Work vehicle system

Abstract

To provide a work vehicle system for forming field ridges into the appropriate shape. [Solution] A work vehicle system according to one embodiment includes a controller. The controller generates an image of the ideal shape of the ridge using a generation AI based on information about the current shape of the ridge in the field and information about the shape of the ridge desired by the user, and generates an image of the estimated shape of the ridge that can be formed using the ridge plastering machine using a generation AI based on information about the current shape of the ridge, the image of the ideal shape of the ridge, and information about the ridge plastering machine that performs ridge plastering work in the field.

Description

Technical Field

[0001] The present invention relates to a work vehicle system.

Background Art

[0002] Conventionally, a seedling transplanter that detects the height of ridges in a field using a camera or the like is known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above-described seedling transplanter, although the height of the ridge can be detected, the shape of the ridge is not detected. Therefore, when a ridge is formed by a ridge painting machine using the information on the ridge obtained by the above-described seedling transplanter, there is a possibility that the shape of the ridge desired by the operator may not be achieved. That is, there is a possibility that the ridge cannot be formed into an appropriate shape.

[0005] The present invention has been made in view of the above, and an object thereof is to provide a work vehicle system capable of forming ridges in a field into an appropriate shape.

Means for Solving the Problems

[0006] To solve the above-mentioned problems and achieve the objective, the work vehicle system (1) according to one embodiment includes a controller (20). The controller (20) generates an image of the ideal shape of the ridge using a generation AI based on information about the current shape of the ridge in the field and information about the shape of the ridge desired by the user, and generates an image of the estimated shape of the ridge that can be formed using the ridge plastering machine (12) using a generation AI based on information about the current shape of the ridge, the image of the ideal shape of the ridge, and information about the ridge plastering machine (12) that performs ridge plastering work in the field. [Effects of the Invention]

[0007] According to one embodiment, the ridges of the field can be formed into an appropriate shape. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a schematic diagram showing a work vehicle system according to an embodiment. [Figure 2] Figure 2 is a schematic plan view of a tractor according to this embodiment. [Figure 3] Figure 3 is a block diagram showing the control system of a tractor according to an embodiment. [Figure 4] Figure 4 is a flowchart explaining the ridge plastering process. [Figure 5] Figure 5 shows an example image of the current shape of a ridge. [Figure 6] Figure 6 is a block diagram showing the flow that generates the ideal shape of the ridge. [Figure 7] Figure 7 shows an example of an image of the ideal shape of a rice paddy ridge. [Figure 8] Figure 8 is a block diagram showing the flow that generates the estimated shape of the ridge. [Figure 9] Figure 9 shows the water supply conditions in the field. [Figure 10] Figure 10 shows an example of a designated field. [Figure 11] Figure 11 shows an example of a designated field. [Modes for carrying out the invention]

[0009] First, an overview of the work vehicle system 1 according to the embodiment will be described with reference to Figure 1. Figure 1 is a schematic diagram showing the work vehicle system 1 according to the embodiment. The work vehicle system 1 is a system for managing the work of work vehicles 2 in a field.

[0010] The work vehicle system 1 comprises a work vehicle 2, a drone 3 (unmanned aerial vehicle), a terminal device 4, and an external device 5. The work vehicle 2, the drone 3, and the terminal device 4 are wirelessly connected to the external device 5 via a network N. The work vehicle 2 and the terminal device 4 are wirelessly connected via a network N. The network N includes communication using Bluetooth®, wireless LAN (Local Area Network), and UWB (Ultra Wide Band), etc. The drone 3, the work vehicle 2, and the terminal device 4 may be wirelessly connected via a network N. In the following explanation, a tractor 2 will be used as an example of the work vehicle 2.

[0011] The drone 3 is equipped with a camera 3a. The drone 3 transmits the image captured by the camera 3a to the tractor 2, the terminal device 4, and at least one of the external device 5.

[0012] Terminal device 4 displays information about the work being done on the tractor 2. Terminal device 4 may, for example, be capable of remotely controlling the tractor 2. That is, terminal device 4 may have the function of a remote control capable of operating the tractor 2. Terminal device 4 may also be capable of operating the drone 3.

[0013] The external device 5 is, for example, a server device. The external device 5 is communicably connected via a network to other external devices, such as a cloud 6. The external device 5 stores information used in the work of the tractor 2. For example, the external device 5 stores information about the field where the tractor 2 performs work, weather information, and the like. The field information includes a field map, the position information of the field, and data on the ridges that form the basis of the field. The data on the basic ridges is, for example, data on ridges formed in the past (such as an image of the ridges formed in the past). The external device 5 can, for example, acquire various information from the cloud 6. Further, the external device 5 may transmit the data acquired from the work vehicle​​​​​​​​​​​​

[0017] The rotational power from the power source 11 is transmitted to the transmission, decelerated by the transmission, and then transmitted to the front wheels 14a and rear wheels 14b which are the driving wheels. The rotational power from the power source 11 can be transmitted to a PTO (Power Take-Off) shaft protruding rearward after being decelerated by the transmission. For example, when the tractor 2 has a traveling motor as the power source 11, it may further include a PTO motor. The rotational power of the PTO motor is transmitted to the PTO shaft.

[0018] A cabin 16 where an operator (also referred to as a driver or a user) can board is provided behind the bonnet 10. Inside the cabin 16, that is, inside the cabin 16, a driver's seat where the operator can sit is provided. Around the driver's seat, a machine operation part including a steering wheel is provided.

[0019] The machine operation part includes operation pedals such as an accelerator pedal, a clutch pedal, and a brake pedal in addition to the steering wheel. Further, the machine operation part includes operation levers such as a forward and reverse lever, a main transmission lever, and a sub-transmission lever. Further, the machine operation part includes operation tools such as various switches.

[0020] Further, the tractor 2 includes a controller 20 (see FIG. 3) which is a control device. The controller 20 can control each part by electronic control, and includes, for example, a processing part having a CPU (Central Processing Unit) and a storage part composed of a hard disk, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. in which various programs and data are stored. The controller 20 is, for example, a vehicle ECU (Electronic Control Unit). The controller 20 may include a plurality of ECUs.

[0021] Furthermore, the tractor 2 is equipped with a positioning device 17 capable of receiving radio waves from satellites orbiting overhead. The positioning device 17 is, for example, a GNSS (Global Navigation Satellite System) positioning device and is installed on top of the cabin 16. The tractor 2 receives radio waves from satellites with the positioning device 17 and can measure its current position (self-position) based on the received radio waves. The positioning device 17 also measures the bearing of the tractor 2.

[0022] The tractor 2 is also equipped with a camera 18. The camera 18 is positioned to photograph the area in front of the tractor 2 and the area diagonally in front of the tractor 2.

[0023] Tractor 2 can be switched between manual and automatic operation by the operator. Tractor 2 can perform automatic operation by the controller 20 controlling each part based on its own position measured by the positioning device 17.

[0024] Next, the control system of the tractor 2, centered on the controller 20, will be described with reference to Figure 3. Figure 3 is a block diagram showing the control system of the tractor 2 according to this embodiment. The controller 20 includes a generative model 20a used in the generative AI (Artificial Intelligence) described later. The controller 20 generates images of the ideal shape of the ridge and images of the estimated shape of the ridge using the generative AI.

[0025] The controller 20 is connected to the power source 11, the transmission 21, the steering device 22, and the ridge-forming machine 12, etc. The controller 20 controls the power source 11, the transmission 21, the steering device 22, and the work equipment 12, etc.

[0026] Furthermore, the controller 20 is connected to a positioning device 17, a camera 18, a rotation sensor 25, a vehicle speed sensor 26, a steering angle sensor 27, and an automatic driving selector switch 28. The controller 20 acquires various signals (information) from the positioning device 17, camera 18, rotation sensor 25, vehicle speed sensor 26, steering angle sensor 27, and automatic driving selector switch 28.

[0027] The rotation sensor 25 detects the rotation speed of the rear wheels 14b. The vehicle speed sensor 26 detects the travel speed (vehicle speed) of the tractor 2. The steering angle sensor 27 detects the steering angle of the front wheels 14a, which are the steering wheels. In other words, the steering angle sensor 27 detects the steering angle of the steering wheel.

[0028] The automatic driving selector switch 28 is a switch that toggles whether or not to perform automatic driving. When the automatic driving selector switch 28 is "ON", automatic driving control is enabled and executed. When the automatic driving selector switch 28 is "OFF", automatic driving control is disabled and not executed. In other words, the automatic driving selector switch 28 is a switch that can enable or disable automatic driving control. The automatic driving selector switch 28 is operated by an operator.

[0029] For example, in straight-line driving controlled by automatic driving control, tractor 2 travels in a straight line along a straight-line travel path. The straight-line travel path is, for example, a travel path parallel to a set reference straight line. The reference straight line is set, for example, by setting a reference start point and a reference end point during travel by operating a reference line setting switch. For example, the reference straight line includes information about the distance and direction of the reference straight line.

[0030] The tractor 2 may travel along a pre-set route using automatic driving control. The tractor 2 travels along the route based on the position information included in the route and the position information of the tractor 2 detected by the positioning device 17.

[0031] When the controller 20 performs autonomous driving control, it steers the steering wheel by controlling the steering cylinder connected to the steering wheel while feeding back the steering angle of the front wheels 14a.

[0032] Even if the automatic driving switch 28 is set to "ON," if the specified conditions are not met, the automatic driving control may not be effective and may not be performed.

[0033] Furthermore, a lifting lever 29 and the like are connected to the controller 20. The lifting lever 29 is a lever that raises and lowers the levee plastering machine 12 (working machine). The lifting lever 29 is, for example, mounted on the handle post and is operated up and down. For example, when the lifting lever 29 is operated upward, the levee plastering machine 12 rises. When the lifting lever 29 is operated downward, the levee plastering machine 12 lowers.

[0034] The controller 20 is also connected to a monitor 30 and a microphone 31. The monitor 30 is located inside the cabin 16. The monitor 30 is located in front of the driver's seat. The monitor 30 displays information about the tractor 2 and the levee plastering machine 12. The monitor 30 can also input information about the levee plastering machine 12 and the work performed by the levee plastering machine 12. For example, the monitor 30 is a touch panel monitor. Images generated by the generation AI, which will be described later, are displayed on the monitor 30.

[0035] The microphone 31 is installed inside the cabin 16. Multiple microphones 31 may be installed. For example, one microphone 31 may be installed at the driver's feet. Another microphone 31 may be installed on the ceiling of the cabin 16. The microphone 31 installed on the ceiling of the cabin 16 can be in a retracted state or an extended state. When the microphone 31 is in the retracted state, it is positioned along the ceiling of the cabin 16. When the microphone 31 is in the extended state, it is moved to a position near the mouth of the operator seated in the driver's seat.

[0036] Furthermore, the controller 20 is connected wirelessly to the terminal device 4, the drone 3, and the external device 5 via the network N through the communication unit 34.

[0037] When tractor 2 is fitted with a levee-forming machine 12 to form levees in a field, it performs the levee-forming process described below. The levee-forming process performed by tractor 2 will be explained with reference to Figure 4. Figure 4 is a flowchart illustrating the levee-forming process.

[0038] The controller 20 acquires information about the current shape of the ridges in the field (S100). The controller 20 acquires information about the current shape of the ridges from images taken by the camera 3a of the drone 3 and the camera 18 installed on the tractor 2. The information about the current shape of the ridges includes images of the ridges and information about the position of the ridges. For example, the controller 20 acquires information about the current shape of the ridges from the drone 3 via the communication unit 34. The controller 20 may also acquire information about the current shape of the ridges from a field map. The controller 20 acquires a field map from an external device 5. For example, the controller 20 may acquire a field map from the cloud 6 via the external device 5.

[0039] The controller 20 displays an image of the current shape of the ridge (S101). The controller 20 displays the image of the current shape of the ridge on the monitor 30. For example, if the image shown in Figure 5 is acquired as information about the current shape of the ridge, the acquired image is displayed on the monitor 30. Figure 5 is a diagram showing an example of an image of the current shape of the ridge. In the image shown in Figure 5, part of the ridge 100 is collapsed. The controller 20 may also display the image of the current shape of the ridge on the terminal device 4.

[0040] The controller 20 acquires information about the desired ridge shape from the worker (S102). The controller 20 acquires information about the desired ridge shape from the worker by, for example, when the monitor 30 or the like is operated by the worker on an image displayed on the monitor 30 or the like. For example, the controller 20 acquires information such as the height, width, and position of the ridge as desired by the worker. The controller 20 may also acquire information about the desired ridge shape from the worker via the microphone 31. For example, the controller 20 acquires information about the desired ridge shape from the worker by performing a predetermined speech recognition process on the sound acquired from the microphone 31.

[0041] Alternatively, for example, the controller 20 may display basic ridge data on a monitor 30 or the like, and obtain information on the desired ridge shape from the basic ridge data. In this case, for example, the controller 20 obtains data on multiple basic ridges from the cloud 6 via an external device 5 and displays it on the monitor 30. Then, the controller 20 uses the basic ridge data selected by the worker from the basic ridge data displayed on the monitor 30 as information on the desired ridge shape.

[0042] The controller 20 generates an image of the ideal ridge shape using a generative AI based on information about the current ridge shape and information about the ridge shape desired by the worker (S103). The generative model 20a used in the generative AI is, for example, a large-scale language model. As shown in Figure 6, the controller 20 generates an image of the ideal ridge shape by inputting information about the current ridge shape and information about the ridge shape desired by the worker into the generative model 20a used in the generative AI. Figure 6 is a block diagram showing the flow of generation of the ideal ridge shape.

[0043] When information about the desired ridge shape from the worker is input, for example via the monitor 30, the controller 20 generates an image of the ideal ridge shape using a generation AI based on the current ridge shape information and the input ridge shape information. For example, the controller 20 generates an image of the ideal ridge shape shown in Figure 7 using the generation AI, based on the current ridge shape shown in Figure 5. Figure 7 is a diagram showing an example of an image of the ideal ridge shape. In Figure 7, an ideal ridge 101 is shown. The controller 20 may also display the image of the ideal ridge shape on the monitor 30 or the terminal device 4.

[0044] The controller 20 generates an image of the estimated shape of the ridge that can be formed using the ridge plastering machine 12, based on information about the current shape of the ridge, an image of the ideal shape of the ridge, and information about the ridge plastering machine 12, using a generation AI (S104). As shown in Figure 8, the controller 20 generates an image of the estimated shape of the ridge by inputting information about the current shape of the ridge, an image of the ideal shape of the ridge, and information about the ridge plastering machine 12 into the generation model 20a used in the generation AI. Figure 8 is a block diagram showing the flow of generation of the estimated shape of the ridge.

[0045] The controller 20, based on the current shape of the ridge, selects the ridge shape that is closest to the ideal shape among the ridges that can be formed using the ridge-forming machine 12, and uses a generation AI to generate an image of the estimated ridge shape. The information of the ridge-forming machine 12 is stored, for example, in the memory unit of the controller 20. Furthermore, the generation AI that generates the image of the estimated ridge shape may be a different generation AI from the generation AI that generates the image of the ideal ridge shape.

[0046] The controller 20 displays an image of the estimated shape of the ridge (S105). The controller 20 displays, for example, the image of the estimated shape of the ridge on the monitor 30. The controller 20 may also display work instructions on the monitor 30 for forming the ridge of the estimated shape. The controller 20 may distinguish between areas where the ridge can be formed by automatic operation and areas where manual steering is required, and display these separately as work instructions. The controller 20 may also display the image of the estimated shape of the ridge on the terminal device 4.

[0047] The controller 20 determines whether the automatic operation changeover switch 28 is "ON" or not (S106). If the automatic operation changeover switch 28 is "ON" (S106: Yes), the controller 20 automatically operates the tractor 2 so that the field ridges have the estimated shape (S107). As a result, the tractor 2 can form ridges of the estimated shape.

[0048] The controller 20 terminates the current process if the automatic operation changeover switch 28 is "OFF" (S106: No). Note that if the automatic operation changeover switch 28 is "OFF", the operator can drive the tractor 2 to form the estimated ridge shape.

[0049] The controller 20 can, for example, generate images of the estimated shape of the ridges at the four corners of the field when the four corners of the field are captured by the drone 3's camera 3a or camera 18. Therefore, the worker can check the estimated shape of the ridges at the four corners of the field on the monitor 30 or the like.

[0050] The controller 20 uses AI to generate an image of the ideal ridge shape based on information about the current ridge shape in the field and information about the ridge shape desired by the worker. The controller 20 also uses AI to generate an image of the estimated ridge shape that can be formed using the ridge forming machine 12, based on information about the current ridge shape, the image of the ideal ridge shape, and information about the ridge forming machine 12. For example, the controller 20 controls the tractor 2 so that the ridge becomes the estimated shape.

[0051] As a result, the work vehicle system 1 can generate an image of the estimated shape of the ridge that is close to the shape desired by the worker and can be formed by the ridge-forming machine 12. When the tractor 2 forms the ridge of the estimated shape, a ridge that matches the shape desired by the worker is formed, and the ridge can be made into an appropriate shape. For example, by displaying the estimated shape of the ridge on the monitor 30, the worker can visually confirm the shape of the ridge that can actually be formed.

[0052] The current shape of the ridge is obtained, for example, from images captured by the camera 3a of the drone 3. This allows the controller 20 to accurately obtain information about the current shape of the ridge. Therefore, the controller 20 can accurately generate an image of the estimated shape of the ridge based on the current shape.

[0053] The work vehicle system 1 may include the following variations.

[0054] The controller 20 may store information about multiple levee plastering machines 12 in its memory unit. The information about the levee plastering machines 12 includes data on the type of levee plastering machine 12 and the degree to which it can modify (form) the levee shape. In other words, the information about the levee plastering machine 12 is the basic data for the levee plastering machine 12. The controller 20 selects a levee plastering machine 12 from the multiple levee plastering machines 12 to form an ideal levee shape. Then, the controller 20 uses a generation AI to generate an image of the estimated levee shape that can be formed using the selected levee plastering machine 12, based on the information about the selected levee plastering machine 12, the information about the current levee shape, and an image of the ideal levee shape.

[0055] The controller 20 may generate images of estimated ridge shapes that can be formed using each ridge-forming machine 12 using AI generation, and display the images of each estimated shape on a monitor 30 or the like. The controller 20 may also select a ridge-forming machine 12 that can form the estimated shape selected by the operator.

[0056] As a result, by using the selected ridge-forming machine 12, the ridge can be formed in the ideal shape desired by the worker.

[0057] The controller 20 may correct the image of the estimated ridge shape according to the weather and at least one of the field soil type. For example, the controller 20 corrects the image of the estimated ridge shape by an inference model that has been deeply trained on the weather and at least one of the field soil type. Information about the weather is obtained, for example, from an external device 5. The field soil type is, for example, the moisture content of the field. The moisture content of the field is detected by a moisture content sensor. The moisture content sensor is installed, for example, on the tractor 2. The moisture content of the field is detected by an operator using the moisture content sensor, and the detection result may be input by a monitor 30 or a terminal device 4, etc. The field soil type may also be the viscosity of the field. The viscosity of the field is detected by a viscosity sensor. The viscosity sensor detects the viscous resistance due to the rotation of the ridge-forming machine 12, for example.

[0058] The controller 20 may generate an image of the estimated shape of the ridge using a generation AI based on information about the current shape of the ridge, an image of the ideal shape of the ridge, information about the ridge-forming machine 12, weather conditions, and at least one piece of information about the soil type of the field.

[0059] This allows the controller 20 to accurately generate an image of the estimated shape of the ridge that can be formed by the ridge-forming machine 12.

[0060] The controller 20 may calculate the difference between the ideal shape of the ridge and the estimated shape of the ridge. Specifically, the controller 20 calculates the difference between an image of the ideal shape and an image of the estimated shape. If the calculated difference is greater than or equal to a predetermined difference, the controller 20 generates at least one revised work process and ridge-forming machine 12 using AI. The predetermined difference is a value set in advance. The controller 20 displays the generated revised work process on, for example, the monitor 30, and proposes the revised work to the worker. For example, the controller 20 displays the revised work process as text. In this way, the controller 20 proposes a revised work process to the worker when there is a large discrepancy between the ideal shape of the ridge desired by the worker and the estimated shape of the ridge that can actually be formed.

[0061] This allows the controller 20 to prevent the formation of ridges that deviate from the shape desired by the worker. Furthermore, the controller 20 can reduce the worker's workload by suggesting revised plans. Additionally, the controller 20 can help the worker understand the revised plans by displaying them as text.

[0062] The controller 20 may compare an image of the levee shape after levee plastering with an image of the estimated levee shape to determine whether the formed levee deviates from the estimated shape. For example, if the formed levee deviates from the estimated shape, the controller 20 displays the cause of the deviation, a method for correction, and a method for adjusting the levee plastering machine 12 on the monitor 30.

[0063] If, after the levee plastering has been completed, a worker gives a correction instruction via voice input, such as "I want to repaint the levee," the controller 20 displays on the monitor 30 the cause of the need to repaint the levee, the correction method, and the adjustment method for the levee plastering machine 12.

[0064] The generation of images of the ridge shape using the generation AI may be performed by an external device 5. The generation model 20a used in the generation AI may be provided in the controller of the external device 5. That is, the controller of the external device 5 may generate images of the ideal shape of the ridge and the estimated shape of the ridge. In this case, the tractor 2 acquires the images generated by the external device 5 via the communication unit 34 and displays them, for example, on a monitor 30. The generation model 20a used in the generation AI may be provided in the cloud 6.

[0065] The controller 20 may determine the extent of weed growth on the ridge from the current image of the ridge. For example, the controller 20 can determine the extent of weed growth by performing image processing on the current image of the ridge. If the controller 20 determines that there is a lot of weed growth, it will suggest to the worker that the weeds be removed.

[0066] The tractor 2 may be equipped with a distance sensor on top of the cabin 16 to measure the distance to the implement 12. The distance sensor is, for example, an ultrasonic sensor. The controller 20 determines the presence or absence of the implement 12 based on the detection result from the distance sensor. The controller 20 may also determine the type of implement 12 based on its size and shape by measuring the distance to the implement 12 and output it to the monitor 30. If the implement 12 is a levee plastering machine 12, the controller 20 may read information about the levee plastering machine 12 from a storage unit or the like based on the determined type of levee plastering machine 12. The controller 20 generates an image of the estimated shape of the levee using the read information about the levee plastering machine 12.

[0067] The work vehicle system 1 may, for example, have an external device 5 display the date and time when water is supplied to the field on a terminal device 4 or the like. The controller of the external device 5 uses a generating AI to determine the date and time when water is supplied to the field based on images of the field, field information, weather, soil characteristics of the field, etc., and displays the determination result on the terminal device 4 or the like. The images of the field may be aerial photographs or images taken by a drone 3. The field information includes the location information of the field and information about the water channels that supply water to the field.

[0068] Specifically, the controller of the external device 5 determines the water supply status in the field, as shown in Figure 9, from, for example, images of the field taken and field information. Figure 9 is a diagram showing the water supply status in the field. In Figure 9, the main irrigation channel 110 is shown with a thick solid line, the branch irrigation channels 111 are shown with a thick dashed line, and the terminal irrigation channel 112 is shown with a thick dotted line. The type of line used to represent each irrigation channel 110 to 112 is the same as in the diagrams described below. Also in Figure 9, the flow of water in each irrigation channel 110 to 112 is indicated by arrows. In Figure 9, fields to which water has been supplied are indicated by hatching. The water flows in the order of the main irrigation channel 110, the branch irrigation channels 111, and the terminal irrigation channel 112. Diversion structures are provided at the connection points of each irrigation channel 110 to 112.

[0069] The controller of the external device 5 uses a generating AI to determine the date and time when water will be supplied to the field specified by the worker, based on the water supply status in the field, the weather, the soil characteristics of the field, etc. Note that the generating AI used may be different from the generating AI that generates an image of the estimated shape of the ridges and the generating AI that generates an image of the ideal shape of the ridges. For example, if a worker specifies a field indicated by hatching in Figure 10 using the terminal device 4 and inputs, for example, "When will water be supplied to this field?", the controller of the external device 5 uses the generating AI to determine the date and time when water will be supplied to the specified field. The controller of the external device 5 then transmits the determination result to the terminal device 4 and displays, for example, "Water will be supplied to this field on May 1st." Figure 8 is a diagram showing an example of a field being specified. In Figure 10, for explanatory purposes, the field to which water has been supplied is shown in black. The controller of the external device 5 may, for example, display the appropriate date for planting seedlings.

[0070] Furthermore, for example, if an operator uses terminal device 4 to specify a field indicated by hatching in Figure 11 and inputs, for example, "When will water be supplied to this field?", the controller of external device 5 uses the generated AI to determine the date and time when water will be supplied to the specified field. If the water is not diverted to the terminal irrigation channel 112 that supplies water to the specified field, the controller of external device 5 transmits the determination result to terminal device 4 and displays, for example, "The date and time when water will be supplied is unknown because the water is not diverted upstream." Figure 11 shows an example of a field being specified. In Figure 11, for explanatory purposes, fields to which water has been supplied are shown in black. Note that the controller of external device 5 may also notify the terminal device 4 of the date and time when water will be supplied once it knows the date and time.

[0071] This allows workers to know when and where water will be supplied to the fields.

[0072] Furthermore, the controller of the external device 5 may predict the amount of water entering the field based on images of the field, field information, weather, and past water distribution trends of the field, and generate an image showing the amount of water in the field on a specific day using a generation AI, which may then be displayed on a terminal device 4 or the like.

[0073] Further effects and modifications can be readily derived by those skilled in the art. Therefore, broader aspects of the present invention are not limited to the specific details and representative embodiments expressed and described above. Accordingly, various modifications are possible without departing from the spirit or scope of the overall concept of the invention as defined by the appended claims and their equivalents. [Explanation of symbols]

[0074] 1. Work Vehicle System 2 Work vehicles 3 Drones 3a Camera 4 Terminal devices 5 External device 12. Ridge plastering machine (working machine) 20 controllers 17 Positioning device 18 Cameras 30 monitors 31 Mike 34 Communication Unit

Claims

1. A work vehicle system having a controller, The aforementioned controller, Based on information about the current shape of the ridges in the field and the information about the shape of the ridges desired by the user, an AI generates an image of the ideal shape of the ridges. A work vehicle system that uses AI to generate an image of the estimated shape of a ridge that can be formed using a ridge-forming machine, based on information about the current shape of the ridge, an image of the ideal shape of the ridge, and information about a ridge-forming machine used to perform ridge-forming work in a field.

2. The aforementioned controller, Based on information regarding multiple ridge-forming machines, select the ridge-forming machine for forming the ideal ridge shape. The work vehicle system according to claim 1, wherein an AI generates an image of the estimated shape of the ridge that can be formed using the selected ridge-forming machine, based on information of the selected ridge-forming machine, information of the current shape of the ridge, and an image of the ideal shape of the ridge.

3. The work vehicle system according to claim 1, wherein the controller controls the work vehicle to achieve the estimated shape.

4. The work vehicle system according to claim 1, wherein the current shape of the ridge is obtained from a field map and at least one of images taken by a camera.

5. The work vehicle system according to claim 1, wherein the controller corrects an image of the estimated shape of the ridge corrected according to at least one of the weather and the soil type of the field.

6. The aforementioned controller, If the difference between the ideal shape of the ridge and the estimated shape of the ridge is greater than or equal to a predetermined difference, the AI ​​generates at least one revised work process and ridge-forming machine. The work vehicle system according to claim 1, which displays the generated revised proposal.

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