KR20210012352A - Leaf wetness duration predicition system and operating method thereof - Google Patents

Abstract

Disclosed is a system for predicting leaf moistness duration time. In one embodiment, the system for predicting leaf moistness duration time predicts whether dew occurs at each of a plurality of positions for each time based on a plurality of weather prediction information and a dew determination model, generates information on the leaf moistness duration time of each of the plurality of positions based on a prediction result of whether the dew occurs for each time at each of the plurality of positions, and generates spatial distribution information on the leaf moistness duration time based on the information on the generated leaf moistness duration time at each of the plurality of positions.

Description

Foliar wetting duration prediction system and its operation method {LEAF WETNESS DURATION PREDICITION SYSTEM AND OPERATING METHOD THEREOF}

The following examples relate to a system for predicting foliar wetting duration.

The Meteorological Agency operates more than 500 automatic weather observation equipment nationwide, and observes four basic variables (temperature, relative humidity, wind direction, wind speed) and additional elements of interest every minute. The Meteorological Administration operates 11 meteorological observation points linked to agriculture, and the expanded points are expressing weather data related to agriculture in connection with the Rural Development Administration.

However, the meteorological observation elements necessary for agricultural workers are still insufficient. In particular, it is difficult to observe the foliar wetness duration (or leaf wetness duration, LWD), which studies agricultural production and has an important effect on the disease and pest environment, and it is not easy to observe because there is no standard observation standard. Producing materials. Therefore, spatial and temporal expansion of LWD data is practically difficult, and to solve this problem, many researchers have developed physical and empirical LWD prediction models.

There are two representative empirical models, Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD) and Number of Hours of Relative Humidity (NHRH). CART/SLD is a method of using a hierarchical decision tree by determining the threshold of relative humidity, dew point temperature, and wind speed as an empirical formula, and it is specialized for a specific region and thus lacks universality and thus lacks prediction accuracy when applied to the Korean Peninsula. . NHRH is a method of determining whether there is dew based on a specific threshold of 90% relative humidity. It is the simplest of the prediction models and it is difficult to expect high accuracy.

A typical physical model is the Penman-Monteith (PM) method, which uses an energy balance to calculate latent heat flux to determine dew. The PM physical model can compensate for the large deviation of the prediction accuracy according to the region, which is raised as a disadvantage of the empirical model. On the other hand, the amount of radiation that has the greatest effect on the performance is difficult to observe, so the use of the PM method is limited.

The artificial intelligence technique, which has been actively studied in recent years, has superior accuracy than the previous model, can be used in various regions, and has the advantage of easy model construction because it uses weather factors that are relatively easy to observe. If the LWD prediction system is constructed using artificial intelligence as described above, high-accuracy LWD information can be provided at an arbitrary point.

The operating method of the foliar wetting duration prediction system according to one side includes: predicting whether or not dew will occur at each of a plurality of locations based on a plurality of weather prediction information and a dew determination model by time; Generating foliar wetting duration information for each of the plurality of locations based on a prediction result of whether dew occurs for each time of the plurality of locations; And generating information on a spatial distribution of foliar wetting duration based on the generated foliar wetting duration information of each of the plurality of locations.

The plurality of weather prediction information may include a prediction value for each time of one or more weather variables of each of the plurality of locations.

The one or more meteorological variables may correspond to one or more of temperature, relative humidity, wind speed, solar radiation, and rainfall.

The generating of the leaf surface wetting duration information of each of the plurality of locations includes counting a time zone in which the occurrence of dew at each of the plurality of locations is predicted, and a daily foliar surface of each of the plurality of locations based on the counting result. It may include the step of generating wet duration information.

When a prediction value for each of a plurality of meteorological variables in a specific time zone at a specific location is received, the dew determination model may predict whether dew will occur in the specific time zone at the specific location.

The dew determination model may include an Extreme Learning Machine (ELM) model.

The step of generating the leaf surface wetness duration spatial distribution information is to generate the leaf surface wetness duration spatial distribution information by integrating the terrain information including the plurality of locations and the generated leaf surface wetness duration information of each of the plurality of locations. It may include the step of.

The operation method may further include displaying the generated information on the spatial distribution of foliar wetting duration time on a display.

The foliar wetting duration prediction system according to one side includes a memory for storing a dew determination model; And predicting by time whether dew will occur at each of a plurality of locations based on a plurality of weather prediction information and the dew determination model, and based on a prediction result of whether dew occurs at each time of the plurality of locations. And a controller configured to generate leaf surface wetness duration information of each of the plurality of locations, and generate leaf surface wetness duration spatial distribution information based on the generated leaf surface wetness duration information of each of the plurality of locations.

The plurality of weather prediction information may include a prediction value for each time of one or more weather variables of each of the plurality of locations.

The one or more meteorological variables may correspond to one or more of temperature, relative humidity, wind speed, solar radiation, and rainfall.

The controller may count a time period in which the occurrence of dew is predicted at each of the plurality of locations, and generate daily foliar wetting duration information of each of the plurality of locations based on the counting result.

When a prediction value for each of a plurality of meteorological variables in a specific time zone at a specific location is received, the dew determination model may predict whether dew will occur in the specific time zone at the specific location.

The dew determination model may include an ELM model.

The controller may generate the foliar wetting duration spatial distribution information by integrating topographic information including the plurality of locations and the generated foliar wetting duration information of each of the plurality of locations.

The controller may display the generated foliar wetting duration spatial distribution information on the display.

1 is a diagram illustrating a system for predicting foliar wetting duration according to an exemplary embodiment.
2 is a flowchart illustrating a method of generating a dew determination model according to an exemplary embodiment.
3 to 4 are views for explaining the operation of the dew determination model according to an embodiment.
5 is a view for explaining the execution time of the foliar wetting duration prediction system according to an embodiment.
6 is a diagram for explaining a spatial distribution of foliar wetting duration time according to an exemplary embodiment.
7 is a flowchart illustrating a method of operating a system for predicting foliar wetting duration according to an exemplary embodiment.
8 is a block diagram illustrating a system for predicting foliar wetting duration according to an exemplary embodiment.
9 is a diagram for explaining an operation of a system for predicting foliar wetting duration and a plurality of user terminals according to an embodiment.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the rights of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes to the embodiments are included in the scope of the rights.

The terms used in the examples are used for illustrative purposes only and should not be interpreted as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of the reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the embodiments, the detailed description thereof will be omitted.

1 is a diagram illustrating a system for predicting foliar wetting duration according to an exemplary embodiment.

Referring to FIG. 1, a leaf wetness duration (LWD) prediction system 110 includes an input data processing unit 110, a dew determination unit 120, and a spatial distribution information generation unit 130.

The input data processing unit 110 preprocesses weather forecast data. For example, the input data processing unit 110 may convert weather prediction data into weather prediction data for each location and/or time according to an input format of a dew determination model to be described later. In other words, the input data processing unit 110 may extract weather prediction data for each location and/or time from the weather prediction data. Here, the location may indicate the location where the Automatic Weather System (AWS) managed by the Meteorological Agency is installed.

The dew determination unit 120 determines whether dew occurs based on the preprocessed weather forecast data and a dew determination model. In other words, the dew determination unit 120 determines whether dew occurs for each location and time based on the pre-processed weather prediction data and a dew determination model. As an example, when the dew determination model of the dew determination unit 120 receives weather prediction data for each time of a specific location, it may predict by time whether dew will occur at a specific location. The generation of the dew determination model will be described later with reference to FIG. 2, and the detailed operation of the dew determination model will be described later with reference to FIGS. 3 to 4.

The dew determination unit 120 generates LWD information for each location based on a determination result of whether dew occurs for each location and time.

The spatial distribution information generation unit 130 generates LWD spatial distribution information based on LWD information for each location. For example, the spatial distribution information generation unit 130 may generate a national LWD spatial distribution map by performing linear interpolation on LWD information for each location.

The LWD prediction system 100 according to an embodiment may generate daily LWD spatial distribution information based on weather prediction data, and thus may provide LWD information of a desired location to a user. In addition, the LWD prediction system 100 according to an embodiment may provide LWD information of a location where there is no weather observation to a user (eg, an agricultural worker), and may help the user to determine agricultural work.

2 is a flowchart illustrating a method of generating a dew determination model according to an exemplary embodiment.

The dew determination model according to an exemplary embodiment may be generated by the model generating apparatus, but is not limited thereto and may be generated by the LWD prediction system 100.

Referring to FIG. 2, the model generating device collects ground observation data (210). Ground observation data may include, for example, one or more of temperature, relative humidity, wind speed, insolation, and dew duration, but is not limited thereto.

The model generation device sets up an Extreme Learning Machine (ELM) (220). As an example, the model generation apparatus sets the number of nodes of the ELM model to 900 and the regulation factor of the ELM model to 0.9.

The model generation device optimizes and verifies the ELM model (230). In other words, the model generating device may train an ELM model based on the collected ground observation data.

The model generating apparatus extracts weights from the optimized ELM model (240). The extracted weights are applied to the LWD prediction system 100. In other words, the extracted weights are applied to the LWD prediction system 100 so that the dew determination model of the dew determination unit 120 may be implemented in the LWD prediction system 100.

3 to 4 are views for explaining the operation of the dew determination model according to an embodiment.

Referring to FIG. 3, the dew determination model may receive weather prediction data for each location/time and predict whether dew occurs for each location/time. Weather forecast data may include predicted values of each of the meteorological variables (eg, temperature, relative humidity, wind speed, solar radiation, and rainfall).

Referring to FIG. 4, when the dew determination model receives weather prediction data for each time at each location, it can predict whether dew will occur at each time zone at each location. As an example, the dew determination model is an hourly predicted value of the temperature of a location A on the map shown in FIG. 4, a predicted value by time of the relative humidity of a location A, a predicted value by time of the wind speed of the location A, a predicted value by time of the insolation amount of the location A, and a location A. It is possible to receive a predicted value for each time of the rainfall of, and it is possible to predict whether or not dew occurs for each time zone at the location A as in the example shown in FIG. 4. In the example shown in FIG. 4, the dew determination model can predict that dew will occur at 3 to 4 o'clock the next day at location A and dew will occur at 4 to 5 o'clock. Likewise, the dew determination model can predict whether dew occurs for each time period at each of the positions B to F.

The dew determination unit 120 may count a time period in which the occurrence of dew is predicted in each of the plurality of locations, and may generate LWD information of each location based on the counting result. In the example shown in FIG. 4, the dew determination unit 120 predicts that dew will occur at 3 to 4 o'clock and 4 to 5 the next day in each of the positions A to E, and at the position F, the dew is 3 to 4 o'clock the next day, and 4 It was predicted to occur at ~5 o'clock and 5-6 o'clock. In this case, the dew determination unit 120 may count the time zones in which the occurrence of dew is predicted in each of the positions A to E as two, and the time zones in which the occurrence of dew in the location F is predicted as three. Accordingly, the dew determination unit 120 may predict the LWD at each of the positions A to E as 2 hours and the LWD at the position F as 3 hours. In other words, the dew determination unit 120 may determine the LWD information for the next day at each of the positions A to E as 2 hours and the LWD information for the next day at the position F to 3 hours.

The positions A to F described with reference to FIG. 4 are merely examples for convenience of description, and the positions are not limited to the positions A to F described with reference to FIG. 4. In addition, the time period in which the occurrence of dew is predicted is also an example for convenience of description, and the time period in which the occurrence of dew is predicted is not limited to that described with reference to FIG. 4.

5 is a view for explaining the execution time of the foliar wetting duration prediction system according to an embodiment.

5, the Local Data Assimilation and Prediction System (LDAPS) starts analysis at 00 UTC. The local forecast model is a model that predicts the weather on the Korean Peninsula, and can predict the weather on the Korean Peninsula up to 36 hours (or 48 hours).

The time for scale detailing is 05 UTC. Scale detailing can represent the work of expanding the prediction data of the local forecast model to a maximum resolution of 100m. The result of performing scale detailing on the prediction data of the local forecast model may be input to the input data processing unit 110 described above. In other words, the scale detailing result may correspond to weather prediction data input to the input data processing unit 110.

The prediction model execution time is 08 UTC. The prediction model may predict whether dew occurs at each location described above by time after 8 hours have elapsed from 00 UTC.

6 is a diagram for explaining a spatial distribution of foliar wetting duration time according to an exemplary embodiment.

Referring to FIG. 6, examples of an LWD spatial distribution map for each day are shown.

The spatial distribution information generator 130 may generate an LWD spatial distribution map for each day based on the LWD information for each location. As an example, the spatial distribution information generation unit 130 may generate a daily LWD spatial distribution map by integrating LWD information and terrain information for each location. In an embodiment, the spatial distribution information generator 130 may utilize an NCL (NCAR Command Language) program to generate a daily LWD spatial distribution map.

7 is a flowchart illustrating a method of operating a system for predicting foliar wetting duration according to an exemplary embodiment.

Referring to FIG. 7, the LWD prediction system 100 predicts whether or not dew will occur at each of a plurality of locations based on a plurality of weather prediction information and a dew determination model (710). In other words, the LWD prediction system 100 may generate prediction information on whether dew occurs for each time at each of a plurality of locations based on a plurality of weather prediction information and a dew determination model. The plurality of locations may represent locations where AWS managed by the Meteorological Administration is installed, but is not limited thereto. The plurality of weather prediction information may correspond to the preprocessed weather prediction data described above. The plurality of weather prediction information may include, for example, prediction values for each time of one or more weather variables of each of the plurality of locations.

In an embodiment, when a prediction value for each of a plurality of weather variables in a specific time zone at a specific location is received, the dew determination model may predict whether dew will occur at a specific time zone at a specific location. Since the operation of the dew determination model has been described in detail with reference to FIG. 4, detailed descriptions are omitted.

The LWD prediction system 100 generates LWD information of each of the plurality of locations based on a prediction result of whether dew occurs for each time of each of the plurality of locations (720). As an example, the LWD prediction system 100 may count a time period in which dew generation is predicted in each of a plurality of locations, and may generate daily LWD information of each of a plurality of locations based on the counting result.

The LWD prediction system 100 generates LWD spatial distribution information based on the generated LWD information of each of a plurality of locations (730 ). For example, the LWD prediction system 100 may generate LWD spatial distribution information by integrating topographic information including a plurality of locations and LWD information for each of the plurality of locations.

A new location may be added or one or more of the existing locations may be removed according to a user request. Due to the flexibility of the system 100, a user may be provided with LWD information of a desired location. In addition, the user may be provided with LWD information for a desired time slot.

The matters described through FIGS. 1 to 6 may be applied to the matters described through FIG. 7, and thus detailed descriptions thereof will be omitted.

8 is a block diagram illustrating a system for predicting foliar wetting duration according to an exemplary embodiment.

Referring to FIG. 8, the LWD prediction system 100 includes a controller 810 and a memory 820.

The memory 820 stores the dew determination model 310. In other words, the memory 820 stores the weights described with reference to FIG. 2.

The controller 810 may implement the input data processing unit 110, the dew determination unit 120, and the spatial distribution information generation unit 130 described above. The controller 810 predicts, by time, whether dew will occur at each of the plurality of locations based on the plurality of weather prediction information and the dew determination model 310. The controller 810 generates LWD information of each of the plurality of locations based on a prediction result of whether dew occurs for each time of each of the plurality of locations. The controller 810 generates LWD spatial distribution information based on LWD information of each of a plurality of locations.

Items described through FIGS. 1 to 7 may be applied to items described through FIG. 8, and thus detailed descriptions thereof will be omitted.

9 is a diagram for explaining an operation of a system for predicting foliar wetting duration and a plurality of user terminals according to an exemplary embodiment.

Referring to FIG. 9, a plurality of user terminals 910-1 to 910-n may access the LWD prediction system 100.

Each of the plurality of user terminals 910-1 to 910-n may correspond to a mobile terminal (eg, a smart phone, a tablet) or a fixed terminal (eg, a PC).

The LWD spatial distribution map may be displayed on the display of each of the plurality of user terminals 910-1 to 910-n according to a user request. Accordingly, each user can check the LWD information of a desired location and/or a desired time zone.

Although not shown in FIG. 9, the daily LWD information generated by the LWD prediction system 100 may be transmitted to the disease and pest prevention system, thereby contributing to the generation of a crop management risk level warning.

The LWD prediction system 100 according to an embodiment may provide information required for agricultural decision-making to agricultural workers, and may provide LWD information that anyone can use. In addition, the LWD prediction system 100 may determine whether dew has occurred by using the ELM model and the prediction system for LWD information at a point where there is no weather observation, and thus generate LWD information for the next day. This LWD information can help farmers to judge agricultural work. In addition, LWD information can be provided to consumers of agricultural information, and can be used as an input to a pest control system to contribute to generating crop management risk level alerts.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments are, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA). , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

As described above, although the embodiments have been described by the limited embodiments and the drawings, various modifications and variations are possible from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the following claims.

Claims (16)

In the operating method of the foliar wetting duration prediction system,
Predicting whether or not dew will occur at each of the plurality of locations based on a plurality of weather prediction information and a dew determination model;
Generating foliar wetting duration information of each of the plurality of locations based on a prediction result of whether dew occurs for each time of each of the plurality of locations; And
Generating spatial distribution information of foliar wetting duration based on the generated foliar wetting duration information of each of the plurality of locations
Containing,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
The plurality of weather prediction information includes prediction values for each time of one or more weather variables of each of the plurality of locations,
Operation method of foliar wetting duration prediction system.
The method of claim 2,
The one or more meteorological variables correspond to one or more of temperature, relative humidity, wind speed, insolation, and rainfall,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
The step of generating leaf surface wetting duration information of each of the plurality of positions,
Counting a time zone in which the occurrence of dew is predicted at each of the plurality of locations, and generating daily foliar wetting duration information of each of the plurality of locations based on the counting result
Containing,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
The dew determination model predicts whether dew will occur in the specific time period of the specific location when a prediction value for each of a plurality of meteorological variables in a specific time period at a specific location is received,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
The dew determination model includes an Extreme Learning Machine (ELM) model,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
The step of generating the leaf surface wetting duration spatial distribution information,
Integrating the topographic information including the plurality of locations and the generated foliar wetting duration information of each of the plurality of locations to generate the foliar wetting duration spatial distribution information
Containing,
Operation method of foliar wetting duration prediction system.
The method of claim 1,
Displaying the generated foliar wetting duration spatial distribution information on a display
Further comprising,
Operation method of foliar wetting duration prediction system.
In the foliar wetting duration prediction system,
A memory for storing a dew determination model; And
Based on a plurality of weather prediction information and the dew determination model, predicting whether dew will occur at each of a plurality of locations by time, and based on a prediction result of whether dew occurs at each of the plurality of locations by time, the A controller that generates leaf surface wetting duration information at each of a plurality of locations, and generates leaf surface wetness duration spatial distribution information based on the generated leaf surface wetness duration information at each of the plurality of locations
Containing,
Foliar wetting duration prediction system.
The method of claim 9,
The plurality of weather prediction information includes prediction values for each time of one or more weather variables of each of the plurality of locations,
Foliar wetting duration prediction system.
The method of claim 10,
The one or more meteorological variables correspond to one or more of temperature, relative humidity, wind speed, insolation, and rainfall,
Foliar wetting duration prediction system.
The method of claim 9,
The controller,
Counting a time zone in which the occurrence of dew is predicted at each of the plurality of locations, and generating daily foliar wetting duration information of each of the plurality of locations based on the counting result,
Foliar wetting duration prediction system.
The method of claim 9,
The dew determination model predicts whether dew will occur in the specific time period of the specific location when a prediction value for each of a plurality of meteorological variables in a specific time period at a specific location is received,
Foliar wetting duration prediction system.
The method of claim 9,
The dew determination model includes an Extreme Learning Machine (ELM) model,
Foliar wetting duration prediction system.
The method of claim 9,
The controller,
Integrating the topographic information including the plurality of locations and the generated foliar wetting duration information of each of the plurality of locations to generate the foliar wetting duration spatial distribution information,
Foliar wetting duration prediction system.
The method of claim 9,
The controller,
Displaying the generated foliar wetting duration spatial distribution information on a display,
Foliar wetting duration prediction system.

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