KR101114513B1 - System and method for forecasting the burst of soilborne plant diseases - Google Patents

Abstract

The present invention provides a system for predicting soil infectious plant diseases and a method thereof, comprising: a meteorological data collection system for collecting meteorological observation data for predicting soil infectious plant diseases; A work processing system for generating soil infectious plant disease prediction information using meteorological observation data collected by the meteorological data collection system; A data storage system for managing data produced, stored, retrieved, and retrieved by the meteorological data collection system, the work processing system, and the web service system; Web service system for delivering the plant disease prediction information generated in the work processing system to the user through a web page; by observing weather data, using the observed data to predict the occurrence of soil infectious plant disease Plant diseases can be predicted.

Soil infectious plant disease, forecasting, forecasting, contaminant estimation, pepper blight, forecasting system

Description

System and method for forecasting the burst of soilborne plant diseases}

The present invention relates to the prevention of soil infectious plant diseases. In particular, it is possible to observe the meteorological data and predict the occurrence of soil infectious plant diseases using the observed data. And to a method thereof.

The incidence of plant diseases is closely related to weather conditions. Due to the recent occurrence of abnormal climate and global warming, the pathogenic ecology may be changed. As a result, sudden crop damage is a concern. To reduce these accidental crop damages, an effective and systematic surveillance system is needed to cope with unexpected disease outbreaks. In other words, there is a need for a system that accurately predicts sudden occurrence of diseases and supports timely control based on the prediction. To this end, the development of mathematical or predictive disease prediction models by identifying the correlation between meteorological environment and plant disease occurrence, and the development of a number of computer programs and systems using the same have been made. Examples are SpecWare ^TM (Spectrum Technologies, Inc.) and MetWin ^TM II (Pessel instruments).

Most of the plant disease surveillance systems developed to date target airborne diseases and use a method of predicting disease occurrence using temperature and leaf wetting duration. A model for predicting the occurrence of some soil infectious diseases and a forecasting system using them have also been developed.However, in reality, it is used to predict the spread of disease in the air by using meteorological data such as temperature, relative humidity and leaf wetting duration. There is not enough to say that the disease occurrence prediction system. Therefore, a system using a model for predicting the spread of infection in the soil or on the soil surface, which is a true soil infectious disease occurrence prediction system, has not been developed yet.

Therefore, the present invention has been proposed to solve the conventional problems as described above, an object of the present invention is to observe the weather data, by using the observed data to predict the occurrence of soil infectious plant diseases can predict plant diseases To provide a soil infectious plant disease prediction system and method therefor.

1 is a block diagram of a soil infectious plant disease occurrence prediction system according to an embodiment of the present invention, Figure 2 is a conceptual diagram showing the operation state of the soil infectious plant disease occurrence prediction system of Figure 1 in detail.

As shown here, a weather data collection system (WDCS) for collecting meteorological observation data for soil infectious plant disease prediction; A Job Processing System (JPS) 20 for generating soil infectious plant disease prediction information using meteorological observation data collected by the meteorological data collection system 10; And a data storage system (DSS) 30 for managing data produced, stored or retrieved and retrieved by the meteorological data collection system 10, the work processing system 20, and the web service system 40. ; It characterized in that it comprises a; Web service system (Web Service System, WSS) (40) for delivering the plant disease prediction information generated by the work processing system 20 to the user through a web page.

3 is a detailed conceptual diagram of a weather data collection system and its neighboring blocks in FIG. 1.

As shown here, the meteorological data collection system 10, the temperature (℃; HMP45C, Vaisala), relative humidity (%, HMP45C, Vaisala), through an unmanned weather station (Automated Weather Station, AWS) installed in the community, Geothermal (° C; 107B, Campbell Scientific), Rainfall (mm; TE525MM, Texas Electronics), Leaf Wet Duration (hr / hr; 237, Campbell Scientific), Wind Direction (ㅀ; 034A, Met One), Wind Speed (m / s ; 034A, Met One), insolation (MJ / m ² ; LI200X, LI-COR), soil moisture content (%; CS615, Campbell Scientific) received and observed, processed into hourly and daily data and the data storage system ( 30), receiving meteorological data through the Meteorological Agency automatic meteorological observation network, collects and decodes meteorological data stored in a binary format, and stores the data in the data storage system 30.

4 is a detailed conceptual view of the job processing system and its peripheral blocks in FIG.

As shown in the drawing, the work processing system 20 generates a network weather interpolation map image having a grid resolution of a specific size (for example, 960m × 960m) from the Meteorological Agency automatic meteorological observation data stored in units of observation points. In addition, using the network weather interpolation map image and the image information file, characterized in that to generate a prediction map of the network vegetation of the same resolution.

The work processing system 20 is characterized by generating a network weather interpolation map for each of the temperature, relative humidity, rainfall, geothermal, leaf surface wet duration.

The creation processing system 20 is characterized in that the weather interpolation map is generated using Inverse Distance Squared Weighting (IDSW) and elevation correction.

The work processing system 20 is characterized in that when there is no actual value of ground temperature, soil moisture, estimates are generated by using different weather factors, growth conditions of crops, and soil characteristic data.

The work processing system 20 generates a mesh plant disease prediction map by driving a prediction model for soil infectious disease.

FIG. 5 is a detailed conceptual view of a data storage system and peripheral blocks thereof in FIG. 1.

As shown here, the data storage system 30, the weather observation data of the community weather station (Automated Weather Station (AWS) weather data and meteorological agency drone weather station data (AWS) weather data, community AWS interpolation image and meteorological office AWS It is characterized by storing the interpolated image data including interpolated weather data, pest vegetation prediction map, altitude map, Saturn map, and drainage map.

FIG. 6 is a detailed conceptual diagram of a web service system and its neighboring blocks in FIG. 1.

As shown in the drawing, the web service system 40 receives an interpolation map image, an administrative boundary boundary and an administrative region name map, and a plant disease prediction map image, and according to a scale range, a state / province, a city / county / It is characterized by displaying the boundaries of administrative districts and the name of administrative districts corresponding to one level among wards, eup / myeon / dong, and dong / ri, superimposed on the weather interpolation and plant disease prediction maps.

8 is a flow chart showing a pepper plague occurrence prediction method according to an embodiment of the present invention.

As shown in the drawing, the work processing system 20 includes observation data such as air temperature, relative humidity, geothermal temperature, high flow rate, leaf wetting duration, wind direction, wind speed, insolation, soil moisture content, and crop coefficient according to pepper growth, and topsoil. Receiving and inputting Saturn; Estimating the data necessary for driving the model (geothermal temperature and soil moisture content in vinyl mulching) based on the observed data when there is no observed data or when the characteristics are different (ST20); Calculating a daily production amount of the cayenne pepper diseased sachets (ST30-50); Judging whether the resident release, direct germination, or remaining resident sac remain according to the estimated temperature (ST60); It is characterized in that it comprises a step of warning the risk of the development of pepper blight, if the germination of the zygote sac for 3 days or the release of the yuzu is possible and the average transmission amount of the three days is 68 or more (ST70).

Soil infectious plant disease prediction system and method according to the present invention can observe the weather data, and predict the occurrence of plant disease by predicting the occurrence of soil infectious plant disease using the observed data, plant growth characteristics data, soil characteristics data It can be effective.

In addition, the present invention also has the advantage of preventing the misuse of pesticides by enabling the control of plant diseases with only a suitable amount of chemical pesticides in a timely manner through appropriate anti-control.

Referring to the accompanying drawings, preferred embodiments of the soil infectious plant disease prediction system and the method according to the present invention configured as described in detail as follows. In the following description of the present invention, detailed descriptions of well-known functions or configurations will be omitted if it is determined that the detailed description of the present invention may unnecessarily obscure the subject matter of the present invention. In addition, terms to be described below are terms defined in consideration of functions in the present invention, which may vary according to intention or precedent of a user or an operator, and thus, the meaning of each term should be interpreted based on the contents throughout the present specification. will be.

First, the present invention is to predict the disease by predicting the occurrence of soil infectious plant disease using the observed weather data, plant growth data, soil characteristics data.

In addition, the present invention is to develop a practical model for predicting soil infectious plant disease, and to establish a practical and efficient predictive control system for soil infectious plant disease which generally causes a great reduction in crop production by killing the whole plant. Practical and efficient forecast control system refers to a system that can contribute to effective control by generating region-specific forecasting information in real time using accurate weather data and accurate forecasting model, and processing it in a form that users can easily understand. In the present invention, the real-time collection and generation system of precise weather data (region-specific weather data) required for driving the newly developed plant disease prediction models, the real-time prediction model driving system, and processing and distributing the prediction information in a form easily available to users We wanted to develop a system that can In particular, the meteorological data collection system (10), which is a real-time collection and generation system of precision meteorological data, utilizes high quality data obtained from the automatic meteorological observation network currently operating on a national scale at Korea Meteorological Administration and Rural Development Administration. It can be developed to produce precise interpolation weather data of 960m grid. In addition, all information generated by the system can be processed and provided in the form of an image map so that users can easily use it. In addition, meteorological factors, crop growth data, and soil characteristics, which measure the temperature and soil moisture content, which are not measured in the meteorological observation network established by the Korea Meteorological Administration, but are affected by the meteorological factors, A method of estimating using the data is included.

1 is a block diagram of a soil infectious plant disease occurrence prediction system according to an embodiment of the present invention.

Thus, the Weather Data Collection System (WDCS) 10 collects meteorological observation data for predicting soil infectious plant disease and transmits it to the work processing system 20.

The job processing system (JPS) 20 generates soil infectious plant disease prediction information by using meteorological observation data collected by the meteorological data collection system 10, and stores the information in a data storage system 30, Transfer to the service system 40 to be delivered to the user.

The data storage system (DSS) 30 manages data that the weather data collection system 10, the work processing system 20, and the web service system 40 produce, store, search and inquire.

The web service system (WSS) 40 transmits plant disease prediction information generated by the work processing system 20 to a user through a web page.

2 is a conceptual diagram showing in detail the operating state of the soil infectious plant disease occurrence prediction system of FIG.

Thus, the meteorological data collection system 10 is a system for automatically collecting meteorological observation data.

The Korea Meteorological Administration operates unmanned automatic weather observation machines at 614 branches nationwide for industrial weather observation. Meteorological data observed by this observation network and converted into hourly data are delivered in real time to the Rural Agricultural Meteorological Data Network.

The Korea Meteorological Administration's Agricultural Meteorological Data Network was designed to transmit weather data every 10 minutes through the Internet FTP connection to the system. We have developed a program that reads the weather data file received in ASCII format and extracts the weather data for each observation point and stores it in the database. I used Java Standard Edition Development Kit 5 (JDK5, http://java.sun.com) as a development tool, and MySQL Server 5.0.22 (MySQL, http: //www.mysql) as database software for managing weather data. .com). This program is run every 10 minutes by the cron daemon process in the operating system.

In addition, the meteorological data collection system (10) developed a micro meteorological observation and data collection system for pepper experiment packaging community.

In order to develop and test soil infectious plant disease predictive model, microclimate in pepper colonies was observed at two sites of pepper packaging (Suwon, Gyeongsangnam-do, Korea). A drone was installed in the community to provide air temperature (℃; HMP45C, Vaisala), relative humidity (%, HMP45C, Vaisala), geothermal temperature (℃; 107B, Campbell Scientific), rainfall (mm; TE525MM, Texas Electronics), foliar wettability Hours (hr / hr; 237, Campbell Scientific), wind direction (ㅀ; 034A, Met One), wind speed (m / s; 034A, Met One), insolation (MJ / m ² ; LI200X, LI-COR), soil moisture The content (%; CS615, Campbell Scientific) was observed every minute, processed into hourly and daily data, and stored in a data collector (CR10X Datalogger, Campbell Scientific). Temperature and relative humidity sensors hang from the observation tower to be 1.4 m above ground, geothermal sensors are embedded horizontally in a 10 cm deep ground, and rainfall hangs just below the lightning rod at the top of the tower (2 m above ground). Hanging the rod at an angle of 45 degrees from the ground at an angle so that it is about 3/4 of the height of the pepper community, and the wind direction and wind speed sensor is a cross-arm, 2m above ground, which is a horizontal support installed under the rainfall sensor of the tower. At the north end, a solar radiation sensor was installed at the south end. The data collector was programmed to provide wireless Internet access to the forecast system server every hour via a CDMA wireless modem (GW-5035C, YISO Wireless). We have developed a program using C language to handle the access of the data collector on the server. The program reads the data stored in the binary format, reads it in binary format, reads it, and stores it in a database. The Internet daemon provided with the operating system always waits for a connection to a predetermined network port, and when the logger comes in, it calls the collection program and leaves the processing of the connection to the collection program. . MySQL was used as database software to manage community weather data.

On the other hand, the work processing system 20 is a system for generating a network plant disease prediction map of the national scale using meteorological office meteorological data. JDK5 was used as a development tool.

The work processing system 20 is internally divided into two stages.

The first step is to create a national network weather interpolation map image with grid resolution of 960m x 960m from the Meteorological Agency's automatic meteorological observation data stored in observation points. We read the weather data from the MySQL database and store the weather interpolation map image in the specified directory on the filesystem.

The second step is to create a national network plant disease prediction map of the same resolution using the network weather interpolation map. The weather interpolation map image is read from the designated directory on the file system and the plant disease prediction map image is also stored in a separate directory.

The magnitude of the interpolation and prediction results was distinguished by assigning the color of the corresponding pixel in the image to a color on a predetermined color spectrum. The 30m grid elevation map (DEM) produced by the Ministry of Environment was converted into a map with 960m x 960m grid resolution and used as basic data when generating weather interpolation maps and plant disease prediction maps.

In this manner, the job processing system generates a mesh weather interpolation map image having a grid resolution of a specific size from the Meteorological Agency automatic meteorological observation data stored in observation point units, and the unit observation pixel of the mesh weather interpolation map image. Based on the positional information of the unit item of the image information file, the network information of the network weather interpolation map image corresponding to the unit item of the image information file, and mapping the information to each corresponding position to provide the network weather. The interpolation map is used to generate a prediction map of the net plant pests of the same resolution.

In addition, the work processing system 20 generates a national network weather interpolation map.

The meteorological interpolation map generated by the production processing system 20 was generated for each of weather, relative humidity, rainfall, geothermal, and foliar wet duration which are essential weather factors for plant disease prediction. On a two-dimensional plane, inverse distance squared weighting (IDSW) equation (1) is used to estimate the weather conditions at one point where no observation is made using data observed at the surrounding observation points. ) Is generally used.

: 추정 대상지점에 대한 기상 추정값

: Weather estimate for the estimated location

: 인접 관측지점들 중

번째 지점에서의 기상 관측값

: Of adjacent observation points

Weather observation at the first point

: 인접 관측지점들 중

번째 지점에서 추정 대상지점까지의 거리

: Of adjacent observation points

From the first point to the estimated target point

Equation 1 is an expression of inverse distance squared weighting, which is an interpolation method used for interpolation of relative humidity and rainfall among weather elements.

In order to construct a network interpolation map of relative humidity and rainfall, a distance squared weighting method (IDSW) such as Equation 1 was used. Since the rainfall value is not observed between 0mm and 0.1mm, the estimated value of rainfall is less than 0.1.

In addition to the IDSW method, an elevation correction method, such as Equation 2, was further applied to create an interpolation map of temperature.

: 추정 대상지점에 대한 기온 추정값

: Estimated temperature for estimated location

: 인접 관측지점들 중 i번째 지점에서의 기온 관측값

: Observation of temperature at the i point of adjacent observation points

: 인접 관측지점들 중 i번째 지점에서 추정 대상지점까지의 거리

: Distance from the i point of adjacent observation points to the estimated target point

: 추정 대상지점의 실제 해발고도

: Actual altitude above sea level

: 추정 대상지점에 대한 해발고도 추정값

: Estimated elevation above sea level

: 건조 대기의 고도에 따른 기온변화율 (-0.0065 ℃/m)

: Temperature change rate according to the altitude of the dry atmosphere (-0.0065 ℃ / m)

: 인접 관측지점들 중 i번째 지점의 해발고도

: Altitude above sea level at adjacent observation points

Equation 2 is a formula of interpolation method used for interpolation of temperature among weather elements. After inverse distance squared weighting is applied, elevation correction is made by considering the effect of temperature changes due to changes in altitude of the dry atmosphere.

Assuming that the elevation of the target point is unknown, the elevation of the target point can be estimated from the elevation of the surrounding observation points by the IDSW method. In the altitude correction method, the weather value calculated by the IDSW is regarded as the weather estimation value for the virtual point of the sea elevation which was previously estimated. Since the altitude above sea level is different from the actual altitude above sea level, the temperature change rate (-0.0065 ℃ / m) according to the altitude change of dry air is applied to this altitude difference. In general, only three observation points adjacent to the target point to be estimated are used for interpolation (nearest neighbor method). However, when the number of adjacent points is small, the distribution of interpolation result shows a discontinuous pattern, and the interpolation map is rough, so all weather observation points are used for interpolation.

In addition, the work processing system 20 includes a model for estimating leaf wetting time, temperature, and soil moisture content in the meteorological office's automatic meteorological observation network. Estimation of leaf wetting time is a conventional model that estimates leaf wetting for one hour when the relative humidity estimate exceeds 90% and that there was no leaf wetting under other conditions (Sutton, JC, Gillespie, TJ). , and Hildebrand, PD 1984. Monitoring weather factors in relation to plant disease.Plant Disease 68: 78-84.]. In addition, foliar wetting was estimated to persist for 1 hour in the presence of rainfall.

For the estimation of geothermal, in 2006, we developed a regression model that estimates the geothermal temperature at the temperature as shown in Equation 3 by analyzing the temperature and geothermal data observed in the red pepper experimental field in Suwon, Gyeonggi-do and Asan, Chungcheongnam-do. This regression model was developed in pepper mulching with vinyl mulching, but it is generally applicable to all vinyl mulching fields.

Td = 0.9198 Ta + 3.192 (R ² = 0.99064)

Td = average daily temperature

Ta = daily average temperature

The soil moisture content of the topsoil layer up to 30 cm deep from the ground was calculated from the water balance and the soil water content of the previous day as shown in Equation 4. In other words, if the soil moisture content at a specific point in time is known and the water balance thereafter can be estimated, the soil moisture content after a specific point can be estimated. As shown in Equation 5, the water balance can be defined as the increase factors of rainfall, irrigation, and capillary rise minus the decrease of evapotranspiration, runoff, and drainage. Again, in the case of the field, the capillary rise may be 0, and assuming that irrigation is not performed after the formulation, the soil moisture content may be calculated by the formula (6). Based on this formula, water is formulated after sufficient water is supplied at the time of planting. So, the soil moisture content after planting is applied when the soil reaches the amount of water to be packaged, and the cumulative runoff is the direct flow rate estimation formula (SCS method, equation 7 ), Cumulative rainfall is estimated from weather observations, cumulative evapotranspiration is estimated in models (Equations 8 and 9) using temperature and relative humidity, and cumulative drainage is the value of pavement volume and the number of days after rain. The soil moisture content was finally determined by considering it.

Wc _n = soil water content in day n (%)

△ S = water balance (mm)

△ S = water balance (mm)

t = time interval

z = space spacing

input = rainfall (D) + irrigation (I) + capillary rise (C)

output = runoff (R) + evapotranspiration (ET) + drainage (G)

Wc _n = soil moisture content on the nth day after planting (%)

Wc ₀ = soil moisture content in days (%)

D = rainfall in mm

R = runoff (mm)

ET = amount of evapotranspiration (mm)

G = displacement (mm)

R = monotonic runoff (mm)

P = total accumulated rainfall (mm)

S = maximum potential reserve (mm)

CN = runoff curve index

     D5 <35.56, CN = 44

     35.56 ≤ D5 ≤ 53.34, CN = 65

     D5> 53.34, CN = 81

D5 = 5 days leading rainfall index = sum of rainfall over the previous 5 days

ET = total integrated evapotranspiration (mm)

Kc = crop coefficients

PET = amount of evapotranspiration of the reference crop without stress (mm / day)

e0 _TM = Saturated water vapor pressure at daily average temperature (mb)

e _TM = mean water pressure in mb daily temperature in mb

TM = daily average temperature

RH = daily average relative humidity

Kc = crop factor

K _cb = reference crop factor = constant already defined according to crop growth

K _a = coefficient according to effective moisture (maximum value is 1)

K _s = coefficient due to rise of topsoil evaporation after rainfall (only up to 4 days after rainfall)

Aw = effective moisture content (%)

Wc = soil moisture content (%)

Wc _fc = Soil Water Content (%) at Paving Water

Wc _pwp = soil moisture content at permanent counterfeit point (%)

t = days after rainfall (1-4)

In addition, the work processing system 20 generates a nationwide net plant disease prediction map.

In other words, by using the weather interpolation map image, the prediction model for late blight was driven to create a prediction map for plant diseases. The weather interpolation values corresponding to one grid point on the interpolation map image for each weather element were used as input values when driving the prediction model, and the result was set to the grid point value at the same position on the plant disease prediction map image. The prediction models are modularized so that the input and output methods have a unified structure. All modeling modules were run hourly within the forecasting system to produce data. Since soil environment is important for the occurrence of red pepper plague, the National Saturn Map of 30m x 30m grid resolution was provided from Rural Development Administration's Agricultural Science and Technology Institute and converted into grid resolution of 960m x 960m and provided as input data to the plague prediction model.

On the other hand, the data storage system 30 is a system for managing data that the weather data collection system 10, the work processing system 20, the web service system 40 to produce and store, search and query. Instead of developing a separate program, the database software MySQL and the file system provided by the operating system were used. The information used in this forecasting system includes meteorological observation information, various map image information, and setting information necessary for the operation of the program for each system. Meteorological observations created and stored tables for each observation point in the database. All map images were saved in PNG (portable network graphic) image format, and images were created by year and month. Some of the program's configuration information is stored in the database and some in the file system as text files.

On the other hand, the web service system 40 is a system that delivers the national network weather interpolation information and plant disease prediction information to the user through a web page. Apache HTTP Server 2.0.55 (http://www.apache.org) was used as web server software. Web pages were written using HTML, and the PHP 5.1.2 (http://www.php.net) scripting language was used for data processing on the server. Dynamic interfaces such as zooming in, zooming out, and panning on a web screen are implemented using JavaScript. In order to speed up the search of the map on the web screen, it is implemented by finding and retrieving the already drawn image without drawing the map image whenever the user requests. According to the scale, superimpose the boundaries of administrative districts and the names of administrative districts corresponding to one of the cities, provinces, cities, counties, districts, towns and villages, and towns and villages on the weather interpolation and plant disease prediction maps. It was. The image of the administrative layer was created by invoking mapserver (http://mapserver.gis.umn.edu) software in a program written in PHP to draw a nationwide map by the magnification level of the map, and then cut out pieces of a certain size like a tile.

3 is a detailed conceptual diagram of a weather data collection system and its neighboring blocks in FIG. 1.

A total of 614 observation points are collected from the Korea Meteorological Agency's UAV, and 528 points of information, which know the exact location coordinates of the observation points, were used in the forecasting system.

The drone installed in the red pepper plantation community is operated by electricity supplied from solar panels (MSX10, Campbell Scientific) because it is difficult to connect a separate commercial power source. Because wireless modems consume a lot of power, it may not be possible to keep the wireless modem on at all times with the power generation of solar panels. The module was used to turn on the wireless modem only when the data collector was connected to the forecasting system server through the wireless modem. This module reduces the supply voltage of 12V from the data collector to 5V of sufficient power for the power consumption of the wireless modem when there is a control signal of the data collector.

4 is a detailed conceptual view of the job processing system and its peripheral blocks in FIG.

It is a structure in which the developed application processes hourly and daily tasks in the order listed in the task list. Worklists are stored in a database. One task consists of a three-step structure: an InputModel that prepares the data needed for the task, a ProcessModel that processes the data in a consistent way, and an OutputModel that stores the processing results. There are two types of work: meteorological interpolation and plant disease surveillance. Table 1 below lists the tasks defined in this forecasting system.

Serial number Job name Execution Time Interval Work content One Meteorological Agency automatic weather interpolation Hourly An hourly national interpolation map is created using the weather forecasting data from the Korea Meteorological Administration. 2 Filling in Hourly Data Blanks Hourly If there is a missing value in the weather meteorological observation data by the Korea Meteorological Agency, copy the data from the previous time and replace it. 3 Daily weather data calculation glance Create a daily national interpolation map using the hourly national interpolation map. 4 Hot Pepper Plague Model Operation glance Run a plague prediction model on a daily basis to create a daily forecast map.

FIG. 5 is a graph of a regression model developed to estimate geothermal temperature using the correlation between daily average temperature and daily average geothermal temperature and temperature in the work processing system of FIG. 4. Zeon estimated by the simple regression model of FIG. 5 was used as input data for the late blight prediction model.

Plant disease predictions were made by tasks 4, 5 and 6 of Table 1. In the ProcessModel of these tasks, we included plant disease prediction models in the form of sub-models. The predicted outcome of late blight is the daily risk of infection, estimated by the concentration of the infectious agent.

FIG. 5 is a detailed conceptual view of a data storage system and peripheral blocks thereof in FIG. 1.

As a result of meteorological interpolation and plant disease prediction stored in the data storage system 30, all image files were stored in a PNG file format with a pixel resolution of 588 × 658.

The processing result data in the work processing system 20 is stored as an image file through a class called MeshImageOutput developed by JDK5. Because the values produced as a result of interpolation or prediction are the result of numerical calculations, the distribution of those values is usually continuous within a map. Theoretically, 386,904 (= 588 ㅧ 658) different values, the total number of lattice points, may appear. Quantization was done to save these values as an image file with a limited number of palettes. In the distribution of values, we used only 248 quantized values that appeared when dividing the interval from minimum value to maximum value into 247 intervals. The 248 quantized values correspond one-to-one to a pre-defined 248 color palette, with the pixel stored in the corresponding color. The palette has a separate palette, depending on the elements of the data. The color palette for temperature, for example, selected 248 colors in sequence in a continuous color spectrum created in the order blue, cyan, yellow, orange, and red to effectively represent temperatures from low to high temperatures. This causes a quantization error due to the difference between the calculated true value and the quantized stored value, and the magnitude of this error does not exceed ((maximum value-minimum value) / 247)). In order to minimize the size of the quantization error, it is designed to automatically generate and store the information file of the image file paired with the image file including the average value, the maximum value and the minimum value of the actual measurement value. This data storage system has the advantage of reducing the storage space through lossless compression of images and reducing the information display time on the web, compared to the conventional binary-based data storage system, but the information below the mechanical error range of the actual equipment. Can be stored.

In addition, the present invention adopts a data storage technique using an image code, as illustrated in FIG. 6, thereby converting input data from a data storage system having an image code table to data into an image code using the table. Image Code Data Utilization Technique using Image Code to control and display this image code by color, which provides data sharing between weather interpolation and pest forecasting using one image code. The storage capacity is reduced, and according to the image code table, data is easily obtained from the image code table list, and various types of information are coded and stored more easily, thereby increasing the usable capacity of the storage unit and efficiently using the memory. Various kinds of tablets By the coded and stored, it has the advantage of being able to take advantage of that provided in the form of an interpolation image memo function as a pest in accordance with the weather forecasting operations interpolation operation.

FIG. 6 is a detailed conceptual diagram of a web service system and its neighboring blocks in FIG. 1.

Thus, the web service system 40 displays the data retrieved for the user's request.

FIG. 7 is a diagram illustrating an example of a screen providing a map presentation interface in the web service system of FIG. 6.

Another example of the development is the development of a pepper plague incidence prediction model through estimation of source density in soil as follows.

8 is a flowchart of a pepper plague occurrence prediction model according to another embodiment of the present invention.

As shown in FIG. 8, the pepper blight occurrence prediction model includes a geothermal estimation step under vinyl mulching; A cumulative total evapotranspiration estimating step; A cumulative daily flow estimating step; Estimating soil moisture content according to water balance calculation and discrimination criteria according to days after rainfall; Calculating a maximum amount of pepper blight yeast capsule produced at the estimated temperature; Calculating the number of stool bags produced today; Judging whether or not the yolk sac remains according to the soil moisture potential; Estimating the seed germination and germination rate according to the temperature; Calculating the amount of infectious agents produced today; It will go through the stage of alarming according to the red pepper plague risk warning criteria.

The method of estimating soil moisture content according to the geothermal estimation method, the cumulative total evapotranspiration estimating method, the cumulative daily runoff estimating method and the water balance calculation and the discrimination criteria according to the days after rainfall in FIG. 8 were described above.

The maximum amount of pepper blight jujube sac generated in the estimated geothermal temperature of FIG. 8 is the maximum amount of pepper blight jujube sac produced in water since the essential condition for generating pepper blight jujube sac is water. Therefore, a regression equation for estimating the maximum production of juvenile pericardium due to temperature was obtained through experiments on the production of capillary periviruses according to temperature.

9 is a diagram showing the maximum production results of pepper blight yeast capsule according to the temperature used in the present invention and the trend line and the regression relation obtained based on the same.

Since the essential condition for the creation of the resident sacklets is water, the maximum number of sacklets produced according to the temperature × soil moisture content / 100 was calculated.

The red pepper plague risk warning criterion of FIG. 8 was developed based on 13 meteorological data and survey data collected from five regions in 2001-2003. The initial outbreak of pepper blight was associated with more than 68 infectious agents in three days, and if all of them were able to germinate or release, all five days later, the disease appeared.

As a result of testing the pepper disease outbreak prediction model using 10 meteorological and pathological data collected from five regions in 2004, it was relatively predicted the first occurrence of pepper disease as shown in Table 2 below.

area The first day observed Forecast date Observation date-forecast date Gangwon Hongcheon 6/10, 6/11 6/3 6, 7 Jeonnam Gokseong 6/26, 6/27 6/25 1, 2 Chungbuk Cheongwon 5 / 28,5 / 30 6/5 -6, -8 Gyeongbuk Nutrition 6/1 5/26 6 Gyeonggi Mars 5/26 6/5 -10 Jeonbuk Jeongeup 5/23 5/26 3

Although the above has been described as being limited to the preferred embodiment of the present invention, the present invention is not limited thereto and various changes, modifications, and equivalents may be used. Therefore, the present invention can be applied by appropriately modifying the above embodiments, it will be obvious that such application also belongs to the scope of the present invention based on the technical idea described in the claims below.

1 is a block diagram of a soil infectious plant disease occurrence prediction system according to an embodiment of the present invention.

2 is a conceptual diagram showing in detail the operating state of the soil infectious plant disease occurrence prediction system of FIG.

3 is a detailed conceptual diagram of a weather data collection system and its neighboring blocks in FIG. 1.

4 is a detailed conceptual view of the job processing system and its peripheral blocks in FIG.

FIG. 5 is a detailed conceptual view of a data storage system and peripheral blocks thereof in FIG. 1.

FIG. 6 is a detailed conceptual diagram of a web service system and its neighboring blocks in FIG. 1.

FIG. 7 is a diagram illustrating an example of a screen providing a map presentation interface in the web service system of FIG. 6.

8 is a flowchart of a work system for driving a pepper pest disease prediction model according to an embodiment of the present invention.

Figure 9 is a graph showing the maximum amount of zygomatic capsule generation according to the temperature used in the present invention.

Explanation of symbols on the main parts of the drawings

10: Meteorological data collection system

20: work processing system

30: data storage system

40: Web Service System

Claims (3)

Collects meteorological observation data for predicting soil infectious plant diseases, and uses the collected meteorological observation data to inform users about plant disease prediction information generated by the work processing system. In the soil infectious plant disease forecasting system to deliver, The job processing system generates a mesh weather interpolation map image having a grid resolution of a specific size from the meteorological office's automatic meteorological observation data stored in units of observation points, and generates a unit observation pixel and an image information file of the mesh weather interpolation map image. Based on the positional information of the unit item, the network weather interpolation map in which the file information of the network weather interpolation map image corresponds to the unit item of the image information file and maps the information prepared by mapping the corresponding information to each of the corresponding positions is used. Generate a mesh plant pest prediction map of the same resolution, The job processing system controls the data storage system to query the data storage system having an image code table with data and convert the image data using the table to display the image code by color. Image-coded and stored various data according to the image code table, and share the image-coded data and the image code of the network weather interpolation map in the work of the network plant pest prediction map, The image code is stored in a color corresponding to the corresponding image pixel, and accordingly, the image color code table shows the temperature from low temperature to high temperature in a continuous color spectrum in the order of blue, cyan, yellow, orange, and red. Set the colors in order and minimize the quantization error so that the magnitude of the quantization error produced by the difference between the computed true value and the quantized stored value does not exceed ((maximum value-minimum value) / 247)). In order to include the average value, the maximum value and the minimum value of the actual measured value, the information file of the image file paired with the image file is also automatically generated and stored together. Image code table of the network interpolation map is generated for the temperature, relative humidity, geothermal, leaf surface wet duration, A meteorological data collection system that collects meteorological observation data for soil infectious plant disease prediction and delivers it to the work processing system, and a web service system and weather that delivers plant disease prediction information generated by the work processing system to a user through a web page. Soil infectious plant disease outbreak prediction system, characterized in that the data collection system, work processing system, a web service system is further provided with a data storage system for managing the data produced, stored or retrieved and retrieved. Collects meteorological observation data for predicting soil infectious plant diseases, and uses the collected meteorological observation data to transmit plant disease prediction information generated by the work processing system to generate soil infectious plant disease prediction information through a web page. In the soil infectious plant disease occurrence prediction method using the soil infectious plant disease occurrence prediction system according to claim 1, Geothermal estimation under vinyl mulching; A cumulative total evapotranspiration estimating step; A cumulative daily flow estimating step; Estimating soil moisture content according to water balance calculation and discrimination criteria according to days after rainfall; Calculating a maximum amount of pepper blight yeast capsule produced at the estimated temperature; Calculating the number of jujube bags produced on the day of generating soil infectious plant disease prediction information; Judging whether or not the yolk sac remains according to the soil moisture potential; Estimating the seed germination and germination rate according to the temperature; Calculating the amount of infectious source produced on the day of generating soil infectious plant disease prediction information; Determining whether to alert according to the risk warning discrimination criteria of red pepper plague occurrence, Dividing and setting the activity state of the resident in the soil residue into a plurality of stages; Measuring weather state information data (temperature, wetting time) and soil moisture content according to a set time interval; Storing the measured weather condition information data and soil moisture content in a data storage system; Based on the measured daily cumulative rainfall, cumulative evapotranspiration, cumulative runoff, and cumulative drainage, a formula for predicting future soil moisture content is calculated. Comparing the calculated predicted soil moisture content with the limiting soil moisture content; Calculating a three-day moving average infectious source amount by estimating the amount of infectious source produced on the day of generating soil infectious plant disease prediction information using measured or estimated daily average temperature and soil moisture content; Generating a late blight alarm on the basis of when the predicted soil moisture content is greater than the limit setting soil moisture content and the average amount of the total produced infectious agents is more than a predetermined value; Soil infectious plant disease occurrence prediction method, characterized in that carried out including. The method of claim 2, In the work processing system, when there is no actual value of geothermal temperature and soil moisture, an estimate is generated by using meteorological factors other than the geothermal temperature and soil moisture, crop growth state, and soil characteristics data. The work processing system includes receiving and transmitting temperature, relative humidity, geothermal temperature, rainfall, leaf wetting duration, wind direction, wind speed, insolation, and soil moisture content, and processing the observed data into periodic data. , The maximum amount of pepper blight jujube capsules produced at the estimated temperature is the maximum amount of pepper blight jujube capsules produced in water because the essential condition for the production of pepper blight jujube capsules is water. A method for predicting the occurrence of soil infectious plant disease, characterized by using a regression equation for estimating the maximum production amount of juvenile pericardium according to temperature through experiments on the generation of juvenile pericardium of germs.

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