KR20160076317A - Apparatus and method for predicting disease and pest of crops - Google Patents

Abstract

Provided is a method for predicting disease and pest of crops, comprising the steps of: storing information regarding the current environment within a greenhouse in which crops are grown according to the passage of time; comparing at least one of disease and pest pattern information and normal pattern information with the stored current environment information; and predicting whether crops have a disease and pest on the basis of the comparison result.

Description

[0001] APPARATUS AND METHOD FOR PREDICTING DISEASE AND PEST OF CROPS [0002]

The present invention relates to an apparatus and a method for predicting the occurrence of pests. More specifically, the present invention relates to an apparatus and a method for predicting the probability of occurrence of a pest of a crop cultivated in a greenhouse.

Along with the development of information technology (IT), a lot of information technology is being applied to agriculture. For example, smart farms, plant factories, and other information technologies have been applied to improve the productivity of crops.

In recent years, greenhouse cultivation, which can improve yields, has been rapidly spreading. The problem is that greenhouse crops grow in accordance with the nutrients in the dense and isolated environment, which may weaken the immunity of crops grown in the greenhouse have. Therefore, it is important to improve the productivity of the crops by reducing the possibility of pests of the crops grown in the greenhouse.

In order to prevent pests, the user carries out the control according to the condition of the crop. In spite of this control work, the pest is always generated. Since only post-pollution prescription or preliminary control work is carried out without analyzing the root cause of the pests occurring in the greenhouse, there may be problems in the stability and cultivation cost of the crop due to frequent control work.

Generally, although related information is provided through a pest system of public institutions, such information is not classified according to the type of crop, the cultivation time, the cultivation area, the cultivation environment, and the driver in the greenhouse. Therefore, There is a problem that it is not possible to grasp whether the working method is immature or the environmental problem inside the greenhouse.

The Korean Registered Publication No. 10-1156594 is a system for diagnosing the occurrence of pests in a greenhouse, wherein a plurality of pest image data for each crop is stored and processed, and then compared with the image data of the crop, Diagnose the occurrence of pests. However, the pest diagnosis based on the image of the crop is a post-process performed after the occurrence of the pests in the crop, and there is a problem that the accuracy of the image acquisition and image reading of the crop against the pest can be poor.

An apparatus and method for predicting the occurrence of a pest according to an embodiment of the present invention aims at accurately predicting the probability of occurrence of a pest in a greenhouse.

In addition, an apparatus and method for predicting the occurrence of pests according to an embodiment of the present invention aims to improve the yield of crops.

According to an embodiment of the present invention,

A method for predicting the occurrence of pests of a crop by a pest-and-disease occurrence predicting device, comprising the steps of: storing current environmental information in a greenhouse in which a crop is grown according to passage of time; Comparing pattern information of at least one of pest pattern information and normal pattern information with the stored current environment information; And a step of predicting whether or not a pest is caused by the crop based on the comparison result.

A method of generating pest pattern information according to another embodiment of the present invention includes:

A method of generating pest pattern information by a pest-insect occurrence predicting apparatus, comprising the steps of: acquiring a plurality of total past environmental information in the greenhouse in which a pest was generated when a pest was generated in a crop previously grown in a greenhouse; Extracting some past environmental information from a time point at which a pest was generated of the previously cultivated crop before a predetermined period of time from each of the plurality of total past environmental information; And generating pest pattern information including an upper limit value and a lower limit value of the environment information based on the extracted partial past environmental information.

According to another embodiment of the present invention,

A storage unit for storing current environmental information in a greenhouse where crops are grown according to the passage of time; And a control unit for comparing at least one of the pattern information of the pest pattern information and the normal pattern information with the stored current environment information to predict whether or not the pest is generated.

The apparatus and method for predicting the occurrence of pests according to an embodiment of the present invention can accurately predict the probability of occurrence of a pest in a greenhouse.

In addition, the apparatus and method for predicting the occurrence of pests according to an embodiment of the present invention can improve crop yield.

FIG. 1 is a diagram showing an environment in which a pest occurrence prediction apparatus according to an embodiment of the present invention is used.
FIG. 2 is a diagram showing a procedure of a method for predicting the occurrence of pests according to an embodiment of the present invention.
3 is a diagram showing a procedure of a method for generating pest pattern information according to an embodiment of the present invention.
Figs. 4 (a) to 4 (c) are diagrams for explaining a method of generating pest pattern information.
5 (a) to 5 (c) are diagrams for explaining a method of generating normal pattern information.
6 is a block diagram showing a configuration of an apparatus for predicting the generation of pests according to another embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. To fully disclose the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

The term " part " used in this embodiment means a hardware component such as software, FPGA, or ASIC, and 'part' performs certain roles. However, 'minus' is not limited to software or hardware. The " part " may be configured to reside on an addressable storage medium and may be configured to play back one or more processors. Thus, by way of example, and by no means, the terms " component " or " component " means any combination of components, such as software components, object- oriented software components, class components and task components, Subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functions provided in the components and parts may be combined into a smaller number of components and parts or further separated into additional components and parts.

In the present specification, 'current environmental information' means environmental information in the greenhouse where crops are currently grown, and 'past environmental information' means environmental information in a greenhouse in which past crops were grown.

1 is a diagram showing an environment in which an apparatus 100 for predicting the occurrence of a pest-disease according to an embodiment of the present invention is used.

Referring to FIG. 1, the apparatus 100 for predicting the occurrence of a pest can be connected to various types of drivers and sensors installed in a greenhouse 10, and a user terminal 200 according to an embodiment of the present invention. According to an embodiment, the pest occurrence prediction apparatus 100 may not be connected to the user terminal 200 but may be provided with a separate output unit to output predetermined information.

Herein, the greenhouse 10 means a facility installed for cultivating crops in a certain space, and may include, for example, a greenhouse, a glasshouse or a plant factory. As shown in FIG. 1, various types of drivers for controlling the cultivation environment of crops may be installed in the greenhouse 10. The driver may include at least one of a skylight, a side window, a light shield, a boiler, a cooling / heating unit, a sprinkler, a nasal fluid, and a carbon dioxide generator, but the present invention is not limited thereto .

In addition, various types of sensors for sensing the environment in the greenhouse 10 may be installed in the greenhouse 10. The sensor may include, but is not limited to, at least one of an air speed sensor, a wind direction sensor, a brightness sensor, a carbon dioxide concentration sensor, a temperature sensor, and a humidity sensor, for example.

The apparatus 100 for predicting the occurrence of pests according to an embodiment of the present invention predicts whether or not a pest is generated based on environmental information transmitted from sensors and actuators in a greenhouse, which will be described with reference to FIG.

FIG. 2 is a diagram showing a procedure of a method for predicting the occurrence of pests according to an embodiment of the present invention.

In step S210, the pest-disease occurrence predicting apparatus 100 stores the current environment information in the greenhouse in which the crop is grown according to the passage of time. For example, if the strawberry is cultivated in the greenhouse from March to June, the pest occurrence prediction device 100 can store the environmental information of the greenhouse from March to June as time passes.

The pest-and-disease occurrence predicting apparatus 100 can receive current environment information from sensors and drivers in the greenhouse according to the passage of time. In other words, the sensor in the greenhouse can measure the concentration of carbon dioxide in the greenhouse, the amount of light, etc. with the passage of time, and then transmit the measured information to the pest occurrence prediction apparatus 100, , Information on how long the nutrient solution is supplied, how much nutrient solution is supplied, and the like can be transmitted to the pest occurrence occurrence prediction apparatus 100.

The present environmental information may include information on at least one of temperature, humidity, light amount, carbon dioxide concentration, nutrient supply period, amount of nutrient solution, and nutrient content in the greenhouse, but is not limited thereto.

In step S220, the pest-disease occurrence predicting apparatus 100 compares at least one pattern information among the pest pattern information and the normal pattern information with the stored current environment information. The pest occurrence predicting apparatus 100 can generate pest pattern information and normal pattern information based on past environmental information corresponding to a crop cultivated in a greenhouse, which will be described later with reference to FIG. 3 to FIG.

In step S230, the pest-insect occurrence predicting apparatus 100 predicts whether or not a pest is generated based on the comparison result in step S220. For example, the pest-incidence prediction apparatus 100 can determine that there is a high possibility that a pest is generated in a crop grown in a greenhouse when the current environmental information according to the time corresponds to the pest pattern information.

When the pest occurrence predicting apparatus 100 predicts that a pest is caused to occur in the crop, the pest occurrence possibility information may be output so that the user can take measures to prevent the occurrence of the pest.

The pest-and-disease occurrence predicting apparatus 100 can output information on whether the environment in the present greenhouse should be controlled and the likelihood of occurrence of a pest is low, while outputting pest-susceptibility-capable information. For example, if the greenhouse temperature information in the current environmental information according to the time corresponds to the pest pattern information, the pest occurrence prediction apparatus 100 can output information to control the temperature in the greenhouse.

Since the apparatus 100 for predicting the occurrence of pests according to an embodiment of the present invention predicts whether or not a pest will be generated in a crop currently being grown using pest pattern information and normal pattern information generated based on past environmental information or the like in a greenhouse, The possibility of occurrence of a pest can be reduced as compared with the prior art. In other words, since the possibility of the occurrence of pests is predicted on the basis of environmental information corresponding to the greenhouse that the user is using, the pests can be predicted by the user.

FIG. 3 is a diagram showing a procedure of a method for generating pest pattern information according to an embodiment of the present invention, and FIGS. 4 (a) to 4 (c) are diagrams for explaining a method for generating pest pattern information to be.

In step S310, when the pest is generated in the crop previously cultivated in the greenhouse, the pest occurrence occurrence predicting apparatus 100 acquires a plurality of total past environment information in the greenhouse in which the pest has occurred. Fig. 4 (a) shows the first past environmental information of the crop in which the pest was generated at the time point p, and Fig. 4 (b) shows the second past environmental information of the crop in which the pest was generated at the point q. The horizontal axis in Figs. 4 (a) and 4 (b) represents time or date, and the vertical axis represents the value of environmental information (for example, temperature, carbon dioxide concentration, etc.). For example, FIG. 4A shows temperature information according to the time stored in 2010, and FIG. 4B shows temperature information according to the time stored in 2013. The pest-incidence prediction apparatus 100 can accumulate past environmental information whenever a pest is generated during the cultivation of the crop in the greenhouse.

In step S320, the pest-insect occurrence predicting apparatus 100 extracts some past environmental information from a point of time when a pest was generated for a crop previously grown from each of the plurality of total past environmental information until a predetermined period. This is because, if a pest is generated at a certain point in time, all past environmental information from the time when the first cultivation is started may not cause the pest.

Some past environmental information can be extracted in various ways. For example, some past environmental information may be past environmental information from a point of time when a pest is generated to a predetermined time. Depending on the implementation, some past environmental information may be extracted based on the growing stage of the crop. When a pest is generated in the harvesting step, the pest occurrence predicting device 100 may collect past environmental information of the whole harvesting step in accordance with some past environmental information , Or extract past environmental information of the entire harvesting step and hair growth step as some past environmental information. Figures 4 (a) and 4 (b) show that the cultivation step is divided into steps A, B, C, and D, but the cultivation step of the crop can be variously changed.

In step S330, the pest-and-insect occurrence predicting apparatus 100 can generate pest pattern information including the upper limit value and the lower limit value of the environment information, based on some past environmental information. 4C is a diagram showing a pest pattern including an upper limit value 410 and a lower limit value 420. As shown in FIG. 4C, environmental information, for example, an upper limit value of temperature (410) and the lower limit value (420) may be determined according to the passage of time. As the past environment information accumulates, the pest occurrence occurrence predicting apparatus 100 can generate more accurate pest pattern information.

5 (a) to 5 (c) are diagrams for explaining a method of generating normal pattern information.

The pest occurrence occurrence predicting apparatus 100 may generate normal pattern information from a plurality of past environmental information in a greenhouse in which no pest was generated when a pest was not generated in the crop previously grown in the greenhouse.

Fig. 5 (a) shows the first past environmental information of the crop in which the pest did not occur, and Fig. 5 (b) shows the second past environmental information of the crop in which the pest did not occur. The horizontal axis in FIG. 5 (a) and the vertical axis in FIG. 5 (b) represent time or date, and the vertical axis represents the value of environmental information (for example, temperature, carbon dioxide concentration and the like). For example, FIG. 5 (a) is temperature information according to the time stored in 2009, and FIG. 5 (b) is temperature information according to the time stored in 2011. FIG. The pest-and-disease occurrence predicting apparatus 100 may store past environmental information on the crop as information for normal pattern information if pests do not occur during the completion of cultivation of the crop in the greenhouse.

The pest-and-disease occurrence predicting apparatus 100 can generate normal pattern information including an upper limit value and a lower limit value of environmental information based on past environmental information. 5C is a diagram showing a normal pattern including an upper limit value 510 and a lower limit value 520. As shown in FIG. 5C, environmental information, for example, an upper limit value of the temperature The lower limit value 510 and the lower limit value 520 may be determined according to the passage of time. As the past environmental information accumulates, the pest occurrence predicting apparatus 100 can generate more accurate normal pattern information.

On the other hand, the pest occurrence prediction apparatus 100 may receive information on the current work tool used in growing the crop from the user. In this case, the pest pattern information and the normal pattern information may include information on the past working tools used for cultivating the crop in accordance with the passage of time. The pest-and-disease occurrence predicting apparatus 100 compares past work tools included in at least one of the pattern information of the pest pattern information and the normal pattern information with the information of the current working tool input by the user, It is possible. That is, comparing the past work tools with the current work tools, not only because of the environmental problems in the greenhouse, but also because there is a possibility that the pests may be generated by the user's work tools. For example, if the pest pattern information contains a tool, a tool, a c tool, and a d tool recorded as past work tools, and the current tool is the a tool, the b tool, the c tool, and the d tool, Since both the present working tool and the past working tool are the same, the pest predicting apparatus 100 may predict that a pest is generated in the crop currently being cultivated. The pest occurrence predicting apparatus 100 predicts that when the past work tools recorded in the pest pattern information according to the passage of time and the work tools having the predetermined number or more among the current work tools stored according to the passage of time are the same, .

On the other hand, if the current environmental information matches both the pest pattern information and the normal pattern information, the pest occurrence occurrence predicting apparatus 100 quantifies the similarity between the current environmental information and the pest pattern information and the similarity between the current environmental information and the normal pattern information It is also possible to predict whether or not the crops will develop pests.

FIG. 6 is a block diagram showing a configuration of an apparatus 600 for predicting the occurrence of a pest according to another embodiment of the present invention.

Referring to FIG. 6, the apparatus 600 for predicting occurrence of a pest may further include a communication unit 610, a storage unit 630, a control unit 650, and an output unit 670 according to another embodiment of the present invention.

The communication unit 610 receives the current environment information from the sensor and the driver in the greenhouse according to the flow of time. The communication unit 610 may perform wired or wireless communication with the sensor and the driver.

The storage unit 630 stores the current environment information according to the flow of time. Also, the storage unit 630 may store a plurality of past environmental information, and may store pest pattern information and normal pattern information generated from a plurality of previously stored past environmental information.

The control unit 650 may compare the current environmental information with at least one of the pest pattern information and the normal pattern information to predict whether or not the pest is generated. The control unit 650 can generate either the pest pattern information or the normal pattern information based on the plurality of past environmental information stored in the storage unit 630. Based on the continuously acquired environmental information, And the normal pattern information.

The output unit 670 outputs pest-occurrence-possible information when it is predicted that a pest is generated in the crop. The output unit 670 may include various types of output devices capable of outputting predetermined information such as a printer, a monitor, and a speaker.

Further, the communication unit 610 may transmit pest-incidence generation information to the user terminal.

The user can take action to reduce the possibility of the occurrence of a pest of the crop by reporting the pest-generating possibility information transmitted to the terminal from the communication unit 610 or the pest-generating possible information output through the output unit 670. [

The apparatus and method for predicting the occurrence of pests according to an embodiment of the present invention can accurately predict the occurrence of pests in greenhouse crops and improve the yield of crops.

The above-described embodiments of the present invention can be embodied in a general-purpose digital computer that can be embodied as a program that can be executed by a computer and operates the program using a computer-readable recording medium.

The computer readable recording medium may be a magnetic storage medium such as a ROM, a floppy disk, a hard disk, etc., an optical reading medium such as a CD-ROM or a DVD and a carrier wave such as the Internet Lt; / RTI > transmission).

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, It will be understood. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

100, 600: Pest insect predicting device
610:
630:
650:
670:

Claims (14)

A method for predicting the occurrence of a pest of a crop by a pest-
Storing current environmental information in the greenhouse in which the crop is grown, over time;
Comparing pattern information of at least one of pest pattern information and normal pattern information with the stored current environment information; And
And predicting whether or not a pest is caused by the crop based on the result of the comparison.
The method according to claim 1,
The method for predicting the occurrence of pests,
Further comprising the step of receiving the current environment information from the sensor and the driver in the greenhouse according to the passage of time.
The method according to claim 1,
The method for predicting the occurrence of pests,
Further comprising the step of generating the pest pattern information from a plurality of past environmental information in the greenhouse where the pest was generated when a pest was generated in the crop previously cultivated in the greenhouse.
The method of claim 3,
Wherein the step of generating the pest pattern information comprises:
And generating the pest pattern information from the plurality of past environmental information from the time of occurrence of the pest of the previously cultivated crop until a predetermined period of time.
The method of claim 3,
Wherein the pest pattern information comprises:
And the upper limit value and the lower limit value of the environmental information in the greenhouse.
6. The method of claim 5,
Wherein the predicting comprises:
And predicting that a pest will occur in the crop if the current environmental information corresponds to the upper limit value and the lower limit value.
The method according to claim 1,
The method for predicting the occurrence of pests,
Further comprising the step of generating said normal pattern information from a plurality of past environmental information in said greenhouse in which no pest was generated when a pest was not generated in a crop previously grown in said greenhouse .
8. The method of claim 7,
Wherein the normal pattern information comprises:
And the upper limit value and the lower limit value of the environmental information in the greenhouse.
9. The method of claim 8,
Wherein the predicting comprises:
And predicting that the pest will not occur in the crop if the current environmental information corresponds to the upper limit value and the lower limit value.
The method according to claim 1,
Wherein the current environment information comprises:
Wherein the information includes at least one of a temperature, a humidity, a light amount, a carbon dioxide concentration, a nutrient supply period, an amount of a nutrient solution and a nutrient solution component in the greenhouse.
The method according to claim 1,
The method for predicting the occurrence of pests,
Further comprising the step of receiving information on a current working tool used in growing the crop from a user,
Wherein the predicting comprises:
Comparing the at least one pattern information among the pest pattern information and the normal pattern information with the information about the input current working tool to predict whether or not a pest is generated in the crop.
The method according to claim 1,
The method for predicting the occurrence of pests,
Further comprising the step of outputting pest occurrence possibility information when it is predicted that a pest is generated in said crop.
A method for generating pest pattern information by a pest-and-disease occurrence predicting apparatus,
Obtaining a plurality of total past environment information in the greenhouse where a pest was generated when a pest was generated in a crop previously grown in the greenhouse;
Extracting some past environmental information from a time point at which a pest was generated of the previously cultivated crop before a predetermined period of time from each of the plurality of total past environmental information; And
And generating pest pattern information including an upper limit value and a lower limit value of environmental information based on the extracted partial past environmental information.
A storage unit for storing current environmental information in a greenhouse where crops are grown according to the passage of time; And
And a controller for comparing at least one of the pattern information of the pest pattern information and the normal pattern information with the stored current environment information to predict whether or not the pest is caused by the crop.

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