Management system, management device, management method, and computer program

Abstract

A management system 1 according to the present disclosure is a system for managing a plurality of trees T arranged along a row direction. The management system 1 comprises: a quality measurement device 2 that is provided in an agricultural machine 6 moving along the row direction and acquires measurement information of fruits of the plurality of trees T; a positioning device 4 that is provided in the agricultural machine 6 and acquires position information of the agricultural machine 6; a shape detection device 5 that detects the shape of a target tree Ta; and a management server 8. The management server 8 includes a processing unit 22 that executes first identification processing for identifying the target tree Ta from among the plurality of trees T on the basis of the position information, and second identification processing for identifying the target tree Ta on the basis of a detection result from the shape detection device 5 when the target tree Ta cannot be identified by the first identification processing.

Description

Management system, management device, management method, and computer program

[0001] This application claims priority to Japanese Patent Application No. 2023-215789 filed on December 21, 2023, and incorporates by reference all of the contents of that application.

[0002] Patent Document 1 discloses a measuring device that irradiates a measurement object such as a fruit with light and performs spectroscopic analysis of the transmitted light to obtain quality values ​​such as sugar content and acidity of the measurement object.

[0003] Japanese Patent Application Laid-Open No. 2020-101409

[0004] The management system disclosed herein is a management system for managing a plurality of plants arranged in a row direction. The management system includes a measuring device mounted on a mobile object moving along the row direction and configured to acquire measurement information of the fruits of the plurality of plants, a positioning device mounted on the mobile object and configured to acquire location information of the mobile object, and a management device configured to execute a first identification process to identify a target plant bearing the fruit measured by the measuring device from among the plurality of plants. In the first identification process, the target plant is identified based on the location information.

[0005] FIG. 1 is a diagram showing an example of the overall configuration of a management system according to an embodiment. FIG. 2 is a block diagram showing an on-board network of an agricultural machine. FIG. 3 is a block diagram showing an example of the configuration of a management server. FIG. 4 is a plan view showing an example of a farm field. FIG. 5 is a diagram for explaining a training method. FIG. 6 is a sequence diagram showing processing when quality measurement is performed by the management system. FIG. 7 is a diagram showing an example of the contents of a training information database. FIG. 8 is a diagram showing an example of a measurement range when measuring the quality of fruit of a tree. FIG. 9 is a flowchart showing an example of a first identification process. FIG. 10 is a diagram showing an example of the contents of a position / shape database. FIG. 11 is a diagram for explaining how to determine a threshold X. FIG. 12 is a diagram schematically showing an example of how point cloud data are compared. FIG. 13 is a diagram showing an example of the contents of a measurement information database. FIG. 14 is a diagram showing an example of a second area. FIG. 15 is a sequence diagram showing a modified example of processing when quality measurement is performed.

[0006] [Problem to be Solved by the Present Disclosure] In general, it is preferable to control the quality of fruit grown in a farm for each tree. To control the quality of fruit for each tree, it is conceivable to assign identification information (ID) to each tree.

[0007] In this case, a worker who performs quality measurement in the field needs to identify the ID of the tree to be measured before measuring the quality of the fruit. Here, in order for the worker to identify the ID of the tree, one possible method is to attach an information tag on which identification information such as an RFID, barcode, or QR code (registered trademark) is recorded to each tree, and the worker can obtain the identification information of the tree to be measured by reading the information tag before measurement.

[0008] However, in the tree identification method using information tags, workers must measure the quality of the fruit and also acquire identification information from the information tags. Furthermore, managers must prepare the information tags and attach them to each tree. Thus, the tree identification method using information tags has the problem of imposing a heavy workload on both workers and managers.

[0009] [Effects of the Present Disclosure] According to the present disclosure, the workload can be reduced.

[0010] First, the contents of the embodiment will be listed and explained.

[0011] (1) A management system disclosed herein is a management system for managing a plurality of plants arranged in a row direction. The management system includes: a measuring device mounted on a mobile object moving along the row direction and configured to acquire measurement information of the fruits of the plurality of plants; a positioning device mounted on the mobile object and configured to acquire position information of the mobile object; a shape detection device mounted on the mobile object and configured to detect the shape of a target plant bearing the fruit measured by the measuring device; and a management device for identifying the target plant. The management device includes a processing unit configured to perform a first identification process for identifying a target plant bearing the fruit measured by the measuring device from among the plurality of plants based on the position information; and a second identification process for identifying the target plant based on the detection result of the shape detection device if the target plant cannot be identified by the first identification process.

[0012] According to the above configuration, a positioning device is provided on a mobile body equipped with a measuring device. Therefore, by knowing the positions of multiple plants in advance, the plant closest to the mobile body can be identified based on the position information acquired by the positioning device. In other words, of the multiple plants, the plant closest to the mobile body can be identified as the target plant. Therefore, the worker performing the measurement does not need to read the information tag, and the manager does not need to prepare the information tag. As a result, the workload on both the worker and the manager can be reduced. Furthermore, if the target plant cannot be identified by the first identification process, the management device can further perform a second identification process to identify the target plant based on the detection results of the shape detection device. Therefore, even if the target tree cannot be identified based on the position information, the target plant can be identified using the detection results of the shape detection device.

[0013] (2) In the management system of (1) above, it is preferable that the second identification process includes a process of acquiring shape information of the target plant based on the detection result, and a process of identifying the target plant from among the multiple plants based on a comparison of the shape information of the target plant with shape information of the multiple plants acquired in advance.

[0014] (3) In the management system of (1) or (2), the shape detection device may include at least one of a LIDAR and a camera. When the shape detection device is a LIDAR, the shape information can be acquired as point cloud data. When the shape detection device is a camera, the shape information can be acquired as image data.

[0015] (4) In the management system of (1), the first identification process may include a comparison process for comparing the distance between the reference position of the plurality of plants and the position of the position information with a predetermined threshold, and a process for identifying the target plant based on the result of the comparison process. In this case, the plant closest to the moving object among the plurality of plants can be identified as the target plant based on the distance between the reference position and the position of the position information. Furthermore, by comparing the distance between the reference position and the position of the position information with a predetermined threshold, the distance to be identified as the target plant can be adjusted. For example, the system can be configured to identify the target plant taking into account an error contained in the position information.

[0016] (5) In the management system of (1), the first identification process preferably includes a process of defining non-overlapping areas around the reference positions of the plurality of plants, and a process of identifying, when the position of the location information is within one of the areas, a plant among the plurality of plants corresponding to the one area as the target plant. In this case, the plant having the reference position closest to the moving object can be identified as the target plant.

[0017] (6) Here, the branches of a plant may be trained to extend in both directions along the row direction relative to the trunk, or may be trained to extend in one direction. When the branches extend in one direction relative to the trunk, and a moving object is located between adjacent trunks, the plant with the trunk closest to the moving object may not necessarily be the target plant. Therefore, in the management system described in (5) above, if the first identification process further includes a process of acquiring the extension direction of branches extending from the reference position along the row direction for the multiple plants, it is preferable that the area be set to a range that differs depending on the extension direction. In this case, by varying the area depending on the extension direction of the branches, the target plant can be appropriately identified.

[0018] (7) In addition, in any one of the management systems (1) to (6) above, the processing unit may be configured to further execute, if the target plant cannot be identified by the first identification process, a process of acquiring the extension direction of branches extending from the trunks of the multiple plants along the row direction, and a third identification process of identifying the target plant based on the position information and the extension direction.

[0019] (8) In the management system of any one of (1) to (7) above, the management device may be configured to further execute a process of storing, when the target plant is identified, identification information assigned to the target plant and the measurement information in association with each other. In this case, the measurement information and the plant identification information can be managed in association with each other, making it easier to manage the measurement information.

[0020] (9) In the management system according to any one of (1) to (7), the fruit may include grapes. In this case, the quality of the grapes can be appropriately managed for each tree.

[0021] (10) In the management system of (9), the measurement information may include at least one of sugar content, acidity, pH, and polyphenol content. In this case, it is possible to manage values ​​appropriate for the quality of grapes.

[0022] (11) From another perspective, the present disclosure provides a management device for managing a plurality of plants arranged in a row direction, the management device including: a processing unit that executes a process of acquiring measurement information of fruits of the plurality of plants by a measurement device provided on a mobile object moving along the row direction; a process of acquiring position information of the mobile object by a positioning device provided on the mobile object; a process of acquiring a detection result of a target plant bearing the fruit measured by the measurement device by a shape detection device provided on the mobile object; a first identification process that identifies the target plant from among the plurality of plants based on the position information; and a second identification process that identifies the target plant based on the detection result if the target plant cannot be identified by the first identification process.

[0023] (12) From another perspective, the present disclosure provides a method for managing a plurality of plants lined up in a row direction, the method including the steps of acquiring measurement information of fruits of the plurality of plants by a measuring device provided on a mobile object moving along the row direction, acquiring position information of the mobile object by a positioning device provided on the mobile object, acquiring a detection result of a target plant bearing the fruit measured by the measuring device by a shape detection device provided on the mobile object, a first identification step of identifying the target plant from among the plurality of plants based on the position information, and, if the target plant cannot be identified by the first identification step, a second identification step of identifying the target plant based on the detection result.

[0024] (13) From another perspective, the present disclosure provides a computer program for causing a computer to execute a process for managing a plurality of plants lined up in a row direction, the computer program causing the computer to execute the following steps: acquiring measurement information of fruits of the plurality of plants using a measuring device provided on a mobile object moving along the row direction; acquiring position information of the mobile object using a positioning device provided on the mobile object; acquiring a detection result of a target plant bearing the fruit measured by the measuring device using a shape detection device provided on the mobile object; a first identification step of identifying the target plant from among the plurality of plants based on the position information; and, if the target plant cannot be identified by the first identification step, a second identification step of identifying the target plant based on the detection result.

[0025] [Details of the embodiment] Preferred embodiments will now be described with reference to the drawings. Note that at least some of the embodiments described below may be combined in any desired manner.

[0026] [Overall Configuration of the Management System] FIG. 1 is a diagram illustrating an example of the overall configuration of a management system according to an embodiment. In FIG. 1 , the management system 1 has a function of managing plants cultivated in a field F. In this embodiment, the field F is, for example, a vineyard for cultivating grapes, which are used to make wine. Accordingly, a plurality of trees T are cultivated in the field F as plants. The plurality of trees T are fruit trees, i.e., grapevines. The plurality of trees T are arranged in multiple rows. The management system 1 has a function of measuring values ​​related to the quality of the grapes bearing on the trees T. More specifically, the management system 1 acquires measurement values ​​related to the quality of the fruit using sensors and measuring devices. The measurement values ​​include sugar content, acidity, pH, polyphenol content, and the like. The management system 1 assigns identification information (tree IDs) to the plurality of trees T and manages the identification information and the measurement values ​​in association with each other. In this way, the management system 1 manages the measurement values ​​for each tree.

[0027] The management system 1 includes a quality measuring device 2, a positioning device 4, a shape detection device 5, an agricultural machine 6, a management server 8, and a management terminal 10. The quality measuring device 2, the positioning device 4, and the shape detection device 5 are mounted on the agricultural machine 6. The quality measuring device 2 is a device that performs measurements related to the quality of the fruit described above. The shape detection device 5 is a device that detects the shape of the tree for which measurements related to fruit quality have been performed.

[0028] The agricultural machine 6, the management server 8, and the management terminal 10 are connected to each other so that they can communicate with each other via a public network NW such as the Internet. The agricultural machine 6 has a communication function using, for example, a mobile communication system. The agricultural machine 6 is connected to the public network NW via a wireless base station BS of the mobile communication system.

[0029] The management server 8 has a function of managing the measurement values ​​for each tree T. The management terminal 10 is a terminal operated by an operator 14 of the management system 1. The management terminal 10 has a function of accepting operations on the management system 1 by the operator 14, and a function of outputting measurement values ​​and the like managed by the management server 8 to the operator 14.

[0030] The agricultural machine 6 is a mobile body that moves within the field F. The agricultural machine 6 is, for example, a tractor. The agricultural machine 6 is capable of traveling within the field F. The agricultural machine 6 travels between rows of multiple trees T within the field F and can approach all of the trees T within the field F. The agricultural machine 6 can travel within the field F by manual operation by an operator, or can travel within the field F by automatic operation based on control commands from the management server 8 or control commands from the management terminal 10 based on input from an operator 14.

[0031] 2 is a block diagram showing the on-board network of the agricultural machine 6. The agricultural machine 6 has an on-board network 6a that complies with a communication standard such as CAN (Controller Area Network). The on-board network 6a includes a control device 18, an input / output device 19, and a communication device 20.

[0032] The communication device 20 functions as a mobile terminal in a mobile communication system. Therefore, the communication device 20 communicates wirelessly with a radio base station BS. The control device 18 is an ECU (Electronic Control Unit) that controls the driving system and work system of the agricultural machine 6. The control device 18 controls each part of the agricultural machine 6 based on the driving operation of the operator. The control device 18 is connected to the public network NW via the communication device 20. Therefore, the control device 18 is connected to the management server 8 and the management terminal 10 so that they can communicate with each other. The control device 18 exchanges necessary information with the management server 8 and the management terminal 10 via the public network NW. If the agricultural machine 6 is capable of autonomous driving, the control device 18 controls cameras and sensors that grasp the surroundings of the agricultural machine 6, as well as the drive system and steering system of the agricultural machine 6, based on control commands and the like from the management server 8 and the management terminal 10, and performs processing to execute autonomous driving. The input / output device 19 has a function to accept operations from the operator and a function to output information and the like to the operator.

[0033] In addition, the quality measurement device 2, the positioning device 4, and the shape detection device 5 are connected to the in-vehicle network 6a. The quality measurement device 2, the positioning device 4, and the shape detection device 5 are connected to the public network NW by the communication device 20. Therefore, the quality measurement device 2, the positioning device 4, and the shape detection device 5 are connected to the management server 8 and the management terminal 10 so as to be able to communicate with each other. This allows the quality measurement device 2, the positioning device 4, and the shape detection device 5 to provide necessary information to the management server 8 and the management terminal 10. Furthermore, the quality measurement device 2, the positioning device 4, and the shape detection device 5 can be controlled by the management server 8 and the management terminal 10.

[0034] The positioning device 4 is a device that measures the vehicle position using GNSS positioning, specifically, a multi-GNSS receiver that supports multiple types of satellite positioning systems. The positioning device 4 measures the vehicle position by communicating with satellites of at least one of the following systems in addition to GPS. Examples of satellite positioning systems other than GPS include Russia's "GLONASS," the European Commission's "Galileo," China's "BeiDou," Japan's "Michibiki (QZSS)," India's "IRNSS," the United States' "WAAS," Europe's "EGNOS," Japan's "MSAS," and India's "GAGAN."

[0035] The positioning method of the positioning device 4 may be either point positioning or relative positioning, but since high accuracy is required in field work, it is preferable to use relative positioning. Therefore, the positioning device 4 may be a mobile station of RTK (Real-Time Kinematics)-GNSS. In this case, the mobile station receives correction information wirelessly from a reference station installed at a location whose coordinates are known, and corrects the detected position using the received correction information. Therefore, the vehicle position can be determined with higher accuracy than when no correction is performed.

[0036] The quality measuring device 2 is a device that measures fruit quality through spectroscopic analysis using near-infrared light. The quality measuring device 2 captures light within a predetermined measurement range and performs spectroscopic analysis. The quality measuring device 2 has the function of separating light from fruit contained within the measurement range and acquiring spectral information in the near-infrared region. The spectral information in the near-infrared region includes information indicating the quality value of the fruit. Therefore, the quality measuring device 2 outputs the spectral information in the near-infrared region as measurement information. The quality measuring device 2 provides the measurement information to the management server 8. The management server 8 acquires measurement values ​​such as sugar content, acidity, pH, and polyphenol content based on the measurement information.

[0037] The quality measurement device 2 includes, for example, a device that primarily performs spectroscopic analysis of light reflected from fruit and a device that primarily performs spectroscopic analysis of light transmitted through fruit. Devices that perform measurements using light reflected from fruit include spectroscopic cameras such as multispectral cameras and hyperspectral cameras. Devices that perform measurements using light transmitted through fruit include devices that include a light-emitting unit and a light-receiving unit that are placed near the fruit.

[0038] The shape detection device 5 is a device that detects the shape of the tree bearing fruit that has been measured by the quality measurement device 2. The shape detection device 5 in this embodiment is, for example, a LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) device. The detection range of the shape detection device 5 is set to include the measurement range of the quality measurement device 2. Thus, the shape detection device 5 detects the presence or absence of an object in the measurement range of the quality measurement device 2, as well as the position and shape of the object. In this way, the shape detection device 5 detects the shape of the tree bearing fruit that has been measured. The detection results are provided to the management server 8. The management server 8 acquires point cloud data based on the detection results of the shape detection device 5. The point cloud data is shape information. The point cloud data indicates the three-dimensional coordinates of each position of an object included in the detection range of the shape detection device 5.

[0039] [Regarding the Management Server 8] Fig. 3 is a block diagram showing an example configuration of the management server 8. As shown in Fig. 3, the management server 8 (management device) is a type of information processing device having a processing unit 22, a storage unit 24, and a communication device 26. The communication device 26 is a communication interface capable of communicating with external devices via a public network NW. The processing unit 22 is, for example, one of various processors suitable for computer control, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), or an FPGA (Field Programmable Gate Array).

[0040] The storage unit 24 is, for example, a flash memory, a hard disk, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The storage unit 24 stores computer programs and necessary information to be executed by the processing unit 22. The processing unit 22 executes computer programs stored in a computer-readable, non-transitory recording medium such as the storage unit 24 to realize various processing functions of the processing unit 22.

[0041] The storage unit 24 also stores a position and shape information database 28, a measurement information database 30, and a tailoring information database 32. These databases 28, 30, and 32 may be stored in a storage device (not shown) connected to the management server 8. These databases 28, 30, and 32 will be described in detail later.

[0042] As described above, the processing unit 22 has the function of acquiring measurement values ​​such as sugar content, acidity, pH, and polyphenol content based on the measurement information provided by the quality measurement device 2. The processing unit 22 also acquires point cloud data based on the detection results from the shape detection device 5. Furthermore, the processing unit 22 executes processes such as a first identification process and a second identification process, and also has the function of managing the measurement values ​​for each tree. These processes will be described in detail later.

[0043] [Regarding fruit quality measurement in a farm field F] Figure 4 is a plan view showing an example of a farm field F. In Figure 4, the top-to-bottom direction of the page corresponds to the north-south direction, and the left-to-right direction of the page corresponds to the east-west direction. Fruit quality measurement is performed by having an agricultural machine 6 travel within the farm field F. In the farm field F shown in Figure 4, for example, a plurality of trees T (eight trees in the example) are cultivated. The trees T are arranged to form rows aligned in the north-south direction. The trees T form two rows. Each row includes four trees T. The distance between the two rows (the distance in the east-west direction) is wider than the distance between a pair of adjacent trees T within a row (the distance in the north-south direction). The agricultural machine 6 travels between the two rows.

[0044] Each of the eight trees T cultivated in the field F is assigned a tree ID as identification information. The eight trees T are assigned integers from 1 to 8 as their tree IDs. The trees T in this embodiment are trained to form hedges.

[0045] Figure 5 is a diagram for explaining the training method. The top part of Figure 5 is a schematic diagram of double-sided training. In the top diagram of Figure 5, the tree T is trained as a hedge. Therefore, branches B extend laterally from the trunk S. In double-sided training, branches B extend on both sides of the trunk S. Double-sided training refers to the training method in which branches B extend on both sides of the trunk S, and the shape of the tree T in which branches B extend on both sides of the trunk S.

[0046] Also, the bottom part of Figure 5 is a schematic diagram of one-sided training. In the bottom diagram of Figure 5, the tree T is also trained as a hedge. In one-sided training, the branch B extends to only one side of the trunk S. One-sided training refers to a training method in which such a branch B extends to one side of the trunk S, and to the shape of the tree T in which the branch B extends to one side of the trunk S. Generally, the training method for multiple trees T cultivated in one field is the same.

[0047] As shown in Figure 4, the tree T in this embodiment is trained on both sides. Furthermore, the branches B of the tree T in this embodiment extend in the north-south direction. The agricultural machine 6 travels along the extension direction of the branches B of the tree T. The quality measuring device 2 is provided on the side of the agricultural machine 6. In principle, the quality measuring device 2 is oriented in a direction perpendicular to the extension direction.

[0048] The work of measuring the quality of fruit in the field F is performed on all eight trees T in the field F. The quality measurement may be performed by operation of the worker driving the agricultural machine 6, or by remote control based on commands from the operator 14. When the quality measurement is performed by the worker, the agricultural machine 6 is manually driven by the worker. Furthermore, the quality measurement by the quality measuring device 2 is performed by manual operation of the worker. When the quality measurement is performed by remote control based on commands from the operator 14, the agricultural machine 6 travels by automatic driving, and the quality measuring device 2 performs the quality measurement by control based on commands from the operator 14. In this embodiment, a case where the worker driving the agricultural machine 6 performs the quality measurement will be described.

[0049] The worker gets on the agricultural machine 6 and travels beside a row of multiple trees T in the field F along the dashed lines in Figure 4. The agricultural machine 6 approaches each tree T in order, starting with the tree T with tree ID = 1, and performs quality measurement. When the worker approaches each tree T and reaches the side of each tree T, the worker stops the agricultural machine 6 and performs quality measurement using the quality measurement device 2. In this way, the quality of the fruit on each tree T is measured.

[0050] Fig. 6 is a sequence diagram showing the processing when quality measurement is performed by the management system 1. Fig. 6 illustrates a case in which an operator parks the agricultural machine 6 to the side (west side) of the tree T with tree ID = 1 as shown in Fig. 4 and measures the fruit of the tree T with tree ID = 1.

[0051] 6 , first, the worker transmits a start notification to the management server 8 indicating that he or she will begin measurement work and the field ID of the field F that is the target of the work (step S1 in FIG. 6 ). The management server 8 manages multiple fields F. A field ID is set for each of the multiple fields F as identification information. By receiving the field ID, the management server 8 can recognize the field F that is the target of the work. The worker may transmit the start notification and the field ID using the input / output device 19 of the agricultural machine 6, or may transmit the start notification and the field ID using a mobile terminal that is owned by the worker and that is connectable to a mobile communication system.

[0052] The management server 8, which has received the start notification and the field ID, acquires the training information corresponding to the received field ID (step S2 in FIG. 6). The management server 8 references the training information database 32 and acquires the training information.

[0053] FIG. 7 is a diagram showing an example of the contents of the training information database 32. Training information is information that indicates the details of training of trees T for each field F. Training information includes "training method" and "extension direction." "Training method" is information that indicates either bilateral training or unilateral training. Therefore, either "bilateral" or "unilateral" is registered in the "training method" column in the training information database 32. "Extension direction" is information that indicates the direction in which the branches of the tree T extend.

[0054] Field IDs and training information are associated and registered in the training information database 32. The management server 8 references the training information database 32 and acquires the training information corresponding to the received field ID.

[0055] Meanwhile, the worker who sent the start notification and the field ID moves the agricultural machine 6 close to the tree T that is the measurement target. In this case, the worker moves the agricultural machine 6 to the side of the tree T with tree ID = 1 and stops it (see FIG. 4). Next, the worker acquires the measurement information and position information (step S3 in FIG. 6).

[0056] The worker operates the quality measurement device 2 and measures the fruit on the tree T with ID=1, including the fruit on the tree T in the measurement range. Figure 8 is a diagram showing an example of the measurement range when measuring the quality of the fruit on the tree T. As shown in Figure 8, the worker uses the quality measurement device 2 to measure fruit K, which is a grape on the tree T.

[0057] FIG. 8 illustrates a case in which the quality measuring device 2 is a spectroscopic analyzer that separates light transmitted through grapes. As shown in FIG. 8 , the quality measuring device 2 includes a light-emitting / receiving unit 2a and a main body 2b. The light-emitting / receiving unit 2a is attached to an arm (not shown) that extends from the side of the agricultural machine 6 in a direction perpendicular to the traveling direction of the agricultural machine 6. The light-emitting / receiving unit 2a has the function of projecting near-infrared light onto a fruit K placed nearby. The light-emitting / receiving unit 2a also has the function of receiving light transmitted through the fruit K. The transmitted light is the near-infrared light projected from the light-emitting / receiving unit 2a that passes through the fruit K. The main body 2b separates the transmitted light and acquires spectral information. The main body 2b provides the spectral information to the management server 8 as measurement information. Therefore, the measurement range of the quality measuring device 2 is a portion of the fruit K irradiated with near-infrared light. If the quality measuring device 2 is a spectroscopic camera, the measurement range is the imaging range of the spectroscopic camera.

[0058] When measuring the quality, the worker advances the arm to bring the light projecting and receiving unit 2a close to the fruit K. Next, the worker causes the light projecting and receiving unit 2a to irradiate near-infrared light onto the fruit K and receive the transmitted light. In this way, the worker causes the quality measuring device 2 to acquire measurement information.

[0059] The positioning device 4 also acquires location information indicating the current location of the agricultural machine 6. The positioning device 4 constantly acquires location information. Therefore, the location information acquired by the positioning device 4 also includes the location information of the agricultural machine 6 when the quality measuring device 2 measured the fruit K.

[0060] The acquired measurement information is transmitted to the management server 8 (step S4 in FIG. 6). The measurement information is transmitted to the management server 8 by the quality measurement device 2. In addition, the position information is transmitted to the management server 8 by the positioning device 4. The position information transmitted to the management server 8 is the position information at the time when the measurement information is acquired or transmitted.

[0061] Upon receiving the measurement information and the location information, the management server 8 executes a first identification process (step S5 in FIG. 6 ). The management server 8 uses the location information received at the same time as the measurement information in the first identification process. The first identification process is a process of identifying, based on the location information, the tree T bearing the fruit K measured by the quality measurement device 2 from among the eight trees T in the field F. In other words, the first identification process is a process of identifying, based on the location information, which of the eight trees T in the field F is the tree T bearing the fruit K measured by the quality measurement device 2. Hereinafter, the identified tree T bearing the fruit K measured by the quality measurement device 2 will also be referred to as the target tree Ta. In the first identification process, the management server 8 identifies the tree ID of the target tree Ta. The first identification process is a process of identifying the tree ID of the target tree Ta based on the location information.

[0062] FIG. 9 is a flowchart showing an example of the first identification process. As shown in FIG. 9, the management server 8 determines whether the field F to be worked on is trained on both sides (step S21 in FIG. 9). If the field F to be worked on is trained on both sides, the management server 8 proceeds to step S22, identifies the target tree Ta based on the first area, and returns to step S5 in FIG. 6. On the other hand, if the field F to be worked on is trained on one side, the management server 8 proceeds to step S23, identifies the target tree Ta based on the second area, and returns to step S5 in FIG. 6. The first and second areas are used to identify the target tree Ta based on the position information from the positioning device 4.

[0063] Here, first, the identification by first area (step S22 in FIG. 9) will be described. In the identification by first area, the management server 8 first refers to the position / shape information database 28. FIG. 10 is a diagram showing an example of the contents of the position / shape information database 28. A plurality of position / shape information databases 28 are stored in the storage unit 24. The plurality of position / shape information databases 28 correspond to a plurality of fields F. Of the plurality of position / shape information databases 28, the management server 8 refers to the position / shape information database 28 of the field F that is the work target.

[0064] As shown in FIG. 10 , tree IDs, tree position information, area information, and reference point cloud data are registered in the position / shape information database 28 (hereinafter also referred to as position / shape DB 28) in correspondence with one another. As described above, the tree ID is identification information assigned to the eight trees T in the field F. The tree position information includes the latitude and longitude of the trunk S of the tree T. In this embodiment, the reference position indicating the position of the tree T is the position of the tree trunk S. The tree position information is position information of the trunk S of the tree T. In other words, the trunk position information is information indicating the reference position of the tree T. The tree position information is registered in the position / shape information database 28 by measuring each tree T in advance.

[0065] The area information is information necessary to set the first area and the second area. The reference point cloud data is point cloud data obtained by scanning each tree T in advance using LIDAR. Therefore, the reference point cloud data is data indicating the three-dimensional coordinates of the position of each part that makes up the tree T. The reference point cloud data is data that represents the three-dimensional shape of the tree T, and is reference shape information that indicates the reference shape of the tree T.

[0066] The management server 8 refers to the position / shape information database 28, calculates the distance D between the position of each of the eight trees T (the position indicated by the tree position information) and the position in the position information, and identifies the tree T with the shortest distance D from among the eight trees T. Here, it is assumed that the agricultural machine 6 is stopped next to the tree T with tree ID = 1, and that the tree T with the shortest distance D is the tree T with tree ID = 1. Therefore, the management server 8 identifies the tree T with the shortest distance D as the tree T with tree ID = 1.

[0067] Next, the management server 8 determines whether the distance D is equal to or less than the threshold X set for the tree T with tree ID = 1. The threshold X is registered as area information in the position / shape information database 28. If the management server 8 determines that the distance D is equal to or less than the threshold X, it identifies the target tree Ta as the tree T with tree ID = 1. If the management server 8 determines that the distance D is greater than the threshold X, it determines that it cannot identify the tree ID of the target tree Ta. Thus, in the first identification process, the management server 8 performs a comparison process in which the distance D between the positions of the trunks S of the multiple trees T and the position indicated by the position information is compared with the threshold X, and a process in which the management server 8 identifies the target tree Ta based on the results of the comparison process.

[0068] The threshold value X is determined by the distance between the tree T with tree ID = 1 and the surrounding trees T. FIG. 11 is a diagram for explaining how to determine the threshold value X. In FIG. 11, point P1 indicates the trunk S of the tree T with tree ID = 1. Point P2 indicates the trunk S of the tree T with tree ID = 2. Point P8 indicates the trunk S of the tree T with tree ID = 8. Point P7 indicates the trunk S of the tree T with tree ID = 7.

[0069] First, the management server 8 identifies the tree T that is closest to the tree T with tree ID = 1 among the other trees T adjacent to it. As described above, the distance between the two rows (distance in the east-west direction) is wider than the distance between a pair of adjacent trees T within the row (distance in the north-south direction). Therefore, the tree T with tree ID = 2 (point P2) is closer to the tree T with tree ID = 1 (point P1) than the trees T with tree IDs = 7 and 8 (points P7 and P8). In other words, the tree T with tree ID = 2 is closest to the tree T with tree ID = 1.

[0070] The management server 8 calculates the threshold value X based on the relationship between point P2 and point P1 of the tree T with tree ID=2. The management server 8 calculates the threshold value X based on the following formula: Threshold value X=((W1) / 2)−Y

[0071] In the above formula, W1 is the distance between points P1 and P2. Point PC is the midpoint between points P1 and P2. Y is the error generated by the positioning device 4. Threshold X is a value obtained by subtracting error Y from half the value of distance W1 (the distance between points P1 and PC). In FIG. 11, the center of the dashed circle is point P1, and the radius of the dashed circle is threshold X. Therefore, when point PN, which is the position indicated by the location information, is within the dashed circle, the distance D is less than or equal to threshold X. In other words, the area surrounded by the dashed circle is the first area A1. Because the first area A1 is determined based on point P2 of the tree T closest to point P1, it does not overlap with first areas similarly determined at other points P2 and P8.

[0072] In this embodiment, the threshold value X is registered in the position / shape information database 28, but the management server 8 may successively obtain the threshold value X when the first identification process is executed. Also, the management server 8 may register the threshold value X obtained once in the position / shape information database 28 and reuse it.

[0073] If point PN is within first area A1, management server 8 identifies tree T with tree ID = 1 as target tree Ta. Conversely, if point PN is outside first area A1, distance D is greater than threshold X. Therefore, management server 8 determines that target tree Ta cannot be identified.

[0074] In this way, the first identification process includes a process of defining non-overlapping first areas A1 around the trunks S of each of the eight trees T using the threshold value X set for each of the eight trees T, and a process of identifying, when the location information indicates the location within one of the multiple first areas A1, the tree T corresponding to one of the first areas A1 among the eight trees T as the target tree Ta. Note that, in the first identification process, the threshold value X is calculated based on the above formula, so that the target tree Ta can be identified while taking into account the error of the positioning device 4.

[0075] 6, after completing the first identification process (step S5 in FIG. 6), the management server 8 proceeds to step S6. If the target tree Ta cannot be identified as a result of the first identification process, the management server 8 proceeds to step S7 and transmits a shape acquisition command to the agricultural machine 6 (step S7 in FIG. 6).

[0076] Upon receiving the shape acquisition command, the agricultural machine 6 outputs the shape acquisition command to the worker via the input / output device 19. In this way, the management server 8 outputs the shape acquisition command to the worker via the agricultural machine 6. The worker, recognizing that the shape acquisition command has been output, uses the shape detection device 5 to detect the shape of the target tree Ta (step S8 in FIG. 6 ). At this time, the detection range of the shape detection device 5 is set to include the measurement range of the quality measurement device 2, as described above. Therefore, the detection result of the shape detection device 5 includes information about the shape of the target tree Ta. The detection result of the shape detection device 5 is transmitted to the management server 8 (step S9 in FIG. 6 ). The detection result is transmitted to the management server 8 by the shape detection device 5.

[0077] Upon receiving the detection results from the shape detection device 5, the management server 8 acquires point cloud data of the target tree Ta based on the detection results and executes a second identification process (step S10 in FIG. 6 ). The second identification process is a process for identifying the tree ID of the target tree Ta based on the point cloud data, which is shape information of the target tree Ta. The management server 8 identifies the tree ID of the target tree Ta based on a comparison of the point cloud data (shape information) of the target tree Ta with the reference point cloud data (shape information) registered in the position and shape information database 28.

[0078] The management server 8 selects, as comparison targets for the point cloud data of the target tree Ta, the tree T with tree ID = 1, which is the tree T closest to the position indicated by the location information, and the tree T with tree ID = 2, which is adjacent to it in the column direction. The management server 8 compares the point cloud data of the target tree Ta with the reference point cloud data of the tree T with tree ID = 1. The management server 8 also compares the point cloud data of the target tree Ta with the reference point cloud data of the tree T with tree ID = 2.

[0079] FIG. 12 is a diagram schematically illustrating an example of how point cloud data are compared. The management server 8 compares point cloud data by extracting feature points of tree T from the point cloud data and matching the feature points. Therefore, the management server 8 extracts feature points of tree T based on each point cloud data. In FIG. 12, multiple feature points of each tree are schematically illustrated within a rectangular frame. The management server 8 matches the feature points of target tree Ta with the feature points of tree T with tree ID = 1 to determine the similarity. The management server 8 also matches the feature points of target tree Ta with the feature points of tree T with tree ID = 2 to determine the similarity. The management server 8 determines that a tree T with a high similarity between the feature points of target tree Ta is the same tree T as target tree Ta. In this embodiment, the management server 8 identifies tree T with tree ID = 1 as target tree Ta.

[0080] In this embodiment, the example shows a case in which the tree T with tree ID = 1, which is the tree T closest to the position indicated by the position information in the first identification process, and the tree T with tree ID = 2, which is adjacent to it in the column direction, are selected as comparison targets for the point cloud data of the target tree Ta, but if there are trees T on both sides of the tree T with tree ID = 1 in the column direction, the trees T on both sides in the column direction may also be used as comparison targets. In this case, it is possible to narrow down the multiple trees T to the tree T that is most likely to be the target tree Ta, and identify it using the point cloud data.

[0081] As shown in Fig. 6, after completing the second identification process (step S10 in Fig. 6), the management server 8 proceeds to step S11 and registers the measurement information (step S11 in Fig. 6). Furthermore, if the management server 8 is able to identify the target tree Ta in step S5, the management server 8 proceeds via step S6 to step S11 and registers the measurement information (step S11 in Fig. 6). The management server 8 registers the measurement information transmitted in step S4 in Fig. 6 in the measurement information database 30.

[0082] 13 is a diagram showing an example of the contents of the measurement information database 30. A plurality of measurement information databases 30 are stored in the storage unit 24. The plurality of measurement information databases 30 correspond to a plurality of fields F. The management server 8 refers to the measurement information database 30 of the field F that is the target of work, out of the plurality of measurement information databases 30.

[0083] 13, tree IDs and measurement information are registered in association with each other in the measurement information database 30 (hereinafter also referred to as the measurement information DB 30). The measurement information is registered in the measurement information DB 30 together with the date and time when the measurement was performed.

[0084] The management server 8 registers the measurement information together with the measurement date and time in the field corresponding to the tree ID of the identified target tree Ta. In this example, the management server 8 registers the measurement information and measurement date and time in the field for tree ID=1. The measurement date and time are provided to the management server 8 by the quality measurement device 2 adding information indicating the measurement date and time to the measurement information. The measurement date and time may also be set to the date and time when the measurement information is received by the management server 8.

[0085] As shown in Fig. 6, once the registration of the measurement information is complete, the management server 8 transmits a movement command to the agricultural machine 6 (step S12 in Fig. 6). The agricultural machine 6 that has received the movement command outputs the movement command to the worker via the input / output device 19. In this way, the management server 8 outputs the movement command to the worker via the agricultural machine 6. The worker, recognizing that the movement command has been output, operates the agricultural machine 6 and moves the agricultural machine 6 to the next tree T (step S13 in Fig. 6).

[0086] Thereafter, the worker sequentially performs quality measurement work on each tree T, and the management server 8 registers the measurement information in the measurement information database 30 while identifying the target tree Ta according to the quality measurement work.

[0087] According to the above configuration, the positioning device 4 is provided on the agricultural machine 6 (mobile body) equipped with the quality measurement device 2. Tree position information for multiple trees T is registered in advance in the position / shape information database 28. Therefore, the plant closest to the agricultural machine 6 can be identified based on the position information acquired by the positioning device 4. That is, of the multiple trees T, the tree T closest to the agricultural machine 6 can be identified as the target tree Ta. Therefore, the worker performing the measurement does not need to read information tags, and the manager does not need to prepare information tags to attach to the trees T. In other words, once the worker performs the quality measurement work and, if necessary, the shape detection work, the measurement values ​​obtained by the quality measurement work are managed for each tree T by tree ID. Therefore, the worker does not need to perform any work related to managing the measurement values. As a result, the workload on both the worker and the manager can be reduced.

[0088] Furthermore, in this embodiment, even if the target tree Ta cannot be identified based on the position information, the target tree Ta can be identified using the detection result of the shape detection device 5. Furthermore, the first identification process of this embodiment includes a comparison process in which a distance D between the positions of the trunks S of the multiple trees T and the position indicated by the position information is compared with a threshold X, and a process in which the target tree Ta is identified based on the result of the comparison process. Therefore, the tree T that is closest to the agricultural machine 6 among the multiple trees T can be identified as the target tree Ta based on the distance D. Furthermore, by comparing the distance D with the threshold X, the distance to be identified as the target tree Ta can be adjusted, and for example, the system can be configured to identify the target tree Ta taking into account an error of the positioning device 4 included in the position information.

[0089] 9 , if it is determined in step S21 of the first identification process that the field is not bilaterally trained (i.e., is single-sided), the management server 8 proceeds to step S23 and identifies the field by a second area. For example, suppose that the training method for field F is single-sided, and the extension direction of branch B faces south. In identifying the field by a second area, the management server 8 references the position and shape information database 28 and sets the second area.

[0090] Fig. 14 is a diagram showing an example of a second area. In Fig. 14, a second area A2 is set between points P1 and P2. In Fig. 14, point P1 is a point indicating the trunk S of a tree T with tree ID = 1. Point P2 is a point indicating the trunk S of a tree T with tree ID = 2.

[0091] The second area A2 has a rectangular shape with sides aligned in the north-south and east-west directions. In Fig. 14, W1 is the distance between point P1 and point P2. The north-south distance W2 of the second area A2 is the distance W1 minus the error Y. The error Y is the error that occurs in the positioning device 4. The east-west distance W3 of the second area A2 is the same as the distance W1.

[0092] In Figure 14, the tree T with tree ID = 1 is trained to one side, and the branch B extends southward, so that the branch B of the tree T with tree ID = 1 extends to the vicinity of point P2, as shown in Figure 14. Therefore, in this case, the management server 8 sets the second area A2 for the tree T with tree ID = 1 between points P1 and P2.

[0093] In a similar manner, the management server 8 sets a second area A2 for each of the eight trees T. When the location information indicates that the location is within one of the second areas A2, the management server 8 identifies the tree T among the eight trees T that corresponds to the one second area A2 as the target tree Ta.

[0094] The distance required to define the second area A2 is registered in the position / shape information database 28. The management server 8 refers to the position / shape information database 28 to obtain and use the distance required to define the second area A2. The management server 8 may also sequentially determine the distance required when executing the first identification process. The management server 8 may also register a value obtained once in the position / shape information database 28 and reuse the value.

[0095] If the target tree Ta cannot be identified in the first identification process in the second area A2, the management server 8 proceeds to steps S7-S10 in Fig. 6 and executes the second identification process. The second identification process is as described above.

[0096] 14, even if point PN, which indicates the position of the agricultural machine 6, is located closer to point P2 than point P1, a branch B of a tree T with tree ID = 1 and whose trunk S is located at point P1 is located to the side of the agricultural machine 6. As described above, there are two ways to train the branch B of the tree T: bilateral training, in which the branch B is trained to extend in both directions along the row direction of the trunk S, and unilateral training, in which the branch B is trained to extend in one direction. In the case of unilateral training, if the agricultural machine 6 is located between adjacent tree trunks S, the tree T with the trunk S closest to the agricultural machine 6 is not necessarily the target tree Ta.

[0097] Therefore, the first identification process of this embodiment further includes a process of acquiring the extension direction of the branches B extending from the trunk S along the row direction for the plurality of trees T, and sets areas (first area A1 and second area A2) having different ranges depending on the extension direction. In this way, by varying the range of the area depending on the extension direction of the branch, it is possible to appropriately identify the target tree Ta.

[0098] [Others] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. For example, in the above embodiment, the shape detection device 5 is a LIDAR, but a camera may be used instead of the LIDAR. When the shape detection device 5 is a camera, the management server 8 acquires image data based on the detection results from the shape detection device 5. Furthermore, the management server 8 identifies an imaged portion of the target tree Ta from the image data and extracts feature points of the target tree Ta based on this imaged portion.

[0099] Furthermore, in the above embodiment, the quality measurement by the quality measurement device 2 (step S3 in FIG. 6) performed by the operator and the shape detection by the shape detection device 5 (step S8 in FIG. 6) performed by the operator can also be performed without operator operation by having the management server 8 remotely control the quality measurement device 2 and the shape detection device 5.

[0100] Furthermore, in the above embodiment, the quality measuring device 2, positioning device 4, and shape detection device 5 are mounted on the agricultural machine 6, which is a mobile body, but the quality measuring device 2, positioning device 4, and shape detection device 5 may be carried by a worker instead of the agricultural machine 6. In this case, the worker himself is a mobile body, and moves around on foot to measure the quality of fruit on the tree T.

[0101] In addition, in the above embodiment, the field F is a vineyard and the trees T are grape trees, but this is not limiting, and the trees T may be vine-type fruit trees other than grapes. Furthermore, the field F may be any field in which plants other than trees are cultivated.

[0102] If the target tree Ta cannot be identified based on the position information of the agricultural machine 6, the target tree Ta may be identified based on training information. Fig. 15 is a sequence diagram showing a modified example of the processing when performing quality measurement. This is a modified example of Fig. 6. In Fig. 15, the same processing as in Fig. 6 is executed when the same reference numerals are used.

[0103] 15, if the target tree Ta cannot be identified based on the location information (No in S32 in FIG. 15), the management server 8 proceeds to step S33 and acquires training information for the field F (step S33 in FIG. 15). After acquiring the training information, the management server 8 performs a third identification process (step S34 in FIG. 15).

[0104] In the third identification process, if the training information indicates that the training method for the field F is one-sided training, the management server 8 identifies the target tree Ta in the third identification process (step S35 in FIG. 15, Yes). That is, in one-sided training, the branch B extends to only one side of the trunk S, so the management server 8 can identify the target tree Ta by referring to the orientation of the direction in which the branch B extends.

[0105] On the other hand, in the identification process, if the training information indicates that the field F is trained bilaterally, the branches B extend on both sides of the trunk S, and therefore the management server 8 determines that the target tree Ta cannot be identified in the third identification process (No in step S35 in FIG. 15 ). If the target tree Ta cannot be identified as a result of the third identification process (No in step S35 in FIG. 15 ), the management server 8 proceeds to step S7 and transmits a shape acquisition command to the agricultural machine 6 (step S7 in FIG. 15 ). Then, the management server 8 acquires point cloud data of the target tree Ta based on the detection results, executes the second identification process, and identifies the tree ID of the target tree Ta based on the point cloud data, which is shape information of the target tree Ta, through the second identification process.

[0106] In this modified example, if the management server 8 cannot identify the target tree Ta by the first identification process, the management server 8 acquires the training information and can identify the target tree Ta based on the location information and the training information. Furthermore, if the management server 8 cannot identify the target tree Ta based on the training information, the management server 8 also identifies the target tree Ta based on its shape.

[0107] The scope of the present invention is defined by the claims, not by the meaning described above, and is intended to include meanings equivalent to the claims and all modifications within the scope thereof.

[0108] REFERENCE SIGNS LIST 1 Management system 2 Quality measurement device 4 Positioning device 5 Shape detection device 6 Agricultural machine 6a In-vehicle network 8 Management server 10 Management terminal 14 Operator 18 Control device 19 Input / output device 20 Communication device 22 Processing unit 24 Memory unit 26 Communication device 28 Shape information database 30 Measurement information database 32 Training information database A1 First area A2 Second area B Branch BS Wireless base station D Distance DB 28 Shape DB 30 Measurement information F Field GNSS Multi ID Tree K Fruit M Measurement range NW Public network S Trunk T Tree Ta Target tree

Claims

1. A management system for multiple plants lined up in a row, comprising: a measuring device provided on a moving body that moves along the row direction and acquires measurement information of the fruits of the multiple plants; a positioning device provided on the moving body and acquires position information of the moving body; a shape detection device provided on the moving body and detects the shape of a target plant with the fruit measured by the measuring device; and a management device that identifies the target plant, wherein the management device has a processing unit that executes: a first identification process that identifies a target plant with the fruit measured by the measuring device from among the multiple plants based on the position information; and a second identification process that identifies the target plant based on the detection result of the shape detection device if the target plant cannot be identified by the first identification process.

2. The management system described in claim 1, wherein the second identification process includes: a process of acquiring shape information of the target plant based on the detection result; and a process of identifying the target plant based on a comparison of the shape information of the target plant with shape information of the multiple plants acquired in advance.

3. The management system according to claim 1 or 2, wherein the shape detection device includes at least one of a LIDAR and a camera.

4. The management system described in claim 1, wherein the first identification process includes: a comparison process for comparing the distance between the reference positions of the multiple plants and the position of the position information with a predetermined threshold value; and a process for identifying the target plant based on the result of the comparison process.

5. The management system described in claim 1, wherein the first identification process includes: a process of defining non-overlapping areas around each of the reference positions of the plurality of plants; and a process of identifying, when the position of the location information is within one of the plurality of areas, a plant among the plurality of plants that corresponds to the one area as the target plant.

6. The management system described in claim 5, wherein the first identification process further includes a process of obtaining an extension direction of branches extending from the reference position along the row direction in the plurality of plants, and the area is set to a different range depending on the extension direction.

7. A management system as described in any one of claims 1 to 6, wherein the processing unit further executes a process of acquiring an extension direction of branches extending from the trunks of the multiple plants along the row direction when the target plant cannot be identified by the first identification process, and a third identification process of identifying the target plant based on the position information and the extension direction.

8. A management system as described in any one of claims 1 to 7, wherein the management device, upon identifying the target plant, further executes a process of correlating and storing identification information assigned to the target plant with the measurement information.

9. The management system according to any one of claims 1 to 8, wherein the fruit includes grapes.

10. The management system according to claim 9, wherein the measurement information includes at least one of sugar content, acidity, pH, and polyphenol content.

11. A management device for multiple plants lined up in a row direction, comprising a processing unit that executes the following processes: a process of acquiring measurement information of the fruits of the multiple plants by a measuring device provided on a moving body that moves along the row direction; a process of acquiring position information of the moving body by a positioning device provided on the moving body; a process of acquiring a detection result of a target plant on which the fruit measured by the measuring device is attached by a shape detection device provided on the moving body; a first identification process of identifying the target plant from among the multiple plants based on the position information; and a second identification process of identifying the target plant based on the detection result if the target plant cannot be identified by the first identification process.

12. A method for managing multiple plants lined up in a row, comprising the steps of: acquiring measurement information of the fruits of the multiple plants by a measuring device provided on a moving body moving along the row direction; acquiring position information of the moving body by a positioning device provided on the moving body; acquiring a detection result of a target plant with the fruit measured by the measuring device by a shape detection device provided on the moving body; a first identification step of identifying the target plant from among the multiple plants based on the position information; and, if the target plant cannot be identified by the first identification step, a second identification step of identifying the target plant based on the detection result.

13. A computer program for causing a computer to execute management processing for multiple plants lined up in a row direction, the computer executing the following steps: acquiring measurement information of the fruits of the multiple plants by a measuring device provided on a moving body moving along the row direction; acquiring position information of the moving body by a positioning device provided on the moving body; acquiring a detection result of a target plant with the fruit measured by the measuring device by a shape detection device provided on the moving body; a first identification step of identifying the target plant from among the multiple plants based on the position information; and a second identification step of identifying the target plant based on the detection result if the target plant cannot be identified by the first identification step.

Images