KR102397375B1 - Integrated plant management system using artificial intelligence - Google Patents

Abstract

In the present specification, provided is a method for a plant management system to manage a plant using an artificial intelligence module. The method comprises the steps of: receiving a reference value for initial setting from a user; setting a set value based on the reference value, and performing irrigation based on the set value; periodically monitoring and storing soil temperature and humidity, RGB values, normalized difference vegetation index (NDVI) values, and bio-impedance values through the artificial intelligence module; and controlling an operation unit based on a result value of the monitoring, wherein the operation unit may include an irrigation pump, a water discharge pump, a growth lamp, and a circulation fan. Accordingly, it is possible to implement a system which can efficiently manage plants.

Description

Integrated plant management system using artificial intelligence {INTEGRATED PLANT MANAGEMENT SYSTEM USING ARTIFICIAL INTELLIGENCE}

The present specification proposes an integrated management system for managing plants through artificial intelligence using RGB, Normalized Difference Vegetation Index (NDVI) and Bio-Impedance measurement values.

A general irrigation system uses a water pump equipped with a timer to irrigate every set specific time, or when a soil humidity sensor that can measure the soil humidity is installed and the humidity of the soil falls below a certain level , it operates by operating a water pump to irrigate.

These operating methods do not directly measure the state of the plant through the irrigation system, but manage the plant through the value measured only in the surrounding environment (eg, soil humidity).

However, since the space to which the plant is applied has different conditions (for example, sunshine, humidity, wind speed, etc.) depending on the environment, the irrigation system does not directly measure the state of the plant, but only the surrounding environment , there is a problem in that when determining whether to water the plant, it is impossible to determine what state the plant is currently in, and the plant dies.

An object of the present specification is to implement an active smart management system that automatically controls plant management conditions by measuring the plant growing environment.

In addition, an object of the present specification is to predict the present and future state of a plant through an artificial intelligence module, using the RGB value, NDVI value, and bioimpedance value of the monitored plant, and implement a system for efficiently managing plants will be.

The technical problems to be achieved by the present specification are not limited to the technical problems mentioned above, and other technical problems not mentioned are clear to those of ordinary skill in the art to which this specification belongs from the detailed description of the following specification can be understood clearly.

An aspect of the present specification provides a method for a plant management system to manage a plant using an artificial intelligence module, the method comprising: receiving, from a user, a reference value for initial setting; setting a set value based on the reference value, and performing irrigation based on the set value; periodically monitoring and storing soil temperature and humidity, RGB values, Normalized Difference Vegetation Index (NDVI) values, and Bio-Impedance values through the artificial intelligence module; and controlling the operation unit based on the result of the monitoring. Including, the operation unit may include an irrigation pump, a drainage pump, a growth light and a circulation fan.

In addition, the reference value may include an irrigation reference value, a soil humidity value for irrigation, a soil humidity value for stopping irrigation, and a non-irrigation period.

In addition, the irrigation reference value may be an average value or a minimum value among individual measurement values of one or more soil humidity sensors.

In addition, the step of controlling the operation unit based on the monitoring result value is based on the monitored RGB value: when the ratio of the yellow area to the green area becomes higher than the first ratio, to reduce the period of irrigation, increasing the soil humidity value; and lowering the soil humidity value to increase the period of irrigation when the ratio of the brown or gray area to the green area is higher than the second ratio; may include

In addition, based on the result value of the monitoring, the step of controlling the operation unit is based on the monitored NDVI value: when the moisture content of the plant is lowered to below a first predetermined value, to reduce the cycle of irrigation, the soil increasing the humidity value; and lowering the soil humidity value in order to extend the period of irrigation when the moisture content of the plant is higher than a second predetermined value; may further include.

In addition, through the artificial intelligence module, based on the stored RGB value, measuring the ratio change amount of the color related to the change, and calculating a first gradient value of the ratio change amount; measuring, through the artificial intelligence module, an NDVI value change amount based on the stored NDVI value, and calculating a second slope value of the NDVI value change amount; measuring, through the artificial intelligence module, a change amount of a bioimpedance value based on the stored bioimpedance value; and predicting, through the artificial intelligence module, a rate of occurrence of a plant problem based on the change amount of the first slope value, the second slope value, and the bioimpedance value; may further include.

In addition, through the artificial intelligence module, on the basis of the plant problem occurrence rate is predicted to exceed a specific value after a certain period of time, transmitting a warning message to the server; may further include.

In addition, based on the monitored soil temperature and humidity, when the soil humidity does not increase more than a specific level after the irrigation is finished, a warning message may be transmitted to the server.

Another embodiment of the present specification is a plant management system for managing plants using an artificial intelligence module, a sensing unit for generating soil and air-related sensing data, an irrigation pump, a drainage pump, a growth light and a circulation fan An operation unit including an RGB camera, a NIR camera (Near-Infrared Spectrometer), a monitoring unit including a bio-impedance sensor, the artificial intelligence module and the sensing unit, the operation unit, the monitoring unit and the a control unit for functionally controlling the artificial intelligence module, wherein the control unit receives a reference value for initial setting from a user, sets a setting value based on the reference value, and performs irrigation based on the setting value, , through the artificial intelligence module, periodically monitor and store soil temperature and humidity, RGB values, NDVI (Normalized Difference Vegetation Index) values, and bioimpedance values, and based on the results of the monitoring, it is possible to control the operation unit .

According to an embodiment of the present specification, it is possible to implement an active smart management system that automatically controls plant management conditions by measuring the plant cultivation environment.

In addition, according to the embodiment of the present specification, using the RGB value, NDVI value, and bioimpedance value of the monitored plant, the present and future state of the plant is predicted through the artificial intelligence module, and a system for efficiently managing the plant can be implemented

The effects obtainable in the present specification are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which this specification belongs from the description below. .

1 is a block diagram illustrating an electronic device related to the present specification.
2 is a block diagram of an artificial intelligence machine learning module to which the present specification can be applied.
3 is a block diagram of a plant management system to which the present specification can be applied.
4 is an embodiment of a plant management system to which the present specification can be applied.
5 is an embodiment of a warning method to which this specification can be applied.
BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to help the understanding of the present specification, provide embodiments of the present specification, and together with the detailed description, explain the technical features of the present specification.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present specification , should be understood to include equivalents or substitutes.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When an element is referred to as being “connected” or “connected” to another element, it is understood that it may be directly connected or connected to the other element, but other elements may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present application, terms such as “comprises” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

1 is a block diagram illustrating an electronic device related to the present specification.

The electronic device 100 includes a wireless communication unit 110 , an input unit 120 , a sensing unit 140 , an output unit 150 , an interface unit 160 , a memory 170 , a control unit 180 , and a power supply unit 190 . ) and the like. The components shown in FIG. 1 are not essential for implementing the electronic device, and thus the electronic device described herein may have more or fewer components than those listed above.

More specifically, the wireless communication unit 110 among the components, between the electronic device 100 and the wireless communication system, between the electronic device 100 and another electronic device 100, or the electronic device 100 and an external server It may include one or more modules that enable wireless communication between them. Also, the wireless communication unit 110 may include one or more modules for connecting the electronic device 100 to one or more networks.

The wireless communication unit 110 may include at least one of a broadcast reception module 111 , a mobile communication module 112 , a wireless Internet module 113 , a short-range communication module 114 , and a location information module 115 . .

The input unit 120 includes a camera 121 or an image input unit for inputting an image signal, a microphone 122 or an audio input unit for inputting an audio signal, and a user input unit 123 for receiving information from a user, for example, , a touch key, a push key, etc.). The voice data or image data collected by the input unit 120 may be analyzed and processed as a user's control command.

The sensing unit 140 may include one or more sensors for sensing at least one of information in the electronic device, surrounding environment information surrounding the electronic device, and user information. For example, the sensing unit 140 may include a proximity sensor 141, an illumination sensor 142, an illumination sensor, a touch sensor, an acceleration sensor, a magnetic sensor, and gravity. Sensor (G-sensor), gyroscope sensor, motion sensor, RGB sensor, infrared sensor (IR sensor: infrared sensor), fingerprint recognition sensor (finger scan sensor), ultrasonic sensor , optical sensor (eg, camera (see 121)), microphone (see 122), battery gauge, environmental sensor (eg, barometer, soil hygrometer, thermometer, radiation sensor) , a thermal sensor, a gas sensor, etc.) and a chemical sensor (eg, an electronic nose, a healthcare sensor, a biometric sensor, etc.). Meanwhile, the electronic device disclosed in the present specification may combine and utilize information sensed by at least two or more of these sensors.

The output unit 150 is for generating an output related to visual, auditory or tactile sense, and includes at least one of a display unit 151 , a sound output unit 152 , a haptip module 153 , and an optical output unit 154 . can do. The display unit 151 may implement a touch screen by forming a layer structure with the touch sensor or being integrally formed. Such a touch screen may function as the user input unit 123 providing an input interface between the electronic device 100 and the user, and may provide an output interface between the electronic device 100 and the user.

The interface unit 160 serves as a passage with various types of external devices connected to the electronic device 100 . The interface unit 160 connects a device equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module. It may include at least one of a port, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. In response to the connection of the external device to the interface unit 160 , the electronic device 100 may perform appropriate control related to the connected external device.

In addition, the memory 170 stores data supporting various functions of the electronic device 100 . The memory 170 may store a plurality of application programs (or applications) driven in the electronic device 100 , data for operation of the electronic device 100 , and commands. At least some of these application programs may be downloaded from an external server through wireless communication. In addition, at least some of these application programs may exist on the electronic device 100 from the time of shipment for basic functions (eg, incoming calls, outgoing functions, message reception, and outgoing functions) of the electronic device 100 . Meanwhile, the application program may be stored in the memory 170 , installed on the electronic device 100 , and driven to perform an operation (or function) of the electronic device by the controller 180 .

In addition to the operation related to the application program, the controller 180 generally controls the overall operation of the electronic device 100 . The controller 180 may provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the above-described components or by driving an application program stored in the memory 170 .

In addition, the controller 180 may control at least some of the components discussed with reference to FIG. 1 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the controller 180 may operate at least two or more of the components included in the electronic device 100 in combination with each other.

The power supply unit 190 receives external power and internal power under the control of the control unit 180 to supply power to each component included in the electronic device 100 . The power supply unit 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

At least some of the respective components may operate in cooperation with each other to implement an operation, control, or control method of an electronic device according to various embodiments described below. In addition, the operation, control, or control method of the electronic device may be implemented on the electronic device by driving at least one application program stored in the memory 170 .

In the present specification, the plant management system may include an electronic device 100 , and the electronic device 100 may be collectively referred to as an electronic device.

2 is a block diagram of an artificial intelligence (AI) machine learning (ML) module to which this specification can be applied.

The artificial intelligence machine learning module 20 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. In addition, the artificial intelligence machine learning module 20 may be included in at least a part of the electronic device 100 shown in FIG. 1 to perform at least a part of AI processing together.

The artificial intelligence machine learning module 20 may include an AI processor 21 , a diagnostic test selection unit 23 , a memory 25 , and/or a communication unit 27 .

The artificial intelligence machine learning module 20 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.

The AI processor 21 may learn the AI model using a program stored in the memory 25 . In particular, the AI processor 21 may learn an AI model for performing a task of predicting the current state of a plant by using the sensed data. For example, the AI processor 21 may be trained to predict the current state of a plant through an RGB camera, a NIR camera, and a bioimpedance sensor.

Meanwhile, the AI processor 21 performing the above-described functions may be a general-purpose processor (eg, CPU), but may be an AI-only processor (eg, GPU) for artificial intelligence learning.

The memory 25 may store various programs and data necessary for the operation of the artificial intelligence machine learning module 20 . The memory 25 may be implemented as a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 25 is accessed by the AI processor 21 , and reading/writing/modification/deletion/update of data by the AI processor 21 may be performed. Also, the memory 25 may store a neural network model (eg, a deep learning model) generated through a learning algorithm for data classification/recognition according to an embodiment of the present specification.

Meanwhile, the AI processor 21 may include a data learning unit that learns a neural network for data classification/recognition. For example, the data learning unit may learn the deep learning model by acquiring learning data to be used for learning and applying the acquired learning data to the deep learning model.

The communication unit 27 may transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device may include other terminals and servers.

On the other hand, the artificial intelligence machine learning module 20 shown in FIG. 2 has been functionally divided into the AI processor 21, the diagnostic test selection unit 23, the memory 25, the communication unit 27, etc. Components may be integrated into one module and referred to as an AI module.

3 is a block diagram of a plant management system to which the present specification can be applied.

Referring to FIG. 3 , the plant management system 300 includes a control unit 310 , a sensing unit 320 , an operation unit 340 , a monitoring unit 330 , and the aforementioned artificial intelligence machine learning module 20 . For example, each part of the plant management system 300 may be implemented with all or part of the configuration of the electronic device 100 .

The control unit 310 may be connected to a management server through a network such as the Internet to receive meteorological information necessary for plant management. For example, the controller 310 may receive temperature, humidity, weather forecast information, etc. that may be obtained through a server of the Korea Meteorological Administration. Also, the control unit 310 may receive outdoor fine dust data from an external server (eg, a server of an official measurement center of the Korea Environment Corporation).

Also, the control unit 310 may be connected to the manager terminal through a network. The manager may remotely control the controller 310 through the terminal, and if necessary, the controller 310 may deliver a warning message to the manager through the manager terminal. The controller 310 may display the acquired sensing information (eg, PM2.5, PM10, methane, and carbon monoxide sensing information) through the sensing unit 320 . Through this, the manager can efficiently monitor the actual cultivation environment of plants.

The sensing unit 320 may include one or more soil temperature and humidity sensors, fine dust sensors, gas sensors, and the like. The sensing unit 320 may periodically transmit sensing data generated through the sensor to the control unit 310 .

The monitoring unit 330 may include an RGB camera, a near-infrared spectrometer (NIR) camera, and a bioimpedance sensor. The monitoring unit 330 may be connected to the AI ML module 20 to transmit monitoring information to the AI ML module 20 .

The operation unit 340 may include an irrigation pump, a drainage pump, a growth light, and a circulation fan for air circulation. The operation unit 340 may receive a command from the control unit 310 to perform an operation for maintaining an optimal cultivation environment.

4 is an embodiment of a plant management system to which the present specification can be applied.

Referring to FIG. 4 , the plant management system 300 may include and control one or more soil humidity sensors. In addition, the operation of the plant management system 300 may be performed through the aforementioned control unit 310 .

The plant management system 300 receives a reference value for initial setting from the user (S410).

For example, the user uses the control unit 310, the terminal, the Internet, etc., according to the actual cultivation environment of the plant, the reference value (eg, irrigation reference value, the soil humidity value for irrigation, the irrigation stop soil humidity value, non-irrigation water period) can be entered.

The plant management system 300 sets a set value based on the reference value, and performs irrigation based on the set value (S420).

For example, in order to set the irrigation reference value, the plant management system 300 may set the irrigation reference value to perform irrigation based on an average value among individual measurement values of one or more soil humidity sensors. Alternatively, the plant management system 300 may be set to execute irrigation based on a minimum value among individual measurement values of one or more soil humidity sensors. For example, the plant management system 300 may be set by receiving the minimum value among the individual measurement values of the soil humidity sensor as the irrigation reference value. In more detail, the plant management system 300 may proceed with irrigation if the irrigation reference value is 40% soil humidity and less than 40%.

In addition, the plant management system 300 may be initially set to stop irrigation when the humidity value of the entire soil reaches the irrigation stop soil humidity value.

If a specific sensor continues to output a low value due to a measurement error or a sensor failure, irrigation may continue. may be initially set to forcibly stop.

The plant management system 300 periodically monitors and stores soil temperature and humidity, RGB values (eg, Green values, Yellow values), NDVI values, and bioimpedance values through the AI ML module 20 (S430).

The AI ML module 20 may store the monitoring value generated by the monitoring unit 330 and learn to predict the current state of the plant by using it.

For example, the soil temperature and humidity may be obtained through a soil temperature and humidity sensor.

The RGB value can be obtained through the image of a plant generated using the above-mentioned RGB camera, and the NDVI value is an index of the growth state of a crop using the reflectance of the NIR (Near Infrared) band, and the above-mentioned NIR camera can be obtained through

In addition, since bioimpedance, which means the degree to which electricity flows according to frequency, varies depending on the composition of the inside of a plant (eg, cell fluid = resistance, cell membrane = capacitor), microcurrent is transmitted and received through the above-described bioimpedance sensor. Thus, the health state of the plant can be measured electrically.

The plant management system 300 controls the operation unit 340 through the AI ML module 20, based on the monitoring result (S440).

1. The plant management system 300 may control the operation unit 340 based on the monitored soil temperature and humidity through the AI ML module 20 . When the temperature and humidity of the soil is excessive or insufficient, the plant management system 300 may control the operation unit 340 to maintain an appropriate plant cultivation environment.

2. The plant management system 300 may control the operation unit 340 based on the monitored RGB values through the AI ML module 20 .

For example, the plant management system 300 may periodically perform irrigation based on an initial setting. If, based on the monitored RGB value, the ratio of the yellow area to the green area on the image of the plant increases to a certain ratio (the first ratio) or more, the AI ML module 20 recognizes this as a drying problem of the plant. can do. For example, the AI ML module 20 predicts the current state of the plant as a dry state, and increases the minimum value (soil humidity value for irrigation) of the soil humidity standard for irrigation to increase the irrigation cycle of the operation unit 340 It can be set short.

Alternatively, based on the monitored RGB value, when the ratio of the Brown or Gray area to the Green area on the image of the plant increases to a certain ratio (the second ratio) or more, the AI ML module 20 solves the problem of over-wetting of the plant. occurrence can be recognized. For example, the AI ML module 20 predicts the current state of the plant as an over-humidity state, and lowers the minimum value of the soil humidity standard for irrigation (soil humidity value for irrigation), so that the operation unit 340 Longer irrigation cycles can be set.

For example, the first ratio and the second ratio may be input from a user or may be calculated by the AI ML module 20 .

3. The plant management system 300 may control the operation unit 340 based on the monitored NDVI value through the AI ML module 20 .

For example, the plant management system 300 may periodically perform irrigation based on an initial setting. If, based on the monitored NDVI value, when the moisture content of the plant is lowered to a predetermined value (a first predetermined value) or less, the AI ML module 20 may recognize this as the occurrence of a drying problem in the plant. For example, the AI ML module 20 predicts the current state of the plant as a dry state, and increases the minimum value (soil humidity value for irrigation) of the soil humidity standard for irrigation to increase the irrigation cycle of the operation unit 340 It can be set short.

Alternatively, based on the monitored NDVI value, when the moisture content of the plant rises above a predetermined value (the second predetermined value), the AI ML module 20 may recognize this as the occurrence of an over-humidity problem in the plant. For example, the AI ML module 20 predicts the current state of the plant as an over-humidity state, and lowers the minimum value of the soil humidity standard for irrigation (soil humidity value for irrigation), so that the operation unit 340 Longer irrigation cycles can be set.

For example, the first predetermined value and the second predetermined value may be input from a user or may be calculated by the AI ML module 20 .

4. The plant management system 300 may control the operation unit 340 based on the monitored bioimpedance value through the AI ML module 20 .

For example, the plant management system 300 may periodically perform irrigation based on an initial setting. If the change rate of the monitored bioimpedance value exceeds a predetermined value, the AI ML module 20 may recognize this as a drying problem of the plant. For example, the AI ML module 20 predicts the current state of the plant as a dry state, and increases the minimum value (soil humidity value for irrigation) of the soil humidity standard for irrigation to increase the irrigation cycle of the operation unit 340 It can be set short.

5 is an embodiment of a warning method to which this specification can be applied.

Referring to FIG. 5 , when a problem is predicted to occur based on the measurement value of the monitoring unit 330 , the AI ML module 20 may notify the management server of this. The management server may warn the manager through the manager terminal or the display unit.

The AI ML module 20 periodically monitors and stores RGB values, NDVI values, and bioimpedance values (S510).

Based on the stored RGB values, the AI ML module 20 measures the amount of change in the ratio of the color related to the change, and calculates a first gradient value of the amount of change in the ratio ( S520 ). For example, the AI ML module 20 may measure a ratio change amount of a color related to a change, such as yellow, brown, and gray, and predict a first gradient value.

The AI ML module 20 measures the change amount of the NDVI value based on the stored NDVI value and calculates a second slope value of the change amount (S530).

The AI ML module 20 measures the amount of change in the bioimpedance value based on the stored bioimpedance value (S540).

The AI ML module 20 predicts the rate of occurrence of plant problems based on the amount of change of the first slope value, the second slope value, and the bioimpedance value (S550). For example, the AI ML module 20 uses the amount of change in the first slope value, the second slope value, and the bioimpedance value in addition to the entered plant data (variety, age, cultivation environment, etc.), cultivation environment information, etc. Thus, it can be learned to perform a task for predicting the rate of occurrence of plant problems.

For example, the AI ML module 20 may predict the rate of occurrence of plant problems after a predetermined period if the amount of slope change exceeds a predetermined level based on the first slope value and/or the second slope value.

In addition, the AI ML module 20 may predict the rate of occurrence of plant problems after a certain period if the amount of change exceeds a certain level based on the amount of change in the bioimpedance value.

The AI ML module 20 transmits a warning message to the server when it is predicted that a problem will occur in the plant because the rate of occurrence of plant problems exceeds a specific value after a certain period of time (S560).

The server may deliver an alarm message to the manager through a push alarm of the terminal, a display unit, a web pop-up window, or the like.

For example, such an alarm message may include information such as 'It is expected that 00% of plants will need maintenance within 00 days'.

In addition, in order to monitor the failure of the soil humidity sensor, the control unit 310 is configured to, if the soil humidity does not increase to a certain level in the sensing data of the soil humidity sensor even after irrigation is completed, a push alarm of the terminal, a display unit, and a web pop-up window It is possible to deliver an alarm message to the manager that irrigation and sensor confirmation are required.

The operation of FIG. 5 may be performed in combination with the operation of FIG. 4 described above.

The above-described specification can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is also a carrier wave (eg, transmission over the Internet) that is implemented in the form of. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present specification should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present specification are included in the scope of this specification.

In addition, although the above description has been focused on services and embodiments, this is merely an example and does not limit the present specification, and those of ordinary skill in the art to which this specification pertains will not depart from the essential characteristics of the present service and embodiments. It can be seen that various modifications and applications not exemplified in the above are possible. For example, each component specifically shown in the embodiments may be implemented by modification. And differences related to such modifications and applications should be construed as being included in the scope of the present specification defined in the appended claims.

Claims (15)

In a method for a plant management system to manage a plant using an artificial intelligence module,
Receiving, from a user, one or more reference values for initial setting; as,
The reference value includes (1) an irrigation reference value, (2) a soil humidity value for irrigation, (3) a soil humidity value for stopping irrigation, and (4) a non-irrigation period,
The irrigation reference value is an average value or a minimum value among individual measurement values of one or more soil humidity sensors,
setting a set value based on the reference value, and performing irrigation based on the set value;
periodically monitoring and storing soil temperature and humidity, RGB values, Normalized Difference Vegetation Index (NDVI) values, and Bio-Impedance values through the artificial intelligence module; and
controlling an operation unit based on a result value of the monitoring;
includes,
The operation unit includes an irrigation pump, a drainage pump, a growth light and a circulation fan,
Based on the result of the monitoring, the step of controlling the operation unit is
Based on the monitored RGB values:
when the ratio of the yellow area to the green area is higher than the first ratio, increasing the soil humidity value for the irrigation to reduce the irrigation cycle; and
When the ratio of the brown or gray area to the green area is higher than the second ratio, lowering the soil humidity value for the irrigation to increase the irrigation cycle;
delete delete delete According to claim 1,
Based on the result of the monitoring, the step of controlling the operation unit is
Based on the monitored NDVI values:
when the moisture content of the plant is lowered to a first predetermined value or less, increasing the soil humidity value for the irrigation to reduce the irrigation cycle; and
when the moisture content of the plant is higher than a second predetermined value, lowering the soil humidity value for the irrigation to increase the irrigation cycle;
Further comprising, a management method.
According to claim 1,
measuring, through the artificial intelligence module, a percentage change amount of a color related to the change, based on the stored RGB values, and calculating a first gradient value of the percentage change amount;
measuring, through the artificial intelligence module, an NDVI value change amount based on the stored NDVI value, and calculating a second slope value of the NDVI value change amount;
measuring, through the artificial intelligence module, a change amount of a bioimpedance value based on the stored bioimpedance value; and
predicting, through the artificial intelligence module, the rate of occurrence of plant problems based on the amount of change in the first slope value, the second slope value, and the bioimpedance value;
Further comprising, a management method.
7. The method of claim 6,
transmitting, through the artificial intelligence module, a warning message to the server based on the fact that the plant problem occurrence rate is predicted to exceed a specific value after a certain period of time;
Further comprising, a management method.
In a plant management system for managing plants using an artificial intelligence module,
A sensing unit for generating sensing data related to soil and air;
Operating parts including irrigation pumps, drainage pumps, growth lights and circulation fans,
A monitoring unit including an RGB camera, a NIR camera (Near-Infrared Spectrometer), and a Bio-Impedance sensor,
the artificial intelligence module and
a control unit for functionally controlling the sensing unit, the operation unit, the monitoring unit, and the artificial intelligence module;
the control unit
One or more reference values for initial setting are input from the user, and the reference values are (1) irrigation reference value, (2) soil humidity value for irrigation, (3) soil humidity value for stopping irrigation, and (4) aesthetics a number of periods, wherein the irrigation reference value is an average value or a minimum value among individual measurement values of one or more soil humidity sensors;
Set a set value based on the reference value, perform irrigation based on the set value, and periodically check soil temperature and humidity, RGB value, NDVI (Normalized Difference Vegetation Index) value and bioimpedance value through the artificial intelligence module. monitoring and storing, and controlling the operation unit based on the result of the monitoring,
Based on the result of the monitoring, to control the operation unit,
Based on the monitored RGB values:
When the ratio of the yellow area to the green area is higher than the first ratio, in order to reduce the irrigation cycle, increase the soil humidity value for the irrigation,
When the ratio of the brown or gray area to the green area is higher than the second ratio, the plant management system lowers the soil humidity value for the irrigation in order to increase the irrigation cycle.
delete delete delete 9. The method of claim 8,
the control unit
Based on the result of the monitoring, to control the operation unit,
Based on the monitored NDVI values:
When the moisture content of the plant is lowered to a first predetermined value or less, in order to reduce the irrigation cycle, increase the soil humidity value for irrigation,
When the moisture content of the plant increases to a second predetermined value or more, in order to increase the period of irrigation, the plant management system for lowering the soil humidity value for the irrigation.
9. The method of claim 8,
The artificial intelligence module
Based on the stored RGB value, measuring a ratio change amount of a color related to the change, calculating a first gradient value of the ratio change amount,
Through the artificial intelligence module, based on the stored NDVI value, measure the amount of change in the NDVI value, and calculate a second slope value of the amount of change in the NDVI value,
Through the artificial intelligence module, based on the stored bioimpedance value, measure the amount of change in the bioimpedance value,
Through the artificial intelligence module, based on the first slope value, the second slope value, and the amount of change in the bioimpedance value, a plant management system for predicting the rate of occurrence of a plant problem.
14. The method of claim 13,
The artificial intelligence module
A plant management system that transmits a warning message to the server based on the fact that the plant problem occurrence rate is predicted to exceed a specific value after a certain period of time.
7. The method of claim 6,
Based on the monitored soil temperature and humidity, when the soil humidity does not increase by more than a specific level after the irrigation is terminated, a warning message is delivered to the server, a management method.

Images