KR20240086899A - Smart farm management method and system using drones - Google Patents

Abstract

A smart farm management method and system using drones is disclosed. A smart farm management method and system using a drone according to an embodiment of the present invention moves to a crop area to be monitored, receives environmental information from a measuring device installed within the crop area, and flies over the crop area while flying over the crop area. A drone that photographs my crops and transmits the captured images of the crops, the environmental information, and the flight record information, and the captured images received from the drone, the environmental information, and the flight record information within the crop area. It includes a management server that analyzes the growth state of the crop and controls the irrigation controller according to the analysis results.

Description

Smart farm management method and system using drones {SMART FARM MANAGEMENT METHOD AND SYSTEM USING DRONES}

Embodiments of the present invention relate to smart farm management technology using drones.

Recently, research and development activities are actively underway to improve agricultural productivity by incorporating technologies related to the 4th Industrial Revolution into agriculture. This technology is called smart farm. Smart farm refers to an intelligent farm using various cutting-edge technologies such as drones, robots, and artificial intelligence, and provides automatic control functions using IoT (internet of things) and big data. Through this, smart farm is a technology that aims to solve problems such as labor shortage and low productivity.

Registered Patent Publication No. 10-1759130 (2017.07.12.)

Embodiments of the present invention are intended to provide a smart farm management method using drones that can manage optimal growth of crops by operating drones.

According to an exemplary embodiment of the present invention, move to a crop area to be monitored, receive environmental information from a measuring device installed within the crop area, photograph crops within the crop area while flying over the crop area, and photograph the crops within the crop area. Analyzing the growth state of crops in the crop area based on the captured image, the drone transmitting the environmental information and flight record information, and the captured image, the environmental information, and the flight record information received from the drone, A smart farm management system using drones including a management server that controls the irrigation controller according to the above analysis results is provided.

The smart farm management system using the drone further includes a representative measurement device and a plurality of auxiliary measurement devices installed at a preset point in the crop area to measure environmental information at each point in the crop area, the representative measurement device being Receives environmental information measured from each of the plurality of auxiliary measurement devices, detects the drone entering the crop area, and generates sensing information. When the sensing information is generated, the measured environmental information and the plurality of received Environmental information can be transmitted to the drone.

The flight path information includes a departure location, arrival location, movement path, movement speed, movement altitude, and movement direction set according to the crop area, and the drone receives flight path information for the crop area from the user or the management server. It is possible to photograph crops within the crop area while flying over the crop area based on the received flight path information.

The preset point may be set so that the representative measurement device and the plurality of auxiliary measurement devices are spaced apart by a preset distance along the border of the crop area.

The preset point may be set in consideration of the slope or vertex of the crop area.

The environmental information may include a unique ID (identifier), location information, wind direction, wind speed, sunlight, rainfall, and soil moisture for each representative measurement device and the plurality of auxiliary measurement devices.

The management server divides the crop area into certain areas based on the location information of the representative measurement device and the plurality of auxiliary measurement devices included in the plurality of environmental information, and provides the flight record information corresponding to the divided areas. Extract captured images for each area based on the GPS information included in the area, analyze the growth status of crops for each area based on the extracted images for each area and environmental information for each area, , Analysis information for each area can be generated based on the analysis results.

The management server generates control information for each area based on the analysis information and the growth data, and based on the generated control information, the amount of water, watering time, and liquid fertilizer for each area are used. The irrigation controller can be controlled to adjust the ratio.

According to embodiments of the present invention, the growth state of crops can be analyzed based on environmental information and captured images of the crop area, and the irrigation controller can be controlled based on the analysis results to create a suitable cultivation environment.

In addition, according to one embodiment of the present invention, environmental information is measured for each area within the crop area using a plurality of measuring devices installed at each point within the crop area, thereby providing an irrigation controller taking into account the weather and soil for each area within the crop area. can be controlled.

1 is a configuration diagram illustrating a smart farm management system according to an embodiment of the present invention.
Figure 2 is a diagram for explaining how measurement devices are installed in a smart farm management system according to an embodiment of the present invention
Figure 3 is a block diagram illustrating a representative measurement device of a smart farm management system according to an embodiment of the present invention.
Figure 4 is a block diagram illustrating an auxiliary measurement device of a smart farm management system according to an embodiment of the present invention.
Figure 5 is a block diagram illustrating a drone of a smart farm management system according to an embodiment of the present invention.
Figure 6 is a block diagram for explaining the management server of the smart farm management system according to an embodiment of the present invention.
Figure 7 is a flowchart illustrating a smart farm management method according to an embodiment of the present invention.
8 is a block diagram illustrating and illustrating a computing environment including a computing device suitable for use in example embodiments.

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The detailed description below is provided to facilitate a comprehensive understanding of the methods, devices and/or systems described herein. However, this is only an example and the present invention is not limited thereto.

In describing the embodiments of the present invention, if it is determined that a detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, the definition should be made based on the contents throughout this specification. The terminology used in the detailed description is only for describing embodiments of the present invention and should in no way be limiting. Unless explicitly stated otherwise, singular forms include plural meanings. In this description, expressions such as “comprising” or “comprising” are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, and one or more than those described. It should not be construed to exclude the existence or possibility of any other characteristic, number, step, operation, element, or part or combination thereof.

Meanwhile, embodiments of the present invention may include a program for performing the methods described in this specification on a computer, and a computer-readable recording medium containing the program. The computer-readable recording medium may include program instructions, local data files, local data structures, etc., singly or in combination. The media may be those specifically designed and constructed for the present invention, or may be those commonly available in the computer software field. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs, DVDs, and media specifically configured to store and perform program instructions such as ROM, RAM, flash memory, etc. Includes hardware devices. Examples of the program may include not only machine language code such as that generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

This is a configuration diagram to explain a smart farm management system according to an embodiment of the present invention.

As shown in FIG. 1, a smart farm management system according to an embodiment of the present invention may include a representative measurement device 100, a drone 300, and a management server 400.

As the representative measurement device 100, drone 300, and management server 400 are connected to each other using a communication network, communication may be possible.

In some embodiments, the communications network may be Wi-Fi, the Internet, one or more local area networks, wire area networks, cellular networks, mobile networks, other types of networks, or such networks. It may include a combination of these.

In addition, the smart farm management system according to an embodiment of the present invention may further include first to Nth auxiliary measurement devices 200-1, .., 200-N.

The representative measurement device 100 and the first auxiliary measurement device to the Nth auxiliary measurement device 200-1, .., 200-N are used in a short distance using Bluetooth, Zigbee, Infrared, etc. Information can be transmitted and received using known communication methods that can be transmitted.

The representative measuring device 100 and the auxiliary measuring device 200 are installed at preset points along the border of the crop area where crops are being grown (e.g., rice field, field, farm, orchard, etc.) and measure the measurement of each point within the crop area. Environmental information can be measured.

The representative measurement device 100 may receive environmental information measured from each of the plurality of auxiliary measurement devices 200-1, .., 200-N. At this time, the representative measurement device 100 and a plurality of auxiliary measurement devices 200-1, .., 200-N may be installed at preset points along the border of the crop area. Here, the preset point may be set so that the representative measurement device 100 and the plurality of auxiliary measurement devices 200-1, .., 200-N are spaced apart from each other by a set distance. Additionally, the preset point may be set considering the slope or vertex. In other words, weather information and soil information depending on the location or altitude of the crop area. For example, in sloping crop areas (e.g., sloping fields, highland areas, orchards, etc.), the installation is installed according to the altitude taking into account the slope of the crop area. You can set a branch. Additionally, in a multi-angled crop area, the installation point can be set to be installed at each vertex of the crop area, as shown in FIG. 2. The environmental information may include sensor measurement information collected from the representative measurement device 100 and a plurality of auxiliary measurement devices 200-1, .., 200-N. Environmental information can be classified and stored by location of the measuring device, type of module, and time. For example, environmental information may include unique IDs (identifiers) of measuring devices, location information (eg, address, GPS coordinates), wind direction, wind speed, amount of sunlight, amount of rainfall, amount of soil moisture, etc.

Figure 3 is a block diagram for explaining a representative measurement device 100 of a smart farm management system according to an embodiment of the present invention.

As shown in FIG. 3, the representative measurement device 100 may include a sensor unit 110, a storage unit 120, a communication unit 130, a detection unit 140, and a control unit 150.

The sensor unit 110 can measure information about the point where the representative measurement device 100 is installed. The sensor unit 110 may include at least one sensor. For example, the sensor unit 110 may include a temperature sensor, a humidity sensor, a wind direction sensor, a wind speed sensor, an optical sensor, a rainfall sensor, a soil moisture sensor, etc. Additionally, the sensor unit 110 may further include a global positioning system (GPS) sensor for measuring the location of the measuring device. Additionally, the sensor unit 110 may generate environmental information based on measured information. Here, the environmental information may include a unique ID (identifier) of the representative measurement device 100, location information (eg, address, GPS coordinates), wind direction, wind speed, amount of sunlight, amount of rainfall, amount of soil moisture, etc.

The storage unit 120 may store information measured by the sensor unit 110 and environmental information received from a plurality of auxiliary measurement devices 200-1, .., 200-N. Additionally, the storage unit 120 may store the unique ID of the representative measurement device 100. Additionally, the storage unit 120 of the representative measurement device 100 may include preset flight path information. Here, the flight path information may include a departure location, arrival location, movement path, movement speed, movement altitude, movement direction, etc. optimized according to the crop area.

The communication unit 130 can transmit and receive information to and from a plurality of auxiliary measurement devices 200-1, .., 200-N and the drone 300. For example, the communication unit 130 may transmit and receive information between measurement devices using known communication methods that can be transmitted in a short distance, such as Bluetooth, Zigbee, and infrared. Additionally, the communication unit 130 may be capable of communicating with the drone 300 using a communication network.

The communication unit 130 may receive environmental information measured from each of the plurality of auxiliary measurement devices 200-1, .., 200-N. Additionally, the communication unit 130 may transmit flight path information and a plurality of environmental information to the drone 300.

The detection unit 140 may detect the drone 300 entering the crop area and generate detection information.

When sensing information is generated from the sensing unit 140, the control unit 150 may control transmission of environmental information to the drone 300 through the communication unit 130. Additionally, the communication unit 130 can control transmission of pre-stored flight path information to the drone.

Figure 4 is a block diagram for explaining an auxiliary measurement device of a smart farm management system according to an embodiment of the present invention.

As shown in FIG. 4, the auxiliary measurement device 200 may include a sensor unit 210, a storage unit 220, and a communication unit 230.

The sensor unit 210 can measure information about the point where the auxiliary measurement device 200 is installed. The sensor unit 210 may include at least one sensor. For example, the sensor unit 210 may include a temperature sensor, a humidity sensor, a wind direction sensor, a wind speed sensor, an optical sensor, a rainfall sensor, a soil moisture sensor, etc. Additionally, the sensor unit 210 may further include a global positioning system (GPS) sensor for measuring the location of the measuring device. Additionally, the sensor unit 210 may generate environmental information based on measured information. Here, the environmental information may include a unique ID (identifier) of the auxiliary measurement device, location information (eg, address, GPS coordinates), wind direction, wind speed, amount of sunlight, amount of rainfall, amount of soil moisture, etc.

The storage unit 220 may store information measured by the sensor unit 210. Additionally, the storage unit 220 may store the unique ID of the auxiliary measurement device 200.

The communication unit 230 can transmit and receive information between measurement devices. For example, the communication unit 230 may transmit measured environmental information to the representative measurement device 100.

Figure 5 is a block diagram for explaining the drone 300 of the smart farm management system according to an embodiment of the present invention.

As shown in FIG. 5, the drone 300 may include a wireless communication unit 310, a storage unit 320, a camera unit 330, a flight recorder 340, and a flight control unit 350.

Meanwhile, the drone 300 may use an unmanned flying vehicle that is generally well known in the technical field to which the present invention pertains. For example, the drone 300 is a flying vehicle that can be controlled remotely without a human on board. It may be a flying vehicle with two wings and has a miniature shape of a typical passenger plane, or it may be an unmanned flying vehicle that has recently been used for various purposes. Since the detailed configuration of the drone 300 is widely known in the technical field to which the present invention pertains, a detailed description thereof will be omitted.

The wireless communication unit 310 may receive a plurality of environmental information from the representative measurement device 100. Additionally, the wireless communication unit 310 may transmit a plurality of received environmental information, captured images, and flight record information to the management server 400. Meanwhile, the wireless communication unit 310 may receive preset flight path information for the crop area to be monitored from the representative measurement device 100, the user, or the management server 400.

The storage unit 320 may store a plurality of environmental information received from the representative measurement device 100. Additionally, the storage unit 320 can store captured images and flight record information. Meanwhile, the storage unit 320 may store preset flight path information received from the representative measurement device 100, the user, or the management server 400.

The camera unit 330 is disposed on the lower side of the drone 300 and can photograph the lower side of the drone 300. For example, the camera unit 330 may photograph crops within the crop area while the drone 300 flies within the crop area according to flight path information through the camera. Additionally, the camera unit 330 may generate a captured image of crops within the crop area, and the captured image may be stored in the storage unit 320.

The flight recorder 340 may measure the flight record of the drone 300 in flight and generate flight record information. For example, the flight recorder 340 records the flight record of the drone 300 in flight through a sensor unit (e.g., GPS sensor, gyro sensor, acceleration sensor, magnetometer, inertial measurement sensor, etc.) installed on the drone 300. can be measured. Here, flight record information includes flight time information, flight path information (travel path, movement speed, movement altitude, movement direction), attitude-related sensor information (information measured from gyro sensor, acceleration sensor, magnetometer, inertial measurement sensor), GPS It may include information, etc.

The flight control unit 350 may generate a flight control signal so that the drone 300 is driven to fly according to the flight path information. For example, the control unit 350 may control the flight (e.g., takeoff/landing, altitude control, direction control, speed control) of the drone 300 according to flight path information received from the measurement device. To this end, the flight control unit 350 may include a power unit for supplying operating power, a drive unit for controlling the flight of the drone 300, and a function control unit for performing functions. Here, the driving unit may include a plurality of motors and propellers, and each motor may rotate the corresponding propeller at a speed determined by path information.

Figure 6 is a block diagram for explaining the management server 400 of the smart farm management system according to an embodiment of the present invention.

As shown in FIG. 6, the management server 400 may include a communication module 410, a storage module 420, an analysis module 430, and a control module 440.

In this specification, a module may mean a functional and structural combination of hardware for carrying out the technical idea of the present invention and software for driving the hardware. For example, the "module" may mean a logical unit of hardware resources for executing a predetermined code and the predetermined code, and does not necessarily mean a physically connected code or one type of hardware. .

The communication module 410 may receive a plurality of environmental information, captured images, and flight record information from the drone 300. Additionally, the communication module 410 may receive growth data required for seasonal growth of crops from an external server. Additionally, the communication module 410 may transmit preset flight path information to the drone 300.

The storage module 420 may store a plurality of environmental information, captured images, and flight record information received from the drone 300. Additionally, the storage module 420 may store growth data received from an external server.

The analysis module 430 can analyze the growth state of crops in the crop area based on a plurality of environmental information, captured images, and flight record information. Specifically, the analysis module 430 may divide the crop area into certain areas based on the location information of the measurement devices included in the plurality of environmental information. In addition, the analysis module 430 extracts captured images for each area based on GPS information included in the flight record information corresponding to the divided areas, and collects captured images for each extracted area and environmental information for each area. Based on this, the growth status of crops in each area can be analyzed. The analysis module 430 may generate analysis information for each area based on the analysis results.

Additionally, the analysis module 430 may generate control information for each area based on analysis information and growth data. For example, when analysis information and growth data are input, the analysis module 430 learns to generate control information for controlling the irrigation controller by performing machine learning based on an already learned environmental control model. It can be.

The control module 440 may control an irrigation controller (not shown) based on control information generated from the analysis module 430. Specifically, the control module 440 can control the irrigation controller to adjust the amount of water, watering time, and ratio of liquid fertilizer for each area based on the control information generated by the analysis module 430. The irrigation controller may be a device that provides a set amount of water or liquid fertilizer to the crop area at set times. That is, the control module 440 controls irrigation controllers that operate collectively according to set conditions for each area, thereby ensuring optimal growth conditions by considering the growth status of crops depending on the location or altitude within the crop area. .

Therefore, according to embodiments of the present invention, the growth state of crops can be analyzed based on environmental information and captured images of the crop area, and the irrigation controller can be controlled based on the analysis results to ensure a suitable cultivation environment. there is.

In addition, according to one embodiment of the present invention, environmental information is measured for each area within the crop area using a plurality of measuring devices installed at each point within the crop area, thereby providing an irrigation controller taking into account the weather and soil for each area within the crop area. can be controlled.

Figure 7 is a flowchart for explaining a smart farm management method according to an embodiment of the present invention. The method shown in FIG. 7 can be performed, for example, by the smart farm management system described above. In the illustrated flow chart, the method is divided into a plurality of steps, but at least some of the steps are performed in a different order, combined with other steps, omitted, divided into detailed steps, or not shown. One or more steps may be added and performed.

First, the drone 300 receives flight path information about the crop area to be monitored and representative measuring device information within the crop area from the user or management server before flight and then flies to the crop area (S702). Here, the flight path information may include departure location, arrival location, movement path, movement speed, movement altitude, movement direction, etc. within the crop area.

Next, the drone 300 receives a plurality of environmental information from the representative measurement device 100 within the crop area (S704). Here, the environmental information may include sensor measurement information collected from the representative measurement device 100 and a plurality of auxiliary measurement devices. Environmental information can be classified and stored by location of the measuring device, type of module, and time. For example, environmental information may include unique IDs (identifiers) of measuring devices, location information (eg, address, GPS coordinates), wind direction, wind speed, amount of sunlight, amount of rainfall, amount of soil moisture, etc.

Next, the drone 300 photographs crops while flying over the crop area based on the received flight path information (S706). Specifically, the drone 300 may use a camera to photograph crops within the crop area while the drone 300 flies according to flight path information within the crop area.

Next, the management server 400 receives a plurality of environmental information, captured images, and flight record information from the drone 300 (S708).

Next, the management server 400 analyzes the growth status of crops in the crop area (S710). Specifically, the management server 400 may analyze the growth state of crops in the crop area based on a plurality of environmental information, captured images, and flight record information. Specifically, the management server 400 may divide the crop area into certain areas based on location information of measurement devices included in a plurality of environmental information. In addition, the management server 400 extracts captured images for each area based on GPS information included in the flight record information corresponding to the divided areas, and provides captured images for each extracted area and environmental information for each area. Based on this, the growth status of crops in each area can be analyzed. The management server 400 may generate analysis information for each area based on the analysis results. Additionally, the management server 400 may generate control information for each area based on analysis information and growth data. For example, when analysis information and growth data are input, the management server 400 learns to generate control information for controlling the irrigation controller by performing machine learning based on an already learned environmental control model. It can be.

Finally, the management server 400 controls the irrigation controller according to the analysis results (S712). Specifically, the management server 400 may control the irrigation controller to adjust the amount of water, watering time, and ratio of liquid fertilizer for each area based on the control information generated from the analysis module 430.

Therefore, according to embodiments of the present invention, the growth state of crops can be analyzed based on environmental information and captured images of the crop area, and the irrigation controller can be controlled based on the analysis results to ensure a suitable cultivation environment. there is.

In addition, according to one embodiment of the present invention, environmental information is measured for each area within the crop area using a plurality of measuring devices installed at each point within the crop area, thereby providing an irrigation controller taking into account the weather and soil for each area within the crop area. can be controlled.

8 is a block diagram illustrating and illustrating a computing environment including a computing device suitable for use in example embodiments. In the illustrated embodiment, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those described below.

The illustrated computing environment 10 includes a computing device 12 . In one embodiment, computing device 12 may be drone 300. Additionally, computing device 12 may be management server 400 .

Computing device 12 includes at least one processor 14, a computer-readable storage medium 16, and a communication bus 18. Processor 14 may cause computing device 12 to operate in accordance with the example embodiments noted above. For example, processor 14 may execute one or more programs stored on computer-readable storage medium 16. The one or more programs may include one or more computer-executable instructions, which, when executed by the processor 14, cause computing device 12 to perform operations according to example embodiments. It can be.

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions executable by the processor 14. In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or an appropriate combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, another form of storage medium that can be accessed by computing device 12 and store desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12, including processor 14 and computer-readable storage medium 16.

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide an interface for one or more input/output devices 24. The input/output interface 22 and the network communication interface 26 are connected to the communication bus 18. Input/output device 24 may be coupled to other components of computing device 12 through input/output interface 22. Exemplary input/output devices 24 include, but are not limited to, a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touch screen), a voice or sound input device, various types of sensor devices, and/or imaging devices. It may include input devices and/or output devices such as display devices, printers, speakers, and/or network cards. The exemplary input/output device 24 may be included within the computing device 12 as a component constituting the computing device 12, or may be connected to the computing device 12 as a separate device distinct from the computing device 12. It may be possible.

Although representative embodiments of the present invention have been described in detail above, those skilled in the art will understand that various modifications can be made to the above-described embodiments without departing from the scope of the present invention. . Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined not only by the claims described later but also by equivalents to the claims.

100: Representative measuring device
110: sensor unit
120: storage unit
130: Department of Communications
140: detection unit
150: control unit
200: Auxiliary measuring device
210: sensor unit
220: storage unit
230: Department of Communications
300: Drone
310: wireless communication department
320: storage unit
330: Camera unit
340: Flight record book
350: Flight control unit
400: Management Server
410: Communication module
420: storage module
430: analysis module
440: Control module

Claims (10)

Move to the crop area to be monitored, receive environmental information from a measuring device installed within the crop area, photograph crops within the crop area while flying over the crop area, capture images of the crops, the environmental information, and Drones that transmit flight log information; and
A drone comprising a management server that analyzes the growth state of crops in the crop area based on the captured image, the environmental information, and the flight record information received from the drone, and controls the irrigation controller according to the analysis results. Smart farm management system used.
In claim 1,
The smart farm management system using the drone is,
It further includes a representative measurement device and a plurality of auxiliary measurement devices installed at a preset point in the crop zone to measure environmental information at each point in the crop zone,
The representative measuring device is,
Receives environmental information measured from each of the plurality of auxiliary measurement devices, detects the drone entering the crop area, and generates sensing information. When the sensing information is generated, the measured environmental information and the plurality of received A smart farm management system using drones that transmits environmental information to the drones.
In claim 2,
The flight path information includes a departure location, arrival location, movement path, movement speed, movement altitude, and movement direction set according to the crop area,
The drone is,
A smart farm management system using a drone that receives flight path information for the crop area from a user or the management server, and photographs crops in the crop area while flying over the crop area based on the received flight path information.
In claim 2,
The preset point is set so that the representative measurement device and the plurality of auxiliary measurement devices are spaced apart by a preset distance along the boundary of the crop area. A smart farm management system using a drone.
In claim 2,
The preset point is set in consideration of the slope or vertex of the crop area. A smart farm management system using a drone.
In claim 2,
The environmental information includes a unique ID (identifire), location information, wind direction, wind speed, sunlight, rainfall, and soil moisture for each representative measurement device and the plurality of auxiliary measurement devices. A smart farm management system using a drone.
In claim 6,
The management server is,
The crop area is divided into certain areas based on the location information of the representative measurement device and the plurality of auxiliary measurement devices included in the plurality of environmental information, and GPS included in the flight record information corresponding to the divided area Based on the information, the captured images for each area are extracted, the growth status of crops for each area is analyzed based on the extracted captured images for each area and the environmental information for each area, and the analysis results A smart farm management system using drones that generates analysis information for each of the above areas based on.
In claim 7,
The management server is,
To generate control information for each area based on the analysis information and the growth data, and to adjust the amount of water, watering time, and ratio of liquid fertilizer for each area based on the generated control information. A smart farm management system using a drone that controls the irrigation controller.
one or more processors, and
A smart farm management method using drones performed on a computing device having a memory for storing one or more programs executed by the one or more processors,
A drone receiving flight path information about a crop area to be monitored from a user or a management server, and flying to the crop area to be monitored;
The drone receiving environmental information from a representative measurement device installed in the crop area;
The drone, photographing crops within the crop area while flying over the crop area based on the received flight path information; and
A smart farm management method using a drone, comprising the step of transmitting, by the drone, a captured image of the crop, the environmental information, and flight record information to a management server.
one or more processors, and
A smart farm management method using drones performed on a computing device having a memory for storing one or more programs executed by the one or more processors,
In the management server, receiving images of crops in the crop area, environmental information of the crop area, and flight record information of the drone from a drone that flew over the crop area;
Analyzing, in the management server, the growth state of crops in the crop area based on the captured image, the environmental information, and the flight record information; and
A smart farm management method using a drone, comprising the step of controlling, in the management server, an irrigation controller according to the analysis results.