Turf management server, turf management method, and turf management program

Abstract

To provide a lawn management server capable of allowing a lawn manager to easily check multiple species data for lawn management.SOLUTION: A lawn management server 50 of a lawn management system includes: a data acquisition unit 531 that acquires images of the grass area taken using a drone, fixed point camera, or handheld camera and NDVI data 5212 in a lawn area, and detect data 523 by sensors installed in the lawn area; and a grass state visualization unit 534 that displays NDVI images based on lawn area images or NDVI data 5212 in the lawn area and data related to a grass condition based on detect data 523 superimposing with each other on the screen of the administrator terminal.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a turf management server, a turf management method, and a turf management program. [Background technology]

[0002] Collection of sensor data using IoT (Internet of Things) such as soil sensors and acquisition of images from the air using drones are being used for turf management and farm management. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] A·R·P Co., Ltd., Soil Moisture Sensor WD-3 WD-5 Series, [online], [Searched on August 20, 2021], Internet<URL:http: / / www.arp-id.co.jp / hp / Soil_Moisture_Sensor.pdf> [Non-Patent Document 2] Drone platform docomo sky, [online], [searched on August 20, 2021], Internet<URL:https: / / www.docomosky.jp / > Summary of the Invention [Problem to be solved by the invention]

[0004] When it comes to turf management, turf managers must go through the cumbersome process of selecting and installing various sensors and drones to obtain various sensor data and drone images, and then checking the various sensor data and drone images on separate corresponding interfaces.

[0005] The present invention has been made in consideration of the above, and aims to provide a turf management server, a turf management method, and a turf management program that allow turf managers to easily check multiple types of data for turf management. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the turf management server of the present invention is a server device that manages the condition of the turf, and is characterized by having an acquisition unit that acquires images of the turf area and vegetation index data for the turf area taken using a drone, fixed camera or handheld camera, and detection data from a sensor installed in the turf area, and a visualization unit that superimposes on the screen of an administrator terminal the image of the turf area or a vegetation index image based on the vegetation index data for the turf area, and data regarding the condition of the turf based on the detection data. Effect of the Invention

[0007] According to the present invention, a turf manager can easily check multiple types of data for turf management. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating the configuration of a turf management system according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of an aerial image of a ground to be managed. [Diagram 3] FIG. 3 is a diagram showing an example of a Normalized Difference Vegetation Index (NDVI) image of a grass area of ​​a ground to be managed. [Figure 4] FIG. 4 is a block diagram showing an example of the configuration of the turf management server shown in FIG. [Diagram 5] FIG. 5 is a diagram showing an example of a screen of the administrator terminal. [Figure 6] FIG. 6 is a diagram showing an example of a screen of the administrator terminal. [Figure 7] FIG. 7 is a diagram showing an example of a screen of the administrator terminal. [Figure 8] FIG. 8 is a diagram showing an example of a screen of the administrator terminal. [Figure 9]FIG. 9 is a diagram showing an example of a screen of the administrator terminal. [Figure 10] FIG. 10 is a flowchart showing the processing steps of a turf condition visualization process for the manager terminal by the turf management server shown in FIG. [Figure 11] FIG. 11 is a flowchart showing the processing steps for an alert notification process performed by the turf management server shown in FIG. 4 to the manager terminal. [Figure 12] FIG. 12 is a diagram illustrating a computer that executes a program. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The following describes in detail an embodiment of the turf management server, turf management method, and turf management program according to the present application with reference to the drawings. Note that the turf management server, turf management method, and turf management program according to the present application are not limited to the embodiment.

[0010] In the following embodiment, the turf management system and the processing flow in the turf management system in the embodiment will be explained in order, and finally the effects of the embodiment will be explained.

[0011] [Embodiment Mode] First, an embodiment will be described. In the embodiment, a turf management system that manages the condition of the turf area of ​​a ground where a sport (rugby, soccer, etc.) is played will be described as an example.

[0012] In the turf management system of the embodiment, a turf management server centrally manages images of the turf area taken by a drone aerial photography, vegetation index data of the turf area, and detection data from various sensors installed in the turf area. The turf management server then visualizes and provides the images of the turf area or the vegetation index data of the turf area and various detection data via a dashboard to a manager terminal owned by the turf manager. The turf manager can easily check multiple types of data for turf management by simply viewing the screen displayed on the dashboard that consolidates various types of data into one.

[0013] [Turf management system] Next, a turf management system according to an embodiment will be described with reference to Fig. 1.

[0014] As shown in Figure 1, the turf management system 100 of the embodiment includes a drone management server 10 of the drone platform, a user data management server 20 of the ground management platform, and an IoT GW (gateway) 30 that communicates with a group of sensors 31 installed on the ground, a turf management server 50 of the turf management platform that connects to an imaging device 40, and an administrator terminal 60 owned by the turf manager.

[0015] The drone management server 10 controls the flight and imaging processes of the drone 11 that takes an aerial photograph of the ground. FIG. 2 is a diagram showing an example of an aerial image of a managed ground. The drone 11 flies along the route Ra shown in FIG. 2, stops at an imaging point (e.g., imaging point Pa) set on the route Ra, and takes an aerial photograph of the ground. The drone 11 acquires an image of the grass area of ​​the ground and NDVI data of the grass area. The drone 11 also acquires moisture content measurement value data of the grass area of ​​the ground. The drone management server 10 transmits drone acquired data to the turf management server 50, in which the image of the grass area acquired by the drone 11, the NDVI data, and the moisture content measurement value data are associated with the coordinates of the ground.

[0016] The user data management server 20 acquires data on users from the wearable devices 21 worn by the users who use the ground. The wearable devices 21 are devices worn by each user of the ground. The wearable devices 21 detect the position data, usage time, acceleration data, or vital data of the user who wears the device, and transmit the measured time-series data to the user data management server 20. The user data management server 20 associates the various detected data received with the identification information of each user, and transmits the data to the turf management server 50. The vital data is, for example, heart rate, body temperature, blood flow, or respiratory rate.

[0017] On the ground, a sensor group 31, an IoT GW 30, and an imaging device 40, which are IoT devices that detect various data, are installed. The IoT GW 30 receives detection data by the sensor group 31 from the sensor group 31 and transmits the received various detection data to a turf management server 50. The imaging device 40 captures images of the grass area of ​​the ground and transmits image data of the grass area to the turf management server 50. The sensor group 31 detects the soil moisture content, electrical conductivity, soil temperature (ground temperature), outside air temperature and humidity, heat index (WBGT (Wet Bulb Globe Temperature)), wind speed, amount of sunlight, amount of hydrogen sulfide, or sunshine hours of the grass area.

[0018] The turf management server 50 receives drone acquired data transmitted from the drone management server 10, user data transmitted from the user data management server 20, detection data transmitted from the IoT GW 30, and image data transmitted from the imaging device 40, and centrally manages these data related to the turf area. The turf management server 50 transmits turf condition data related to the condition of the turf on the ground to the manager terminal 60.

[0019] Specifically, the turf management server 50 visualizes and provides the image of the turf area or the NDVI data of the turf area and various detection data via a dashboard to the manager terminal 60 owned by the turf manager. The image of the turf area or the NDVI image based on the NDVI data of the turf area and the data on the condition of the turf based on the detection data are superimposed on the screen of the manager terminal 60. FIG. 3 is a diagram showing an example of an NDVI image of the turf area of ​​a ground under management. In FIG. 3, an NDVI image Gn of the turf area is superimposed on an aerial image Gs of the ground. As shown in FIG. 3, the NDVI image Gn is an image in which the NDVI data acquired by the drone 11 at each point is arranged and the growth condition is displayed as a heat map.

[0020] When the NDVI data or detection data exceeds a predetermined threshold, the turf management server 50 notifies the manager terminal 60 of information indicating that the NDVI data or detection data has exceeded the predetermined threshold. The turf management server 50 also identifies abnormal areas within the turf area where the condition of the turf is abnormal based on the image of the turf area, the NDVI data of the turf area, and the detection data, and displays information indicating the abnormal areas on the screen of the manager terminal 60.

[0021] The turf manager's manager terminal 60 displays, via the dashboard, a screen that combines images of the turf area or NDVI data of the turf area with various detection data. There are multiple combinations of data that can be displayed on the screen of the manager terminal 60, and the turf manager can switch between them as appropriate. By visually checking the screen of the manager terminal 60, the turf manager can easily check multiple types of data for turf management, and can use various data for repairing and maintaining the turf.

[0022] The manager terminal 60 is also notified of information indicating that the NDVI data or the detection data has exceeded a threshold value. By checking the notification to the manager terminal 60, the turf manager can recognize whether or not there is an abnormality in the turf condition. The screen of the manager terminal 60 displays information indicating abnormal areas within the turf area where the turf condition is abnormal. The turf manager can recognize the location of the abnormal area simply by checking the screen of the manager terminal 60 without having to directly check the condition of the turf on the ground, and can therefore carry out repairs and maintenance accurately and quickly.

[0023] [Turf management server] Next, we will explain the turf management server 50. Figure 4 is a block diagram showing an example of the configuration of the turf management server 50 shown in Figure 1. As shown in Figure 4, the turf management server 50 has a communication unit 51, a memory unit 52, and a control unit 53.

[0024] The communication unit 51 communicates with other devices wirelessly or wired. The communication unit 51 is a communication interface that transmits and receives various information to and from other devices connected via a network or the like. The communication unit 51 is realized by a NIC (Network Interface Card) or the like, and communicates between other devices and a control unit 53 (described later) via an electric communication line such as a LAN (Local Area Network) or the Internet. For example, the communication unit 51 receives drone acquisition data transmitted from the drone management server 10, user data transmitted from the user data management server 20, detection data transmitted from the IoT GW 30, and image data transmitted from the imaging device 40. The communication unit 51 also transmits grass condition data regarding the state of the grass on the ground to the manager terminal 60.

[0025] The memory unit 52 is a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. The memory unit 52 may be a semiconductor memory in which data can be rewritten, such as a random access memory (RAM), a flash memory, or a non-volatile static random access memory (NVSRAM). The memory unit 52 stores an operating system (OS) and various programs executed by the turf management server 50. The memory unit 52 also stores various information used in the execution of the programs. The memory unit 52 has drone acquired data 521, user data 522, detection data 523, image data 524, and feedback data 525.

[0026] The drone acquired data 521 includes drone image data 5211, NDVI data 5212, and moisture measurement data 5213, each of which is associated with a coordinate. The drone image data 5211 is a color image. The NDVI data 5212 is calculated based on image data captured using R (red) light and near-infrared light, based on the correlation between the red wavelength range, which is an absorption band, and the near-infrared wavelength range, which is difficult to absorb. Data on the growth state of the grass area can be obtained from the NDVI data 5212.

[0027] User data 522 is time-series data of the user's location data, usage time, acceleration data, or vital data. The usage time and frequency of the grass area can be detected from the user's location data, usage time, and acceleration data. Users include athletes who play on the ground, as well as grass managers who repair and maintain the grass on the ground.

[0028] Detection data 523 receives detection data from the sensor group 31 and transmits the various detection data received to the turf management server 50. Detection data 523 includes detection data from the sensor group 31 in the turf area of ​​the ground, such as soil moisture data 5231, temperature and humidity data 5232 including the soil temperature and outside air temperature and humidity, electrical conductivity data 5233, heat index, wind speed, amount of sunlight, amount of hydrogen sulfide, or sunshine hours. Data on the growth condition of the turf can be obtained from the detection data 523.

[0029] The image data 524 is image data of the grass area of ​​the ground captured by the imaging device 40. From the image data 524, data such as the frequency of use of the grass (how much it is stepped on) and the hours of sunlight can be obtained.

[0030] The feedback data 526 is data indicating the point at which the injury occurred and the divot point on the grass, which is fed back by the user of the grass, and is registered in association with the feedback time.

[0031] The control unit 53 controls the entire turf management server 50. The control unit 53 is, for example, an electronic circuit such as a central processing unit (CPU) or a micro processing unit (MPU), or an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 53 also has an internal memory for storing programs that define various processing procedures and control data, and executes each process using the internal memory. The control unit 53 also functions as various processing units by running various programs. The control unit 53 has a data acquisition unit 531 (acquisition unit), a visualization request acceptance unit 532, an identification unit 533, a turf condition visualization unit 534 (visualization unit), a notification unit 535, and a feedback unit 536.

[0032] The data acquisition unit 531 acquires images of the grass area captured by aerial photography using a drone, NDVI data of the grass area, and detection data from the sensor group 31 installed in the grass area. The data acquisition unit 531 acquires drone-acquired data transmitted from the drone management server 10, user data transmitted from the user data management server 20, detection data transmitted from the IoT GW 30, and image data transmitted from the imaging device 40.

[0033] The visualization request receiving unit 532 receives a request to visualize various data related to the turf condition from the manager terminal 60 via a dashboard displayed on the screen of the manager terminal 60, which allows the user to view various data related to the turf area.

[0034] The identification unit 533 identifies abnormal areas of the grass area where the grass condition may be abnormal based on the image of the grass area, the NDVI data of the grass area, and the detection data. For example, the identification unit 533 analyzes the image data of the grass area and identifies abnormal areas where divots are present. The identification unit 533 also identifies areas where poor grass growth is occurring as abnormal areas by mapping the NDVI data of the grass area and abnormal data of the detection data on the image data of the grass area.

[0035] For example, the identification unit 533 detects the usage time and frequency of the grass area from the user's position data, usage time, and acceleration data, obtains data regarding the growth condition of the grass from the detection data 523, and obtains data such as the frequency of grass use (how much it is stepped on) and hours of sunlight from the image data 524.

[0036] The identification unit 533 then extracts from the detection data areas in the NDVI data where grass growth is worse than standard, areas where the user uses the area more frequently than other areas, areas where the sunshine hours are less than other areas, areas where the soil moisture content is less than other areas, areas where the electrical conductivity is higher than other areas, or areas where the temperature and humidity differ from other areas by a predetermined amount or more, and identifies the extracted areas as abnormal areas. This is because areas where the sunshine hours are less than other areas are considered to have worse grass growth than other areas. This is because areas where the user uses the area more frequently than other areas are considered to have more damage to the grass than other areas. Also, areas where the temperature and humidity differ from other areas by a predetermined amount or more are considered to have different grass growth compared to other areas.

[0037] The grass condition visualization unit 534 displays, on the screen of the manager terminal 60, an image of the grass area or an NDVI image based on the NDVI data of the grass area and data regarding the grass condition based on the detection data 523 in a superimposed manner.

[0038] FIG. 5 is a diagram showing an example of a screen of the manager's terminal 60. As shown in FIG. 5, the turf manager can view various data related to the turf area via a dashboard. For example, when the turf manager operates the manager's terminal 60 to select soil information of ground A at a specified date and time from the dashboard, the visualization request receiving unit 532 receives a visualization request for the soil information of ground A at this date and time. Then, the turf condition visualization unit 534 causes the manager's terminal 60 to display, on the aerial image of ground A, superimposed balloons E1 to E3 showing the soil temperature, soil electrical conductivity, and soil moisture content at the specified date and time, corresponding to the installation positions of soil sensors 1, 2, and 3 installed on ground A, as shown in screen M1 of FIG. 5. At this time, for reference, the turf condition visualization unit 534 causes the weather information, humidity, and wind speed at the specified date and time to be displayed in frame W1 of screen M1.

[0039] The turf condition visualization unit 534 displays information indicating the abnormal area identified by the identification unit 533 on the screen of the manager terminal 60. The turf condition visualization unit 534 displays the abnormal area superimposed on an image of the turf area or an NDVI image on the screen of the manager terminal 60. Figures 6 to 8 are diagrams showing examples of the screen of the manager terminal 60.

[0040] For example, when the turf manager operates the manager terminal 60 to select divot information of ground A from the dashboard, the visualization request receiving unit 532 receives a request to visualize the divot information of ground A. The turf condition visualization unit 534 divides the aerial photograph of ground A into a grid as shown on screen M2 in FIG. 6. Then, based on the identification result by the identification unit 533, the turf condition visualization unit 534 causes the manager terminal 60 to display a comment C21 indicating that there is a divot in area K2 of the various areas of ground A as shown on screen M2 in FIG. 6. At the same time, the turf condition visualization unit 534 causes area K2 of the aerial photograph to be superimposed with, for example, an abnormal area shown in red.

[0041] Alternatively, the turf condition visualization unit 534 may divide the NDVI image of ground A into a grid, as shown on screen M3 in Figure 7, and display on the administrator terminal 60, together with the NDVI data, a comment C31 indicating that there is a divot in area K2.

[0042] Furthermore, when the identifying unit 533 identifies a plurality of abnormal regions, the abnormal regions and the details of the abnormalities may be displayed together with the priority order of repair.

[0043] For example, the turf condition visualization unit 534 displays a comment C41, a comment C42, and a comment C43, as shown on screen M4 in FIG. 8. The comment C41 indicates that there is a divot in the area K2. The comment C42 indicates that there is a possibility that the turf growth is poor in the area D3 due to a lack of sunlight. The comment C43 indicates that there is a possibility that the turf condition is poor in the area L2 because the area is used more frequently than the other areas. Then, the turf condition visualization unit 534 displays the priority of repairs on each of the areas K2, D3, and L2 of the NDVI image of the ground A divided into a grid pattern. This priority is determined, for example, according to a preset rule.

[0044] When the NDVI data or the detection data exceeds a predetermined threshold, the notification unit 535 notifies the administrator terminal 60 or a mobile terminal carried by the administrator of alert information indicating that the NDVI data or the detection data exceeds the predetermined threshold. For example, the notification unit 535 displays an alert comment on the dashboard screen of the administrator terminal 60. The threshold is set for each type of detection data. The threshold is set according to the season and the region. The notification unit 535 may notify the alert information when either one of the NDVI data and the detection data exceeds a predetermined threshold, and may also notify the alert information when a predetermined number of data exceed their corresponding thresholds.

[0045] Fig. 9 is a diagram showing an example of a screen of the manager terminal 60. When the threshold value of the soil temperature is 0°C, the notification unit 535, as shown in screen M5 of Fig. 9, superimposes balloons indicating the soil temperature, the electrical conductivity of the soil, and the soil moisture content in association with the installation positions of the soil sensors 1, 2, and 3 on the aerial image of the ground A, and then displays a comment C51 indicating that the soil temperature of the ground is below freezing on the manager terminal 60. The notification unit 535 may also notify the turf manager of the alert information by email via the manager terminal 60 or the like.

[0046] In addition, if the notification unit 535 determines that there is a risk of ill health based on the temperature and humidity detected by the sensor group 31 and the user's vital signs data, etc., it may send an alert indicating the risk of ill health to the user's wearable device 21 or the mobile device of the supervisor supervising the user, and urge the user to take a break or stop working.

[0047] The feedback unit 536 accepts feedback such as the points where injuries occurred by turf users and data indicating the turf condition by the turf manager, and registers the data in the feedback data 525 in association with the feedback time. The turf condition visualization unit 534 may display the various data registered as the feedback data 525 on the manager terminal 60 together with an image of the turf area or an NDVI image.

[0048] [Turf management server processing] Next, there will be described the processing of the turf management server 50. Fig. 10 is a flow chart showing the processing steps of the turf condition visualization process for the manager terminal 60 by the turf management server 50 shown in Fig. 4.

[0049] The turf management server 50 acquires images of the turf area taken by aerial photography by a drone, NDVI data for the turf area, and detection data from the sensor group 31 installed in the turf area (step S1). When the turf management server 50 receives a request to visualize various data related to the turf condition (step S2), it identifies abnormal areas of the turf area where the turf condition may be abnormal based on the images of the turf area, the NDVI data for the turf area, and the detection data (step S3).

[0050] The turf management server 50 then instructs the manager terminal 60 to visualize the turf condition data (step S4), and causes the screen of the manager terminal 60 to superimpose an image of the turf area or an NDVI image based on the NDVI data of the turf area and data regarding the turf condition based on the detection data 523. At this time, the turf management server 50 may instruct the manager terminal 60 to display information indicating the abnormal area identified in step S3, and cause the information indicating the abnormal area to be displayed on the screen of the manager terminal 60.

[0051] [Alert notification processing] Next, there will be described the alert notification process of the turf management server 50. Fig. 11 is a flowchart showing the processing steps of the alert notification process to the manager terminal 60 by the turf management server 50 shown in Fig. 4.

[0052] As shown in Figure 11, the turf management server 50 acquires NDVI data for the turf area and detection data from the sensor group 31 installed in the turf area (step S11). The turf management server 50 determines whether any of the NDVI data or detection data exceeds a predetermined nuclear threshold (step S12). If no data exceeds the predetermined thresholds in the NDVI data or detection data (step S12: No), the turf management server 50 returns to step S11 and continues acquiring data.

[0053] If the NDVI data or detection data contains data that exceeds each of the specified thresholds (step S12: Yes), the turf management server 50 notifies the manager terminal 60 of alert information indicating that the NDVI data or detection data has exceeded the specified thresholds (step S13).

[0054] [Effects of the embodiment] In the embodiment, the turf management server 50 acquires and centrally manages images of the turf area taken by aerial photography by drone, NDVI data of the turf area, and detection data from various sensors installed in the turf area, and displays the images of the turf area or the NDVI images of the turf area superimposed on the various detection data on the administrator terminal 60 via a dashboard.

[0055] For example, the turf management server 50 may display a list of detection data from the sensor group 31 on the manager terminal 60, or may display multiple image data in layers or as a list, thereby visualizing the condition of the turf in a composite manner that would be difficult to determine from a single image. The turf management server 50 maps the detection data and information from the user to the position of the turf, visualizing the growth status from both the image and data perspectives. Therefore, according to the embodiment, the turf manager can easily check multiple types of data for turf management by simply viewing the screen displayed on the dashboard that consolidates various types of data into one.

[0056] When the NDVI data or detection data exceeds a predetermined threshold, the turf management server 50 notifies the manager terminal 60 of information indicating that the NDVI data or detection data has exceeded the predetermined threshold. The turf management server 50 also identifies abnormal areas within the turf area where the condition of the turf is abnormal based on the image of the turf area, the NDVI data of the turf area, and the detection data, and displays information indicating the abnormal areas on the screen of the manager terminal 60.

[0057] This allows the turf manager to identify the location of abnormal areas simply by checking the screen of the manager's terminal 60 without having to directly check the condition of the turf on the ground, allowing for accurate and rapid repairs and maintenance. Therefore, according to the embodiment, it is possible to reduce the amount of work required by the turf manager by reducing the time spent searching for damaged turf areas and reducing the number of missed searches, and it is also possible to improve the quality of the turf and the quality of turf management.

[0058] The turf management system 100 according to the present embodiment is not limited to the management of turf on a ground, but can also be applied to the management of turf laid on a golf course or the like. The turf management system 100 is also applicable not only to turf management, but also to the management of the growth conditions of agricultural crops in agricultural land. The present embodiment has been described with an example in which an image of a turf area, NDVI data of the turf area, and moisture measurement value data of the turf area are obtained by aerial photography using a drone, but the image of a turf area, NDVI data of the turf area, and moisture measurement value data of the turf area may also be obtained using a handy camera or a fixed camera owned by a manager or the like.

[0059] [System configuration, etc.] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or a part of it can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. Furthermore, each processing function performed by each device can be realized in whole or in part by a CPU or GPU and a program analyzed and executed by the CPU or GPU, or can be realized as hardware using wired logic.

[0060] Furthermore, among the processes described in this embodiment, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

[0061] [program] It is also possible to create a program in which the processes performed by the turf management server 50 described in the above embodiment are written in a language executable by a computer. For example, it is also possible to create a program in which the processes performed by the turf management server 50 in the above embodiment are written in a language executable by a computer. In this case, the same effects as those of the above embodiment can be obtained by having a computer execute the program. Furthermore, such a program can be recorded on a computer-readable recording medium, and the program recorded on the recording medium can be read into a computer and executed to achieve the same processes as those of the above embodiment.

[0062] Fig. 12 is a diagram showing a computer that executes a program. As shown in Fig. 12, a computer 1000 includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070, and these components are connected by a bus 1080.

[0063] The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012, as exemplified in Fig. 12. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090, as exemplified in Fig. 12. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0064] 12, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, the above programs are stored in, for example, the hard disk drive 1090 as program modules in which instructions to be executed by the computer 1000 are written.

[0065] Moreover, the various data described in the above embodiment are stored as program data, for example, in the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 and the program data 1094 stored in the memory 1010 or the hard disk drive 1090 into the RAM 1012 as necessary, and executes various processing procedures.

[0066] Note that the program module 1093 and program data 1094 relating to the program are not limited to being stored in the hard disk drive 1090, and may be stored in, for example, a removable storage medium and read by the CPU 1020 via a disk drive or the like. Alternatively, the program module 1093 and program data 1094 relating to the program may be stored in another computer connected via a network (such as a local area network (LAN) or wide area network (WAN)) and read by the CPU 1020 via the network interface 1070.

[0067] The above-described embodiments and their modifications are included in the technology disclosed in this application, as well as in the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0068] 10 Drone Management Server 11. Drone 20 User data management server 21 Wearable Devices 30 IoT Gateway 31 Sensor Group 40 Imaging device 50 Turf Management Server 51 Communications Department 52 Storage section 53 Control section 521 Drone Acquired Data 522 User Data 523 Detection Data 524 Image data 525 Feedback Data 5211 Drone image data 5212 NDVI data 5213 Moisture content measurement data 5231 Moisture content data 5232 Temperature and humidity data 5233 Electrical Conductivity Data

Claims

1. A turf management server for managing the condition of turf, An acquisition unit that acquires an image of a grass area taken using a drone, a fixed camera, or a handheld camera, vegetation index data of the grass area, and detection data by a sensor installed in the grass area; An identification unit that identifies an abnormal area in the grass area where the grass condition may be abnormal based on the image of the grass area, the vegetation index data of the grass area, and the detection data; A visualization unit that displays, on a screen of a manager terminal owned by a turf manager, an image of the turf area or a vegetation index image based on the vegetation index data of the turf area and data on the condition of the turf based on the detection data in a superimposed manner, and displays information indicating the abnormal area on the screen of the manager terminal; having The detection data includes soil moisture content, electrical conductivity, soil temperature, outside temperature and humidity, heat index, wind speed, amount of sunlight, amount of hydrogen sulfide, sunshine hours, image data of the grass area, position data of users who are at least one of the athletes competing in the grass area and the grass manager who repairs and / or maintains the grass of the grass area, usage time of the users, acceleration data of the users, or vital data of the users, The turf management server is characterized in that the identification unit extracts areas of the vegetation index data from the detection data where grass growth is worse than standard, areas where the user uses the data more frequently than other areas, areas where the sunshine hours are less than other areas, areas where the soil moisture content is less than other areas, areas where the electrical conductivity is higher than other areas, or areas where the temperature and humidity differ from other areas by a predetermined amount or more, and identifies the extracted areas as abnormal areas.

2. a notification unit that notifies the manager terminal or a mobile terminal carried by the lawn manager of information indicating that the vegetation index data or the detection data has exceeded a predetermined threshold when the vegetation index data or the detection data has exceeded a predetermined threshold. The turf management server of claim 1 further comprising:

3. The turf management server according to claim 1, characterized in that the visualization unit superimposes the abnormal area on an image of the turf area or the vegetation index image on the screen of the administrator terminal.

4. The turf management server described in any one of claims 1 to 3, characterized in that the acquisition unit acquires moisture content measurement data of a turf area photographed using a drone, a fixed camera or a handheld camera.

5. A turf management method executed by a turf management server that manages the condition of the turf, A step of acquiring an image of a grass area taken using a drone, a fixed camera, or a handheld camera, vegetation index data of the grass area, and detection data by a sensor installed in the grass area; Identifying an abnormal area of ​​the grass area where the grass condition may be abnormal based on the image of the grass area, the vegetation index data of the grass area, and the detection data; A process of displaying, on a screen of a manager terminal owned by a turf manager, an image of the turf area or a vegetation index image based on the vegetation index data of the turf area and data regarding the condition of the turf based on the detection data in a superimposed manner, and displaying information indicating the abnormal area on the screen of the manager terminal; Including, The detection data includes soil moisture content, electrical conductivity, soil temperature, outside temperature and humidity, heat index, wind speed, amount of sunlight, amount of hydrogen sulfide, sunshine hours, image data of the grass area, position data of users who are at least one of the athletes competing in the grass area and the grass manager who repairs and / or maintains the grass of the grass area, usage time of the users, acceleration data of the users, or vital data of the users, A turf management method characterized in that the identification step extracts areas of the vegetation index data from the detection data where grass growth is worse than standard, areas where the user uses the data more frequently than other areas, areas where the sunshine hours are less than other areas, areas where the soil moisture content is less than other areas, areas where the electrical conductivity is higher than other areas, or areas where the temperature and humidity differ from other areas by a specified amount or more, and identifies the extracted areas as abnormal areas.

6. A step of acquiring an image of a grass area taken using a drone, a fixed camera, or a handheld camera, vegetation index data of the grass area, and detection data by a sensor installed in the grass area; Identifying an abnormal area of ​​the grass area where the grass condition may be abnormal based on the image of the grass area, the vegetation index data of the grass area, and the detection data; A step of displaying, on a screen of a manager terminal owned by a turf manager, an image of the turf area or a vegetation index image based on the vegetation index data of the turf area and data regarding the condition of the turf based on the detection data in a superimposed manner, and displaying information indicating the abnormal area on the screen of the manager terminal; Run the following on your computer: The detection data includes soil moisture content, electrical conductivity, soil temperature, outside temperature and humidity, heat index, wind speed, amount of sunlight, amount of hydrogen sulfide, sunshine hours, image data of the grass area, position data of users who are at least one of the athletes competing in the grass area and the grass manager who repairs and / or maintains the grass of the grass area, usage time of the users, acceleration data of the users, or vital data of the users, The identification step is a turf management program for extracting from the detection data areas in the vegetation index data where grass growth is worse than standard, areas where the user uses the area more frequently than other areas, areas where the sunshine hours are less than other areas, areas where the soil moisture content is less than other areas, areas where the electrical conductivity is higher than other areas, or areas where the temperature and humidity differ from other areas by a predetermined amount or more, and identifying the extracted areas as abnormal areas.

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