JP2023081814A - Farm field evaluation device, method for evaluating farm field, and farm field evaluation program - Google Patents

Abstract

To provide a farm field evaluation device, a method for evaluating a farm field, and a farm field evaluation program that can objectively evaluate the work efficiency of a farm field on the assumption that the work is done by an automatic operation machine.SOLUTION: In a farm field evaluation device 1, an information acquisition unit 11 acquires farm field information on the shape of at least one target farm field. A route generation unit 12 generates a driving route on which an automatic driving machine moves in the target farm field on the basis of the farm field information. A time calculation unit 13 calculates the work time of the automatic driving machine in the target farm field on the basis of the route generated by the route generation unit 12. A farm field evaluation unit 14 evaluates the work efficiency of the automatic driving machine in the target farm field on the basis of the work time.SELECTED DRAWING: Figure 4

Description

The present invention relates to a field evaluation device, a field evaluation method, and a field evaluation program.

In recent years, self-driving agricultural machines have become widespread. Such an agricultural machine automatically generates a driving route in the field based on the shape of the field. Therefore, as a result of generating various driving routes depending on the shape of the field, work efficiency differs depending on the shape even if the area is the same. Therefore, there is a need for a technology that objectively expresses the efficiency of work in a field, on the premise of using an automatically operated agricultural machine.

Patent Document 1 discloses a first acquisition unit that acquires farm work data of farm work performed by an agricultural machine, and an evaluation calculation unit that calculates a score of farm work for each field based on the farm work data acquired by the first acquisition unit. A farming evaluation system is disclosed comprising:

Patent Literature 2 discloses a system that evaluates actual agricultural work based on work hours per area.

In Patent Document 3, when evaluating land based on its shape, a location/drawing input unit for inputting a target figure formed of polygons approximating the land to be evaluated, and an internal maximum and a shape depreciation rate determination unit having internal maximum rectangle detection means for detecting rectangles, and an evaluation system for evaluating a target land based on the shape depreciation rate is disclosed.

The systems described in Patent Literatures 1 and 2 are for evaluating agricultural work, and are different from those for evaluating fields.

The system described in Patent Literature 3 is not based on the premise of route generation by an automatic driving agricultural machine, and is primarily a system for evaluating residential land.

Patent Publication JP 2017-068533 Patent Publication JP 2017-129939 Patent Publication JP 2008-040663

Provided is a field evaluation device that objectively evaluates field work efficiency on the premise that work is performed by an automatic driving machine.

In order to achieve the above object, an agricultural field evaluation apparatus according to one aspect of the present invention includes an information acquisition unit that acquires agricultural field information related to the shape of one or more target agricultural fields; A time calculation unit that calculates the working time of the automatically operating machine, and a field evaluation unit that evaluates the working efficiency of the automatically operating machine in the target field based on the working time.

a route generation unit that generates a driving route along which the automatic driving machine moves in the target farm field based on the farm field information, wherein the time calculation unit generates, based on the route generated by the route generation unit, The work time by the automatically operating machine may be calculated.

The information acquisition unit acquires the field information related to the plurality of target fields to be evaluated, the route generation unit generates an inter-field route for traveling between the plurality of target fields, and the time calculation unit may calculate the travel time of the inter-field route, and the farm field evaluation unit may evaluate the work efficiency based on the travel time and the work time.

A storage unit that stores a field shape model and the work time in association with each other, and the time calculation unit approximates the shape of the target field to one or more of the shape models by image identification processing, and calculates the shape. The working time in the target field may be calculated based on the working time associated with the model.

The time calculation unit may calculate the working time in the target field based on at least one of a perimeter of the target field, the number of vertices, and an uneven shape of the outer periphery of the target field. .

a second storage unit that stores work in association with the type or movement mode of the automatic driving machine used for the work, wherein the information acquisition unit selects a work as an evaluation target in the target field; Selection of a plurality of works may be accepted, and the time calculation unit may refer to the second storage unit and calculate the work time based on the type or movement mode of the selected work.

In order to achieve the above object, an agricultural field evaluation method according to another aspect of the present invention includes information acquisition processing in which a computer acquires agricultural field information related to the shape of one or more target agricultural fields; A time calculation process for calculating the working time of the automatically driving machine in the target field and a field evaluation process for evaluating the working efficiency of the automatically driving machine in the target field based on the working time are executed.

In order to achieve the above object, an agricultural field evaluation program according to still another aspect of the present invention provides a computer with an information acquisition process for acquiring agricultural field information related to the shape of one or more target agricultural fields, and a time calculation process for calculating the working time of the automatically driving machine in the target field; and a field evaluation process for evaluating the working efficiency of the automatically driving machine in the target field based on the working time .
The computer program can be stored in various data-readable recording media and provided, or can be provided in a downloadable manner via a network such as the Internet.

It is possible to objectively evaluate the work efficiency of the field assuming work by automatic driving machines.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a perspective view of a drone, which is an example of an automatic driving machine that the agricultural field evaluation device according to the present invention is assumed to be used for work. 1 is an overall conceptual diagram of an agricultural field evaluation device according to the present invention; FIG. It is a functional block diagram which the said drone has. 1 is a functional block diagram of a field evaluation device according to the present invention and a drone connected to the field evaluation device through a network; FIG. It is one example of the table stored in the memory|storage part which the said agricultural field evaluation apparatus has. It is a schematic diagram which shows one example of the path|route produced|generated by the said agricultural field evaluation apparatus in an agricultural field. FIG. 5 is a schematic diagram showing another example of a route generated in the agricultural field; It is a schematic diagram which shows the example of the path|route between fields which moves between several fields produced|generated by the said farm field evaluation apparatus. Examples showing the evaluation results of fields by the field evaluation device, including (a) an example of a histogram showing the distribution of evaluation values for each field, and (b) a histogram showing the distribution of evaluation values for each corporation that manages the fields. Examples are (c) a histogram showing the distribution of evaluation values for each district having fields, and (d) an example of a scatter diagram showing the relationship between field area, efficiency, and working time. It is a flowchart which shows the flow which the said agricultural field evaluation apparatus evaluates an agricultural field.

The farm field evaluation device 1 is a device that evaluates a farm field from the viewpoint of workability of an automatically moving agricultural machine. The field evaluation device 1 can evaluate one or more fields as target fields. The farm field evaluation device 1 acquires field information such as the number, area, or shape of the farm fields, and also acquires the working time when the automatically operated agricultural machine works in the farm fields. Further, the field evaluation device 1 calculates the working efficiency in the field based on this working time.

Embodiments for carrying out the present invention will be described below with reference to the drawings. All figures are illustrative. In the following detailed description, for purposes of explanation, certain details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, embodiments are not limited to these specific details. Also, well-known structures and devices are schematically shown to simplify the drawings.

First, the configuration of a drone will be described as an example of an automatic driving machine that is assumed to be used in agricultural work by the agricultural field evaluation device according to the invention.
As shown in FIG. 1, rotors 101-1a, 101-1b, 101-2a, 101-2b, 101-3a, 101-3b, 101-4a, 101-4b (also called rotors) connect drone 100 to It is a means to fly, and considering the balance of flight stability, aircraft size, and power consumption, it is equipped with 8 aircraft (4 sets of two-stage rotor blades). Each rotary wing 101 is arranged on four sides of the housing 110 by arms protruding from the housing 110 of the drone 100 . That is, rotor blades 101-1a and 101-1b are on the left rear in the direction of travel, rotor blades 101-2a and 101-2b are on the left front, rotor blades 101-3a and 101-3b are on the right rear, and rotor blades 101- on the right front. 4a and 101-4b are arranged respectively. Note that the drone 100 is traveling in the x direction in FIG.

Lattice-like propeller guards 115-1, 115-2, 115-3, and 115-4 forming a substantially cylindrical shape are provided on the outer periphery of each set of rotor blades 101 to prevent rotor blades 101 from interfering with foreign matter. Radial members for supporting the propeller guards 115-1, 115-2, 115-3, and 115-4 are not horizontal but have a tower-like structure. This is to prevent the member from interfering with the rotor by promoting the buckling of the member to the outside of the rotor blade at the time of collision.

Rod-shaped legs extend downward from the rotating shaft of the rotor blade 101, respectively.

Motors 102-1a, 102-1b, 102-2a, 102-2b, 102-3a, 102-3b, 102-4a, 102-4b are connected to rotors 101-1a, 101-1b, 101-2a, 101- Means for rotating 2b, 101-3a, 101-3b, 101-4a, 101-4b (typically an electric motor, but may be a motor, etc.), one machine provided for one rotor blade It is Motor 102 is an example of a propeller. The upper and lower rotors in one set (e.g. 101-1a and 101-1b) and their corresponding motors (e.g. 102-1a and 102-1b) are used for drone flight stability etc. The axes are collinear and rotate in opposite directions.

Four nozzles 103-1 and 103-2 are provided as means for downwardly spraying the material to be sprayed. In the specification of the present application, the sprayed material generally refers to a liquid or powder that is sprayed on a field, such as pesticides, herbicides, liquid fertilizers, insecticides, seeds, and water.

The tank 104 is a tank for storing sprayed material, and is provided at a position close to and lower than the center of gravity of the drone 100 from the viewpoint of weight balance. The hoses 105-1, 105-2, 105-3, 105-4 are means for connecting the tank 104 and the respective nozzles 103-1, 103-2, are made of a hard material, and serve to support the nozzles. may also serve as A pump 106 is a means for discharging the spray from the nozzle.

FIG. 2 shows an overall conceptual diagram of the flight control system of the drone 100. As shown in FIG. This figure is a schematic diagram and not to scale. In the figure, the drone 100, the controller 401, and the small portable terminal 401a are each connected to the base station 404, and only the controller 401 is connected to the farming cloud 405; is not limited to Drone 100, controller 401, small portable terminal 401a, and base station 404 are connected to farming cloud 405, respectively. These connections may be wireless communication by Wi-Fi, a mobile communication system, or the like, or part or all of them may be wired.

The operation unit 401 is operated by the user 402 to transmit commands to the drone 100, and to display information received from the drone 100 (for example, position, amount of sprayed material stored, remaining battery level, camera image, etc.). and may be implemented by a portable information device such as a general tablet terminal that runs a computer program. The operation device 401 has an input section and a display section as a user interface device. The drone 100 according to the present invention is controlled to fly autonomously, but manual operation may be performed during basic operations such as takeoff and return, and in an emergency. In addition to the portable information device, an emergency operator (not shown) having a dedicated emergency stop function may be used. The emergency operation device may be a dedicated device equipped with a large emergency stop button or the like so that a quick response can be taken in case of emergency. Furthermore, apart from the operation device 401, the system may include a small portable terminal 401a, such as a smart phone, capable of displaying part or all of the information displayed on the operation device 401. FIG. Further, it may have a function of changing the operation of the drone 100 based on information input from the small portable terminal 401a. The small portable terminal 401a is connected to a base station 404, for example, and can receive information and the like from the farming cloud 405 via the base station 404. FIG.

A farm field 403 is a rice field, a field, or the like targeted for spraying by the drone 100 . In reality, the topography of the farm field 403 is complicated, and there are cases where a topographic map cannot be obtained in advance, or there are cases where the topographic map differs from the situation of the field. Fields 403 are usually adjacent to houses, hospitals, schools, other crop fields, roads, railroads, and the like. Moreover, there may be intruders such as buildings and electric wires in the field 403 . Fields 403 may include fields and paddy fields.

The base station 404 is a device that provides a Wi-Fi communication base unit function, etc., and may also function as an RTK-GPS base station and provide an accurate position of the drone 100 (Wi-Fi Fi communication master unit function and RTK-GPS base station may be independent devices). Also, the base station 404 may be able to communicate with the farming cloud 405 using a mobile communication system such as 3G, 4G, and LTE.

The farming cloud 405 is typically a group of computers and related software operated on a cloud service, and may be wirelessly connected to the operation device 401 via a mobile phone line or the like. The farming cloud 405 may be composed of hardware devices. The farming cloud 405 may acquire an image of the farm field 403 captured by the drone 100 . In addition, the drone 100 may be provided with topographical information and the like of the field 403 that has been saved. In addition, a history of flight and captured images of the drone 100 may be accumulated and various analysis processes may be performed.

The small portable terminal 401a is, for example, a smart phone. The display unit of the small portable terminal 401a displays information about the expected operation of the drone 100, more specifically, the scheduled time for the drone 100 to return to the departure/arrival point 406, and the work to be done by the user 402 when returning. Information such as the content is displayed as appropriate. Also, the operation of the drone 100 may be changed based on the input from the small portable terminal 401a.

Normally, the drone 100 takes off from the departure/arrival point 406 outside the field 403 and returns to the departure/arrival point 406 after spraying the sprayed material on the field 403 or when the sprayed material needs to be replenished or charged. The flight path (entry path) from the starting point 406 to the target field 403 may be stored in advance in the farming cloud 405 or the like, or may be input by the user 402 before starting takeoff. Landing point 406 may be a virtual point defined by coordinates stored in drone 100, or may be a physical landing pad.

FIG. 3 shows a block diagram showing the control functions of an embodiment of the spray drone according to the present invention. The flight controller 501 is a component that controls the entire drone, and specifically may be an embedded computer that includes a CPU, memory, related software, and the like. Flight controller 501 controls motors 102-1a and 102-1b via control means such as ESC (Electronic Speed Control) based on input information received from operation device 401 and input information obtained from various sensors described later. , 102-2a, 102-2b, 102-3a, 102-3b, 104-a, and 104-b, the flight of the drone 100 is controlled. The actual rotation speeds of motors 102-1a, 102-1b, 102-2a, 102-2b, 102-3a, 102-3b, 104-a, and 104-b are fed back to flight controller 501 to ensure normal rotation. It is configured to be able to monitor whether or not Alternatively, an optical sensor or the like may be provided on the rotor blade 101 and the rotation of the rotor blade 101 may be fed back to the flight controller 501 .

The software used by the flight controller 501 is rewritable through a storage medium or the like, or through communication means such as Wi-Fi communication or USB, in order to extend/change functions or correct problems. In this case, protection is provided by encryption, checksum, electronic signature, virus check software, etc. to prevent rewriting by unauthorized software. Also, part of the calculation processing used for control by the flight controller 501 may be executed by another computer existing on the operation device 401, on the farming cloud 405, or at another location. Due to the high importance of flight controller 501, some or all of its components may be duplicated.

The flight controller 501 communicates with the controller 401 via the Wi-Fi slave device function 503 and via the base station 404, receives necessary commands from the controller 401, and sends necessary information to the controller. You can send to 401. In this case, the communication may be encrypted to prevent fraudulent acts such as interception, impersonation, and device hijacking. The base station 404 also has a function of an RTK-GPS base station in addition to a Wi-Fi communication function. By combining the signals from the RTK base station and the signals from the GPS positioning satellite, the flight controller 501 can measure the absolute position of the drone 100 with an accuracy of several centimeters. Due to the high importance of the flight controller 501, it may be duplicated or multiplexed, and each redundant flight controller 501 should use a different satellite in order to cope with the failure of a particular GPS satellite. may be controlled.

The 6-axis gyro sensor 505 is means for measuring the acceleration of the drone body in three mutually orthogonal directions, and is means for calculating the velocity by integrating the acceleration. The 6-axis gyro sensor 505 is means for measuring changes in the attitude angle of the drone body in the three directions described above, that is, angular velocity. The geomagnetic sensor 506 is a means of determining the direction of the drone body by measuring geomagnetism. The air pressure sensor 507 is a means of measuring air pressure, and can also indirectly measure the altitude of the drone. The laser sensor 508 is means for measuring the distance between the drone body and the ground surface using reflection of laser light, and may be an IR (infrared) laser. Sonar 509 is a means of measuring the distance between the drone body and the ground using the reflection of sound waves such as ultrasonic waves. These sensors may be selected according to the drone's cost targets and performance requirements. Also, a gyro sensor (angular velocity sensor) for measuring the inclination of the airframe, a wind sensor for measuring wind force, etc. may be added. Also, these sensors may be duplicated or multiplexed. If there are multiple sensors for the same purpose, the flight controller 501 may use only one of them and switch to an alternative sensor if it fails. Alternatively, multiple sensors may be used at the same time and a failure is assumed to occur if their measurements do not match.

Flow rate sensors 510 are means for measuring the flow rate of the spread material, and are provided at multiple locations along the path from tank 104 to nozzle 103 . A liquid exhaustion sensor 511 is a sensor that detects when the amount of sprayed matter has fallen below a predetermined amount.

The growth diagnosis camera 512a is means for photographing the field 403 and acquiring data for image analysis. Also, the growth diagnosis camera 512a is, for example, a multispectral camera, but may be a camera that receives visible light. The pathological diagnosis camera 512b is means for photographing crops growing in the field 403 and acquiring data for pathological diagnosis. The pathological diagnosis camera 512b is, for example, a camera that receives visible light. The growth diagnostic camera 512a and the pathological diagnostic camera 512b may be realized by one hardware configuration.

The intruder detection camera 513 is a camera for detecting drone intruders, and is different from the growth diagnosis camera 512a and the pathology diagnosis camera 512b in terms of image characteristics and lens orientation. It is another device. Switch 514 is a means for user 402 of drone 100 to make various settings. The intruder contact sensor 515 is a sensor for detecting that the drone 100, especially its rotor or propeller guard portion, has come into contact with an intruder such as an electric wire, building, human body, standing tree, bird, or other drone. . Note that the intruder contact sensor 515 may be replaced by the 6-axis gyro sensor 505 . The cover sensor 516 is a sensor that detects that the operation panel of the drone 100 and the internal maintenance cover are open. The inlet sensor 517 is a sensor that detects that the inlet of the tank 104 is open.

These sensors may be selected, duplicated or multiplexed depending on the drone's cost targets and performance requirements. Also, a sensor may be provided outside the drone 100 at the base station 404, the controller 401, or at another location to transmit the read information to the drone. For example, a wind sensor may be provided in the base station 404 to transmit information on wind force and wind direction to the drone 100 via Wi-Fi communication.

The flight controller 501 transmits a control signal to the pump 106 to adjust the discharge amount and stop the discharge. The current status of the pump 106 (eg, number of revolutions, etc.) is configured to be fed back to the flight controller 501 .

The LED 107 is display means for informing the operator of the drone of the state of the drone. Display means such as a liquid crystal display may be used in place of or in addition to LEDs. A buzzer is an output means for informing the state of the drone (especially an error state) by means of an audio signal. A Wi-Fi slave device function 519 is an optional component for communicating with an external computer or the like for transferring software, for example, separately from the operation device 401 . In place of or in addition to the Wi-Fi slave unit function, infrared communication, Bluetooth (registered trademark), ZigBee (registered trademark), other wireless communication means such as NFC, or wired communication means such as USB connection may be used. Also, instead of the Wi-Fi slave device function, it may be possible to communicate with each other by a mobile communication system such as 3G, 4G, and LTE. The speaker 520 is output means for notifying the state of the drone (especially error state) by means of recorded human voice, synthesized voice, or the like. Weather conditions can make it difficult to see the visual display of the drone 100 in flight, and in such cases, audible status communication is effective. A warning light 521 is a display means such as a strobe light that indicates the state of the drone (especially an error state). These input/output means may be selected, duplicated or multiplexed according to the drone's cost targets and performance requirements.

●Overview of Agricultural Field Evaluation Device As shown in FIG. 4, the agricultural field evaluation device 1 is communicably connected to, for example, a drone 100 and a network NW. However, within the technical scope of the present invention, it is not necessary to be connected to the drone 100, and it is sufficient if the agricultural field evaluation device 1 has the function of generating the driving route of the automatic driving machine.

The farm field evaluation device 1 may have a hardware configuration, or may be configured on the farm management cloud 405 . The field evaluation device 1 may be composed of a plurality of devices connected wirelessly or partially or wholly by wires.

Functional part of the field evaluation device 1 The field evaluation device 1 includes an arithmetic device such as a CPU (Central Processing Unit) for executing information processing, and storage devices such as RAM (Random Access Memory) and ROM (Read Only Memory). It has an information acquisition unit 11, a route generation unit 12, a time calculation unit 13, a field evaluation unit 14, an output unit 15, a communication processing unit 16 and a storage unit 20 as software resources.

The storage unit 20 is an example of a first function unit and a second function unit, and is a function unit that stores at least information referred to in field evaluation.
FIG. 5 is a diagram showing an example of the table T1 stored in the storage unit 20. As shown in FIG. The table T1 stores work in association with the type or mode of movement of the automatic driving machine used for the work. Work items include, for example, plowing, plowing, rice planting, pesticide application, and fertilizer application. The table T1 stores the types of automatic driving machines used for these tasks. Autonomous driving machines refer to all machines that automatically generate driving routes and operate automatically. Automatic driving machines are, for example, agricultural machines such as cultivators, tractors, and rice transplanters. Also, the self-driving machine may be a drone that performs agricultural work. Note that the automatic driving machine is not limited to a machine that performs agricultural work, and may be a machine that performs work by moving comprehensively in a predetermined area, such as a cleaning robot.

Different types of automated driving machines may be stored depending on the type of work, or there may be automated driving machines that can be used for both pesticide spraying and fertilizer spraying, such as the drone shown in the figure. In addition, the table T1 may also store manual movement modes that do not use an automatic driving machine for each task.

The mode of movement indicates the mode of each task in each automatic driving machine. The mode of movement includes, for example, working width, movement speed during work, movement speed during movement between fields, assumed horsepower, and the like. As for the moving speed when moving between fields, in addition to the moving speed due to automatic operation, the moving speed assumed during manual operation by the worker may be stored separately from the moving speed due to automatic operation. Further, the movement mode may include a movement speed when moving in the field without working. Additionally, the mode of movement may include the speed at which the autonomous machine turns, eg, turns. For example, in the case of a machine such as a drone that is used for multiple tasks, it may move in different modes depending on the task to be performed.

The information acquisition unit 11 is a functional unit that acquires field information regarding the shape of a target field. The farm field information may be, for example, field coordinate data or survey data published by a public institution such as the Ministry of Agriculture, Forestry and Fisheries. Moreover, the field information may be survey data acquired in advance using a device having GNSS. This survey data may be data acquired by patrolling the farm field with a smartphone having a GNSS function, a predetermined survey device, or the like. Moreover, the survey data may be data obtained by flying a drone having a GNSS function. The survey data may be two-dimensional data, or may be three-dimensional data in consideration of height differences.

The farm field information may include information on restricted areas that the automatic driving machine cannot enter. The no-entry area is defined in the horizontal direction and in the height direction and is an area extending in the three-dimensional direction, for example, a rectangular parallelepiped area drawn around an obstacle. Note that the no-entry area may be a spherical area drawn around the obstacle.

The information acquisition unit 11 may call up the farm field information from an appropriate storage unit of the farm field evaluation device 1, or may acquire the farm field information from a separate information source through the network NW.

The information acquisition unit 11 may accept a plurality of fields separated from each other as target fields. The selection of multiple fields is, for example, when a single land owner owns multiple scattered fields, or when the same worker works on multiple fields using the same automatic driving machine. is performed on According to such a configuration, it is possible to evaluate the working efficiency of a plurality of fields, assuming a case where a plurality of fields are collectively worked.

In addition, the information acquisition unit 11 may receive selection of one or more works to be evaluated in the target field. The user who selects the work can select the work to be evaluated according to the use of the field and the type of work requiring evaluation. The information acquisition unit 11 may receive work selection via the communication processing unit 16, or may receive work selection by appropriate input means provided in the field evaluation device 1. FIG.

The route generation unit 12 is a functional unit that generates a driving route along which the automatic driving machine moves in the target field based on the field information.

The route generation unit 12 may determine, from the field shape included in the field information, the areas excluding the no entry area from the target field as the movement permitted areas 70i and 80i (see FIGS. 6 and 7). Note that the route generation unit 12 may vary the no-entry area for each type of automatic driving machine. For example, in a machine that travels on land, it is necessary to avoid obstacles regardless of their height while the drone 100 flies in the air. It is possible to fly According to the configuration that determines whether or not to set the area as a no-entry area based on the size of the obstacle in the height direction, depending on the type of the automatic driving machine, it is possible to more accurately evaluate the work efficiency for each machine type.

The route generator 12 generates a driving route within the movement-permitted area.
As shown in FIG. 6, for example, the route generation unit 12 generates a driving route for scanning the movement-permitted area 70i substantially exhaustively by reciprocating.

Further, as shown in FIG. 7, the route generation unit 12 may divide the movement-permitted area 80i into a plurality of areas and generate a route for each divided area. More specifically, the route generator 12 divides the movement-permitted area 80i into one or more shaped areas 81i and one or more odd-shaped areas 82i and 83i smaller than the shaped area 81i. The route generating unit 12 divides the movement-permitted area 80i particularly when the movement-permitted area 80i has a concave polygonal shape when viewed from above. A concave polygon is a polygon in which at least one of the interior angles of the polygon is greater than 180°, in other words a polygon with a concave shape. When a concave portion is found, the route generation unit 12 performs division processing of the area. If no concave portion is found, it is determined that further division is unnecessary, and the process ends.

Further, the route generating unit 12 may further divide the shaping area 81i into an outer peripheral area 811i and an inner area 812i, and generate a driving route for each area. The outer peripheral area 811i is an annular area having the working width of the automatic driving machine. The working width of an automated driving machine varies depending on the type of automated driving machine and the work content. For example, the working width when the drone 100 sprays chemicals is the spray width of the chemicals. Also, the working width when the drone 100 performs surveillance is the monitorable width when it is a surveillance drone. Thus, the working width is different than the housing width of the autonomous machine.

The odd-shaped areas 82i and 83i are areas each having a smaller area than the shaped area 81i, and are areas in which the outer peripheral area 811i and the inner area 812i cannot be defined. The route generation unit 12 determines whether or not each area 811i, 812i, 82i, 83i to be determined is an area in which route generation is possible, and determines an area for which route generation is to be performed. This is because the shaped area 81i and the deformed areas 82i and 83i may not be drivable due to their shapes. The route generation unit 12 determines whether or not the area allows route generation based on a predetermined value determined based on the driving performance of the automatic driving machine. The driving performance of an automatically-operated machine includes the run-up distance required for the automatically-operated machine to reach constant-speed operation and the stopping distance required from constant-speed operation to stop. In addition, the operating performance of automated driving machines includes the working width for chemical spraying and monitoring.

For example, the route generation unit 12 determines that the long side of the outer peripheral area 811i or the inner area 812i is less than a predetermined value determined based on the starting distance required for the automatic driving machine to reach constant speed operation and the stopping distance required for stopping. In this case, it may be determined not to generate a route for the outer peripheral area 811i or the inner area 812i. For example, when the long side of the outer area 811i or the inner area 812i is less than the sum of the approach distance and the stop distance, it is determined not to generate the route. Further, the route generation unit 12 does not generate a route when the shortest side of the outer area 811i or the inner area 812i is less than a predetermined value determined based on the working width of the automatic driving machine. More specifically, route generation is not performed when the shortest side of the outer peripheral area 811i or the inner area 812i is less than the working width of the automatic driving machine. This is because a route cannot be generated if the value is less than the predetermined value.

In addition, the route generation unit 12 determines whether or not the drone 100 can be driven in each of the determined odd-shaped areas 82i and 83i. The route generation rules for the odd-shaped areas 82i and 83i are rules for generating a one-way flight route in the long side direction or a one-way route. Therefore, when the shortest sides of the irregularly shaped areas 82i and 83i are less than a predetermined value determined based on the working width of the automatically operating machine, the route generating unit 12 prevents the automatically operating machine from generating a route in the irregularly shaped area. .

In addition, when the long sides of the deformed areas 82i and 83i are less than a predetermined value determined based on the approach distance required for the automatic driving machine to reach constant speed operation and the stopping distance required for stopping, route generation is not performed. make a decision. For example, when the long sides of the odd-shaped areas 82i and 83i are less than the sum of the approach distance and the stopping distance, route generation is not performed.

The route generation unit 12 shown in FIG. 5 generates a driving route in the route generation target area based on a predetermined route generation rule. The route generation unit 12 generates a driving route for each of the outer area 811i, the inner area 812i, and the odd-shaped area 83i, and connects the generated driving routes to generate the driving route for the movement-permitted area 80i. good. The order of concatenation is arbitrary. The route generator 12 generates driving routes based on different route generation rules for each area. For example, the route generation unit 12 generates a circular route 811r that circles once in the outer peripheral area 811i. In addition, the route generation unit 12 makes a round trip in the route generation target area a plurality of times in the inner area 812i, and scans such that the adjacent round trip paths or the adjacent outward and return paths spread or narrow from the outbound route starting point side to the outbound route end point side. A round-trip driving route 812r may be generated. The route generator 12 may generate a route that moves in one direction in the long side direction of the area, or an irregular-shaped area driving route 83r that makes one round trip in the irregular-shaped area 83i.

The route generation unit 12 generates different driving routes depending on the type of automatic driving machine. The route generation unit 12 refers to the information on the movement mode stored in the table T1, applies this information as a coefficient to the route generation rule, and generates a driving route. That is, the route generation unit 12 generates driving routes based on the same generation rule and different coefficients for each type of automatic driving machine. For example, an automatic driving machine with a wide working width has fewer reciprocations than a machine with a narrow working width, but it may not be possible to create a path in a narrow area and work in the area. Further, the route generation unit 12 may generate driving routes based on different route generation rules depending on the automatic driving machine. The route generation unit 12 can generate routes for various automatic driving machines by appropriately adding and storing route generation rules generated based on a predetermined format.

The route generating unit 12 may generate an in-farm moving route for moving without working in the field separately from a driving route for moving while working. Although it is preferable that the movement route in the field is as short as possible, it is difficult to drive in a so-called single stroke without duplication in the field. Normal.

Further, as shown in FIG. 8, when the information acquisition unit 11 acquires information indicating that a plurality of fields are to be evaluated as target fields, the route generation unit 12 generates a route for moving between the plurality of fields F. , that is, to generate an inter-field route r. The field-to-field route r may be a route that moves from one farm field to another farm field, or may be a concept that indicates the entire route that tours a plurality of farm fields.

The time calculation unit 13 is a functional unit that calculates the working time of the automatic driving machine in the target field according to the field information. Based on the driving route generated by the route generating unit 12, the time calculating unit 13 calculates the working time of the automatically driving machine.

The time calculation unit 13 may calculate the work time by simulating the movement of the automatically driving machine based on at least the route length. In this case, the work time is calculated by dividing the route length by the movement speed. In addition, the time calculation unit 13 may calculate the working time taking into account the number of turns or turns on the route. This is because turning and turning takes more time than going straight, so even if the route length is the same, the route with more turns takes longer to work.
Note that the time calculation unit 13 may calculate the working time by measuring the time taken when the automatic driving machine actually moves along the route and performs the work.

Further, the time calculation unit 13 may calculate the work time by applying the manual work time to the area of the area where the driving route cannot be generated by the route generation unit 12 . In this case, the time calculation unit 13 calculates the working time in the target field by adding up the working time of the automatic driving machine and the working time of the manual operation. The work time calculated by the time calculation unit 13 increases as the area in which the driving route cannot be generated increases.

When an inter-field route is generated by the route generation unit 12, the time calculation unit 13 may calculate the travel time of the inter-field route. The time calculation unit 13, for example, refers to the table T1 and calculates the travel time based on the inter-field travel route length and the travel speed during the inter-field travel. The time calculation unit 13 may employ the travel speed of automatic operation or the travel speed expected in manual operation as the travel speed during inter-field travel. Also, the time calculation unit 13 may receive an input via the information acquisition unit 11 to select which movement speed to adopt. Further, when there is an intra-field movement route for moving without working in the field, the time calculation unit 13 calculates the movement time for intra-field movement based on the route and the movement speed during movement within the field. may The time calculation unit 13 adds one or more of the travel time of the inter-field route, the travel time of the intra-field route, and the manual work time to the work time of the automatic driving machine, thereby calculating the work time in the target field. calculate. The time calculation unit 13 calculates a longer work time as the travel time of the inter-field route, the travel time of the intra-field travel route, and the manual work time are longer.

The time calculation unit 13 may approximate the shape of the target field to one or a plurality of shape models by image identification processing, and calculate the work time in the target field based on the work time associated with the shape model. For example, when the shape of the field can be approximated to a rectangle, the time calculation unit 13 shortens the path within the field and also shortens the overlapping paths, resulting in a short working time. Further, the time calculation unit 13 may calculate the work time based on the area of the target field that deviates from the shape model. More specifically, the larger the area that deviates from the approximate model, the longer the work time may be calculated. Furthermore, the time calculation unit 13 may calculate the working time according to the number of shape models applied to the target field. The route of the automatic driving machine is generated for each shape model, for example, and it is assumed that a plurality of generated routes are connected with the movement route in the field to be the route in the target field. Therefore, the larger the number of shape models that approximate the target field, the longer the movement path in the field and the longer the work time.

The time calculation unit 13 may calculate the working time in the target field based on at least one of the perimeter length of the target field, the number of vertices, and the uneven shape of the outer periphery of the target field. For example, the time calculation unit 13 calculates a longer working time as the perimeter of the target field is longer. This is because when the perimeter of the target field is long, it is assumed that the target field has a large area or a complicated shape. Since it is assumed that the shape of the target field is more complicated as the perimeter is longer relative to the area, the time calculation unit 13 may calculate the working time based on the area and the perimeter. . Further, the time calculation unit 13 calculates a longer working time as the number of vertices of the target field increases. This is because it is assumed that the more the number of vertices, the more complicated the shape of the target field. Furthermore, the time calculation unit 13 determines whether or not the outer circumference of the target field includes a concave shape, and if the concave shape is included, the working time is calculated longer. Further, the time calculation unit 13 may calculate a longer working time as the number of concave shapes increases. Since the route of the automatic driving machine is assumed to be a route that scans a rectangular farm field back and forth, the more recesses there are, the more it becomes necessary to divide the farm field and generate a route. This is because, as a result, it is assumed that the movement route in the field will be longer, and thus the working time will be longer. According to such a configuration, the calculation processing load is reduced compared to a configuration in which the route of the automatic driving machine is actually generated and the working time is calculated.

The time calculation unit 13 may refer to the storage unit 20 and calculate the work time based on the type or movement mode of the work selected by the information acquisition unit 11 . According to this configuration, the working time for the selected work can be calculated more accurately.

The farm field evaluation unit 14 is a functional unit that evaluates the working efficiency of the automatic driving machine in the target farm field based on the working time of the target farm field. The field evaluation unit 14 calculates an evaluation value by, for example, dividing the work time by the field area. In addition, the field evaluation unit 14 evaluates work efficiency based on travel time and work time. The travel time may include the travel time for inter-field travel and the travel time for intra-field travel.

In other words, it can be said that a field in which a route for traveling by an automatic driving machine can be drawn concisely is a field in which work efficiency is high. In addition, when the intra-field route or the inter-field route is long, or the field requires a route with many turns, the work time is longer than the above-mentioned fields, and the work efficiency evaluation value is small. Furthermore, if there is a large area in which work cannot be performed by the automatic driving machine, manual work will occur, resulting in an increase in work time and a small work efficiency evaluation value. According to such a configuration, it is possible to objectively evaluate the work efficiency according to the shape of the field, assuming that the work is performed by the automatic driving machine. As a result, it can be a factor to more accurately evaluate the productivity of agricultural fields when evaluating land assets and calculating credit risk. Further, according to the configuration that can calculate the evaluation value according to the selected work, it is possible to help crop selection by specifying the work suitable for the target field. Furthermore, since the degree of efficiency improvement that can be expected by the introduction of the automatic driving machine is calculated, it can be used as an opportunity for the administrator to introduce the automatic driving machine.

Further, in the table T1, when a plurality of automatically driving machines are associated with the same work, the route generation unit 12 generates different movement routes for each type of the automatically driving machine, and the time calculation unit 13 calculated the work time, the field evaluation unit 14 can calculate an evaluation value for each type of automatic driving machine in the target field. This evaluation value can also be used by a farm field manager or an automatic driving machine manufacturer/distributor to help select a machine to be introduced to the farm field.

The output unit 15 is a functional unit that outputs the evaluation result of the target field determined by the field evaluation unit 14 to an appropriate display unit. The output unit 15 may be displayed on a terminal connected via the network NW in addition to the display unit provided in the agricultural field evaluation device 1 . The terminal may be any appropriate one such as a personal computer, a tablet terminal, or a smart phone. The output unit 15 may display an evaluation value for each type of automatic driving machine. The output unit 15 may also display the evaluation value for each selected target field. Furthermore, the output unit 15 may display the evaluation value of the selected work on the display unit. The output unit 15 may display the results of land asset evaluation and credit risk calculation. For example, the output unit 15 may refer to a table storing a plurality of relationships between land asset evaluation results and work efficiency evaluation values, and estimate and display the land asset evaluation value according to the evaluation value of the target field. good.

FIG. 8 is a diagram showing the result of generating the inter-field route r output by the output unit 15. As shown in FIG. In the figure, a plurality of farm fields F and inter-field routes r traveling between the farm fields are numbered. According to the configuration in which the output unit 15 outputs the inter-field route generation result, the evaluation process becomes clear.

FIG. 9 is a diagram showing an example of the evaluation result of the target field. FIG. 9(a) is an example of a histogram showing the distribution of evaluation values for each field, FIG. 9(b) is an example of a histogram showing the distribution of evaluation values for each corporation that manages the fields, and FIG. , is an example of a histogram showing the distribution of evaluation values for each district having fields. For example, after displaying the histogram on an appropriate display unit, the output unit 15 displays the bar to which the evaluation target field belongs in a manner different from that of the other bars. According to this configuration, the viewer can visually confirm the distribution or relative evaluation of work efficiency. Further, information exchange between the manager of a field with a high evaluation value and the manager of a field with a low evaluation value may contribute to the improvement of efficiency.

FIG. 9(d) is an example of a scatter diagram showing the relationship between field area, efficiency, and working time. A plot P1 shows the relationship between the field area and the work efficiency evaluation value, and a plot P2 shows the relationship between the field area and the work time. As shown in the figure, even in fields of the same field area, work efficiency and work time vary depending on the shape of the field, and there are variations. The output unit 15 may display the scatter diagram on an appropriate display unit.

The communication processing unit 16 is a functional unit that transmits and receives information to and from appropriate devices through the network NW. The communication processing unit 16 may receive information regarding selection of a field or work from, for example, an appropriate input device. The communication processing unit 16 may receive field information from a device that stores field coordinate data or survey data published by a public institution such as the Ministry of Agriculture, Forestry and Fisheries, or from a device that has a GNSS function. The communication processing unit 16 may receive information on work, machine type, and movement mode, and store the information in the table T1 of the storage unit 20. FIG. The communication processing unit 16 may receive the actual value of the working time in the target farm field from the drone 100 or other automatic operation machine. The communication processing unit 16 may transmit the field evaluation results to an output device having a display unit, for example.

●Processing Flow As shown in FIG. 10, first, selection of one or more target fields is accepted (S11). Next, the field information of the selected target field is acquired (S12). Next, selection of one or more target works is accepted (S13). Next, the route generator 12 generates a driving route for the selected target field (S14). When a plurality of target fields are selected, a driving route in each target field is generated. In addition, the route generation unit 12 may generate an intra-field movement route. Next, the work time in the field generated in the target field is calculated (S15). Next, when a plurality of target fields are selected (Y in S16), the route generation unit 12 generates an inter-field route for traveling between the target fields (S17). Further, the travel time required for the inter-field route is calculated by the time calculation unit 13 (S18). After step S18, or when there are not a plurality of target fields in step S16, the process proceeds to step S19. In step S19, the field evaluation unit 14 calculates an evaluation value of work efficiency.

(Technically Remarkable Effects of the Present Invention)
According to the agricultural field evaluation apparatus of the present invention, it is possible to objectively evaluate the working efficiency of the agricultural field on the premise that the work is performed by an automatic driving machine.

Claims (6)

a time calculation unit that calculates a working time for work in the target field according to the shape of one or more target fields;
a field evaluation unit that evaluates work efficiency in the target field based on the work time;
A field evaluation device.
A storage unit that stores the field shape model and the work time in association with each other,
The time calculation unit approximates the shape of the target field to one or more shape models by image identification processing, and calculates the work time in the target field based on the work time associated with the shape model. ,
The field evaluation device according to claim 1.
The time calculation unit calculates the working time in the target field based on at least one of the length of the circumference of the target field, the number of vertices, and the uneven shape of the outer circumference of the target field.
The field evaluation device according to claim 1 or 2.
a second storage unit that associates and stores the work and the mode of movement in the work;
further comprising
The time calculation unit refers to the second storage unit and calculates the work time based on the movement mode in one or more of the work to be evaluated in the target field.
The field evaluation device according to any one of claims 1 to 3.
the computer
a time calculation process for calculating the working time of the work in the target field according to the shape of one or more target fields;
A field evaluation process for evaluating work efficiency in the target field based on the work time;
, a field evaluation method.
to the computer,
a time calculation process for calculating the working time of the work in the target field according to the shape of one or more target fields;
A field evaluation process for evaluating work efficiency in the target field based on the work time;
A field evaluation program that runs

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