JP2018173917A - Information processing apparatus, program, information processing system and data structure - Google Patents

Abstract

PROBLEM TO BE SOLVED: To further improve agricultural productivity by using a robot or an IT.SOLUTION: The information processing apparatus includes a management device for managing one or more agricultural products cultivated in a farm field. The management device includes, for example, a position information acquisition unit that acquires position information indicating positions of one or more agricultural products in respective farm fields, a quality information acquisition unit that acquires quality information on a quality of each of the one or more agricultural products, and a management data generation unit that generates management data in which the position information acquired by the position information acquisition unit and the quality information acquired by the quality information acquisition unit are associated with each other.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus, a program, an information processing system, and a data structure.

In recent years, using unmanned agricultural machinery or sensing technology to improve the efficiency of agriculture has been studied. For example, Non-Patent Document 1 discloses an unmanned small weeding robot. Non-Patent Document 2 discloses a method of topdressing according to the growth state utilizing sensing technology.
[Prior art documents]
[Non-patent literature]
[Non-Patent Document 1] Nao Kondo et al., “Development of Weeding Weeding Robot”, [on line] [searched on August 12, 2016], Internet <URL: http: //www.aptech.kais.kyoto-u. ac.jp/activity/date/grass_cutter_robot.pdf>
[Non-patent document 2] Hokkaido Research Institute, List of test research results, "Summary of results (created in January 2006)" [on line] [searched on October 20, 2016], Internet <URL : Https://www.hro.or.jp/list/agricultural/center/kenkyuseika/gaiyosho/h18gaiyo/f5/2006508.pdf>

  Agricultural productivity cannot be improved sufficiently by simply introducing agricultural robots. It is expected to further improve agricultural productivity using robots or IT.

  In a first aspect of the present invention, an information processing apparatus is provided. The information processing apparatus includes a management device, for example. In the above information processing apparatus, the management apparatus manages, for example, one or more agricultural products cultivated in the farm field. In the information processing apparatus, the management apparatus may include a position information acquisition unit that acquires position information indicating the position of each of the one or more agricultural products in each field. In the information processing apparatus, the management apparatus may include a quality information acquisition unit that acquires quality information regarding the quality of each of the one or more agricultural products. In the information processing apparatus, the management apparatus may include a management data generation unit that generates management data in which the position information acquired by the position information acquisition unit is associated with the quality information acquired by the quality information acquisition unit. In the information processing apparatus, the position information may include information indicating a position in a substantially horizontal direction on the field and information indicating an altitude.

  In the information processing apparatus, the position information acquisition unit may include an image data acquisition unit that acquires image data of each image of one or more agricultural products. In the information processing apparatus, the position information acquisition unit may include a stock specifying unit that specifies a stock in which each agricultural product is grown based on the image data acquired by the image data acquisition unit. In the information processing apparatus, the position information acquisition unit is configured to: (i) a representative position of each agricultural product and (ii) a reference position of a stock in which each agricultural product is grown based on the image data acquired by the image data acquisition unit. You may have a relative relationship acquisition part which acquires the information which shows a relative relationship. In the information processing apparatus, the position information acquisition unit is based on (i) information on the reference position of the stock specified by the stock specifying unit, and (ii) information indicating the relative relationship acquired by the relative relationship acquisition unit. You may have a position analysis part which acquires the position information of each agricultural product.

  In the information processing apparatus, the position information acquisition unit may include an image data acquisition unit that acquires image data of each image of one or more agricultural products. In the information processing apparatus, the position information acquisition unit includes an area specifying unit that specifies an area that is a part of the field and on which each agricultural product is grown, based on the image data acquired by the image data acquisition unit. You may have. In the information processing apparatus, the position information acquisition unit is configured to determine (i) a representative position of each agricultural product and (ii) a reference position of an area where each agricultural product grows based on the image data acquired by the image data acquisition unit. You may have a relative relationship acquisition part which acquires the information which shows a relative relationship. In the information processing apparatus, the position information acquisition unit is based on (i) information related to the reference position of the area specified by the stock specifying unit and (ii) information indicating the relative relationship acquired by the relative relationship acquisition unit. You may have a position analysis part which acquires the position information of each agricultural product.

  In the information processing apparatus, the position information acquisition unit may include an image data acquisition unit that acquires image data of each image of one or more agricultural products. In the information processing apparatus, the position information acquisition unit may include an imaging condition acquisition unit that acquires imaging condition information indicating a condition under which an image is captured. In the information processing apparatus, the position information acquisition unit acquires position information of each agricultural product based on (i) the image data acquired by the image data acquisition unit and (ii) the imaging condition information acquired by the imaging condition acquisition unit. There may be included a position analyzing unit. In the information processing apparatus described above, the information on quality may include at least one of (i) information on the appearance of each agricultural product and (ii) information on the components included in each agricultural product.

  In a second aspect of the present invention, an information processing apparatus is provided. The information processing apparatus may include a support device. In the above information processing apparatus, the support apparatus supports, for example, work on one or more agricultural products cultivated in a farm field. In the above information processing apparatus, the support apparatus is information related to demand for one or more agricultural products, and (i) quality conditions related to the quality required by the consumer for the agricultural product and (ii) the agricultural product requested by the consumer. You may provide the demand information acquisition part which acquires the demand information containing the quantity conditions regarding quantity. In the information processing apparatus, the support apparatus stores (i) position information indicating the position of each of the one or more agricultural products in the field and (ii) quality information regarding the quality of each of the one or more agricultural products in association with each other. The management data storage unit may be referred to, and an extraction unit may be provided that extracts agricultural products satisfying quality conditions from one or more agricultural products. In the information processing apparatus described above, the support device refers to the management data storage unit and at least each of the agricultural products extracted by the extraction unit when at least the number of the agricultural products extracted by the extraction unit satisfies the quantity condition. (Ii) a separation target determination unit that determines a separation target to be separated from the stock on a specific work day or work period from the agricultural products extracted by the extraction unit based on the position information; It's okay.

  In the above information processing apparatus, the support apparatus gives the quality of other agricultural products when at least one of the agricultural products extracted by the extraction unit is separated from the stock on a specific working day or working period. A quality prediction unit that predicts the influence based on at least the position information may be provided. In the information processing apparatus, the demand information may include a time condition related to a time when the consumer requests agricultural products. In the information processing apparatus, the separation target determination unit is configured to predict the profits obtained from one or more agricultural products based on (i) the prediction result of the quality prediction unit and (ii) the quality condition, the quantity condition, and the time condition. The separation target may be determined so that the amount satisfies a predetermined condition. In the information processing apparatus, the support apparatus may include an attribute information acquisition unit that acquires attribute information indicating an attribute of the worker or the work machine. In the information processing apparatus, the separation target determination unit may determine the separation target based on (i) position information and (ii) attribute information acquired by the attribute information acquisition unit. In the information processing apparatus described above, the support device refers to (i) the management data storage unit, acquires the position information of the separation target determined by the separation target determination unit, and (ii) the worker based on the position information. Or you may provide the work route determination part which determines the route of a working machine.

  In the information processing apparatus, the support apparatus acquires image data of an image captured by the imaging apparatus from an imaging apparatus of a worker or a work machine that is performing the separation work of agricultural products or the preparation of the separation work in the field. A working image acquisition unit may be provided. In the information processing apparatus, the support device may include a separation target recognition unit that recognizes a separation target determined by the separation target determination unit among at least one agricultural product included in the image. In the information processing apparatus described above, the support device may cause the worker or the work machine to separate agricultural products other than the separation target determined by the separation target determination unit from the stock by analyzing the image data acquired by the work image acquisition unit. A warning unit may be provided for outputting a warning to the worker or the work machine when it is determined that the value is high.

  In the third aspect of the present invention, a program is provided. A non-transitory computer readable medium storing the above program may be provided. The program may be a program for causing a computer to function as the information processing apparatus. The program may be a program for causing a computer to execute an information processing procedure in the information processing apparatus.

  In a fourth aspect of the present invention, an information processing system is provided. The information processing system includes, for example, the information processing apparatus according to the first aspect and the information processing apparatus according to the second aspect.

  In a fifth aspect of the invention, a data structure is provided. The data structure described above may be a data structure of data used in a management system that manages growing agricultural products. In the above data structure, for example, position information indicating the position of one or more agricultural products in each field is associated with quality information regarding the quality of each of the one or more agricultural products. In the above data structure, the position information may include information indicating a position in a substantially horizontal direction on the field and information indicating an altitude.

  It should be noted that the above summary of the invention does not enumerate all the necessary features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

1 schematically shows an example of a system configuration of a farm work support system 100. An example of an internal configuration of the management server 110 is schematically shown. An example of an internal structure of the growth management part 220 is shown schematically. An example of an internal configuration of position monitoring part 352 is shown roughly. An example of an internal configuration of work management part 230 is shown roughly. An example of an internal configuration of work plan part 530 and work monitoring part 540 is shown roughly. An example of screen 700 is shown roughly. An example of the support by the farm work support system 100 is schematically shown. An example of data table 900 is shown roughly.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention. In the drawings, the same or similar parts are denoted by the same reference numerals, and redundant description may be omitted.

[Outline of the farm work support system 100]
FIG. 1 schematically shows an example of the system configuration of the farm work support system 100. In this embodiment, the farm work support system 100 manages the growth status of agricultural products cultivated in the farm field 10. The farm work support system 100 may divide the agricultural field 10 into a plurality of subareas 20 and manage the growth status of agricultural products cultivated in each of the plurality of subareas 20. For example, the farm work support system 100 manages the growth state of one or more stocks 12. The farm work support system 100 may manage the growth state of one or more fruits 14 growing on the stock 12. The fruit 14 may be an example of an agricultural product. Examples of agricultural products include cereals, vegetables (including root vegetables), fruits, flowers, branches and leaves (for example, tea leaves, ornamental branches or leaves, etc.), mushrooms, mycelia, and the like.

  To produce agricultural products, work such as soil sampling, soil analysis, fertilization, establishment, sowing or planting, field monitoring, pest control, weeding, thinning or pruning, topdressing, judgment of harvest time, harvesting, etc. Will occur. The farm work support system 100 may manage the work related to at least one of the stock 12 and the fruit 14. The farm work support system 100 may support workers and work machines engaged in the above work. For example, the farm work support system 100 supports an operation in which a worker or a work machine separates a part of the stock (sometimes referred to as a work target or a separation target) from the stock. Examples of the work for separating a part of the stock from the stock include weeding, thinning or summarization, and harvesting. The fruit 14 may be an example of a work target or a separation target.

  For example, an outline of support by the farm work support system 100 will be described by taking, as an example, a case where a specific quantity of fruits 14 having a specific quality is harvested on a specific work day or work period. In the present embodiment, the farm work support system 100 determines a fruit 14 to be a harvest work target (sometimes referred to as a work target) among one or more fruits 14 cultivated in the field 10. Thereafter, the farm work support system 100 notifies the worker or the work machine of the position of the fruit 14 to be worked when the worker or the work machine performs the harvesting work.

  For example, the farm work support system 100 guides the worker about the contents of the next work or guides the work route. Although the notification method is not particularly limited, the farm work support system 100 displays, for example, information on the work target on the display device of the worker's communication terminal. The farm work support system 100 may use augmented reality technology to display information on the work target superimposed on at least one of an image in real space and an image. The farm work support system 100 may notify the worker or the work machine of a warning when the worker or the work machine tries to harvest the fruit 14 that is not the work target.

  In one embodiment, the farm work support system 100 plans which fruits 14 to harvest based on at least one of current order status and future demand forecast. The farm work support system 100 may plan which fruit 14 to harvest at which time based on at least one of the current order status and future demand forecast. Thereby, the profitability of the field 10 can be increased.

  By the way, when a plurality of fruits 14 are grown in the same strain 12, when one or a plurality of fruits 14 are harvested, the fruits 14 that have not been harvested yet are owing to sugar translocation, change in light irradiation, etc. Growth rate changes. Moreover, the growth rate of the fruit 14 of the other strains 12 changes as the amount of the fertilizer component distributed to the other strains 12 changes. Therefore, the farm work support system 100 predicts how the growth status of other fruits 14 changes by harvesting a specific fruit 14, and which fruit 14 is to be harvested based on the prediction result. You may plan. The farm work support system 100 may plan which fruits 14 are to be harvested based on (i) the above prediction result and (ii) at least one of the current order status and future demand prediction. The farm work support system 100 may plan a time for harvesting the fruits 14.

  In another embodiment, the farm work support system 100 plans which fruits 14 to harvest based on the attributes of the worker or work machine. The farm work support system 100 may plan which fruits 14 to harvest based on the attributes of the worker or work machine and (ii) at least one of the current order status and future demand forecast. The farm work support system 100 may plan a time for harvesting the fruits 14. Thereby, for example, the burden on the operator can be reduced.

  For example, there is a case where an experience of harvesting agricultural products is implemented for tourists. In this case, attributes such as age, height, physical strength, judgment, medical history, taste, and taste vary depending on the tourist. A tourist can be a consumer as well as a consumer. Therefore, the farm work support system 100 acquires information on the attributes of tourists via an arbitrary input terminal, for example, before the harvest experience is implemented. Then, the farm work support system 100 determines the fruits 14 to be harvested in consideration of the acquired tourist attributes. Thereafter, the farm work support system 100 acquires information indicating a tourist's evaluation regarding the harvested fruit 14 via the input terminal. The farm work support system 100 may further determine the fruit 14 to be harvested in consideration of information indicating the above evaluation. Thereby, a tourist's satisfaction can be improved.

[Overview of each part of the farm work support system 100]
In this embodiment, the farm work support system 100 includes a management server 110, for example. The farm work support system 100 may include a worker terminal 120. The farm work support system 100 may include a work machine 130. In the present embodiment, the work machine 130 includes, for example, a work robot 140, a work drone 150, and a monitoring camera 162. In this embodiment, at least one of the work robot 140 and the work drone 150 may acquire an arbitrary function by rearranging various units (sometimes referred to as an attachment). For example, the work robot 140 includes an imaging unit 142, a sense unit 144, an instruction unit 146, and a farm work unit 148. In addition, the work drone 150 includes an imaging unit 152.

  The farm work support system 100 may be an example of a management device, a support device, an information processing device, and an information processing system. The management server 110 may be an example of a management device, a support device, an information processing device, and an information processing system. The imaging unit 142 may be an example of an image data acquisition unit and an imaging condition acquisition unit. The imaging unit 152 may be an example of an image data acquisition unit and an imaging condition acquisition unit. The monitoring camera 162 may be an example of an image data acquisition unit and an imaging condition acquisition unit. The sense unit 144 may be an example of a quality information acquisition unit.

  In the present embodiment, each unit of the farm work support system 100 may send and receive information to and from each other via the communication network 30. The communication network 30 may be a wired communication transmission line, a wireless communication transmission line, or a combination of a wireless communication transmission line and a wired communication transmission line. The communication network 30 may include a wireless packet communication network, the Internet, a P2P network, a dedicated line, a VPN, a power line communication line, and the like. The communication network 30 may include (i) a mobile communication network such as a mobile phone network, (ii) a wireless MAN (for example, WiMAX (registered trademark)), a wireless LAN (for example, WiFi (registered trademark)). Or a wireless communication network such as Bluetooth (registered trademark), Zigbee (registered trademark), or NFC (Near Field Communication).

  In the present embodiment, the management server 110 manages one or more agricultural products cultivated in the agricultural field 10. For example, the growth status of the agricultural product is managed. The management server 110 may manage the growth status of agricultural products cultivated in each sub area 20 for each of the sub areas 20. The management server 110 may manage the growth state of one or more stocks 12. The management server 110 may manage the growth state of one or more fruits 14 growing on the stock 12. In the present embodiment, the management server 110 may support work on one or more agricultural products cultivated in the farm field 10.

  In the present embodiment, the worker terminal 120 may be a communication terminal used by the worker. The worker terminal 120 may be any device that can send and receive information to and from the management server 110 via the communication network 30, and details thereof are not particularly limited. Examples of the worker terminal 120 include a personal computer and a portable terminal. Examples of the portable terminal include a mobile phone, a smartphone, a PDA, a tablet, a notebook computer or a laptop computer, and a wearable computer.

  In the present embodiment, the work machine 130 may be a machine that performs an arbitrary work. The work machine 130 executes, for example, various types of farm work and monitoring work. The work machine 130 may be a fixed type or a movable type. The work machine 130 may have an autonomous traveling function or an autonomous navigation function, may be steered by remote operation, or may be steered by an operator who has entered the work machine 130.

  For example, in the present embodiment, the work robot 140 may be a working autonomous traveling robot. The work drone 150 may be a work drone operated by remote control. The monitoring camera 162 may be a fixed type or may be installed on a carriage that moves on a rail installed on the farm field 10.

  In the present embodiment, the imaging unit 142, the imaging unit 152, and the monitoring camera 162 image the inside of the farm field 10 or the periphery of the farm field 10. The imaging unit 142, the imaging unit 152, and the monitoring camera 162, for example, image one or more stocks 12, one or more fruits 14, one or more workers, one or more work machines, and the like. The imaging unit 142, the imaging unit 152, and the monitoring camera 162 may transmit image data obtained by imaging to the management server 110.

  In the present embodiment, the sense unit 144 measures a physical quantity related to the growth of the strain 12 or the fruit 14. For example, the sense unit 144 measures a physical quantity related to the quality of one or more fruits 14. Thereby, the sense unit 144 acquires information on the quality of one or more fruits 14 (sometimes referred to as quality information). The sense unit 144 may transmit information indicating the measurement result to the management server 110.

  The sense unit 144 may acquire identification information of each of the one or more fruits 14 (the identification information may be abbreviated as ID). The sense unit 144 may acquire the ID of each fruit by reading the information of the tag attached to each fruit or the vicinity of each fruit. In one embodiment, the sense unit 144 acquires the ID of each fruit based on the analysis result of the image captured by the imaging unit 142. The image analysis may be performed by the computer of the work robot 140 or by the computer of the management server 110. In another embodiment, the sense unit 144 uses a technology for exchanging IDs via short-range wireless communication such as RFID (radio frequency identifier), and tags attached to each fruit or the vicinity of each fruit. Get the ID embedded in. The sense unit 144 may be transmitted to the management server 110 in association with identification information and quality information of each of the one or more fruits 14.

  In the present embodiment, the instruction unit 146 outputs an instruction regarding the work to a worker or work machine that performs the work on the farm 10. The mode of outputting the above instruction is not particularly limited. For example, the instruction unit 146 displays the content of the instruction on the display screen. The instruction unit 146 may output the content of the instruction as a voice. The instruction unit 146 may physically indicate the stock 12 or the fruit 14 that is the target of the instruction. The instruction unit 146 may indicate the strain 12 or the fruit 14 that is the target of the instruction by irradiating the electromagnetic wave (for example, light) to the strain 12 or the fruit 14 that is the target of the instruction. .

  In the present embodiment, the farm work unit 148 may be an attachment for farm work. For example, a farm work unit 148 corresponding to the application is attached to the work robot 140 and performs farm work. Thereby, the work robot 140 can cope with a plurality of different types of farm work. Therefore, the introduction cost of the work machine 130 can be suppressed as compared with the case where the dedicated work machine 130 is introduced for each farm work. In addition, a larger number of work machines 130 can be introduced at the same cost as when dedicated work machines 130 are introduced for each farm work. As a result, the farm work can be easily performed by the plurality of work robots 140. By using a plurality of work robots 140, it is possible to provide a necessary amount of resources at a required time in a series of farm work cycles. As a result, the productivity of farm work can be greatly improved.

  Each of the farm work units 148 may be a unit specialized for one or more farm works. Agricultural work unit 148 includes chemical spraying device for pest control, fertilizer spraying device for spraying fertilizer, gas spraying device for spraying gas, stand-up device, seedling dropping device, sowing device, tilling device, unnecessary leaves and leaves A manipulator having a jig for carrying out, a manipulator having a jig for plucking agricultural products, a mowing device (for example, a grass mowing device for neglected fields), a watering device, a bird and animal threat device (for example, voice, Examples are devices that threaten birds and beasts with light.), Thinning devices, flowering devices, fruit tree picking devices, bag hanging devices (for example, bag hanging devices for fruit trees), and the like.

[Specific Configuration of Each Part of Farm Work Support System 100]
Each unit of the farm work support system 100 may be realized by hardware, may be realized by software, or may be realized by hardware and software. At least a part of each part of the farm work support system 100 may be realized by a single server or may be realized by a plurality of servers. At least a part of each part of the farm work support system 100 may be realized on a virtual server or a cloud system. At least a part of each part of the farm work support system 100 may be realized by a personal computer or a portable terminal. Examples of the portable terminal include a mobile phone, a smartphone, a PDA, a tablet, a notebook computer or a laptop computer, and a wearable computer. Each unit of the farm work support system 100 may store information using a distributed ledger technology such as a block chain or a distributed network.

  When at least a part of the constituent elements constituting the farm work support system 100 is realized by software, the constituent elements realized by the software define operations related to the constituent elements in an information processing apparatus having a general configuration. It may be realized by starting up software or a program. The information processing apparatus having the above general configuration includes (i) a data processing apparatus having a processor such as a CPU and a GPU, a ROM, a RAM, a communication interface, and the like, and (ii) a keyboard, a pointing device, a touch panel, a camera, and voice input. Input devices such as devices, gesture input devices, various sensors, GPS receivers, (iii) output devices such as display devices, audio output devices, vibration devices, and (iv) storage devices such as memory, HDD, SSD (external) Including a storage device).

  In the information processing apparatus having the general configuration, the data processing apparatus or the storage device may store the software or the program. The software or program is executed by a processor to cause the information processing apparatus to execute an operation defined by the software or program. The above software or program may be stored in a non-transitory computer-readable recording medium. The software or program may be a program for causing a computer to function as the farm work support system 100 or a part thereof. The software or program may be a program for causing a computer to execute information processing in the farm work support system 100 or a part thereof.

  FIG. 2 schematically shows an example of the internal configuration of the management server 110. In the present embodiment, the management server 110 includes a request processing unit 210, a growth management unit 220, a work management unit 230, and a storage unit 240. In the present embodiment, the storage unit 240 includes a growth information storage unit 242, a reference position storage unit 244, an attribute information storage unit 246, an order information storage unit 248, a history information storage unit 250, and a prediction model storage unit 252. And have.

  The growth management unit 220 may be an example of a management device. The work management unit 230 may be an example of a support device. The storage unit 240 may be an example of a management data storage unit. The growth information storage unit 242 may be an example of a management data storage unit.

  In the present embodiment, the request processing unit 210 accepts various requests. For example, the request processing unit 210 receives a request for ordering the fruit 14 from the consumer terminal 40. The request processing unit 210 processes the received request.

  In the present embodiment, the growth management unit 220 manages the growth status of each of the one or more fruits 14. For example, the growth management unit 220 manages the position of each of the one or more fruits 14. The growth management unit 220 may manage the quality of each of the one or more fruits 14. Details of the growth management unit 220 will be described later.

  In the present embodiment, the work management unit 230 manages work related to each of the one or more fruits 14. The work management unit 230 may create a work plan. For example, the work management unit 230 determines the fruit 14 to be a work target on a specific work day or work period. The work management unit 230 may manage the progress of work. The work management unit 230 may generate information for supporting the work. Details of the work management unit 230 will be described later.

  In the present embodiment, the storage unit 240 stores various types of information. In the present embodiment, the growth information storage unit 242 stores, for example, information regarding the growth status of each of the one or more stocks 12. The growth information storage unit 242 may store information on the growth status of each of the one or more fruits 14.

  In the present embodiment, the reference position storage unit 244 stores position information of various reference positions. The position information may be (i) latitude information and longitude information, or (ii) latitude information, longitude information, and altitude information. For example, the reference position storage unit 244 stores position information of the positions of points representing each of the plurality of subareas 20 (sometimes referred to as reference positions). The reference position storage unit 244 may store position information of each reference position of one or more stocks 12.

  In the present embodiment, the attribute information storage unit 246 stores information indicating worker attributes. The attribute information storage unit 246 may store information indicating the attribute of the work machine. Examples of worker attributes include age, gender, physical characteristics, preferences, work requirements, or work restrictions. Examples of physical characteristics include height, weight, presence / absence or degree of physical strength, presence / absence or degree of chronic illness, medical history, and the like. Examples of preferences include work preferences, taste preferences, and the like. As requirements for work or restrictions on work, it is possible to exemplify whether or not a work table or a ladder can be used, and whether or not work with dirt can be performed. Examples of the work machine attribute include the specification or property of the work machine, the specification or property of the mounted attachment, and the like.

  In the present embodiment, the order information storage unit 248 stores information indicating the contents of the accepted order. Examples of order contents include delivery date, quality, quantity, price, and the like. In the present embodiment, the history information storage unit 250 stores information related to various histories related to agricultural products. Examples of the history include a history of work on agricultural products, a history of the growth status of agricultural products, a history of demand for agricultural products, a history of market prices of agricultural products, and the like. In the present embodiment, the prediction model storage unit 252 stores, for example, various models used for prediction or simulation and parameters of the model.

  FIG. 3 schematically shows an example of the internal configuration of the growth management unit 220. FIG. 3 schematically shows an example of the internal configuration of the work robot 140.

[Overview of work robot 140]
In the present embodiment, the work robot 140 includes a base unit 310, a travel unit 320, a support unit 330, and a farm work unit 148. In the present embodiment, the base unit 310 includes a control unit 312 and a power supply unit 314. In the present embodiment, the support unit 330 includes an imaging unit 142, a sense unit 144, and an instruction unit 146.

  In the present embodiment, one or more attachments can be attached to the base unit 310. The base unit 310 may have a mounting portion for mounting the attachment to the base unit 310.

  In the present embodiment, the control unit 312 controls the work robot 140. For example, the work robot 140 receives a GPS signal, a beacon signal, and the like, and acquires information indicating the current position of the work robot 140. The work robot 140 controls autonomous travel of the work robot 140 based on (i) information indicating the current position of the work robot 140 and (ii) information indicating the destination and information indicating the travel route. May be. Note that the work robot 140 may analyze the image data acquired by the imaging unit 142 and acquire information indicating the current position of the work robot 140.

  In the present embodiment, the power supply unit 314 supplies power to each unit of the work robot 140. The traveling unit 320 may be a traveling attachment. The traveling unit 320 may generate power for moving the work robot 140 using the electric power supplied from the power supply unit 314. The support unit 330 may be an attachment for support work. The support unit 330 supports various operations for one or more agricultural products cultivated in the field 10.

[Overview of the growth manager 220]
In the present embodiment, the growth management unit 220 includes a position monitoring unit 352, a quality monitoring unit 354, and a data update unit 356. The position monitoring unit 352 may be an example of a position information acquisition unit. The quality monitoring unit 354 may be an example of a quality information acquisition unit. The data update unit 356 may be an example of an information processing device, a management device, and a management data generation unit.

  In the present embodiment, the position monitoring unit 352 acquires the position information of each of the one or more fruits 14 cultivated in the farm field 10. A specific configuration of the position monitoring unit 352 will be described later.

  The position information only needs to be information indicating the position in the field 10, and the specific format is not particularly limited. The position information may include latitude information and longitude information. The position information may include latitude information, longitude information, and altitude information. The position information may include information indicating the position in the substantially horizontal direction on the farm field 10 and information indicating the altitude. The position in the substantially horizontal direction in the farm field 10 may be represented using two orthogonal axes on a plane representing the ground of the farm field 10. The position in the substantially horizontal direction in the agricultural field 10 may be expressed using an azimuth and a distance. The position in the substantially horizontal direction in the farm field 10 may be represented using identification information of a specific point in the farm field 10. The position information may include identification information of the reference position in the agricultural field 10 and information indicating a relative relationship with the reference position. The position in the substantially horizontal direction in the agricultural field 10 may be represented using identification information of a specific point, an azimuth and a distance from the point.

  In one embodiment, the position monitoring unit 352 acquires image data of an image captured by the imaging unit 142. The position monitoring unit 352 may analyze the image and acquire position information of one or a plurality of fruits 14 included in the image.

  In another embodiment, the position monitoring unit 352 acquires information indicating a condition (sometimes referred to as an imaging condition) when the above image is captured. The position monitoring unit 352 may acquire information indicating image capturing conditions and image data of the image. The position monitoring unit 352 may acquire the position information of the one or more fruits 14 included in the image by analyzing the imaging conditions. The information indicating the imaging condition of the image includes (i) information indicating the latitude and longitude of the imaging device that captured the image, (ii) information indicating the position and height in the substantially horizontal direction in the field of the imaging device, iii) information indicating the distance between the imaging device and the subject of the image, (iv) information indicating the azimuth of the optical axis of the imaging device, and (v) information indicating the angle of the optical axis of the imaging device with respect to the vertical direction. , (Vi) imaging magnification, (vii) angle of view, (viii) at least one of the specifications of the imaging device.

  In the present embodiment, the quality monitoring unit 354 acquires quality information of each of the one or more fruits 14 cultivated in the farm field 10. In one embodiment, the quality monitoring unit 354 acquires image data of an image of the fruit 14 captured by the imaging unit 142. The quality monitoring unit 354 may analyze the image data and generate information indicating the quality of the fruit 14. In another embodiment, the quality monitoring unit 354 acquires information indicating the measurement result of the sense unit 144. The quality monitoring unit 354 may generate the information indicating the quality of the fruit 14 by analyzing the obtained measurement result.

  The quality information may include at least one of (i) information related to the appearance of the fruit 14 and (ii) information related to components included in the fruit 14. The quality information includes (i) at least one of shape, color, pattern and size, (ii) presence / absence of abnormality in appearance, position of the abnormality, and at least one of the size of the abnormality, (iii) At least one of hardness and load bearing capacity (iv) at least one of density and specific gravity, (v) presence / absence of a cavity, position of the cavity, and size of the cavity, and (vi) sugar content, acidity It may be information indicating at least one selected from the group consisting of at least one of water content, protein content, scent component content, calcium content, and magnesium content. An abnormality in appearance may be treated as a color or a pattern.

  In the present embodiment, the data updating unit 356 receives the position information of the fruit 14 from the position monitoring unit 352 and receives the quality information of the fruit 14 from the quality monitoring unit 354. The data update unit 356 associates position information and quality information with each of the one or more fruits 14 and stores them in the growth information storage unit 242. The data stored in the growth information storage unit 242 may be an example of management data. The data update unit 356 may update the data stored in the growth information storage unit 242 when the position or quality of the fruit 14 is changed.

  FIG. 4 schematically shows an example of the internal configuration of the position monitoring unit 352. In the present embodiment, the position monitoring unit 352 includes an image recognition unit 410, a reference position specifying unit 420, a relative position calculating unit 430, and a position specifying unit 440.

  The image recognition unit 410 may be an example of an image data acquisition unit, a stock specifying unit, and an area specifying unit. The reference position specifying unit 420 may be an example of an imaging condition acquisition unit. The relative position calculation unit 430 may be an example of a relative relationship acquisition unit. The position specifying unit 440 may be an example of a position analyzing unit.

[Recognition process of fruit 14]
In the present embodiment, the image recognition unit 410 acquires image data of each image of one or more fruits 14. For example, the image recognition unit 410 acquires image data of an image captured by any of the imaging unit 142, the imaging unit 152, and the monitoring camera 162. The image recognition unit 410 may acquire image data of an image including a plurality of fruits 14.

  The image recognition unit 410 recognizes each of one or a plurality of fruits 14 included in the image. The image recognition unit 410 recognizes each of the one or more fruits 14 included in the image using, for example, an arbitrary image recognition technique used in face recognition processing or the like. The image recognition unit 410 may recognize each of one or a plurality of fruits 14 based on a plurality of images taken at the same time. For example, when the difference in imaging time of a plurality of images is within 1 hour (preferably within 30 minutes, more preferably within 10 minutes, and even more preferably within 5 minutes), the plurality of images are simultaneously It may be considered that the image was taken. The image recognition unit 410 may recognize each of one or a plurality of fruits 14 based on a plurality of images captured at different times. The image recognition unit 410 may determine the ID of each of the one or more fruits 14 based on the recognition result.

  In one embodiment, the image recognition unit 410 recognizes each of one or a plurality of fruits 14 included in the image by using the appearance characteristics of the individual fruits 14 as an authentication pattern. For example, when ID is given to each fruit 14, the feature of the appearance of the fruit is registered as an authentication pattern of the fruit. Thereby, ID of the fruit 14 contained in the said image can be determined by analyzing an image.

  In another embodiment, the image recognition unit 410 includes (i) the positional relationship between the plurality of fruits 14, (ii) the plurality of fruits 14, and one or more feature points of the strain 12 on which the fruits are growing. The positional relationship or (iii) the positional relationship of the plurality of feature points of the stock 12 on which the plurality of fruits 14 are grown is used as an authentication pattern to recognize the plurality of fruits 14 included in the image. For example, when an ID is assigned to each of the plurality of fruits 14, the positional relationship of the plurality of fruits 14 is registered as an authentication pattern of the plurality of fruits 14. Thereby, ID of the fruit 14 contained in the said image can be determined by analyzing an image.

  In still another embodiment, the image recognition unit 410 may recognize a plurality of fruits 14 by a procedure similar to a technique called global alignment or global mapping. Specifically, the image recognition unit 410 first compares the first image including the plurality of fruits 14 and the second image including the plurality of fruits 14 to determine the plurality of images included in the first image. Each of the fruits 14 is associated with one of the plurality of fruits 14 included in the second image. For example, (i) the image recognizing unit 410 next calculates the sum of the errors of the positions of the corresponding fruits 14, and (ii) the first so that the error satisfies a predetermined condition. The plurality of fruits 14 included in the image are associated with the plurality of fruits 14 included in the second image. When each of the plurality of fruits 14 included in the second image has already been recognized, the ID of each of the plurality of fruits 14 included in the first image can be determined by the above processing.

[Exception handling in recognition processing]
When the image recognition unit 410 recognizes an image, a pattern that is recognized as a fruit but cannot identify an ID may be extracted from an image that is a target of recognition processing. At this time, for example, the image recognition unit 410 notifies the user to that effect, and prompts the user for an instruction. When receiving an instruction from the user, the image recognizing unit 410 assigns a new ID to the pattern or specifies an ID according to the instruction.

  The image recognition unit 410 may determine whether or not the result of the image recognition process is normal. When it is determined that the result of the image recognition process regarding the specific stock is not normal, the image recognition unit 410 may execute the image recognition process under different conditions. For example, the image recognition unit 410 executes image recognition processing based on image data obtained by imaging the specific stock from different angles.

  For example, the image recognition unit 410 determines whether or not the result of the image recognition process is normal according to the following procedure. According to the present embodiment, the image recognition unit 410 first calculates the total yield of one or more fruits 14 recognized by the image recognition process. Examples of the yield of the fruit 14 include (i) number, (ii) mass estimated from the size of the image, and the like. The image recognition unit 410 may calculate the yield per reference unit. The image recognition unit 410 may calculate the yield per reference unit in consideration of the growth condition. Examples of the reference unit include (i) a unit area of the field, (ii) the number of unit strains, and (iii) a unit volume of a space where the fruit 14 can be harvested.

  The space in which the fruit 14 can be harvested may be a virtual space in which a stock or a part of the stock can be accommodated. For example, when considering a virtual space in which the entire stock is accommodated, the virtual space has (i) a rectangular parallelepiped shape, and (ii) its height is accommodated in the space. (Iii) its width may be the maximum value of the width of the stock housed in the space, and (iv) its depth is inside the space. It may be the maximum depth of stock contained. The shape of the virtual space is not limited to a rectangular parallelepiped. The virtual space may be a cylindrical shape, a polygonal prism shape, a conical shape, a polygonal pyramid shape, or a combination thereof.

  Next, the image recognition unit 410 determines whether or not the result of the image recognition process is normal based on the calculated total yield or the yield per reference unit. In one embodiment, the image recognition unit 410 has a normal result of the image recognition process based on whether the calculated total value of yield or the yield per reference unit satisfies a predetermined condition. It is determined whether or not. For example, the image recognizing unit 410 may calculate the total value of the calculated yield or the yield per reference unit, and (i) the actual value in the same period of the normal year or (ii) the actual value in the same growth state, or (iii) ) Compare the predicted value based on the above actual value. For example, the image recognition unit 410 determines that the result of the image recognition processing is normal when (i) the difference between the two does not fall within a predetermined range, or (ii) the difference between the two is statistically significant. It is determined that

  In another embodiment, the image recognition unit 410 calculates (i) the total value of the yield of each strain or the yield per reference unit for a plurality of strains, and (ii) the image recognition processing result of each strain is normal. It may be determined statistically whether or not. For example, the image recognition unit 410 compares the total value of the yield of a specific strain or the yield per reference unit with the total value of the total yield or the yield per reference unit. When it does not fall within the determined range, or (ii) when the difference between the two is statistically significant, it is determined that the result of the image recognition process is normal.

[Specific processing of stock 12]
In the present embodiment, the image recognition unit 410 identifies the stock 12 on which each fruit grows based on the image data of each image of one or more fruits 14. The identification process for the stock 12 may be executed by the same procedure as the recognition process for the fruit 14.

[Specific processing of sub-area 20]
In the present embodiment, the image recognition unit 410 identifies the sub-area 20 where each fruit grows based on the image data of each image of one or more fruits 14. The identification process of the sub-area 20 may be executed by the same procedure as the fruit 14 recognition process.

  In the present embodiment, the reference position specifying unit 420 specifies a reference position that can be used for specifying the position of the fruit for at least a part of the one or more fruits 14. The reference position indicates, for example, the position of a known point, object, or part of the object that can be used to calculate the position of each fruit.

  In one embodiment, the reference position specifying unit 420 may specify the position of the strain 12 where the fruit 14 is growing as the reference position of the fruit. The position of the representative point of the stock 12 may be used as the position of the stock 12 described above. In another embodiment, the reference position specifying unit 420 may specify the position of the sub-area 20 where the fruit 14 is growing as the reference position of the fruit. The position of the representative point of the sub area 20 may be used as the position of the sub area 20 described above.

  In still another embodiment, the reference position specifying unit 420 obtains information indicating an imaging condition of an image including the fruit 14 as a subject (sometimes referred to as imaging condition information). Among the imaging conditions, at least one of (i) information indicating the latitude and longitude of the imaging device that captured the image, and (ii) information indicating the substantially horizontal position and height of the imaging device in the field 10 Is included, the reference position specifying unit 420 may specify the position indicated by the imaging condition as the reference position of the fruit.

  In the present embodiment, the relative position calculation unit 430 acquires or calculates information indicating the relative relationship between the representative position of each fruit and the reference position of each fruit for at least a part of one or more fruits 14. In one embodiment, the relative position calculation unit 430 analyzes an image including each fruit and each reference position, and determines a relative relationship between them. The relative position calculation unit 430 may determine the relative relationship between the two in consideration of the imaging conditions. In another embodiment, when the imaging condition includes distance measurement information indicating the distance between the imaging device that captured the image and the subject, the relative position calculation unit 430 uses the distance measurement information. Thus, the relative relationship between the two may be determined. The relative position calculation unit 430 may determine the relative relationship between the representative position of each fruit and the reference position of each fruit by a combination of the above embodiments.

  In this embodiment, the position specifying unit 440 includes (i) the reference position of each fruit specified by the reference position specifying unit 420, and (ii) the representative position of each fruit acquired or calculated by the relative position calculation unit 430, and The position of each fruit is specified based on the relative relationship of the reference position of each fruit. Thereby, according to one Embodiment, the position monitoring part 352 can acquire the positional information on each fruit based on the image data of the image which contains each fruit as a to-be-photographed object. According to another embodiment, the position monitoring unit 352 can acquire position information of each fruit based on image data of an image including each fruit as a subject and imaging condition information of the image.

  FIG. 5 schematically shows an example of the internal configuration of the work management unit 230. In the present embodiment, the work management unit 230 includes an order management unit 510, a demand prediction unit 520, a work plan unit 530, and a work monitoring unit 540. The work planning unit 530 may be an example of an information processing device and a support device.

  In the present embodiment, the order management unit 510 receives an order regarding the fruit 14. For example, the order management unit 510 transmits data of an input screen for receiving an order to the consumer terminal 40 in response to a request from the consumer terminal 40. The order management unit 510 acquires information input to the consumer terminal 40 by the consumer as order information. The order management unit 510 may manage the progress of the received order. The order management unit 510 may store information indicating the contents of the received order in the order information storage unit 248.

  In the present embodiment, the demand prediction unit 520 predicts the demand for the fruit 14. The algorithm for demand prediction is not particularly limited. For example, the demand prediction unit 520 uses the demand prediction model stored in the prediction model storage unit 252 to predict the demand for the fruit 14 at a specific date or time. The demand prediction unit 520 may analyze the data related to the past demand stored in the history information storage unit 250 to predict the demand for the fruit 14 at a specific date or time.

  In the present embodiment, the work plan unit 530 creates a work plan for the fruit 14. For example, the work planning unit 530 determines the fruit 14 to be separated from the stock 12 on a specific work day or work period. The work plan unit 530 may create a work plan related to a plurality of agricultural products. Details of the work planning unit 530 will be described later.

  In the present embodiment, the work monitoring unit 540 monitors work performed by at least one of the worker and the work machine 130. For example, the work monitoring unit 540 monitors whether the work is progressing according to the work plan created by the work planning unit 530. The work monitoring unit 540 may create support information for supporting at least one of the worker and the work machine 130 based on the work plan. The work monitoring unit 540 may determine whether or not at least one of the worker and the work machine 130 may deviate from the work plan. The work monitoring unit 540 may generate warning information for issuing a warning when it is determined that at least one of the worker and the work machine 130 may deviate from the work plan. Details of the work monitoring unit 540 will be described later.

  FIG. 6 schematically shows an example of the internal configuration of the work planning unit 530 and the work monitoring unit 540. In the present embodiment, the work planning unit 530 includes an extraction condition determination unit 632, a quality prediction unit 634, and a work target determination unit 636. In the present embodiment, the work monitoring unit 540 includes an image recognition unit 642, a support information generation unit 644, and an attribute adjustment unit 646.

  The extraction condition determination unit 632 may be an example of a demand information acquisition unit and an attribute information acquisition unit. The quality prediction unit 634 may be an example of a quality prediction unit. The work target determination unit 636 may be an example of an extraction unit and a separation target determination unit. The image recognition unit 642 may be an example of a work image acquisition unit and a separation target recognition unit. The support information generation unit 644 may be an example of a work route determination unit and a warning unit.

  In the present embodiment, the extraction condition determination unit 632 is separated from the stock 12 by the work target determination unit 636 from one or more fruits 14 cultivated in the field 10 on a specific work day or work period. The conditions for extracting the power fruit 14 (sometimes referred to as extraction conditions) are determined. The extraction condition determination unit 632 includes, for example, (i) information related to the demand for the fruit 14 (may be referred to as demand information), and (ii) information indicating the attribute of the worker or the work machine 130 (may be referred to as attribute information). .) And (iii) determining the extraction condition based on at least one of the prediction results regarding the quality of the fruit 14 at a specific date and time in the future.

  In one embodiment, the extraction condition determination unit 632 acquires information related to the demand for the fruit 14 (sometimes referred to as demand information). For example, the extraction condition determination unit 632 acquires information indicating the content of the order received by the order management unit 510. The extraction condition determination unit 632 may access the order information storage unit 248 to acquire the above information, or may request the above information from the order management unit 510 and receive the information. The extraction condition determination unit 632 acquires information indicating a future demand trend predicted by the demand prediction unit 520. For example, the extraction condition determination unit 632 requests the above information from the demand prediction unit 520 and receives the information.

  The demand information includes, for example, (i) a quality condition related to the quality requested by the consumer, (ii) a quantity condition related to the quantity of the agricultural product requested by the consumer, and (iii) the consumer requests the agricultural product. Includes at least one timing condition for timing. The extraction condition determination unit 632 may determine the extraction condition based on the demand information.

  For example, the extraction condition determination unit 632 determines the extraction condition so that the extraction result satisfies the quality condition and the quantity condition. More specifically, the extraction condition determination unit 632 may determine, as the extraction condition, a condition that the number of fruits 14 that satisfy the quantity condition is extracted from one or more fruits 14 that satisfy the quality condition. .

  The extraction condition determination unit 632 may determine the extraction condition so that the extraction result satisfies the quality condition, the quantity condition, and the time condition. More specifically, the extraction condition determination unit 632 determines, as the extraction condition, a condition that the number of fruits 14 satisfying the quality condition satisfies the quantity condition at the time at the time indicated by the time condition. It's okay.

  The extraction condition determination unit 632 obtains the number of fruits 14 satisfying the quality condition at the time indicated by the time condition by one or more fruits 14 cultivated in the field 10 that satisfy the quantity condition at the time. A condition that the expected amount of profit to be satisfied satisfies a predetermined condition may be determined as the extraction condition. The predetermined conditions are as follows: (i) a condition that the estimated amount is larger than a threshold; (ii) the estimated amount calculation process is executed a plurality of times, and the estimated amount is the most For example, a condition that it is large, and (iii) a condition that a function that represents the estimated amount or a function that approximates the estimated amount is maximized at least in a specific region.

  In another embodiment, the extraction condition determination unit 632 acquires attribute information of the worker or the work machine 130. For example, the extraction condition determination unit 632 accesses the attribute information storage unit 246 and acquires the above information. The extraction condition determination unit 632 may determine the extraction condition based on the attribute information. For example, the extraction condition determination unit 632 determines, as the extraction condition, a condition that the fruit 14 existing at a position corresponding to the height of the worker is extracted. The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 that matches the taste taste of the worker and is present at a position corresponding to the height of the worker is extracted.

  The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 existing at a position corresponding to at least one of the worker's age, handicap, and medical history is extracted. The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 including a component corresponding to at least one of the current physical condition of the worker, the current medical condition, and the presence or absence of allergy is extracted. .

  The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 existing at a position corresponding to at least one of the clothes and footwear of the worker is extracted. The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 existing at a position corresponding to the desired harvest amount of the worker is extracted. The extraction condition determination unit 632 may determine, as the extraction condition, a condition that the fruit 14 existing at a position corresponding to the number or attribute of the worker's companion is extracted. The extraction condition determination unit 632 may determine a condition for extracting the fruit 14 having quality according to the number or attributes of the worker's companion as the extraction condition.

  The extraction condition determination unit 632 may determine at least one of the position and quality of the fruit 14 to be harvested next by the worker based on at least one of the number and the quality of the fruit 14 already harvested by the worker. For example, the extraction condition determination unit 632 adjusts the difficulty level of harvesting the next fruit 14 to be harvested according to the number of the fruits 14 already harvested by the operator. The extraction condition determination unit 632 determines the component or taste of the fruit 14 to be harvested next in accordance with the component or taste of the fruit 14 already harvested by the operator. The extraction condition determination unit 632 may determine a condition that the fruit 14 corresponding to at least one of the position and quality of the fruit 14 to be harvested next is extracted as the extraction condition.

  The extraction condition determination unit 632 may acquire data related to a reaction when the worker harvests the fruit 14 or a reaction when the worker eats the fruit 14. For example, the extraction condition determination unit 632 collects audio data or image data when the worker harvests the fruit 14 or when the worker eats the fruit 14. The extraction condition determination unit 632 may analyze the collected data to determine at least one of the position and quality of the fruit 14 to be harvested next by the worker. For example, when the worker shows an unfavorable reaction, a quality different from the previously harvested fruit 14 is set as the quality of the fruit 14 to be harvested next. On the other hand, when the worker shows a favorable reaction, the same quality as the previously harvested fruit 14 is set as the quality of the fruit 14 to be harvested next. The extraction condition determination unit 632 may determine a condition that the fruit 14 corresponding to at least one of the position and quality of the fruit 14 to be harvested next is extracted as the extraction condition.

  In the present embodiment, the quality predicting unit 634 performs (i) future quality and (ii) separating the fruit from the stock for at least a part of the one or more fruits 14 cultivated in the field 10. At least one of the effects on the quality of other fruits may be predicted. For at least a part of the fruit 14 that satisfies the extraction condition determined by the extraction condition determination unit 632, the quality prediction unit 634 (i) future quality and (ii) other by separating the fruit from the stock At least one of the effects on fruit quality may be predicted. When the quality prediction unit 634 separates a specific fruit 14 (a single fruit 14 or a plurality of fruits 14) from the stock 12 on a specific work day or work period. In addition, the influence on the quality of the other fruit 14 may be predicted based on at least one of (i) the position of the specific fruit 14 and (ii) the position of the other fruit 14.

  The prediction may be a quantitative prediction or a qualitative prediction. Further, the algorithm for quality prediction is not particularly limited. For example, the quality prediction unit 634 predicts the quality of the fruit 14 at a specific date or time using a quality prediction model stored in the prediction model storage unit 252. The quality prediction unit 634 uses a quality prediction model stored in the prediction model storage unit 252 to separate other specific dates and times from a specific fruit 14 at a specific date and time. Other fruits 14 in may be predicted. The quality prediction unit 634 may analyze the data related to the past growth of the fruit 14 stored in the history information storage unit 250 to predict the quality.

  The quality prediction unit 634 may determine the extraction condition based on the prediction result. The quality prediction unit 634 may correct the extraction condition determined by the extraction condition determination unit 632 based on the prediction result.

  In the present embodiment, the work target determination unit 636 determines a fruit 14 to be a specific work target (sometimes referred to as a work target) from one or more fruits 14 cultivated in the field 10. . The work target determination unit 636 may extract, as a work target, the fruit 14 that matches the extraction condition determined by at least one of the extraction condition determination unit 632 and the quality prediction unit 634. In addition, the fruit 14 used as the object of the operation | work which isolate | separates the fruit 14 from the stock | strain 12 may be an example of the isolation | separation object.

  In one embodiment, the work target determination unit 636 first refers to the growth information storage unit 242 and extracts the fruit 14 that satisfies the quality condition included in the extraction condition from the one or more fruits 14. Next, the work target determination unit 636 refers to the growth information storage unit 242 and acquires the position information of the extracted fruit 14. Next, the work target determination unit 636 may determine the fruit 14 to be a work target from the extracted fruits 14 based on the acquired position information. For example, the work target determining unit 636 preferentially determines the fruit 14 existing in a region where the density of the fruit 14 exceeds a predetermined threshold as a work target. The work target determination unit 636 may preferentially determine the fruit 14 existing in a region where the density of the fruit 14 is lower than a predetermined threshold as a work target. The work target determining unit 636 may execute the above process when the number of the extracted fruits 14 satisfies the quantity condition included in the extraction condition.

  When the number of the extracted fruits 14 does not satisfy the quantity condition included in the extraction condition, for example, the work target determination unit 636 may confirm that the quantity is insufficient, the quantity is insufficient, or the current quantity via the worker terminal 120. Information about the fruit 14 having quality close to the quality condition is notified to the worker. The work target determining unit 636 continues the processing according to the operator's instruction for the notification.

  In one embodiment, the work target determination unit 636 cancels the current order and assigns the fruits 14 that currently do not meet the quality requirements to other future orders. In another embodiment, when it can be expected that the supply cannot catch up with the demand for the fruit 14 having a specific quality, the work target determining unit 636 determines the person who wants to purchase the fruit 14 and the amount of money to purchase by the auction method. You can do it. Similarly, when it can be predicted that the price fluctuation of the fruit 14 having a specific quality will exceed a predetermined threshold, the work target determination unit 636 determines the person who wants to purchase the fruit 14 and the amount of money to purchase by the auction method. You may decide.

  In another embodiment, when the extraction condition includes a condition related to the attribute of the worker or the work machine 130, the work target determination unit 636 performs (i) position information and (ii) the worker or the work machine 130. The fruit 14 to be worked may be determined on the basis of the condition relating to the attribute. For example, the work target determining unit 636 extracts the fruit 14 present at a position corresponding to the height of the worker as a work target. The work target determining unit 636 may preferentially determine the fruit 14 present at a position corresponding to the height of the worker among the fruits 14 satisfying the quality condition as a work target.

  In yet another embodiment, the work target determination unit 636 uses the prediction result of the quality prediction unit 634 to determine that the number of fruits 14 that satisfy the quality condition at the time indicated by the time condition is the quantity condition at that time. To determine the fruit 14 to be worked. In still another embodiment, the work target determination unit 636 includes one or more of the following based on (i) the prediction result of the quality prediction unit 634, and (ii) the quality condition, quantity condition, and timing condition included in the extraction condition. The work target is determined so that the expected amount of profit obtained from the fruit 14 satisfies a predetermined condition.

  For example, the work target determining unit 636 first determines a temporary work target in the first date and time or period. Specifically, a specific fruit 14 (a single fruit 14 or a plurality of fruits 14 may be used) so as to satisfy the quality condition and the quantity condition in the first date and time or period. Is determined as a temporary work target. Next, the work target determination unit 636 uses the prediction result of the quality prediction unit 634 to determine a temporary work target at the second date and time or time indicated by the time condition. Similarly, the work target determining unit 636 determines temporary work targets for all the dates and times included in the time condition. Next, the work target determination unit 636 calculates the total value of profits obtained by a series of work. The work target determining unit 636 performs the above process a plurality of times while changing the combination of the fruits 14 to be worked. The work target determining unit 636 determines a temporary work target in a trial in which the total value of profits is the maximum among a plurality of trials as the current work target.

  In the present embodiment, the image recognition unit 642 captures an image from the imaging device of the worker who is performing the separation work of the fruit 14 or the preparation of the separation work or the imaging device of the work machine 130 in the field 10. Get the image data of the image. For example, the image recognition unit 410 acquires image data of an image captured by any of the imaging unit 142, the imaging unit 152, and the monitoring camera 162. The image recognition unit 410 may acquire image data of an image captured by an imaging device mounted on the worker terminal 120. The image recognition unit 410 may acquire image data of an image including a plurality of fruits 14.

  In the present embodiment, the image recognition unit 642 recognizes each of one or more fruits 14 included in the image. The image recognition unit 642 recognizes each of the one or more fruits 14 included in the image using, for example, an arbitrary image recognition technique used in face recognition processing or the like. The image recognizing unit 642 may recognize the fruit 14 determined as the work target by the work target determining unit 636 from one or a plurality of fruits 14 included in the image. The image recognition unit 642 may recognize the fruit 14 or determine the ID of the fruit 14 by the same procedure as the image recognition unit 410.

  In the present embodiment, the support information generation unit 644 generates information for supporting the work of the worker or the work machine 130. The support information generation unit 644 may transmit the generated information to the worker terminal 120 or the work machine 130. In one embodiment, the support information generation unit 644 generates a command for causing the display device of the worker terminal 120 to display information related to the work target. The above-mentioned command may be a command for superimposing and displaying information about a work object on at least one of an image in real space and an image using augmented reality technology.

  In another embodiment, the support information generation unit 644 first refers to the growth information storage unit 242, and acquires the position information of the plurality of fruits 14 determined by the work target determination unit 636 as the work target. Next, the support information generation unit 644 determines the route of the worker or the work machine 130 based on the positions of the plurality of fruits 14. The support information generation unit 644 may generate a command for causing the display device of the worker terminal 120 to display information indicating the route. The command may be a command for superimposing and displaying information indicating the route on at least one of an image in real space and an image using augmented reality technology.

  In still another embodiment, the support information generation unit 644 may notify the worker or the work machine of a warning when the worker or the work machine 130 tries to harvest the fruit 14 that is not a work target. Good. For example, first, the image recognition unit 642 analyzes the image data of the work site, and determines whether or not the worker or the work machine 130 is likely to separate the fruit 14 other than the work target from the stock 12. When the image recognition unit 642 determines that the possibility is high, the support information generation unit 644 generates a warning for the worker or the work machine 130. The support information generation unit 644 outputs the above warning to the worker terminal 120 or the work machine 130. For example, the support information generation unit 644 transmits the above warning to the worker terminal 120 or the work machine 130.

  In the present embodiment, the attribute adjustment unit 646 adjusts the attributes of the worker or the consumer based on the input from the worker or the consumer. For example, the attribute adjustment unit 646 acquires information indicating an evaluation of the taste of the fruit from a consumer who has tasted the specific fruit 14 via an arbitrary input device. The attribute adjustment unit 646 finely adjusts the difference between the reference on the farm work support system 100 and the consumer's reference regarding the taste-related preference based on the evaluation result. When the attribute adjustment unit 646 adjusts the attribute, the attribute adjustment unit 646 may transmit information indicating that the attribute has been adjusted to the work target determination unit 636. When the work target determining unit 636 receives information indicating that the attribute has been adjusted, the work target determining unit 636 may execute the work target determining process again. Thereby, a customer's satisfaction improves.

  FIG. 7 schematically shows an example of the screen 700. In the present embodiment, the screen 700 includes an icon 710 indicating information on each of the one or more fruits 14, an icon 722 indicating the fruits 14 to be harvested, and an icon 724 indicating the fruits 14 to be thinned. And an icon 730 indicating the order of work, and an icon 740 indicating the work route. In the present embodiment, the images of the stock 12 and the fruit 14 may be real space images or images. That is, the screen 700 may be a screen for superimposing on the real space image of the stock 12 and the fruit 14, and the screen 700 may be a screen including images of the stock 12 and the fruit 14.

  In the present embodiment, a tag 702 for specifying the stock 12 or the subarea 20 is arranged in the vicinity of the stock 12. The tag 702 may be attached with information indicating a reference position. Information indicating the reference position may be embedded in the tag 702. The tag 702 may store information indicating the reference position. Information indicating the reference position may be encoded or encrypted. The tag 702 may be assigned the ID of the stock 12 or the sub-area 20 or may be embedded with the ID of the stock 12 or the sub-area 20. The ID of the stock 12 or the subarea 20 may be encoded or encrypted.

  In the present embodiment, the stock 12 is provided with a tag 704 for identifying the stock 12. The tag 704 may have information indicating a reference position. Information indicating the reference position may be embedded in the tag 704. The tag 704 may store information indicating the reference position. Information indicating the reference position may be encoded or encrypted. The tag 704 may be attached with the ID of the stock 12 or may be embedded with the ID of the stock 12. The ID of the stock 12 may be encoded or encrypted.

  FIG. 8 schematically shows an example of support by the farm work support system 100. In the present embodiment, a tag 802 is disposed near the fruit 14. In the tag 802, the ID of the fruit 14 is embedded as an RFID. In the present embodiment, the worker wears the worker terminal 120, the wearable display 820, the scissors 822, and the gloves 824 to perform the work. In the present embodiment, the wearable display 820 displays a support screen such as the screen 700. The wearable display 820 may display the support screen so as to overlap the real space image. In this embodiment, the scissors 822 and the gloves 824 are provided with a communication chip 826 having an RFID reception function and a short-range wireless communication function. The scissors 822 and the gloves 824 may be examples of work tools.

  In the present embodiment, the work robot 140 uses the instruction unit 146 to indicate the position of the fruit 14 to be worked to the worker. Then, when the operator brings the scissors 822 and the gloves 824 close to the fruits 14 in order to harvest or thin the fruits 14, the communication chip 826 arranged in the scissors 822 or the gloves 824 receives the ID embedded in the tag 802. Receive. The communication chip 826 transmits the ID embedded in the tag 802 to the worker terminal 120 or the management server 110. The computer of the worker terminal 120 or the management server 110 compares the ID embedded in the tag 802 with the ID of the fruit 14 to be worked, and the ID embedded in the tag 802 becomes the work target. It is determined whether or not it is included in the ID of the fruit 14.

  When the ID embedded in the tag 802 is included in the ID of the fruit 14 to be worked, the worker terminal 120 or the management server 110 notifies the worker that the fruit 14 is the work target. It is not necessary to notify. On the other hand, when the ID embedded in the tag 802 is not included in the ID of the fruit 14 to be worked, the worker terminal 120 or the management server 110 informs the worker that the fruit 14 is the work target. Notice. Thereby, the agricultural work support system 100 can support work.

  FIG. 9 schematically shows an example of the data table 900. The data table 900 may be an example of growth information stored in the growth information storage unit 242. The data table 900 may be an example of a data structure. In the present embodiment, the data table 900 includes, for example, a fruit ID 910 for identifying the fruit 14, a type 920 of the fruit 14, position specifying information 930 indicating the position of the fruit 14, and quality information 940 regarding the quality of the fruit 14. Are stored in association with each other. The position specifying information 930 includes, for example, at least one of (i) information indicating the reference position 932 and the relative position 934, and (ii) information indicating the absolute position 936. The reference position 932 includes, for example, an ID of a subarea where each fruit is cultivated, an ID of a strain in which each fruit is grown, and an ID of a feature point of the strain. The quality information 940 includes, for example, at least one of (i) information about the appearance 942 and (ii) information about the component 944.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. In addition, the matters described in the specific embodiment can be applied to other embodiments within a technically consistent range. Moreover, each component may have the same characteristics as other components having the same name and different reference numerals. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

  The order of execution of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown in the claims, the description, and the drawings is particularly “before” or “prior to”. It should be noted that the output can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the description, and the drawings, even if it is described using “first”, “next”, etc. for convenience, it means that it is essential to carry out in this order. It is not a thing.

  10 farms, 12 stocks, 14 fruits, 20 subareas, 30 communication networks, 40 consumer terminals, 100 farm work support systems, 110 management servers, 120 worker terminals, 130 work machines, 140 work robots, 142 imaging units, 144 senses Unit, 146 Instruction unit, 148 Agricultural work unit, 150 Work drone, 152 Imaging unit, 162 Monitoring camera, 210 Request processing part, 220 Growth management part, 230 Work management part, 240 Storage part, 242 Growth information storage part, 244 Reference position Storage unit, 246 Attribute information storage unit, 248 Order information storage unit, 250 History information storage unit, 252 Prediction model storage unit, 310 Base unit, 312 Control unit, 314 Power supply unit, 320 Travel unit, 330 Support unit, 352 Position monitoring unit, 354 quality monitoring unit, 356 data update unit, 410 image recognition unit, 420 reference position specifying unit, 430 relative position calculating unit, 440 position specifying unit, 510 order management unit, 520 demand prediction unit, 530 work planning unit 540 Work monitoring unit, 632 Extraction condition determination unit, 634 Quality prediction unit, 636 Work target determination unit, 642 Image recognition unit, 644 Support information generation unit, 646 Attribute adjustment unit, 700 screen, 702 tag, 704 tag, 710 icon , 722 icon, 724 icon, 730 icon, 740 icon, 802 tag, 820 wearable display, 822 scissors, 824 gloves, 826 communication chip, 900 data table, 910 fruit ID, 920 types, 930 position specifying information, 932 reference position, 934 phase Position, 936 the absolute position, 940 quality information 942 appearance, 944 component

Claims (15)

A management device for managing one or more agricultural products cultivated in the field;
The management device
A position information acquisition unit that acquires position information indicating the position of each of the one or more agricultural products in the field;
A quality information acquisition unit for acquiring quality information regarding the quality of each of the one or more agricultural products;
A management data generation unit that generates management data in which the positional information acquired by the positional information acquisition unit and the quality information acquired by the quality information acquisition unit are associated;
Comprising
Information processing device. The position information includes information indicating a position in a substantially horizontal direction in the field and information indicating an altitude.
The information processing apparatus according to claim 1. The position information acquisition unit
An image data acquisition unit for acquiring image data of each image of the one or more agricultural products;
Based on the image data acquired by the image data acquisition unit, a strain identification unit that identifies the strain in which each agricultural product is growing,
Based on the image data acquired by the image data acquisition unit, a relative relationship acquisition that acquires (i) a representative position of each agricultural product and (ii) a relative relationship between a reference position of the stock on which each agricultural product grows And
(I) Based on the information regarding the reference position of the stock specified by the stock specifying unit, and (ii) information indicating the relative relationship acquired by the relative relationship acquiring unit, the position information of each agricultural product is acquired. A position analysis unit;
Having
The information processing apparatus according to claim 1 or 2. The position information acquisition unit
An image data acquisition unit for acquiring image data of each image of the one or more agricultural products;
Based on the image data acquired by the image data acquisition unit, an area specifying unit that specifies a part of the farm field where each agricultural product is growing,
Based on the image data acquired by the image data acquisition unit, a relative relationship acquisition that acquires information indicating a relative relationship between (i) a representative position of each agricultural product and (ii) a reference position of the area where each agricultural product is growing. And
(I) Based on the information regarding the reference position of the area specified by the stock specifying unit, and (ii) information indicating the relative relationship acquired by the relative relationship acquiring unit, the position information of each agricultural product is acquired. A position analysis unit;
Having
The information processing apparatus according to claim 3. The position information acquisition unit
An image data acquisition unit for acquiring image data of each image of the one or more agricultural products;
An imaging condition acquisition unit that acquires imaging condition information indicating a condition under which the image is captured;
A position analysis unit that acquires the position information of each agricultural product based on (i) the image data acquired by the image data acquisition unit and (ii) the imaging condition information acquired by the imaging condition acquisition unit;
Having
The information processing apparatus according to claim 1 or 2. The information on the quality includes at least one of (i) information on the appearance of each agricultural product, and (ii) information on components contained in each agricultural product.
The information processing apparatus according to any one of claims 1 to 5. A support device for supporting work on one or more agricultural products cultivated in the field;
Information relating to the demand for the one or more agricultural products, comprising: (i) a quality condition relating to the quality required by the consumer and (ii) a quantity requirement relating to the quantity of the agricultural product requested by the consumer. A demand information acquisition unit to acquire;
(I) referring to a management data storage unit that associates and stores position information indicating the position of each of the one or more agricultural products in the field, and (ii) quality information regarding the quality of each of the one or more agricultural products. An extraction unit for extracting the agricultural product satisfying the quality condition from the one or more agricultural products;
When at least the number of agricultural products extracted by the extraction unit satisfies the quantity condition, (i) referring to the management data storage unit, obtain the position information of each of the agricultural products extracted by the extraction unit; ii) From the agricultural products extracted by the extraction unit based on the position information, a separation target determination unit that determines a separation target to be separated from the stock on a specific work day or work period;
Comprising
Information processing device. The support device has an influence on the quality of other agricultural products when at least one of the agricultural products extracted by the extraction unit is separated from the stock on the specific working day or working period. A quality prediction unit that predicts based on at least the position information;
The demand information further includes a time condition related to a time when the consumer requests agricultural products,
The separation target determining unit includes (i) a prediction result of the quality prediction unit, and (ii) an estimated amount of revenue obtained from the one or more agricultural products based on the quality condition, the quantity condition, and the time condition. Determining the separation target so that satisfies a predetermined condition.
The information processing apparatus according to claim 7. The support device further includes an attribute information acquisition unit that acquires attribute information indicating an attribute of the worker or the work machine,
The separation target determination unit determines the separation target based on (i) the position information and (ii) attribute information acquired by the attribute information acquisition unit.
The information processing apparatus according to claim 7 or 8. The support device (i) refers to the management data storage unit, acquires position information of a separation target determined by the separation target determination unit, and (ii) an operator or a work machine based on the position information A work route determination unit for determining the route of
The information processing apparatus according to any one of claims 7 to 9. The support device includes:
In the field, a work image acquisition unit that acquires image data of an image captured by the imaging device from an imaging device of a worker or work machine that is performing the separation work of the agricultural product or the preparation of the separation work;
A separation target recognition unit that recognizes a separation target determined by the separation target determination unit among at least one agricultural product included in the image;
Further comprising
The information processing apparatus according to any one of claims 7 to 10. The support device is highly likely that the worker or the work machine separates agricultural products other than the separation target determined by the separation target determination unit from the stock by analyzing the image data acquired by the work image acquisition unit. A warning unit that outputs a warning to the worker or the work machine when it is determined that
The information processing apparatus according to claim 11.   A program for causing a computer to function as the information processing apparatus according to any one of claims 1 to 12. An information processing apparatus according to any one of claims 1 to 6,
An information processing device according to any one of claims 7 to 12,
An information processing system comprising: A data structure used in a management system for managing one or more agricultural products cultivated in a field,
Position information indicating the position of each of the one or more agricultural products in the field;
Quality information regarding the quality of each of the one or more agricultural products;
Are associated,
The position information includes information indicating a position in a substantially horizontal direction in the field and information indicating an altitude.
data structure.