. To make this system more useful, other types of prediction models and other kinds of modeling methods should be added to this system. The prediction results would become more accurate on the basis of complementary advantages of different prediction methods. In order to improve the reliability of results, multiple prediction results using different methods should be showed in overlay format. Thus a scientific basis would be provided for the integrated management of wheat stripe rust.

Fig. 1 .

 1 Fig. 1. Overall structure of the web-based prediction system for wheat stripe rust.

Fig. 2 .

 2 Fig. 2. Function structure chart of the web-based prediction system for wheat stripe rust.

Fig. 3 .

 3 Fig. 3. The system interface for inputting data.

Fig. 5 .

 5 Fig. 5. The prediction results of wheat stripe rust for Gangu County using the web-based prediction system.

Table 1 .

 1 Disease prediction model table.

	Building database	Disease prediction	Building model
		function
				Historical
	Collected models			disease data
			Diseases data	Key factor
		Result display	input	selection
	Model database
				Regression
				model
		GIS maps		Test
			No	Significant ?
	Model querying	Factor value input
			Yes
	Field name	Data type	Comments
	fid	int
	fdisesse	nvarchar(50)	Disease name
	fprov	nvarchar(50)	Provinces where the disease occurred
	fcity	nvarchar(50)	counties where the disease occurred
	fmodel	nvarchar(50)	Model type
	Fx1, Fx2, Fx3, Fx4, Fx5 and Fx6	float	The coefficient value of impact factor 1, 2, 3, 4, 5 and 6
	fchang	float	The values of constant term
	fexplain	text	Use instructions of the models
	The regression prediction models of wheat stripe rust that were applied in Ganggu
	County in Gansu Province, Longnan in Gansu Province, Hanzhong in Shaanxi

Table 2 .

 2 Some historical prediction model stored in the model database of the web-based prediction system for wheat stripe rust.

	The suitable
	region for
	using	Prediction model	Variable	Reference
	prediction
	model
		y = -4.699158 +
	Hanzhong in Shaanxi Province	0.199445x 1 0.052963x 2 0.314299x 3 0.014257x 4	+ + + +
		0.008677x 5

Acknowledgments

. This work was supported in part by National Key Technology R&D Program (2012BAD19B04) and Special Fund for Agro-scientific Research in the Public Interest (200903004 and 200903035).


Lihua Jiang
email: jianglh@caas.net.cn

Hong-Bin Zhang

, Xiaorong Yang

Nengfu Xie

Research on Semantic Text Mining Based on Domain Ontology

Keywords: ontology, domain ontology, text mining, semantic text mining 1

Text mining is an effective means of detecting potentially useful knowledge from large text documents. However conventional text mining technology cannot achieve high accuracy, because it cannot effectively make use of the semantic information of the text. Ontology provides theoretical basis and technical support for semantic information representation and organization. This paper improves the traditional text mining technology which cannot understand the text semantics. The author discusses the text mining methods based on domain ontology, and sets up domain ontology and database at first, then introduces the "concept-concept" correlation matrix and identifies the relationships of conceptions, and puts forward the text mining model based on domain ontology at last. Based on the semantic text mining model, the depth and accuracy of text mining is improved.

Introduction

Natural language is the main means to exchange and express thoughts and ideas in social-economic living for people. Though study of natural language for a long time, the ability of interpretation is still limited. The existed technology has solved single sentence analysis, but it is hard to cover all language phenomenon, especially for the whole paragraph and chapter.

In the early nineteenth century, the data mining technology based on statistical technology had developed maturely, and application in the large-scale structural relational database achieved success. So people want to apply data mining technology to analyze natural language text and the method is called text mining or knowledge discovery in text. Difference with the traditional natural language processing words and sentences, the main goal of text mining is concentrated on founding hidden meaningful knowledge in mass text set, that is the understanding of text set and the relationship among texts. Now most of the text mining applications are lack of semantic level consideration, only in grammar level processing, so the obtained result is not pretty.

Text Mining Based on Domain Ontology

Text mining or knowledge discovery in text is the process of extracting unknown, useful and understandable pattern or knowledge in mass text data [START_REF] Hearstma | Text datamining Issues, techniques, and the relationship to information access[END_REF][START_REF] Feldman | Knowledge discovery in textual databases (KDT)[A[END_REF]. The research object is semi-structural or unstructured and natural language text contains multi-level ambiguities. So text mining has brought a lot of difficulties.

The traditional text mining method based on vector space model represented the text to lexical frequency vectors and mined the vectors [START_REF] Mothe | Information mining -use of the document dimensions to analyse interactively a document set[END_REF][START_REF] Ghanem | A grid infrastructure for mixed bioinformatics data and text mining[END_REF].

The defect of this method is only dealing with document forms and ignoring the semantic role. This is the primary cause that traditional text mining can not live up to

Key Technology of Text Mining Based on Domain Ontology

At present, Most of the ontology systems are almost the same in basic structure. Most of ontology is described for the entity, conception, generally properties and relationship. That is, by the way of some rules, the characteristic features and the corresponding parameters of the entities or conceptions are studied. At the same time, the relationship of entity and conception is described.

Knowledge Presentation Based on Domain Ontology

Knowledge presentation is the foundation of text mining. The quality of the knowledge representation is related to the efficiency of text mining. This paper uses WordNet and OWL to organize and present knowledge. This paper studies the following ideas: after data preprocessing, universal transformation format and conception extraction, domain ontology and ontology database are built by WordNet and OWL. Knowledge construction is defined and knowledge index is built in conceptual level.

In this paper, we take agriculture ontology as an example. Agriculture ontology is the system including agriculture terms, definition and standard relationship of terms.

It is also formalize expression of conceptions and the relationship among conceptions.

At the same time, it can not only deal with the inner relations among agriculture subject tale, but also more formal special relations. Formalize agriculture ontology is defined:

Agri_Onto=(Onto_Info, Agri_Concept, AgriCon_Relation, Axion)

Therein, Onto_Info is the basic information of ontology including name, creator, design time, modification time, aim and knowledge resource and so on; Agri_Concept is the set of agriculture conceptions; AgriCon_Relation is the conceptual relation set including hierarchical relationship and the hierarchical relationships; Axion includes existing axiomatic set in ontology.

Construction conceptual semantic correlation matrix

Combined with the features of domain ontology and text mining, this paper proposes the text mining model based on domain ontology, and the main idea is as following:

at first, "conception-conception" correlative matrix of domain ontology is constructed, and then key words in the collected documents are extracted in the light of domain ontology and the documents are represented to vector space model in order to calculate similarity between documents by reference to the "conception-conception" correlation matrix to realize document clustering. If new conception is found in the process of document clustering, the ontology database should be enlarged.

In domain ontology, there are vocabulary to represent classes and conceptions which are not only the bridge to communicate with classes and conceptions but also basic elements to represent classes. The relationships of domain ontology depend on the words to connect, so vocabulary is the key of construction domain ontology. In this study, the class vocabulary and conception vocabulary are extracted from text documents to make up vocabulary set

} ,..., , { 2 1 T C C C C 
, in which T is the sum of conceptions in ontology. This paper uses matrix to structure representation method of conception relatedness, as follows:

              ) ,
T T T T T T C C R C C R C C R C C R C C R C C R C C R C C R C C R R In the matrix, ) , ( 1 T C C R represents semantic relativity of 1 C and T C .The result of ) , ( 1 T C C R is semantic relativity of 1
C and T C . A lot of scholars have discussed the conception relativity calculation. Generally speaking, there are two kinds of methods, information capacity method and the concept distance method. This paper adopts concept distance method, because in the ontology conceptions are arranged in tree.

The method of concept distance can not only reduce complexity of algorithm but also easy to calculate. Before calculation the relativity, first of all to do the following description:

① if i C and j C is similar, the   j i C , C R =1; ② if i
C and j C is not similar, according document [START_REF] Wu Jian | Web Service Discovery Based on Ontology and Similarity of Words [J[END_REF] this paper adopts the following formula to calculate semantic relativity:

In the formula,   is the total weights in the shortest root from i C to j C in domain ontology tree. Dep is the max depth of ontology tree.  is a controllable parameter, generally more than equal to zero.

Identification of relationship of conceptions

The aim of text mining is to find the inherent, useful knowledge and implicit relationship. This paper introduces the identification mode to judge weather there is relation  

j i C C  between i
C and j C .The method of this paper is to set a threshold to judge the relation between i C and j C . If

                    1 , max * * 2 * , d * * , , i j i j i j j i j i C d C d Dep C C CE C d C C C Dist C C R      

Conclusion

In order to improve the depth and accuracy of the text mining, a semantic text mining model based on domain ontology is proposed. In this model, conceptual semantic correlation matrix is applied to semantic analysis so that the semantic relations can be extracted accurately. The text mining model based on domain ontology in this paper can mine deep semantic knowledge from text documents. The pattern got has great potential applications.

  clustering text documents[START_REF] Hotho | Wordnet improves text clustering[END_REF]. Song etc. suggested an automated method for document classification using an ontology, which expressed terminology information and vocabulary contained in web documents by way of a hierarchical structure (Song Mh, 2005). In our country, knowledge engineering lab of computer science department in Tsinghua university developed text mining platform based semantic web. And also there are some researchers who discussed applications of semantic processing technology in text mining. Xuling Zheng etc.proposed a corpus based method to automatically acquire semantic collocation rules from a Chinese phrase corpus, which was annotated with semantic knowledge according to HowNet(Zheng Xuling etc., 2007). By establishing domain ontology as the way of knowledge organization, Guobing Zhou etc. introduced a novel information search model based on domain ontology in semantic context (Zou Guobing etc., 2009) . An Intelligent search method based on domain ontology for the global web information was proposed to solve the problem of low efficiency typical in traditional search engineers based on word matched technology by Hengmin Zhu (Zhu Hengmin etc., 2010) . In order to improve the depth and accuracy of text mining, a semantic text mining model based on domain ontology was proposed by Yufeng zhang etc.. And in this model, semantic role labeling was applied to semantic analysis so that the semantic relations can be extracted accurately (Zhang Yufeng etc., 2011). Taken together, the research of semantic text mining based on domain ontology is still in the domestic research theory spread stage, and relative actively in foreign countries. But there is few whole text mining based on domain ontology solutions results. And the research scope is only in foundation of shallow knowledge such as classification and clustering of text (Bingham,2001; Montes-y-Gómez, 2001)but rarely in rich useful deep semantic knowledge such as semantic association foundation(Zelikovitz,2004)、topic tracking(Aurora, 2007) and trend analysis (Pui Cheong Fung, 2003)and so on.

  threshold, there is correlation between i C and j C . At the same time, the implicit correlation is found.

Fig1.

  Fig1. Text Mining Model Based on Agriculture Ontology

Acknowledgment:

The work is supported by the special fund project for basic science research business Fee, AIIS "Tibet agricultural information personalized service system and demonstration" (No.2012-J-08) and The CAAS scientific and technological fund project "Research on 3G information terminal-based rural multimedia information service" (No.201219).


Lutao Gao
email: gaolutao_11@163.com

Linnan Yang

Lin Peng

Yingjie Chen

Yongzhou Yu

The research and design of the Android-based facilities environment multifunction remote monitoring system ※

Keywords: Mobile Internet, Android Smartphone, Facilities Environment, Remote Monitoring

For the actual demand of national facilities environment remote monitoring system combined with mobile Internet technology is cheaper, simpler to operate and better performance of mobility management. This article describes the research of the Android-based facilities environment multifunction remote monitoring system that based on the Android Smartphone as the terminal, was combined with wireless camera, relay group, wireless AP + temperature and humidity (light intensity) sensor and so on. The system implements remote facilities environment factors monitoring, real-time video monitoring, maintenance and management for remote server. The test and application shows that is stable, cheap, good mobility and easy to operate, it is a strong practicality and application prospects.

Introduction

With the rapid development of Chinese agricultural facilities environment remote monitoring system is an important factor to improve facilities for agricultural production automation and efficient [START_REF] Zhongfu | GPRS and WEB based data acquisition system for greenhouse environment[END_REF] . In recent years, the national agricultural engineering researchers used computer-controlled technology, web technology, GPRS and GSM technology [START_REF] Guoxiang | Development and application on GSM based monitoring system for digital agriculture[END_REF][START_REF] Yao Li-Bo | Design of Supervisory Control System for Greenhouse Based on Industrial Ethernet and Web Browse[END_REF] based on the PC (Personal Computer), PDA (Personal Digital Assistant) application terminal, develop and design a range of facilities environment remote monitoring and control systems [START_REF] Jiandong | Technology of a fertilizing expert system PDA for crop growing[END_REF][START_REF] Li Wei | Research on automation testing on Windows mobilebased device[END_REF] , has played a positive role in promoting the development of Chinese agricultural facilities.

This year, with the rapid development of mobile Internet and networking technology continues to mature, especially the world's rapidly growing popularity of smart phones, the mobile network terminals application is more widely. In the present facilities of agricultural environmental monitoring system, there are lots of PDA ※ Agriculture Science Technology Achievement Transformation Fund (2011GB2F300013) Lutao Gao, Linnan Yang, Lin Peng, Yingjie Chen, Yongzhou Yu application terminal for greenhouse control system, but because the PDA is a single function, and development environment in the human-computer interaction, crossplatform performance is poor, limiting the effect of promotion and application of its facilities in agricultural environments. In recent years, the Android operating system and Android Smartphone has been rapid development, especially the launch of the Android 3G Smartphone fewer than 1000 Yuan (RMB), the Android Smartphone market share in Chinese rising [START_REF] Hall | OPERATING SYSTEMS FOR MOBILE COMPUTING[END_REF] . In the one hand, the Android smart phone is the set of calls, multimedia, Internet smart terminal; it is not only inexpensive, but also multifunction [START_REF] Han | Design and Implementation of Locust Data Collecting System Based on Android[END_REF] . The other hand, the Android operating system is open source and free, it not only reduces the system development costs, but also has a better humancomputer interaction technology because of object-oriented Java language supporting. Therefore, Android Smartphone application terminal with mobile Internet technology has a positive meaning to build multi-purpose environmental monitoring system for Chinese agricultural facilities to further improve our facilities for agricultural automation and intelligent.

The article describes the system is based on Android technology, Socket technology and Java technology, group of wireless relay, wireless sensors and wireless cameras, over Android application terminals, research and development facility environment based on the Android multi-function remote monitoring system.

System architecture design

The article based on Android Smartphone application terminal for the real facilities environment monitoring system status, combines the working principle of wireless cameras and wireless sensors, and gives the design of system architecture. The figure of system architecture shows as As Figure 1 shows, the system architecture contains user, data transfer and data layers. User layer is based on the Android Smartphone application terminal for sending and checking relevant instruction and information by human-computer interaction. Data transfer layer refers to the entire system operation that depends on network environment. The system is connected to the Internet by WIFI or 3G network

The research and design of the Android-based facilities environment multifunction remote monitoring system※ 3 for data transmission. Data layer contains video, environmental factors (temperature and humidity, light intensity) collection of information, such as, sun visor, water screen, heating and other facilities environmental site implementing agencies, as well as for data storage, centralized management of servers in the data center, and so on.

System design and implementation

The system based on the Android Smartphone application terminal to achieve the following three main features:

(1) Implementation remote real-time video monitoring of the facility agriculture;

(2) Combination of wireless relay group and wireless sensors such as temperature and humidity, light intensity, remote real-time monitoring to agricultural environment;

(3) Remote control of facility agriculture data center servers.

Core system class diagram is Figure 2: You can see from Figure 3, this opening session and video get the thread method, to achieve the functionality of video transmission and session continued. At the same time, this article also uses memory auto clean mechanism, thereby ensuring that the video transmission in the process, due to lack of memory of the Terminal and is causing the lockup issues.

System design and realization of real-time video monitoring

Design and implementation of system facilities environment monitoring module

The module to temperature humidity and light strength sensor for information collection Terminal, to wireless AP for frontal network access device, to wireless following electrical group for implementation body control device, and to the Android smart phone for application terminal, achieved remote real-time monitoring facilities environment within temperature humidity, illumination strength. It is able to through remote control unlimited following electrical group to achieve remote real-time control facilities environment system, its system implementation flowchart as Figure 4:

The research and design of the Android-based facilities environment multifunction remote monitoring system※ 5 As shown in Figure 3, the article point to the transfer protocol for wireless sensor to achieve the real-time data acquisition terminal for data transmitted to mobile terminals, automatic alarm when facilities environment exceeds a predetermined value. At the same time, using wireless relay group for weak electricity to force electricity controling. It achieves that mobile terminal directly control facilities environmental site visor, water screen, heater and functions of system.

Start

Design and implementation of remote server control module

This module is primarily in Smartphone application for terminal objects servercontrol. It achieves remote management features and maintains a data center server by Android mobile phone with coordinate positioning technologies and Socket like technologies. Its server control activity figure as shown in Figure 5: This system is applied by the agricultural science and technology achievements transformation Fund "Test and demonstration of Yunnan information service platform for Standardization of characteristic fruit and vegetable production ". The part of system interface as shown in Figure 7: The system uses the mobile Internet technology, to Android Smartphone application for the Terminal, combined with wireless data collection Terminal, using Android technology research and development "facility environment multifunction remote monitoring system based on Android", achieved through the Android Smartphone to remote facilities environment monitoring of video information, environmental factors, as well as the ability to remotely controlling, managing, and maintaining a data center server.

Figure 1

 1 Figure 1 System architecture

Figure 2

 2 Figure 2 System class diagram As Figure 2 shows, This article achieves the SendData, SendPacketRunnable, SengStringRunnable, WifiSendSocket, WifiReciveSocket, ReceiveImageRunnable, and other core classes by overriding methods of the parent class and inheritance and overrides the appropriate methods. The core class diagram design for code optimization and integration of the system of development laid the Foundation important technologies.

3 :Figure 3

 33 Figure 3 Technical route flowchart

Figure 4

 4 Figure 4 Facilities environment monitoring system flowchart

Figure 6

 6 Figure 6 System user operations flowchart

Figure 7

 7 Figure 7 Part of system interface

Lutao Gao, Linnan Yang, Lin Peng, Yingjie Chen, Yongzhou Yu The article point to the achievement of control server from Android mobile phone by Socket technology and Android technology. As the remote control of server activity figure shows, the module achieves following functions: When the user clicks the phone screen, a click command sent to the server. When the user double-clicks on a mobile phone screen, a double-click command sent to the server. When a user under the mobile phone screen, pop up the dialog box that you are able to choose right click, delete, copy, cut, paste operations. At the same time it is able to do function over PC such as IE browsing, file management, system management, and so on.

System integration and implementation

The system application program development environment is built over JDK +Eclipse +Android SDK+ADT. It support for the JAVA programming language [START_REF] Shang Minghua | Information collection system of wheat production risk based on Android smartphone[END_REF] . It is better than QT application development platform based on C/C++ on security, human-computer interaction, and cross-platform [START_REF] Shang Minghua | Information collection system of wheat production risk based on Android smartphone[END_REF][START_REF] Xiaoqian | Development and Research of Application Based on Android Platform[END_REF] .

In this development environment, using Android, Java and Socket technology, combine human-computer interaction technology with Android, to integrate and apply for facility environment multifunction remote monitoring system based on Android. The user operations flowchart as shown in Figure 6: Lutao Gao, Linnan Yang, Lin Peng, Yingjie Chen, Yongzhou Yu Display of system features and application effect: (1) Using Android technology, based on the major mobile Internet devices -smart phones replace traditional PC, and applied to the facilities environment monitoring systems, reducing the cost of system development and investment;

(2) Compared with the single network applications such as PDA Terminal, Android smart phone applications more powerful, more large and universal application, have better market infrastructure and its application development environment based on JAVA language to make them better human-computer interaction and cross-platform performance;

(3) The system is feature-rich, secure and reliable, reducing labor costs, and good mobile performance by actual application in project. It gets very good application value.


Ping Han

Pengxin Wang
email: wangpx@cau.edu.cn

Shuyu Zhang
email: shuyuzhang88@sina.com

Junming Liu

Dehai Zhu

Pengxin Wang

Application of the ARIMA Models in Drought Forecasting Using the Standardized Precipitation Index

Keywords: drought forecasting, standardized precipitation index, autoregressive integrated moving average model

The standardized precipitation index (SPI) was used to quantify the classification of drought in the Guanzhong Plain, China. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecast the SPI series. Most of the selected ARIMA models are seasonal models (SARIMA). The forecast results show that the forecasting power of the ARIMA models increases with the increase of the time scales, and the ARIMA models are more powerful in short-term forecasting. Further study was made on the correlation coefficient between the actual SPIs and the predicted ones for the forecasting. It is shown that the ARIMA models can be used to forecast 1month leading values of all SPI series, and 6-month leading values for SPI with time scales of 9, 12 and 24 months. Our study shows that the ARIMA models developed in the Guanzhong Plain can be effectively used in drought forecasting.

Introduction

Drought is a slow-onset and creeping natural hazard that occurs in all regions of the world. Prolonged multiyear drought has caused significant damages in natural environment as well as in human lives. The Estimation for the cost of drought in the United States ranges from $6 to $8 billion annually [START_REF] Schubert | Predicting Drought on Seasonal-to-decadal Time Scales[END_REF]. In China, the amount of loss caused by drought ranks the first in all natural hazards. With the increase of the population and the severity of drought, an effective mitigation of the impacts caused by drought is imperative. In 1965, Palmer presented a drought index that incorporated antecedent precipitation, moisture supply, and the pioneering evapotranspiration [START_REF] Palmer | Meteorological Drought[END_REF]. McKee et al. developed the SPI as an alternative to the Palmer Index for Colorado [START_REF] Mckee | The Relationship of Drought Frequency and Duration to Time Scales[END_REF]. The SPI is considered to have several advantages over the PDSI [START_REF] Mckee | The Relationship of Drought Frequency and Duration to Time Scales[END_REF][START_REF] Guttman | Comparing the Palmer Drought Severity Index and the Standardized Precipitation Index[END_REF][START_REF] Hayes | Monitoring the 1996 Drought Using the SPI[END_REF]. The first advantage is that the SPI is based only on precipitation [START_REF] Mckee | The Relationship of Drought Frequency and Duration to Time Scales[END_REF]. Due to this reason, the SPI is also not adversely affected by topography. Second, the SPI is calculated on various timescales, which allows it to describe the various types of droughts: the shorter time scales for meteorological and agricultural droughts, and the longer ones for hydrological drought. Third, because the SPI is normally distributed, the frequencies of drought events at any location for any time scale are consistent. Hayes et al. argued that the SPI detects moisture deficits more rapidly than the PDSI, which has a response time scale of approximately 8 to 12 months [START_REF] Hayes | Monitoring the 1996 Drought Using the SPI[END_REF]. Paulo et al. used several drought indices in Portugal, and found that the SPI showed its superiority for the purpose of drought monitoring [START_REF] Paulo | Analysis of Local and Regional Droughts in Southern Portugal Using the Theory of Runs and the Standardized Precipitation Index[END_REF][START_REF] Paulo | Drought Concepts and Characterization, Comparing Drought Indices Applied at Local and Regional Scales[END_REF]. Labedzki used SPI to analyse the local meteorological drought and evaluate the drought risk in Bydgoszcz, Poland [START_REF] Labedzki | Estimation of Local Drought Frequency in Central Poland Using the Standardized Precipitation Index[END_REF]. The time series forecasting has been widely applied and become an important approach of drought forecasting. One of the most widely used time series model is the autoregressive integrated moving average (ARIMA) model [START_REF] Box | Time Series Analysis Forecasting and Control[END_REF]. The wide application of the ARIMA model in many areas is due to its flexibility and systematic search (identification, estimation and diagnostic check) in each stage for an appropriate model [START_REF] Zhang | Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model[END_REF]. The ARIMA model has several advantages over other approaches, such as moving average, exponential smoothing, neural network, and in particular, its forecasting capability and its richer information on time-related changes [START_REF] Yurekli | Application of Linear Stochastic Models to Monthly Flow Data of Kelkit Stream[END_REF][START_REF] Hu | Weather Variability and the Incidence of Cryptosporidiosis: Comparison of Time Series Poisson Regression and SARIMA Models[END_REF]. The ARIMA models have also been used to analyze and model hydrologic time series [START_REF] Yurekli | Application of Linear Stochastic Models to Monthly Flow Data of Kelkit Stream[END_REF][START_REF] Fernando | Generation and Forecasting of Monsoon Rainfall Data[END_REF][START_REF] Modarres | Streamflow Drought Time Series Forecasting[END_REF]. Fernandz et al. used SARIMA model to forecast stream-flow in a small watershed in Galicia [START_REF] Fernandez | Streamflow Drought Time Series Forecasting: a Case Study in a Small Watershed in North West Spain[END_REF]. Durdu developed linear stochastic models for forecasting droughts in Turkey using SPI series as drought index [START_REF] Durdu | Application of Linear Stochastic Models for Drought Forecasting in the Buyuk Menderes River Basin, Western Turkey[END_REF]. The Guanzhong Plain is located in the northwest of China. This area is subjected to water stress and drought conditions. The frequency of drought is on average about once in 7 years [START_REF]Committee on Brief Actual Records of Historical Natural Disasters for Shaanxi: Brief Actual Records of Historical Natural Disasters for Shaanxi[END_REF]. The Plain has flat terra and fertile soil, and is the political and economical center of Shaanxi Province. Drought forecasting for this area can help to mitigate the effects of drought, and is in favor of effective water resource management. In this paper, The SPI is used as a drought index to describe the drought condition of the Guanzhong Plain. The SPI time series of multiple time scales in the Guanzhong Plain are calculated. The ARIMA models are applied to simulate and forecast the SPI series.

SPI time series forecasting models

The SPI time series of multiple time scales can be computed (typically 3, 6, 9, 12 and 24 months) according to the McKee's method [START_REF] Guttman | Comparing the Palmer Drought Severity Index and the Standardized Precipitation Index[END_REF]. The classification of dry and wet spells resulting from the values of the SPI is shown in table 1. The SPI data set from 1966 to 2003 is used for model development. The data set of 2004 is used for model validation. Based on the steps for developing the ARIMA models [START_REF] Bowerman | Forecasting and Time Series: an Applied Approach[END_REF][START_REF] Box | Time Series Analysis: Forecasting and Control[END_REF], the ARIMA models fit for SPI3, SPI9, SPI12 and SPI24 are identified respectively. They are SPI3, MA(2); SPI6, ARIMA(1,0,0)(0,0,1) 6 ; SPI9, ARIMA(1,0,0)(0,0,1) 9 ; SPI12, ARIMA(1,0,0)(0,0,1) 12 ; and SPI24, ARIMA(0,1,0)(2,1,1) 24 . It is observed that most of the SPI time series have the seasonal features. As the time scale increases, the seasonal feature is more and more distinct, and the series need to be seasonally differenced. The forecast is done for 12 leading months using the selected models, i.e., forecast the SPI values in 2004. By comparing the predicted data with the original data, the forecasting capability of the models is discussed.

Results and discussion

SPI3, SPI6, SPI9, SPI12 and SPI24 are respectively fitted by the selected best models from historical data. The forecast is done with 12-month lead-time. The values of SPI in 2004 are predicted. Fig. 1 shows the plots of fitting (forecasts for 1966-2003) and forecasting (forecasts for 2004) using the chosen ARIMA models, in which only the values of SPI in 1990-2004 are displayed for clarity. Observing the fitting figure, how well the chosen model fits the data series is indirectly shown. In Fig. 1, it is obvious that the fitted data follow the original data very closely, especially for higher SPI series (SPI12 and SPI24). This indicates that the chosen best ARIMA models capture the patterns of the SPI series. However, the models that fit the data well do not all have good forecasting capability. The predicting power of the ARIMA models should be valuated. The forecast with 12month lead-time are analyzed. It is found that the predicted SPI values fit the original ones better and better with the increase of the time scale in Fig. 1. The values of SPI3 predicted for 2004 tend to be constant. However, this situation is changed in SPI6, SPI9, SPI12 and SPI24. The predicted data gradually tends to present the stochastic change of the SPI series. It indicates that the forecasting power of the ARIMA models is improved as the time scale increases. By comparing the calculated values with the predicted ones, the absolute percentage errors (APE,

100 / ) (   i i i X X X
) are calculated as an analysis on the forecasting power, as shown in Table 2. It is obvious that all SPI series have less APEs on 1month ahead forecasting. The mean absolute percentage error (MAPE) is 5.8. This demonstrates that the ARIMA models have better result on short-term forecasting.

The APEs seem to be less for higher SPI series (SPI12 and SPI24). For 1-month ahead, the APEs of all SPI series decrease with the increase of the time scale. For 1-9 month ahead, the APEs of SPI12 are all less than 6, and the APEs of SPI24 are generally less than 10, with only two exceptions (13.3 and 17.4). These results indicate that if the SPI series have a longer time scale, the ARIMA models will have a better forecasting power. In order to exactly determine how good the forecasting power of the ARIMA models is, the forecasts are done with 1-month to 8-month lead-time. For example, 2-month lead-time forecast means that during March 2004 the forecast for May 2004 is performed. The correlation coefficients between the actual SPIs and the predicted ones from the ARIMA models are used as the criteria to evaluate the fit. Table 3 provides the coefficients for 1-8 month lead-time. It is observed that with more and more longer lead-time the coefficient decreases. To a given lead-time, the coefficient gradually increases with the increasing of the time scale, which shows that the ARIMA model is an appropriate model for the SPI series, and its power of forecasting is better for higher SPI series (SPI9, SPI12 and SPI24). Evidently, the ARIMA models give good forecast results for 1-month lead-time with the correlation coefficients ranging from 0.873 to 0.980. For high SPI series (SPI9, SPI12 and SPI24), the forecasts have good results up to 6-month lead-time with the correlation coefficients larger than 0.7. Therefore, the selected ARIMA models have a better power of forecasting for the SPI series. The ARIMA models can be used to forecast the change of the series of SPI3 and SPI6 one to two months ahead, and the future change of the series of SPI9, SPI12 and SPI24, six months ahead. The ARIMA model therefore exhibits a strong forecasting capability.

Conclusions

The Guanzhong Plain is one of the areas subjected to drought. In this study, the ARIMA models are developed to forecast drought using the SPI as the drought index.

The results show that the selected ARIMA models are appropriate for the SPI series.

Exactly the same as in many other applications [START_REF] Zhang | Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model[END_REF][START_REF] Modarres | Streamflow Drought Time Series Forecasting[END_REF], the ARIMA models demonstrate better capability in short-term forecasting. The absolute percentage errors (APE) between the actual SPIs and the predicted ones for 12-month lead-time are all less on 1-month ahead forecasting. The correlation coefficients between the actual data and the predicted ones are all larger on 1-2 month lead-time forecasting. Moreover, in this study, the ARIMA models also demonstrate a better forecasting power even on 6 month leading values for SPI9, SPI12 and SPI24. The study also shows that the ARIMA models have a good forecasting capability for the SPI series with longer time scales. The APEs are less for higher SPI series (SPI12 and SPI24). To a given lead-time, the correlation coefficient gradually increases with the time scale increasing. This may be because the increase in the length of the time scale effectively reduces the noise of the SPI series. Therefore, the selected best ARIMA models developed from the SPI time series can be used for drought forecasting in the Guanzhong Plain. This study contributes to the exploration of a feasible way on drought forecasting in the Plain. The results demonstrate that the developed ARIMA models have a good forecasting power and can be used for drought forecasting. Further study should be focused on improving the precision of the model forecasting, and on studying the types of droughts described by the SPI series with different time scales, which will effectively assist the local authorities to mitigate the impacts caused by drought, and to reasonably use the water resources.

Fig. 1 .

 1 Fig. 1. The SPI series, the fitted series and the forecasts for 12 months ahead (1990-2004). The curve with stars is for the calculated SPI. The one without stars is for fitting and forecasting. The predicted values of SPI for 2004 are on the right side of the reference line.

Table 1 .

 1 Drought classification of SPI.

	SPI value	Class
	>=2	Extremely wet
	1.5 to 1.99	Very wet
	1.0 to 1.49	Moderately wet
	-0.99 to 0.99	Near normal
	-1 to -1.49	Moderately dry
	-1.5 to -1.99	Severely dry
	<=-2	Extremely dry

Table 2 .

 2 Absolute percentage errors of SPI3, SPI6, SPI9, SPI12 and SPI24 for 12-month leadtime. Only values less than 20 are shown.

Table 3 .

 3 Correlation coefficients of different lead-time. Blank cells are for the non-existing correlation coefficients.

	SPI	One-	Two-	Three	Four-	Five-	Six-	Seven	Eight-
	series	month	month	-month	month	month	month	-month	month
		lead-	lead-	lead-	lead-	lead-	lead-	lead-	lead-
		time	time	time	time	time	time	time	time
	SPI	0.87	0.417	0.360
	3	3
	SPI	0.88	0.762	0.673	0.580	0.514	0.24
	6	8					8
	SPI	0.95	0.889	0.830	0.786	0.772	0.80	0.695	0.523
	9	5					3
	SPI	0.98	0.985	0.985	0.985	0.935	0.74	0.538	0.388

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 41071235 and 40871159), the China National Support Program of Science and Technology (Grant No. 2012BAH29B03), and China Agricultural University's Grant, 2012JC004.


Wei Shen

Long Yang

Jin Zhang
email: j_zhang@shou.edu.cn

Guangxiong Peng

The Survey of Fishery Resources and Spatial Distribution Using DIDSON Imaging Sonar Data

Keywords: DIDSON, Survey, Fishery Resources, Spatial Distribution

In recent years, the DIDSON (Dual-frequency IDentification SONar),which can provide almost-video-quality images to identify objects even in turbid water has been used in enumerating fish populations, underwater structures inspection, oil/gas leakage detection and identification, underwater security, evidence searching, ship's hulls and ports safety inspection, and underwater navigation and so on.

In this paper, a designed vessel-mounted observing systems collected DIDSON data and GPS data according to designed lines in the Dishui Lake, shanghai city. Every line area was calculated according to the depth and sonar open-angle. Trained observers counted manually each fish image in every DIDSON file. Then based on the area and number of fish observed, the average density and zoning density of the Dishui lake was calculated. At last the whole fish number and distribution was calculated based on the ARCGIS software.

The practice proved the method based on DIDSON Data is very feasible, effective and accurate for fishery resources estimating.

Introduction

The Dishui Lake of Shanghai Lingang Town was completed on 2003. The area of whole lake is 5.56 square kilometers, the average depth is 3.7 m, and the most deep depth is 6.2 m. Since 2007, the management of the lake has put about 315 ton fry into the lake so far, the fry were mainly silver carp and bighead carp. At present, how much fish in the lake is a question. So the lakes management decides to detect the fishery resources for the further catching and management.

In the water , sonar equipment is the main observation and investigation equipment [START_REF] Donald | Surveys of Fisheries Resources[END_REF]. Acoustic fisheries resources survey is fast and efficient investigation of biological resources, and no damage for fisheries [START_REF] Donald | Surveys of Fisheries Resources[END_REF][START_REF] Elliott | A comparison of three methods for assessing the abundance of Arctic charr, Salvelinus alpinus, in Windermere (northwest England)[END_REF][START_REF] Tan | Hydroacoustic survey of fish density, s patial distribution, and behavior upstream and downstream of the Changzhou Dam on the Pe arl River[END_REF].

Currently, the main advanced acoustic instruments for fishery resources survey are Simrad EY-60 [START_REF] Tan Xichang | Hydroacoustic assessment of fish resources in the Lake Qinghai with EY60 echosounder[END_REF], Bisonics DT-X [START_REF] Wang Chong-Rui | Hydroacoustic assesment of abundance and spatial distribution of Gymnocypris przewalskii in Qinghai Lake with BioSonics DT-X echosounder[END_REF], and etc.. These instruments generally utilize echo integration method [START_REF] Diner | Correction of school geometry and density: an approach based on acoustic image simulation[END_REF][START_REF] Misund | Mapping the shape, size, and density of fish schools by echo integration and a high-resolution sonar[END_REF][START_REF] Gauthier | Acoustic observation of diel vertical migration and shoaling behaviour in Atlantic redfishes[END_REF] to assess the total amount of resources. When using these acoustic instruments, firstly the target strength of fish should be acquired, and then the standardized calibration should be implemented. Usually the acoustic image displayed on the screen are not dynamic, the accuracy of the fish counting is not very high. In addition, these methods also need to set the various parameters of fish, which will lead to significant differences on the identifying result [START_REF] Tan Xichang | Hydroacoustic assessment of fish resources in the Lake Qinghai with EY60 echosounder[END_REF]. This project utilized DIDSON (Dual-Frequency Identification Sonar) to detect the Dishui lake fishery resources, and acquired three days first-hand data. Based on echo counting method [START_REF] Wang Jing | Acoustic assessment of silver carp and bighead ca rp in Qinghe Reservoir[END_REF], the number of fish was counted from DIDSON image. Then the average density and zoning density of fish were calculated. At last, the total amount was calculated and the fish spatial distribution was plotted on a map based on ARCGIS software.

DIDSON

DIDSON (Dual-frequency IDentification SONar), which is invented by Washington University, can provide almost-video-quality images to identify objects even in turbid water. DIDSON has been used in fisheries management, underwater structures inspection, oil/gas leakage detection and identification, underwater security, evidence searching, ship's hulls and ports safety inspection, and underwater navigation and so on.

No other sonar in its class is able to deliver the image quality and frame rate of the DIDSON. The resulting real-time, near video-quality data is clear enough to study the behavior of darting fish, even in opaque waters.

A defacto standard for the exacting marine biology audience, this dual frequency so nar is also ideal for construction inspection, pipe laying observation, hull and berth sw eeps and other mission critical jobs down to 300 meters.

The DIDSON is versatile, too, with a 1.1 MHz detection frequency capable of imag ing targets up to 30 meters away.Stored images can be converted to AVI files and JPE Gs, and a size and distance tool is included, making analysis accurate and presentation easy.

Fish counting automatically based on DIDSON image

Using DIDSON image (Fig. 4) and semi-automated counting software [START_REF] Han | DIDSON-based Acoustic Counting Method for Juvenile Ayu Plecoglossus altivelis Migrating Upstream[END_REF][START_REF] Han | Automated Acoustic Method for Counting and Sizing Fa rmed Fish during Transfer using DIDSON[END_REF] developed ourself, the fish number of every line was counted and the location was recorded [START_REF] Tong | Mosaicing of Acoustic Video Images for Underwater Structure Inspection[END_REF][START_REF] Tong Jian-Feng | Preliminary Research on the Image Processing of Acoustic Camera and Its Application in Fishery[END_REF]. The fish length was calculated based on the pixel value of identified fish in the image. At last, The number checked via manual review would be saved to table ( Table 1) for late statistical analysis.

Catch statistics

By the actual measurement data form the Dishui Lake management Company, the fish species composition, body length, weight, and other relevant data were acquired.

Based on caught 41 silver carp, the relationship between weight and length (Fig. 5) was established using the power function regression method.

Fishery Resources Quantitative Assessment Methods

The average density method and zoning density method are used in fish resources quantitative assessment in this paper.

The Average Density Method

The whole lake fish quantity value N is defined as

S N .   ( 2
)
where  is fish average density, S is the whole lake area. The value  is calculated by every line fish

density i  .    n i i n 1 1   (3)
The i  is calculated by the fish number and the area of line i .

i i i S N   (4)
Where the value i S is defined as

     n n n n l r 1 i i i 2 1 2 R L S (5)
Where the i L is length of line i and the i R is the slant-range from the transducer to the water bottom. The Based on these methods above, the length, area, fish number, fish density of each survey line are calculated. The whole lake fish density  is 0.0608261349 via formula 3, and the Dishui lake area is 5.56 sq.km so the whole lake fish number N is 338193 .

By the site statistics data, the mean size of Dishui lake fish is 64.26cm. So the fish average weight is 2.847kg, and the whole lake fish gross is 962835kg.

The Zoning Density Method

The fish average density method ignored the number difference of different regional, and it could not express the actual distribution of the fish, and it's accuracy was affected by the survey lines distribution. So the zoning density method was designed to improve the count accuracy.

The value K1(num/m 2 )was designed to express the fish density of every survey point.

d l n K1   (6)
Where value n is the detected fish number, value l is the distance between two survey points, value d is the DIDSON scan width.

The value K2(kg/m 2 )was designed to express the fish grass of every survey point.

d l nW 2 K   2.5865 L d l 0.00006n    (7)
Where value L (cm) is length of fish, W (kg) is the fish weight.

Based on formula 6 & 7, the value K1 and K2 was calculated.

Then using Inverse Distance Weighted (IDW) interpolation within ARCGIS, the whole lake fish density were processed as shown Fig. 6. Based on the K1 Grid data, the whole Dishui lake fish number was calculated via ARCGIS Surface Volume tool. And the whole number is 345253.

Based on the K2 Grid data, the whole Dishui lake fish gross was calculated via ARCGIS Surface Volume tool. And the whole fish gross is 939885kg.

Fishery Resources Spatial Distribution

In order to understand the fish spatial distribution of Dishui lake, the pie charts of detected fish number were plotted on a map based on the counted fish number of every points. From the map, the Dishui lake fish spatial location are expressed visually (Fig. 7). Actually the Figure 6 expresse the fish spatial distribution also.

[ 9 ]Fig. 1 .

 91 Fig.1. Standard DIDSON (Left: main body, Right: acoustic lens )

Fig. 2 .

 2 Fig. 2. The actual detecting route in Dishui lake, shanghai, China

Fig. 3 .

 3 Fig.3. Measurement of vessel and equipment installation

Fig. 4 .

 4 Fig.4. Fish Identification and Tracking

Fig. 5 .

 5 Fig.5. Relationship between full-length and weight of silver carp (R2 is fitting degree, n is the number of samples)

nr

  is changed by the water bottom terrain from time 1 to n seconds. The n l is computed by GPS positioning data.

Fig. 6 .

 6 Fig. 6. Map of K1 zoning density (unit: num/m2,grid resolution 10m)

Fig. 7 .

 7 Fig. 7. Map of Dishui lake fish distribution

Table 1 .

 1 The fish count table from DIDSON image

	N	E	Local X	Local Y	Num	Len(cm)
	30.90368	121.9308	-36690.4516	44345.14614	1	96
	30.90346	121.9308	-36714.39585	44347.71281	2	60
	30.90338	121.9308	-36723.94869	44348.47933	2	72

Table 2 .

 2 The detection data of each survey line

	Line	Length(m)	Area(m2)	fish number	fish density (num/m2)
	No1 No2	3109 4561	15206.1	485	0.031895095
	No3	1871	5697.7	385	0.067571125

Supported by the Innovation Program of Shanghai Municipal Education

Commission(12ZZ159), and China Postdoctoral Science Foundation (2011M501296, 2012T50832).

Analysis and Discussion

Summary and Analysis

In this paper, two kinds of assessment methods were used to calculate the total fish amount in Dishui lake. Whith average density method, the Dishui lake fish number is 338193 and the fish gross is 962835kg. And the number is 345253 via zoning density method, and the fish gross is 939885kg.

The assessment value by two methods is close, and the fish fry data supplied by Dishui lake management department was analyzed to prove that the two methods are effective. Overall, the average density method is simple, easy to operate. The zoning density method needs a large amount of calculation and requires the assist by GIS software.

Practice proved that the average method is simple and effective, can be directly applied to the assessment of fishery resources. The zoning density method witch can N visually analyse fish distribution and the amount of the different regions needs lots of equally distributed detection data to interpolate the whole area data.

In comparison with the lake depth map, the fish were mainly concentrated in two deep-water areas and several estuaries. Because the main fish silver carp like warm area in winter, so they lied in deep water and lakebed. In addition, the bait was rich in estuary, so they foraged there.

Question and Discussion

Due to technical and capacity constraints, there are several deficiencies need for improvement.

In this detection, the fish species relatively simple and mainly concentrated in the bottom of the lake, so we used a flat density. But in many cases, different fish distributed in different water depth. So the 3D fish density in order to more accurately measure the 3D distribution will be survey in the next study.

Sonar instruments have certain blind spots, for example DIDSON has a big noise away from the lens 1 meter, where the fish can not be quantified. So how to solve this question is a key point for the next research.

When using the acoustic instruments to survey fishery resources underwater, due to the movement of the boat and the noise of boat motor, part of the fish will be driven.

So how to decide the drive coefficient is very difficult but necessary, and the current research is still very scarce.

Applying DIDSON sonar to detect and assess fishery resources in China has just begun, but the practices proved that it could be used effectively in large-waters fishery resources detection and assessment.