KR20180076766A - Artificial Intelligence Based Smart Farm Management System - Google Patents

Artificial Intelligence Based Smart Farm Management System Download PDF

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KR20180076766A
KR20180076766A KR1020160181276A KR20160181276A KR20180076766A KR 20180076766 A KR20180076766 A KR 20180076766A KR 1020160181276 A KR1020160181276 A KR 1020160181276A KR 20160181276 A KR20160181276 A KR 20160181276A KR 20180076766 A KR20180076766 A KR 20180076766A
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박아론
임준기
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농업회사법인 만나씨이에이 주식회사
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Abstract

An intelligent smart farm management system is disclosed.
One embodiment of the present invention proposes a method of controlling a plurality of control target devices by using an environment control algorithm in order to efficiently manage a smart farm, learning an environment control algorithm using an artificial intelligence algorithm, periodically And controlling the device to be controlled.

Description

Artificial Intelligence Based Smart Farm Management System [

The present embodiment relates to an intelligent smart farm management system. Here, the artificial intelligent smart farm management system can learn and update the environment control algorithm through an artificial intelligence algorithm.

The contents described below merely provide background information related to the present embodiment and do not constitute the prior art.

In general, smart farms (or plant factories), a concept introduced to maximize the production and quality of agricultural products through precise environmental control, are seen as solutions to the food security crisis facing humanity. Here, smart farm mainly controls the temperature, humidity, light amount, carbon dioxide concentration in the greenhouse, and the concentration of individual nutrients constituting the nutrient solution temperature, pH, electric conductivity or nutrient solution is also controlled in a farm using hydroponic cultivation method. However, it is unclear how such a smart farm environment control system is showing a significant increase in crop yield and quality from a long-term perspective. Because the smart farm environment control system compares the optimal growth environment data for each crop and the measurement data from various sensors installed in the greenhouse, the control command is issued. In many cases, even after the control command is executed, the environment of the greenhouse is set to the set target value This is because the climate of the area where the greenhouse is located and the composition of the control elements (motors, pumps, etc.) are all different. In addition, although the optimal growth environment of the cultivated crops may vary depending on various conditions such as the type of seeds used in the farm, the seedling growing conditions, and the unique environment of the individual greenhouse, the present Smart Farm environment control system does not sufficiently take such uncertainty into consideration there is a problem.

Therefore, there is a need for an environment control system that can efficiently manage smart farms using artificial intelligence technology that has achieved rapid technological progress in recent years.

The present invention is intended to supplement the above-mentioned deficiencies with respect to SmartPharm.

In order to efficiently manage the smart farm, we propose a method to control a plurality of control target devices by using an environment control algorithm, learn environment control algorithm using artificial intelligence algorithm, and periodically update the control target device The purpose is to do.

According to an aspect of the present invention, there is provided a sensor module including at least one sensor module installed in a farm to generate sensing information, a sensor module for collecting the sensing information at a predetermined period during a growing period of a crop in the farm, A database for storing the collected sensing information and the control information, and a controller for controlling the environment control algorithm through an artificial intelligence algorithm using the sensing information and the control information of the database, A control module for controlling the plurality of control target devices connected in a wired manner according to the control information; And a gateway for communicatively connecting the sensor module and the control module with the central control module.

Here, the sensing information may include at least one of temperature, humidity, CO2, light quantity, soil moisture, soil nutrients, nutrient solution temperature, nutrient solution pH, nutrient solution EC, individual nutrient solution component, atmospheric environment, nutrient solution environment, .

Here, the control information may include at least one of on / off control, motor drive direction control, step control, and time control.

The update unit learns and updates the environment control algorithm through the artificial intelligence algorithm using the sensing data and control information stored in the database and the crop data.

Here, the crop data includes at least one of seed type data, seed seed status at seed purchase, seedling status data, production amount data according to status, growth period data, and quality data.

According to the present invention, an environment control algorithm is learned and periodically updated in a smart farm using an artificial intelligence algorithm, thereby controlling the device to be controlled in the most efficient manner.

In addition, environmental control algorithms are continually updated using environmental data before and after environmental control commands are executed in individual greenhouses, and in the greenhouses where the system is installed using the production history data of agricultural products, it is possible to determine in what environment the crops grow best There is an effect.

In addition, when the smash farm management system that utilizes artificial intelligence is successfully distributed to domestic and overseas farmers, data on the current status and prices of future agricultural products are constructed by using the data on crops, Operators will be able to select which crops to grow during the next growing season, which will enable systematic production control, which will enable farm prices and farm imports to be safeguarded.

In addition, the smart farm management system using artificial intelligence enables the selection of cultivated crops, the timing of harvest and the yields, as well as controlling the customized smart farm environment optimized for individual farmers.

1 is a schematic view of an artificial intelligent smart farm management system according to an embodiment of the present invention.
FIG. 2 is a schematic view of a central environment control module of the artificial intelligent smart farm management system according to the present embodiment.
FIG. 3 is a flowchart illustrating an operation procedure of the smart smart management system according to the present embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to exemplary drawings. It should be noted that, in adding reference numerals to the constituent elements of the drawings, the same constituent elements are denoted by the same reference symbols as possible even if they are shown in different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In describing the components of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. Throughout the specification, when an element is referred to as being "comprising" or "comprising", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise . In addition, '... Quot ;, " module ", and " module " refer to a unit that processes at least one function or operation, and may be implemented by hardware or software or a combination of hardware and software.

1 is a schematic view of an artificial intelligent smart farm management system according to an embodiment of the present invention.

The artificial intelligence smart farm management system includes a sensor module 100, a central environment control module 200, a control module 300, and a gateway 400.

At least one sensor module 100 is installed in a farm to generate sensing information. Here, the sensing information includes at least one of temperature, humidity, CO2, light amount, soil moisture, soil nutrients, nutrient solution temperature, nutrient solution pH, nutrient solution EC, individual nutrient solution component, atmospheric environment, nutrient solution environment and soil environment information .

The sensor module 100 senses at least one of a plurality of sensing information to generate sensing information. The sensor module 100 can generate sensing information by sensing temperature and humidity information, for example. In addition, sensing information can be generated by sensing temperature, humidity, CO2, and light amount information of the sensor module 100.

The central environment control module 200 collects sensing information from the sensor module 100 at predetermined intervals during the growth period of the crop in the farm. The central environment control module 200 collects sensing information at 6-hour intervals for six months, for example, during the growth period of the crop.

The central environment control module 200 generates control information using an environment control algorithm according to the sensing information. Here, the environment control algorithm receives sensing information as an input value and calculates control information that is the most suitable output value according to the sensing information. Here, the control information includes at least one of on / off control, motor drive direction control, step control, and time control.

The central environment control module 200 stores the collected sensing information and control information.

The central environment control module 200 learns and updates the environment control algorithm through the artificial intelligence algorithm using the sensing information and the control information.

The control module 300 controls a plurality of control target devices 600 connected by wire according to the control information. Here, the plurality of control target devices 600 are devices located on a farm such as a motor, an LED lighting device, a humidifier, etc. for controlling the opening and closing operation of a heating device, an entrance window or a skylight.

The gateway 400 communicates between the sensor module and the control module and the central control module.

1, the sensor module 100, the control module 300, the central control module 200, and the gateway 400 are connected to the communication network 500 to transmit and receive data.

FIG. 2 is a schematic view of a central environment control module of the artificial intelligent smart farm management system according to the present embodiment.

The central environment control module 200 includes a main control unit 210, a database 220, and an update unit 230.

The main control unit 210 collects sensing information from the sensor module at predetermined intervals during the growth period of the crop in the farm, and generates control information using an environment control algorithm according to the sensing information. The main control unit 210 collects the sensing information at intervals of 12 hours for four months, for example, the growth period of the crop. The environment control algorithm inputs sensing information as an input value and calculates control information which is the most suitable output value according to sensing information.

The database 220 stores the collected sensing information and control information.

The update unit 230 learns and updates the environment control algorithm through the artificial intelligence algorithm using the sensing information and the control information of the database 220.

Here, artificial intelligence technology is represented as a representative technology group such as machine learning and deep learning, and is a technology for judging new input by utilizing a huge amount of database.

Here, machine learning, which is a field of artificial intelligence, is literally a machine learning, and it can analyze the big data of large scale and predict the future of the future. Machine learning can design complex algorithms and programming, and can be applied to various fields. Three methods are used to create an algorithm for machine learning.

First, it is a supervised learning method that inputs data created by the user in advance and then outputs the data. Since the supervised learning method is more precise than the input data, the reliable output value is calculated as the amount of data increases.

Next, it is an unsupervised learning method which models the pattern by only inputting without output. The non-supervised learning method is a method that the computer learns by itself and then draws the desired output value by applying it. It requires a high level of computation ability and is used in the data mining technique.

Finally, it is a reinforcement learning method which is a method of generating an algorithm by re-learning feedback after learning a large amount of data by computer itself.

Machine learning involves analyzing patterns by learning data through the above three methods, and then having the ability to make judgments directly according to changing situations.

Deep learning consists of several layers of artificial neural networks and is one of the learning methods belonging to machine learning. Deep learning allows you to judge a variety of situations without having to learn various situations yourself.

In order for deep running to be used effectively, a lot of data must be accumulated to yield reliable results. Deep learning is a big data that can identify a certain pattern, not only shape and form, but also can analyze abstract objects.

The update unit 230 learns and updates the environment control algorithm through artificial intelligence algorithm using the sensing data and control information stored in the database 220 and the crop data. Here, the crop data includes at least one of seed type data, seed seed status at seed purchase, seedling status data, production amount data according to status, growth period data, and quality data.

The updating unit 230 learns and updates the environment control algorithm through the artificial intelligence algorithm using the sensing information and the control information about the crops cultivated in the farm based on the crop data.

For example, the updating unit 230 may update the crop data based on at least one crop data among the crop type data, seed condition data at the time of seed purchase of the crop, status data of the crop seedling, status data according to the status, The environment control algorithm is learned through the artificial intelligence algorithm by using the sensing information and the control information of the environment in which the environment is grown.

FIG. 3 is a flowchart illustrating an operation procedure of the smart smart management system according to the present embodiment.

At least one sensor module 310 is installed in the farm to generate sensing information (S310).

The central environment control module 200 collects the sensing information generated by the sensor module 310 (S320).

The central environment control module 200 generates control information using an environment control algorithm (S330).

The central environment control module 200 transmits control information to the control module 300 (S340).

The control module 300 controls the control target device 600 according to control information received from the central environment control module 200 at step S350. Here, the control target device 600 is a device located in a farm such as a motor, an LED lighting device, a humidifier, etc. for controlling the opening and closing operation of a heating device, an entrance window or a skylight.

The central environment control module 200 collects sensing information from the sensor module 100 at predetermined intervals during the growth period of the crop in the farm (S360).

The central environment control module 200 learns and updates the environment control algorithm through the artificial intelligence algorithm (S370). The central environment control module 200 learns and updates the environment control algorithm through the artificial intelligence algorithm using the crop data in addition to the sensing information and the control information.

The central environment control module 200 instructs the updated environment control algorithm to use in step S330 (S380).

3, it is described that steps S310 to S380 are sequentially executed. However, this is merely illustrative of the technical idea of the present embodiment, and it should be understood by those skilled in the art that, It will be understood that various modifications and changes may be made to the embodiments of the present invention without departing from the essential characteristics thereof by changing the order described in FIG. 3 or by executing one or more of steps S310 through S380 in parallel. But is not limited thereto.

The above description is merely illustrative of the technical idea of the present embodiment, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the embodiments. Therefore, the present embodiments are to be construed as illustrative rather than limiting, and the scope of the technical idea of the present embodiment is not limited by these embodiments. It is to be understood that the scope of the present invention is to be construed as being limited only by the scope of the appended claims.

100: sensing module
200: Central environment control module
300: control module
400: Gateway
500: Network
600: Control target device
210:
220: Database
230:

Claims (5)

A sensor module installed in the farm to generate sensing information;
A main controller for collecting the sensing information from the sensor module at a predetermined period during a growing period of the crop in the farm and generating control information using an environment control algorithm according to the sensing information, A central environment control module for learning and updating the environment control algorithm through an artificial intelligence algorithm using the sensing information and control information of the database;
A control module for controlling the plurality of control target devices connected by wire according to the control information; And
A sensor module and a gateway for communicating between the control module and the central control module
A smart smart farm management system including. The method according to claim 1,
The sensing information,
Wherein the information includes at least one of temperature, humidity, CO2, light amount, soil moisture, soil nutrients, nutrient solution temperature, nutrient solution pH, nutrient solution EC, individual nutrient solution component, atmospheric environment, nutrient solution environment, Intelligent smart farm management system. The method according to claim 1,
The control information includes:
On / off control, motor drive direction control, step control, and time control. The method according to claim 1,
The update unit
And the environment control algorithm is learned through the artificial intelligence algorithm by using the sensing data and the control data stored in the database and the crop data, thereby updating the intelligent smart farm management system. 5. The method of claim 4,
The crop data may include:
Wherein the seed data includes at least one of seed type data, seed seed data at the time of seed purchase, seedling seed state data, production amount data depending on the state, growth period data, and quality data.
KR1020160181276A 2016-12-28 2016-12-28 Artificial Intelligence Based Smart Farm Management System KR20180076766A (en)

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Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109637102A (en) * 2018-12-12 2019-04-16 青岛盛景电子科技有限公司 A kind of number agricultural land information monitoring system
KR20200026519A (en) * 2018-09-03 2020-03-11 농업회사법인 만나씨이에이 주식회사 Farm Environment Control System Having Self-Diagnostics Capabilities
KR102094775B1 (en) * 2019-08-02 2020-03-30 (주)메이티 System for providing simulation based smartfarm education service for smart farm management in virtual environment
KR102096000B1 (en) 2019-10-11 2020-04-01 주식회사 라인인포 Remote control system for smart farm
KR20200044604A (en) 2018-10-20 2020-04-29 주식회사 지농 Smart Farm Control System Using Smart Farm Control History
KR20200049131A (en) 2018-10-31 2020-05-08 충남도립대학교 산학협력단 Smart farm
KR102097660B1 (en) 2019-01-29 2020-05-26 추봉수 System for selling agricultureal products using smart farm
KR102084441B1 (en) * 2019-07-31 2020-05-29 주식회사 팜팜랩스 Method for managing a smart farm using sensor module and control module and apparatus using the same
KR20210025796A (en) * 2019-08-28 2021-03-10 주식회사 어밸브 Method for maximizing growth of crops using machine learning
KR20210047005A (en) * 2019-10-21 2021-04-29 부산과학기술대학교 산학협력단 Method for managing smart farm based on AI(artificial intelligence) and smart farm based on AI using the same
KR20210101054A (en) * 2020-02-07 2021-08-18 경북대학교 산학협력단 Apparatus for managing smart farm and control method thereof
WO2021201339A1 (en) * 2020-03-31 2021-10-07 (주)씨엔에스아이엔티 Smart agricultural site management system and method
KR102331141B1 (en) * 2021-01-05 2021-12-01 농업회사법인 주식회사 편농 the improved smart farm management system
WO2021256621A1 (en) * 2020-06-18 2021-12-23 주식회사 글로벌코딩연구소 Smart farm crop transaction system using artificial intelligence, and method therefor
KR20210157516A (en) * 2020-06-19 2021-12-29 주식회사 다함 Integrated monitoring and control system of smart greenhouse
KR20220042687A (en) 2020-09-28 2022-04-05 김학철 Method of Determining Whether A Smart Farm Sensor has failed using a Recurrent Neural Network(RNN)
WO2022118095A1 (en) * 2021-09-03 2022-06-09 Bagheri Hamed Hamed fd: farming, ai-based doctor for all
KR20220083181A (en) * 2020-12-11 2022-06-20 한국전자통신연구원 Apparatus and method for selecting collected data for smart farm dataset validation
KR102514475B1 (en) * 2021-10-29 2023-03-27 에스케이임업 주식회사 A management system that combines an air quality meter and an irrigation system
KR20230056082A (en) 2021-10-19 2023-04-27 스마트쿱(주) Artificial intelligence used smart farm edge computing system
KR102540853B1 (en) 2022-11-11 2023-06-07 제레스팜 주식회사 Multiple smart farm artificial intelligence integrated management system
KR20240077597A (en) 2022-11-24 2024-06-03 송봉준 Smart farm controlling system with outdoors control device installed and agriculture record writing method using thereof

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200026519A (en) * 2018-09-03 2020-03-11 농업회사법인 만나씨이에이 주식회사 Farm Environment Control System Having Self-Diagnostics Capabilities
WO2020050569A1 (en) * 2018-09-03 2020-03-12 농업회사법인 만나씨이에이 주식회사 Self-diagnostic farm environment control system
KR20200044604A (en) 2018-10-20 2020-04-29 주식회사 지농 Smart Farm Control System Using Smart Farm Control History
KR20200049131A (en) 2018-10-31 2020-05-08 충남도립대학교 산학협력단 Smart farm
CN109637102A (en) * 2018-12-12 2019-04-16 青岛盛景电子科技有限公司 A kind of number agricultural land information monitoring system
KR102097660B1 (en) 2019-01-29 2020-05-26 추봉수 System for selling agricultureal products using smart farm
KR102084441B1 (en) * 2019-07-31 2020-05-29 주식회사 팜팜랩스 Method for managing a smart farm using sensor module and control module and apparatus using the same
KR102094775B1 (en) * 2019-08-02 2020-03-30 (주)메이티 System for providing simulation based smartfarm education service for smart farm management in virtual environment
KR20210025796A (en) * 2019-08-28 2021-03-10 주식회사 어밸브 Method for maximizing growth of crops using machine learning
KR102096000B1 (en) 2019-10-11 2020-04-01 주식회사 라인인포 Remote control system for smart farm
KR20210047005A (en) * 2019-10-21 2021-04-29 부산과학기술대학교 산학협력단 Method for managing smart farm based on AI(artificial intelligence) and smart farm based on AI using the same
KR20210101054A (en) * 2020-02-07 2021-08-18 경북대학교 산학협력단 Apparatus for managing smart farm and control method thereof
WO2021201339A1 (en) * 2020-03-31 2021-10-07 (주)씨엔에스아이엔티 Smart agricultural site management system and method
WO2021256621A1 (en) * 2020-06-18 2021-12-23 주식회사 글로벌코딩연구소 Smart farm crop transaction system using artificial intelligence, and method therefor
KR20210157516A (en) * 2020-06-19 2021-12-29 주식회사 다함 Integrated monitoring and control system of smart greenhouse
KR20220042687A (en) 2020-09-28 2022-04-05 김학철 Method of Determining Whether A Smart Farm Sensor has failed using a Recurrent Neural Network(RNN)
KR20220083181A (en) * 2020-12-11 2022-06-20 한국전자통신연구원 Apparatus and method for selecting collected data for smart farm dataset validation
KR102331141B1 (en) * 2021-01-05 2021-12-01 농업회사법인 주식회사 편농 the improved smart farm management system
WO2022118095A1 (en) * 2021-09-03 2022-06-09 Bagheri Hamed Hamed fd: farming, ai-based doctor for all
KR20230056082A (en) 2021-10-19 2023-04-27 스마트쿱(주) Artificial intelligence used smart farm edge computing system
KR102514475B1 (en) * 2021-10-29 2023-03-27 에스케이임업 주식회사 A management system that combines an air quality meter and an irrigation system
KR102540853B1 (en) 2022-11-11 2023-06-07 제레스팜 주식회사 Multiple smart farm artificial intelligence integrated management system
KR20240077597A (en) 2022-11-24 2024-06-03 송봉준 Smart farm controlling system with outdoors control device installed and agriculture record writing method using thereof

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