JP6616377B2 - Information processing system and program - Google Patents

Description

  The present invention relates to an information processing system and a program.

In recent years, the use of unmanned agricultural machines, sensing technology, information technology (IT), and the like has been studied to improve agriculture efficiency. 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 a weeding weeding robot”, [on line] [searched on October 18, 2017], 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 18, 2017], Internet <URL : Https://www.hro.or.jp/list/agricultural/center/kenkyuseika/gaiyosho/h18gaiyo/f5/2006508.pdf>

  When using IT to make farm work more efficient, it is necessary to collect a large amount of data related to farm work. In particular, it is desirable to easily collect data on the harvest.

  In a first aspect of the present invention, an information processing system is provided. Said information processing system is provided with the harvest position specific | specification part which specifies the position where the one agricultural product was harvested, for example in the field which grows one or more agricultural products. The information processing system includes, for example, a quality information acquisition unit that acquires quality information indicating the quality of one agricultural product. The information processing system described above includes, for example, (a) information indicating the position specified by the harvest position specifying unit, and (b) harvest information that generates the harvest information associated with the quality information acquired by the quality information acquiring unit. A generation unit is provided.

  Said information processing system may be provided with the mass information acquisition part which acquires the mass information which shows the mass of one agricultural product. In the information processing system, the harvest information generation unit includes (a) information indicating the position specified by the harvest position specifying unit, (b) quality information acquired by the quality information acquisition unit, and (c) a mass information acquisition unit. Harvest information associated with the mass information acquired by may be generated.

Said information processing system may be provided with the 1st image data acquisition part which acquires the data of the image of one agricultural product imaged before being harvested.
The above information processing system analyzes the data acquired by the first image data acquisition unit, and (i) the characteristics of a part of one agricultural product to be harvested, and (ii) exists around the part to be harvested. A feature information generation unit that generates feature information indicating at least one of the features of a part of one agricultural product that is not harvested may be provided.

  Said information processing system may be provided with the cultivation condition acquisition part which acquires the work information which shows the work history regarding one agricultural product. The information processing system may include an evaluation unit that evaluates at least a part of the work included in the work history based on the information acquired by the growing condition acquisition unit and the harvest information generated by the harvest information generation unit. Good. In the information processing system, the growing condition acquisition unit may further acquire weather information in at least a part of the growing period of one agricultural product.

  Said information processing system may be provided with the 2nd image data acquisition part which acquires the data of the image by which the mode that the one agricultural product was harvested was imaged. The information processing system may include an imaging position acquisition unit that acquires information indicating a position when the imaging device that has captured the image captures the image. In the above information processing system, the harvest position specifying unit may analyze the data acquired by the second image data acquisition unit and determine a relative positional relationship between the imaging device and one agricultural product. In the information processing system, the harvest position specifying unit is a position where one agricultural product is harvested in the field based on the position of the imaging device indicated by the information acquired by the imaging position acquisition unit and the relative positional relationship. May be specified.

  In the above information processing system, the imaging device may be mounted on a moving body that moves inside the farm field. In the above information processing system, for example, the moving body includes a plurality of storage units that store one or more farm products that are harvested (a). In the information processing system described above, for example, (b) when one agricultural product is accommodated in any of a plurality of accommodating units, the moving object is one agricultural product depending on the accommodating unit in which the one agricultural product is accommodated. A quality determining unit for determining the quality of In the information processing system described above, the mobile body includes, for example, (c) a transmission unit that transmits quality information indicating the quality of one agricultural product determined by the quality determination unit to the quality information acquisition unit. In the information processing system, the quality information acquisition unit may acquire the quality information from the mobile body via the communication network.

  In the information processing system described above, the moving body may include (d) at least one mass measurement unit that measures the mass of an object accommodated in at least one of the plurality of accommodation units. In the information processing system, the transmission unit may transmit mass information indicating the mass of one agricultural product measured by at least one mass measurement unit to the mass information acquisition unit. In the above information processing system, the mass information acquisition unit may acquire mass information from a moving body via a communication network.

  In the above information processing system, the moving body may move so as to be positioned behind the worker in the traveling direction of the worker who harvests one agricultural product. In the above information processing system, the moving body may move so as to be positioned in front of the worker in the traveling direction of the worker who harvests one agricultural product.

  In the second aspect of the present invention, a program is provided. The program may be a program for causing a computer to function as the information processing system. A computer readable medium storing the above program may be provided. The computer readable medium may be a non-transitory computer readable medium. The computer readable medium may be a computer readable recording medium.

  In a third aspect of the present invention, a moving body is provided. Said mobile body has an autonomous movement function, for example. Said moving body is provided with the some accommodating part which accommodates the 1 or more agricultural products which (a) each harvested, for example. For example, (b) when one agricultural product is accommodated in any of the plurality of accommodating units, the moving body determines the quality of the one agricultural product according to the accommodating unit in which the one agricultural product is accommodated. A quality determination unit is provided. The mobile body includes, for example, (c) a transmission unit that transmits quality information indicating the quality of one agricultural product determined by the quality determination unit to an external information processing apparatus.

  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 configuration of the work data collection unit 222 is schematically shown. An example of an internal configuration of analysis part 232 is shown roughly. An example of an internal configuration of work support part 234 is shown roughly. An example of the data format of various information stored in storage part 240 is shown roughly. An example of the system configuration of the farm work robot 120 is shown schematically. An example of the spraying unit 150 is shown schematically. An example of the accommodation unit 160 is shown schematically.

  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 includes a management server 110. The farm work support system 100 may include a farm work robot 120. In the present embodiment, the farm work robot 120 includes a base unit 130, a moving unit 132, and an imaging unit 134. The farm work robot 120 may include one or a plurality of sensor units 136. The farm work robot 120 may include one or more farm work units 138.

  The farm work support system 100 may be an example of an information processing system. The management server 110 may be an example of an information processing system. The farm work robot 120 may be an example of a moving body. The farm work robot 120 may be an example of a work machine or an unmanned work machine. The farm work robot 120 may be an example of an agricultural machine.

  In order to produce the agricultural product 30, soil sampling, soil analysis, fertilization, establishment, sowing or planting, field monitoring, pest control, weeding, thinning or abstracting, topdressing, judgment of harvest time, harvesting, etc. Work occurs. An operation for producing the agricultural product 30 may be referred to as an agricultural operation. Agricultural work may be an example of work. The soil may be an example of a medium for the agricultural product 30.

  In the present embodiment, the farm work support system 100 manages farm work related to each of the one or more farm products 30. The farm work support system 100 may support at least one of the worker 12 and the farm work robot 120 engaged in farm work. For example, the farm work support system 100 supports at least one of the worker 12 and the farm work robot 120 by instructing at least one of the worker 12 and the farm work robot 120 about the content of the next work to be performed.

[Outline of Information Processing in Farm Work Support System 100: Collection of Information]
According to one embodiment of the farm work support system 100, the management server 110 collects information related to the work of the worker 12. Examples of information related to the work include information related to the contents of the work, information related to the agricultural product 30 to be worked, and the like. The management server 110 may collect information on the field where the agricultural product 30 is grown.

  The management server 110 collects the above information by acquiring the data of the image captured by the imaging device arranged on the farm field, the work tool, the worker 12, the farm robot 120, and the like via the communication network 10. Good. The management server 110 may collect the above information by acquiring observation data of various sensors arranged in the farm field, the work tool, the worker 12, the farm robot 120, and the like via the communication network 10. At least one of the image data and the observation data may be transmitted to the management server 110 via at least one of the communication terminal 22 and the farm work robot 120.

  In this embodiment, the farm work robot 120 has an autonomous traveling function. The farm robot 120 may have a self-position estimation function. The farm robot 120 may store the information indicating the time and the information indicating the self position in the storage device of the farm robot 120 in association with each other. Thereby, the movement history of the farm work robot 120 can be recorded. The farm work robot 120 may transmit the information indicating the time and the information indicating the self position to the management server 110 in association with each other. The farm work robot 120 may transmit the information indicating the time and the information indicating the self position to the management server 110 in association with each other for a predetermined period.

  The unit for representing time is not particularly limited. Any unit corresponding to the purpose may be used as a unit representing the time. Examples of time units include milliseconds, seconds, minutes, hours, and days. When the purpose is to specify the positions of the worker 12 and the farm robot 120, it is preferable to use “minute” or a unit shorter than that as the unit representing time, and “second” or shorter. More preferably, units are used. For the purpose of specifying the position of the agricultural product 30, it is preferable to use “day” or a shorter unit as a unit for representing time, and use “minute” or a shorter unit. It is preferable. For the purpose of specifying the position of the subject in the image, it is preferable to use “minute” or a unit shorter than that, and more preferable to use “second” or a unit shorter than that.

  The method for representing the position is not particularly limited. The location may be represented by a spatial reference by coordinates in an arbitrary coordinate system (sometimes referred to as a direct reference), or a spatial reference by any geographic identifier (sometimes referred to as an indirect reference). May be represented by Examples of the geographic identifier include (i) an address, (ii) a zip code, and (iii) an identifier assigned to a section having a predetermined geographical range (sometimes referred to as a mesh or a grid). can do. Examples of the section include a UTM grid or an MGRS (Military Grid Reference System). The information indicating the position may include information indicating the height from the reference position. Examples of the reference position include a point 0 m above sea level, a point representing an agricultural field, and the like.

  As the geographic identifier of the agricultural product 30, the identification information of the stock in which the agricultural product is growing may be used. In any storage device, identification information of one or more stocks and the position of the stock may be stored in association with each other. As the geographic identifier of the agricultural product 30, identification information of the stock in which the agricultural product is growing and information indicating the position of the agricultural product in the stock may be used. The information indicating the position of the agricultural product in the stock may be information indicating the relative position between the reference position of the stock and the agricultural product. The accuracy of the relative position is not particularly limited. The relative position may be expressed by a numerical value, or may be indicated by a section indicating a position in the stock. The division indicating the position in the stock is, for example, “upper part”, “central part”, “lower part” and the like.

  The method for estimating the self position is not particularly limited. As a method for estimating the self-position, (i) using an internal sensor that measures internal information of the farm robot 120 such as an encoder, an inertial sensor, and a gyro sensor, the amount of movement of the farm robot 120 from the reference position is integrated. (Ii) information on landmarks whose absolute positions are known, using an external sensor for observing the environment around the agricultural robot 120 such as an imaging device and a distance measuring device, A method for estimating the self-position based on information from an external sensor, (iii) a method for estimating the self-position using a GPS signal, a radio wave from a radio base station, and the like (iv) a combination of these A method for estimating the position can be exemplified.

  In this embodiment, the farm work robot 120 moves so as to be positioned behind the worker 12 in the traveling direction of the worker 12 who is working, for example. The farm work robot 120 may move so as to be positioned in front of the worker 12 in the traveling direction of the worker 12 who is working. The farm work robot 120 may move forward or backward of the worker 12 while keeping the distance from the worker 12 constant. The farm work robot 120 may approach the worker 12 or move away from the worker 12 based on an instruction from the worker 12. The instruction of the worker 12 may be an instruction by voice or an instruction by gesture.

  The farm work robot 120 images at least one of the state of the worker 12 working and the state of the object of the work using the imaging unit 134 while moving forward or backward of the worker 12. The farm robot 120 may store captured image data or the analysis result of the image in the storage device of the farm robot 120. Thereby, the work history of the worker 12 can be recorded. The farm work robot 120 may transmit captured image data or an analysis result of the image to the management server 110. The image may be a moving image or a still image.

  In one embodiment, the captured image may be associated with the time when the image was captured. Thereby, the management server 110 can associate the captured image with the position of the agricultural robot 120 when the image is captured, for example, using time as a key. In another embodiment, the captured image may be associated with the position of the farm robot 120 when the image is captured.

[Outline of Information Processing in Agricultural Work Support System 100: Analysis of Information]
According to the present embodiment, the management server 110 collects, for example, a large amount of (i) information related to the work of one or more workers 12, (ii) information related to one or more fields, and the like. can do. The management server 110 may generate the information for supporting the work of the worker 12 or the farm robot 120 by analyzing the collected information. For example, the management server 110 analyzes the collected information and generates one or a plurality of analysis models. The analysis model includes (i) a function of one or more parameters, (ii) a function of one or more random variables and one or more parameters, (iii) a mathematical model for numerical analysis, (iv) a machine A learned model by learning or deep learning can be exemplified.

  In one embodiment, the management server 110 analyzes a plurality of data relating to a specific type of agricultural product in a specific agricultural field, and generates an analysis model specific to the agricultural field regarding the agricultural product. The analysis model defines, for example, a relationship between a parameter related to the work on the agricultural product and a parameter related to the growth state of the agricultural product. Examples of the growth state of the agricultural product include a growth stage and a growth state.

  For example, in the above analysis model, (i) information indicating the type of agricultural product, (ii) information indicating a target relating to the harvest time, quality, yield, etc. of the agricultural product, and (iii) indicating the current growth state of the agricultural product When information and (iv) information indicating the contents of work performed on agricultural products in a specific period are input, the analysis model is (i) the type of work to be performed from now on, and (ii) The information indicating the position where the work should be performed and (iii) the time when the work should be performed and / or the work intensity is output. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

  The specific period is, for example, a period from the start of the cultivation of agricultural products to the present in the current cultivation cycle. For example, as the work intensity of the spraying work, the spraying amount per spraying, the spraying frequency, etc. can be exemplified.

  In another embodiment, the management server 110 analyzes a plurality of data related to a specific type of agricultural product in a plurality of fields, or data on the agricultural product in a plurality of locations of a single field, and performs general-purpose related to the agricultural product. A simple analysis model. The analysis model defines, for example, the relationship between the parameters related to the work on the agricultural products, the parameters related to at least one of soil and weather, and the growth of the agricultural products 30.

  For example, in the above analysis model, (i) information indicating the type of agricultural product, (ii) information indicating a target relating to the harvest time, quality, yield, etc. of the agricultural product, and (iii) indicating the current growth state of the agricultural product When information, (iv) information indicating the contents of work performed on agricultural products in a specific period, and (v) information on at least one of soil and weather in a field where agricultural products are cultivated are input, The analysis model indicates (i) the type of work to be performed from now on, (ii) the position where the work is to be performed, and (iii) at least one of the time when the work is to be performed and the work intensity. Output information. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

  According to yet another embodiment, the management server 110 uses information related to the work of an experienced worker 12 as a learning data set (may be referred to as learning data or teacher data). Execute machine learning or deep learning. The learning data set may be an example of teacher information. Thereby, the management server 110 generates a learned model for presenting appropriate work to the user according to the user's situation. The user is, for example, an inexperienced worker 12. The user may be the worker 12 who has abundant experience.

  For example, when a plurality of data relating to a specific type of agricultural product in a specific field is input as learning data, a learned model specific to the field regarding the agricultural product is generated. In the learned model, for example, (i) information indicating the type of agricultural product, (ii) information indicating a target relating to the harvest time, quality, yield, etc. of the agricultural product, and (iii) the current growth state of the agricultural product When the information to be shown and (iv) information indicating the contents of the work performed on the agricultural product in a specific period are input, the learned model is (i) the type of work to be performed from now on, (Ii) Output information indicating the position where the work should be performed, and (iii) at least one of the time when the work should be performed and the work intensity. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

  For example, when a plurality of data relating to a specific type of agricultural product in a plurality of fields or data on the agricultural product in a plurality of locations in a single field are input as learning data, A model is generated. In the learned model, for example, (i) information indicating the type of agricultural product, (ii) information indicating a target relating to the harvest time, quality, yield, etc. of the agricultural product, and (iii) the current growth state of the agricultural product When the information to be displayed, (iv) information indicating the contents of the work performed on the agricultural product in a specific period, and (v) information regarding at least one of the soil and the weather of the field where the agricultural product is grown are input. The learned model includes (i) the type of work to be performed from now on, (ii) the position at which the work is to be performed, and (iii) at least one of the time at which the work is to be performed and the work intensity. The information indicating is output. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

  Moreover, in this embodiment, the management server 110 manages the log | history of the work implemented in each position of the said agricultural field for every agricultural field. Moreover, the management server 110 manages the history of the growth state of the agricultural product 30 cultivated at each position of the field for each field. Furthermore, the management server 110 manages at least one of the quality and the harvest amount of the agricultural product 30 harvested at each position of the farm field for each farm field.

  Thereby, the management server 110 (i) The history of the work performed in the specific position of the specific field, (ii-1) The growth state of the agricultural product 30 cultivated in the position, (ii-2) The quality of the agricultural product 30 harvested at the position and (ii-3) at least one of the yield of the agricultural product 30 at the position can be associated with each other. As a result, the management server 110 can evaluate at least some of the plurality of operations performed on the agricultural product 30. The management server 110 may generate learning data for machine learning or deep learning based on the evaluation result.

[Specific example of information processing in farm work support system 100: in case of spraying work]
According to one embodiment shown in FIG. 1, the farm work support system 100 collects information related to work performed by a worker 12 engaged in spraying work. The farm work support system 100 manages the collected information.

  In the present embodiment, for example, the worker 12 sprays substances such as seeds, water, fertilizer, and agricultural chemicals while carrying the spraying unit 150 on the back. The spraying unit 150 associates data indicating the spraying time with data indicating the spraying amount and transmits the data to the communication terminal 22, the farm robot 120, or the management server 110. The spreading unit 150 may have a self-position estimation function. In this case, the spraying unit 150 may associate the information indicating the time with the information indicating the self position and transmit the information to the communication terminal 22, the agricultural robot 120, or the management server 110. The communication terminal 22 and the farm work robot 120 may transfer the data received from the spraying unit 150 to the management server 110.

  In other embodiments, the spraying unit 150 may be mounted on the farm robot 120. The farm robot 120 may control the spraying unit 150 to spray the substance. The spraying unit 150 may be an example of an agricultural work unit 138 or an attachment for agricultural work.

  On the other hand, in the present embodiment, the farm work robot 120 images the worker 12 performing the spraying work while moving in front of or behind the worker 12. The farm work robot 120 may image the state in which the substance is dispersed. Thereby, for example, when the worker 12 is spraying the pesticide, information indicating the spraying direction or spray destination of the pesticide can be obtained by analyzing the captured image. For example, information indicating whether the application destination of the agricultural chemical is the front side of the leaf, the back side of the leaf, the stem, or the root is obtained. Further, by analyzing the captured image, information on the work performed by the worker 12 can be obtained immediately before the worker 12 sprays the agricultural chemical or immediately after spraying the agricultural chemical. The farm work robot 120 transmits the imaged image data or the analysis result of the image to the management server 110.

  In the present embodiment, the management server 110 acquires various types of information from at least one of the communication terminal 22, the agricultural robot 120, and the spraying unit 150, and manages the information. The management server 110 learns information related to the scattering work and generates model data for the scattering work. In response to a request from the worker 12 or the farm robot 120, the management server 110 presents information such as the time when the substance should be sprayed, the position where the substance should be sprayed, and the amount of the substance to be sprayed at the position. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

[A specific example of information processing in the farm work support system 100: harvesting work]
According to another embodiment shown in FIG. 1, the farm work support system 100 collects information regarding work performed by a worker 12 engaged in harvesting work. The farm work support system 100 manages the collected information.

  For example, the worker 12 harvests the agricultural product 30 while moving in the field. The worker 12 stores the harvested agricultural product 30 in the storage unit 160 of the farm work robot 120. In the present embodiment, the storage unit 160 has a plurality of storage portions. And the operator 12 judges the quality of the harvested agricultural product 30, and accommodates the harvested agricultural product 30 in the accommodating part according to the quality. The accommodation unit 160 may be an example of an agricultural work unit 138 or an attachment for agricultural work.

  On the other hand, in the present embodiment, the farm work robot 120 identifies a storage unit in which the harvested agricultural product 30 is stored among a plurality of storage units. For example, the farm robot 120 measures the mass of the contents in each container. And when the mass of the accommodation of one accommodation part increases, the farm work robot 120 detects that the agricultural product 30 was accommodated in the said one accommodation part. According to this embodiment, since each accommodating part is matched with the quality of the agricultural product 30, the agricultural work robot 120 can specify the quality of the harvested agricultural product 30. The farm work robot 120 may specify the mass of the harvested agricultural product 30.

  Moreover, in this embodiment, the farm work robot 120 images the worker 12 who is performing the harvesting work while moving in front of or behind the worker 12. The farm work robot 120 may image the crop 30 before harvesting, or may capture the harvested crop 30. The farm work robot 120 transmits the imaged image data or the analysis result of the image to the management server 110. Thereby, the information regarding the criterion for harvesting the agricultural product 30 is obtained. For example, if what part of the agricultural product 30 is in what state, information indicating whether or not the agricultural product 30 should be harvested can be obtained. The farm work robot 120 transmits the imaged image data or the analysis result of the image to the management server 110.

  In the present embodiment, the management server 110 acquires various types of information from at least one of the communication terminal 22, the agricultural robot 120, and the spraying unit 150, and manages the information. In one embodiment, the management server 110 uses at least one of the quality and mass information of the harvested agricultural product 30 to generate learning data for various operations. In another embodiment, the management server 110 learns information related to harvesting work and generates model data for harvesting work. In response to a request from the worker 12 or the farm robot 120, the management server 110 presents information such as the time when the substance should be sprayed, the position where the substance should be sprayed, and the amount of the substance to be sprayed at the position. Thereby, the worker 12 can appropriately control the growth of the agricultural product 30.

[Overview of each part of the farm work support system 100]
In the present embodiment, the management server 110 transmits and receives information to and from the agricultural robot 120 via the communication network 10. The management server 110 may send and receive information to and from the communication terminal 22 of the worker 12 via the communication network 10.

  In the present embodiment, the communication network 10 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 10 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 10 may include (i) a mobile communication network such as a mobile phone line network, (ii) a wireless MAN (for example, WiMAX (registered trademark)), a wireless LAN (for example, WiFi (registered trademark)). ), Bluetooth (registered trademark), Zigbee (registered trademark), wireless LAN that may include wireless communication networks such as NFC (Near Field Communication), Bluetooth (registered trademark), Zigbee (registered trademark), and NFC may be an example of near field communication.

  In the present embodiment, the communication terminal 22 transmits and receives information to and from the management server 110 via the communication network 10. The communication terminal 22 may send and receive information to and from the agricultural robot 120 via the communication network 10. The communication terminal 22 may function as a user interface when the worker 12 accesses the management server 110. The communication terminal 22 may be used for user authentication processing by the management server 110. The communication terminal 22 may function as a user interface when the worker 12 operates the farm robot 120. The communication terminal 22 may be used for user authentication processing by the farm work robot 120.

  Examples of the communication terminal 22 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 management server 110 manages information related to one or more agricultural products 30 that are cultivated in the field. Examples of the agricultural product 30 include cereals, vegetables (including root vegetables), fruits, flowers, branches and leaves (for example, tea leaves, ornamental branches or leaves), mushrooms, mycelia, and the like. .

  In one embodiment, the management server 110 manages at least one of information on the type of the agricultural product 30, information on the growth stage of the agricultural product 30, and information on the growth status of the agricultural product 30 as the information on the agricultural product 30. The information regarding the growth status may be information indicating an evaluation compared with an arbitrary standard. Evaluation may be represented by a continuous numerical value and may be represented by a stepwise division. The evaluation represented by the graded category includes two-level evaluation such as “excellent”, “good” and “bad”, three-level evaluation such as “excellent”, “good” and “impossible”, “excellent”, 4-level evaluation such as “good”, “acceptable” and “impossible”, 5-level evaluation such as “S”, “A”, “B”, “C”, “D”, 10-level evaluation from 0 to 9 Etc. can be illustrated. The information related to the growth status of the agricultural product 30 may be an example of information related to the outcome of the work described below.

  In another embodiment, the management server 110 manages information regarding work performed on the agricultural product 30 as information regarding the agricultural product 30. Examples of the information related to the work can include at least one of information indicating the time when the work is performed, information indicating the worker 12 who performed the work, information regarding the content of the work, and information regarding the result of the work. . As information regarding the result of the work, at least one of information indicating the quality of the harvest, information indicating the yield, and information indicating the yield can be exemplified.

  In still another embodiment, the management server 110 may manage information about a farm field where the farm product is cultivated as information about the farm product. As information regarding a field, at least one of the information regarding the soil of a field and the information regarding the weather of a field can be illustrated.

  The management server 110 may divide the field into a plurality of subareas and manage information on one or more agricultural products cultivated in each of the plurality of subareas. Each sub-area may be one of virtually divided areas or one of physically partitioned areas. Each sub-area may have an arbitrary shape. The position of the worker 12, the agricultural product 30, the farm work robot 120, etc. may be represented by an identification symbol given to the subarea in which they are present, and any position that can indicate a specific position within the subarea. You may represent by the coordinate in a coordinate system.

  In the present embodiment, the farm work robot 120 may be a machine that performs an arbitrary work. The farm work robot 120 performs, for example, various farm work, monitoring work, and the like. The farm robot 120 may be fixed or mobile. The farm work robot 120 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 farm work robot 120. The farm work robot 120 is preferably an unmanned work machine having an autonomous traveling function or an autonomous navigation function.

The farm work robot 120 may be a small robot. For example, the volume per farm robot 120 may be 2 m ³ or less, 1 m ³ or less, 0.5 m ³ or less, or 0.25 m ³ or less. Thereby, it can move freely within the field. In addition, the agricultural product 30 can be observed from an angle difficult for humans.

  In the present embodiment, the base unit 130 holds the moving unit 132, the imaging unit 134, the sensor unit 136, and the farm work unit 138. Each of the moving unit 132, the imaging unit 134, the sensor unit 136, and the farm work unit 138 may be configured to be detachable from the base unit 130. Thereby, the farm work robot 120 can cope with a plurality of different types of work.

  In the present embodiment, the moving unit 132 moves the farm robot 120. The power of the moving unit 132 may be electric power, an internal combustion engine, or a steam engine. Details of the moving unit 132 will be described later.

  In the present embodiment, the imaging unit 134 images various subjects. For example, the imaging unit 134 images at least one of the worker 12, the agricultural robot 120, the agricultural product 30, and the surrounding environment of the agricultural product 30. As the surrounding environment of the agricultural product 30, the inside of a farm field or the circumference | surroundings of a farm field can be illustrated. The imaging unit 134 may image the soil at the position where the agricultural product 30 is growing as the surrounding environment of the agricultural product 30.

  Data of an image captured by the imaging unit 134 may be stored in the storage device of the farm robot 120 or transmitted to the management server 110. The farm robot 120 may analyze data of an image captured by the imaging unit 134 and transmit the analysis result to the management server 110. Details of the imaging unit 134 will be described later.

  In the present embodiment, the sensor unit 136 measures various physical quantities. For example, the sensor unit 136 measures at least one of a physical quantity related to the worker 12, a physical quantity related to the agricultural robot 120, a physical quantity related to the agricultural product 30, and a physical quantity related to the surrounding environment of the agricultural product 30.

  Examples of the physical quantity related to the agricultural product 30 include the color, shape, size, composition, content of specific components, hardness, and the like of the agricultural product 30. Examples of the physical quantity relating to the surrounding environment of the agricultural product 30 include a physical quantity relating to the soil of the field where the agricultural product 30 is cultivated, a physical quantity relating to the weather of the agricultural field, and the like.

  The measurement result of the sensor unit 136 may be stored in the storage device of the farm robot 120 or transmitted to the management server 110. The farm work robot 120 may analyze the measurement result of the sensor unit 136 and transmit the analysis result to the management server 110. Details of the sensor unit 136 will be described later.

  In the present embodiment, the farm work unit 138 may be an attachment for farm work. For example, a farm work unit 138 corresponding to the application is attached to the base unit 130. Thereby, the farm work robot 120 can carry out a plurality of different types of farm work. Details of the farm work unit 138 will be described later.

  Each of the farm work units 138 may be a unit specialized for one or more farm works. Agricultural work unit 138 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 cutting, a manipulator having a jig for harvesting agricultural products, a mowing device (for example, a 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 machine 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.

  In this embodiment, the case where the management server 110 and the farm robot 120 execute the information processing in the farm work support system 100 has been described. However, the sharing method of information processing in the farm work support system 100 is not limited to this embodiment. In other embodiments, the communication terminal 22 or the farm robot 120 may execute at least part of the information processing of the management server 110 in the present embodiment, and the communication terminal 22 or the management server 110 may perform the farm work in the present embodiment. At least a part of the information processing of the robot 120 may be executed.

  FIG. 2 schematically shows an example of the internal configuration of the management server 110. In the present embodiment, the management server 110 includes an input / output control unit 212, a work data collection unit 222, an agricultural field data collection unit 224, an analysis unit 232, a work support unit 234, and a storage unit 240. In the present embodiment, the storage unit 240 includes a work information storage unit 252, a result information storage unit 254, a soil information storage unit 262, a weather information storage unit 264, a growth information storage unit 266, and a model information storage unit 282. And have. Each unit of the management server 110 may send and receive information to and from each other.

  The work data collection unit 222 may be an example of a work information generation unit, a harvest information acquisition unit, a quality information acquisition unit, a harvest information generation unit, and a breeding condition acquisition unit. The field data collection unit 224 may be an example of a soil information acquisition unit and a growth information acquisition unit. The analysis unit 232 may be an example of an evaluation unit and a teacher information generation unit.

  In the present embodiment, the input / output control unit 212 controls input / output of information of the management server 110. The input / output control unit 212 may accept information input to the input device. Examples of the input device include a keyboard, a pointing device, a voice input device (for example, a microphone), an image input device (for example, a camera), a voice recognition device, and an image recognition device. The input / output control unit 212 may output information to the output device. Examples of the output device include a display, a printer, a speaker, an ultrasonic output device, and a vibration output device.

  The input / output control unit 212 may control communication with an external information processing apparatus. For example, the input / output control unit 212 exchanges information with at least one of the communication terminal 22, the farm work robot 120, the spraying unit 150, and the storage unit 160. The input / output control unit 212 may be a communication interface. The input / output control unit 212 may support one or more communication methods.

  In the present embodiment, the work data collection unit 222 acquires information regarding various works from at least one of the communication terminal 22, the farm work robot 120, the spraying unit 150, and the storage unit 160. The work data collection unit 222 analyzes the above information and generates work information in which information indicating a position where the work is performed and information indicating the content of the work are associated with each other. For example, the work data collection unit 222 stores information indicating the position where the work is performed and information indicating the content of the work in the work information storage unit 252 in association with each other.

  In the present embodiment, the work data collection unit 222 relates to information indicating the position in the farm field and the produce 30 harvested at the position from at least one of the communication terminal 22, the farm work robot 120, the spreading unit 150, and the storage unit 160. Information associated with information is acquired. For example, the work data collection unit 222 acquires quality information indicating the quality of the harvested agricultural product 30 from the farm work robot 120. For example, the work data collection unit 222 acquires mass information indicating the mass of the harvested agricultural product 30 from the farm work robot 120.

  In the present embodiment, the work data collection unit 222 generates harvest information in which information indicating the position where the agricultural product 30 is harvested and information indicating at least one of the quality and mass of the harvested agricultural product 30 are associated with each other. . For example, the work data collection unit 222 associates information indicating the position where the agricultural product 30 is harvested with information indicating at least one of the quality and mass of the harvested agricultural product 30 and stores the information in the result information storage unit 254. .

  In the present embodiment, the farm field data collection unit 224 acquires information about the farm field. As information regarding a field, at least one of the information regarding the agricultural product 30 currently grown in the field and the information regarding the surrounding environment of the agricultural product 30 can be illustrated. Examples of information related to the surrounding environment of the agricultural product 30 include information related to the soil in the field, information related to the weather in the field (sometimes referred to as weather data), and the like. Examples of the weather data include weather, air temperature, water temperature, humidity, rainfall, wind speed, wind direction, amount of sunlight, and statistical values thereof.

  In one embodiment, the agricultural field data collection unit 224 acquires soil information in which information indicating a position in the agricultural field and information indicating the characteristics of the soil at the position are associated with each other. In another embodiment, the field data collection unit 224 acquires growth information in which information indicating a position in the field and information indicating a growth state of one or more agricultural products 30 at the position are associated with each other. As information which shows the growth state of agricultural product 30, the information which shows the growth stage of agricultural product 30, the information which shows the growth state of agricultural product 30, etc. can be illustrated.

  The farm field data collection unit 224 acquires information about the farm field from, for example, at least one of the communication terminal 22, the farm work robot 120, and a sensor arranged in the farm field. A plurality of types of sensors may be arranged in the farm field. The field data collection unit 224 may acquire weather data regarding an area including each field from an information distribution apparatus that distributes weather data. The weather data may be information on past weather or information on future weather prediction.

  In the present embodiment, the analysis unit 232 generates a learning data set for machine learning or deep learning in the work support unit 234. The analysis unit 232 may generate a learning data set using at least a part of data acquired or generated by at least one of the work data collection unit 222 and the farm field data collection unit 224. The analysis unit 232 extracts data according to the purpose or use of the learned model generated by the work support unit 234 from the data acquired or generated by the work data collection unit 222, and processes the extracted data. Thus, a learning data set may be generated.

  The analysis unit 232 may generate a learning data set related to a specific work based on the harvest information acquired or generated by the work data collection unit 222. For example, the analysis unit 232 generates data for learning by extracting data when the quality and yield of the harvest satisfy a specific condition from the data acquired or generated in the past. To do. The analysis unit 232 may generate a learning data set related to a specific work based on information indicating the growth state of the agricultural product 30 acquired by the field data collection unit 224. For example, the analysis unit 232 extracts data when the growth state of the agricultural product 30 satisfies a specific condition as a result from data acquired or generated in the past, and generates a learning data set.

  For example, the analysis unit 232 generates a learning data set related to a specific work based on the work information related to one or more first workers generated by the work data collection unit 222. Examples of specific work include spraying work and harvesting work. According to one embodiment, a worker who has abundant experience in the specific task is selected as the first worker. According to other embodiment, the worker whose growth state of the agricultural product 30 which the worker cultivates is good or bad is selected as the first worker. According to still another embodiment, in the past harvesting operation, an operator whose quality or yield of the agricultural product 30 cultivated by the operator is good or bad is selected.

  The analysis unit 232 may generate a learning data set based on the work information of the one or more first workers and the soil information acquired by the field data collection unit 224. The analysis unit 232 may generate a learning data set based on the work information of the one or more first workers and the growth information acquired by the field data collection unit 224. The analysis unit 232 may generate a learning data set based on the work information of the one or more first workers and the soil information and the growth information acquired by the field data collection unit 224. The analysis unit 232 may generate a learning data set based on the work information of the one or more first workers and the harvest information acquired by the work data collection unit 222. The analysis unit 232 is based on the work information of the one or more first workers described above, at least one of the soil information and the growth information acquired by the field data collection unit 224, and the harvest information acquired by the work data collection unit 222. Thus, a learning data set may be generated.

  In the present embodiment, the analysis unit 232 performs multivariate analysis or sensitivity analysis using at least a part of data acquired or generated by at least one of the work data collection unit 222 and the field data collection unit 224. The analysis unit 232 may determine an explanatory variable having a large influence on the objective variable among one or more explanatory variables related to the objective variable. Examples of the objective variable include the growth state of the agricultural product 30, the quality of the harvest, the mass of the harvest, and the like.

  In the present embodiment, the analysis unit 232 may correct information acquired by at least one of the work data collection unit 222 and the field data collection unit 224 based on the result of the multivariate analysis or the sensitivity analysis. The analysis unit 232 may generate the learning data set using the corrected data by the same procedure as that when generating the learning data set using the data before correction. The analysis unit 232 may use data corrected for some types of information among the plurality of types of information used for the learning data set.

  In the present embodiment, the work support unit 234 provides information for supporting at least one of the worker 12 and the farm robot 120 to the worker 12 and the farm robot 120. The work support unit 234 provides information for supporting the work of at least one of the worker 12 and the farm work robot 120 based on information acquired or generated by at least one of the work data collection unit 222 and the field data collection unit 224. You can do it.

  The work support unit 234 may provide information for supporting the work of at least one of the worker 12 and the farm robot 120 using a machine learning or deep learning method. The work support unit 234 supports the work of at least one of the worker 12 and the agricultural robot 120 by using information on the magnitude or sensitivity of the influence of the explanatory variable on the objective variable calculated by the analysis unit 232. Information may be provided.

  In the present embodiment, the storage unit 240 stores various types of information. In the present embodiment, the work information storage unit 252 stores information indicating the position where various operations on the agricultural product 30 are performed and information indicating the contents of the work in association with each other. For example, the work information storage unit 252 stores the work information generated by the work data collection unit 222. In the present embodiment, the result information storage unit 254 stores result information indicating the results of various operations on the agricultural product 30. For example, the result information storage unit 254 stores the harvest information generated by the work data collection unit 222.

  In the present embodiment, the soil information storage unit 262 stores information regarding the soil of the field for each of the one or more fields. The soil information storage unit 262 may store information indicating a geographical distribution related to one or more characteristics of the soil for each of one or more fields. For example, the soil information storage unit 262 stores the soil information acquired by the farm field data collection unit 224.

  In the present embodiment, the weather information storage unit 264 stores information related to the weather of the field for each of the one or more fields. For example, the weather information storage unit 264 stores the weather information acquired by the farm field data collection unit 224.

  In the present embodiment, the growth information storage unit 266 stores information indicating the position of the agricultural product 30 and information indicating the growth state of the agricultural product 30 in association with each other. For example, the growth information storage unit 266 acquires the growth information acquired by the farm field data collection unit 224.

  In the present embodiment, the model information storage unit 282 stores learned models (including secondary models and tertiary models) obtained by machine learning or deep learning. The model information storage unit 282 stores the learned model generated by the work support unit 234, for example. The model information storage unit 282 may store information on the magnitude or sensitivity of the influence of the explanatory variable on the objective variable, calculated by the analysis unit 232.

  FIG. 3 schematically shows an example of the internal configuration of the work data collection unit 222. In the present embodiment, the work data collection unit 222 includes a work position specifying unit 310, a work content specifying unit 322, an accompanying work specifying unit 324, a result information acquiring unit 326, and a work information generating unit 332. In the present embodiment, the work position specifying unit 310 includes an image data acquisition unit 312, an imaging position specifying unit 314, a relative position specifying unit 316, and an absolute position specifying unit 318. Each unit of the work position specifying unit 310 may transmit and receive information to and from each other.

  The work position specifying unit 310 may be an example of a position specifying unit, a spraying position specifying unit, and a harvesting position specifying unit. The image data acquisition unit 312 may be an example of a first image data acquisition unit and a second image data acquisition unit. The work content specifying unit 322 may be an example of a content specifying unit, a spray amount specifying unit, and a spraying direction specifying unit.

  In the present embodiment, the work position specifying unit 310 specifies the position where the work is performed for each of one or more works performed on the agricultural product 30. For example, the work position specifying unit 310 specifies at least one of the position of the worker 12 in the field and the position of the work place in the field. In one embodiment, when the worker 12 wears the spraying unit 150 and performs the spraying work, the work position specifying unit 310 includes at least the position of the worker 12 in the field and the nozzle position of the spraying unit 150. Specify one. In another embodiment, when the worker 12 is performing the harvesting operation of the agricultural product 30, the position where the agricultural product 30 is harvested may be specified.

  In the present embodiment, the image data acquisition unit 312 acquires image data obtained by capturing an image of the work being performed. In one embodiment, when the worker 12 is wearing the spraying unit 150 and performing the spraying operation, the image data acquisition unit 312 is such that at least one of the worker 12 and the spraying unit 150 is spraying the substance. The data of the imaged image is acquired. In another embodiment, when the worker 12 is performing the harvesting work of the agricultural product 30, the image data acquisition unit 312 may acquire image data of the agricultural product 30 captured before being harvested. In addition, the image data acquisition unit 312 may acquire image data obtained by capturing an image of the crop 30 being harvested.

  In the present embodiment, the imaging position specifying unit 314 acquires information indicating the position when the imaging device that captured the image captures the image. For example, when the imaging unit 134 mounted on the farm robot 120 captures the above image, the imaging position specifying unit 314 displays the representative point of the farm robot 120 when the above image is captured or the imaging unit 134. Information indicating the position is acquired. The imaging position specifying unit 314 may output the position of the representative point of the farm work robot 120 as the position of the imaging unit 134.

  In this embodiment, the farm work robot 120 has a self-position estimation function, and records information indicating time and information indicating the self-position at the time in association with each other. In addition, the imaging unit 134 records the shooting time and the image in association with each other. Thereby, the imaging position specifying unit 314 can specify the position of the representative point of the farm robot 120 when the above-described image is taken. Further, the relative positional relationship between the representative point of the agricultural robot 120 and the imaging unit 134 (sometimes referred to as a relative position) is known. Therefore, the imaging position specifying unit 314 can specify the position of the imaging unit 134 as necessary.

  In the present embodiment, the relative position specifying unit 316 analyzes the data of the image acquired by the image data acquisition unit 312, and the relative positional relationship between the imaging device that captured the image and the position where the work is performed. To decide.

  In one embodiment, when the worker 12 wears the spraying unit 150 and performs the spraying work, and the imaging unit 134 captures an image, the relative position specifying unit 316 includes the imaging unit 134 and the worker. 12 and at least one of the spraying units 150 are determined. The relative position specifying unit 316 may output the relative position between the representative point of the imaging unit 134 or the farm robot 120 and the worker 12 as the relative position of the imaging unit 134 and the spraying unit 150.

  In another embodiment, when the worker 12 performs a harvesting operation of the agricultural product 30 and the imaging unit 134 captures an image, the relative position specifying unit 316 includes the imaging unit 134 and at least one of the agricultural products 30. To determine the relative position. The relative position specifying unit 316 may output the relative position between the representative point of the imaging unit 134 or the farm robot 120 and the worker 12 as the relative position of the imaging unit 134 and the agricultural product 30.

  In the present embodiment, the absolute position specifying unit 318 specifies the position where the work has been performed based on the position of the imaging device specified by the imaging position specifying unit 314 and the relative position specified by the relative position specifying unit 316. .

  In one embodiment, when the worker 12 wears the spraying unit 150 to perform the spraying work and the imaging unit 134 captures an image, the absolute position specifying unit 318 is specified by the imaging position specifying unit 314. Based on the position of the imaging unit 134 and the relative position specified by the relative position specifying unit 316, at least one of the position of the worker 12 in the field and the position of the spraying unit 150 in the field is specified.

  In another embodiment, when the worker 12 performs the harvesting work of the agricultural product 30 and the imaging unit 134 captures an image, the absolute position specifying unit 318 specifies the imaging unit specified by the imaging position specifying unit 314. Based on the position of 134 and the relative position specified by the relative position specifying unit 316, the position where the agricultural product 30 is harvested in the field is specified.

  In the present embodiment, the work content identification unit 322 identifies the content of the work. For example, the work content identification unit 322 identifies the content of the work based on at least one of the information acquired by the work data collection unit 222 and the information acquired by the field data collection unit 224. The work content identification unit 322 may identify the content of at least one of the work performed by the worker 12 and the work performed on the agricultural product 30. Examples of the contents of the work include the purpose of the work, the type of work, the strength of the work, the procedure of the work, the type of material used in the work, the type of equipment used in the work, and the like.

  In one embodiment, when the worker 12 performs the spraying work (including the case where the worker 12 is performing the spraying work), the work content specifying unit 322 is specified by the work position specifying unit 310. In position, identify the amount of material spread. The work content specifying unit 322 may specify the cumulative value of the amount of the substance dispersed during the predetermined period. The work content specifying unit 322 may specify the material spraying direction at the position specified by the work position specifying unit 310. For example, the work content specifying unit 322 acquires measurement data of a flow meter arranged in the spraying unit 150 and specifies the amount of material sprayed.

  In another embodiment, when the worker 12 performs the harvesting work (may include the case where the worker 12 is performing the harvesting work), the work content specifying unit 322 is specified by the work position specifying unit 310. The at least one of the quality and mass of the harvested agricultural product 30 is specified at the position. The work content specifying unit 322 may specify the cumulative value of the mass of the agricultural product 30 harvested during the predetermined period for each predetermined period. The work content specifying unit 322 acquires, for example, measurement data of a mass meter arranged in the storage unit 160, and specifies at least one of quality and mass of the harvested agricultural product 30.

  In this embodiment, the work content identification unit 322 analyzes the data acquired by the image data acquisition unit 312 and (i) the characteristics of the harvested part of the agricultural product 30 and (ii) the surroundings of the harvested part. Feature information indicating at least one of the features of the non-harvested part of the agricultural product 30 existing in the farm may be generated. The feature information may be an example of information indicating work content.

  In the present embodiment, the incidental work specifying unit 324 performs the specific work before or after the worker 12 performs the specific work accompanying the specific work at the position specified by the work position specifying unit 310. The work carried out after the execution (sometimes referred to as accompanying work) is specified. The accompanying work identification unit 324 may identify an accompanying work by analyzing an image obtained by imaging the state of the work. For example, when the worker 12 performs the spraying work, the worker 12 checks the state of the back side of the leaf before performing (i) the spraying work, or (ii) Check the wetness of the.

  In the present embodiment, the result information acquisition unit 326 acquires result information indicating the result of work. In one embodiment, the result information acquisition unit 326 acquires growth information indicating the growth state of the agricultural product 30. The result information acquisition unit 326 may acquire growth information from the field data collection unit 224. In another embodiment, the result information acquisition unit 326 acquires quality information indicating the quality of the agricultural product 30. The result information acquisition unit 326 may acquire quality information from the farm robot 120 via the communication network 10. In still another embodiment, the result information acquisition unit 326 acquires mass information indicating the mass of the agricultural product 30. The result information acquisition unit 326 may acquire mass information from the farm robot 120 via the communication network 10.

  In the present embodiment, the work information generation unit 332 generates work information in which information indicating the position where the work is performed and information indicating the content of the work are associated with each other. The work information generation unit 332 includes (i) information indicating the position specified by the work position specifying unit 310, (ii-1) information indicating the work content specified by the work content specifying unit 322, and (ii-2) The work information associated with at least one piece of information indicating the work content of the accompanying work specified by the accompanying work specifying unit 324 may be generated.

  For example, when the worker 12 performs the spraying work, the information indicating the work content may include information indicating the spraying amount, information indicating the spraying direction, and the like. In this case, the work information generation unit 332 may generate work information in which information indicating the position where the spraying work is performed is associated with at least one of information indicating the spraying amount and information indicating the spraying direction.

  In another embodiment, when the worker 12 performs the harvesting work, (i) information indicating the position specified by the work position specifying unit 310, (ii-1) quality information acquired by the result information acquiring unit 326, (Ii-2) Harvest information associated with at least one of the mass information acquired by the result information acquisition unit 326 may be generated.

  FIG. 4 schematically shows an example of the internal configuration of the analysis unit 232. In the present embodiment, the analysis unit 232 includes a data analysis unit 410. In the present embodiment, the analysis unit 232 includes a condition determination unit 420 and a learning data generation unit 430. In the present embodiment, the learning data generation unit 430 includes a correction unit 432, a merge unit 434, an extraction unit 436, and a shaping unit 438. Each unit of the analysis unit 232 may transmit and receive information to and from each other without being limited by the direction of the arrow in the drawing and the presence or absence of the arrow.

  In the present embodiment, the data analysis unit 410 analyzes information acquired or generated by at least one of the work data collection unit 222 and the field data collection unit 224. The data analysis unit 410 may store the analysis result in the storage unit 240. The data analysis unit 410 may transmit the analysis result to the correction unit 432. The data analysis unit 410 may transmit the analysis result to the work support unit 234.

  In one embodiment, the data analysis unit 410 performs multivariate analysis or sensitivity analysis using at least a part of data acquired or generated by at least one of the work data collection unit 222 and the field data collection unit 224. The analysis unit 232 may determine an explanatory variable having a large influence on the objective variable among one or more explanatory variables related to the objective variable.

  The objective variable may be any parameter related to the agricultural product 30. Examples of the objective variable include the growth state of the agricultural product 30, the quality of the harvested product, the mass of the harvested product, the occurrence of pests, or a combination thereof. Examples of the quality include color, shape, size, composition, content of specific component, hardness, and the like.

  The explanatory variable may be an arbitrary parameter related to the work. Examples of the explanatory variable include at least one of the type of work, the timing of the work, the intensity of the work, the work interval, the work frequency, the work accuracy, the skill of the worker, and a combination thereof. Can do. The explanatory variable may be any parameter related to the agricultural product 30 or the surrounding environment of the agricultural product 30. Examples of explanatory variables include types of agricultural products 30, parameters relating to soil characteristics, parameters relating to weather, and the like.

  In one embodiment, the analysis unit 232 refers to information (influence degree, contribution rate, influence function, etc.) indicating the magnitude of the influence of the explanatory variable on the objective variable for at least one of the one or more explanatory variables. There is a case.) In another embodiment, the analysis unit 232 determines, for at least one of the one or more explanatory variables, information (sometimes referred to as sensitivity) indicating how the objective variable changes with respect to the change of the explanatory variable. To do.

  The analysis unit 232 may determine an explanatory variable having a large influence on the target variable among the one or more explanatory variables based on the magnitude or sensitivity of the influence. For example, the analysis unit 232 determines the specific explanatory function as an explanatory variable having a large influence on the objective variable when the magnitude or sensitivity of the above-described influence regarding the specific explanatory variable satisfies a predetermined condition. To do. Examples of the predetermined condition include a condition that it is larger than a predetermined threshold, a condition that it falls within a predetermined range, a condition that it does not fall within a predetermined range, and the like.

  The analysis unit 232 may collect a plurality of pieces of work information related to the same type of work and handle them as a single work group. The analysis unit 232 may collect a plurality of pieces of work information related to the same type of work performed at the same position or region and handle them as a single work group. The region may be a plurality of fields, a single field, or a part of a plurality or a single field. The analysis unit 232 may collect a plurality of pieces of work information related to the same type of work performed in the same time zone at the same position or region and handle them as a single work group. The analysis unit 232 may select any period of the day as the above time zone. The analysis unit 232 may collect a plurality of pieces of work information related to the same type of work performed at the same position or region for each predetermined period and handle them as a single work group. The predetermined period may be one day, one week, or one month. In this case, the analysis unit 232 may execute multivariate analysis or sensitivity analysis using any parameter related to the work group as an explanatory variable.

  The parameters relating to the work group include (a) statistics of work intensity indicated by each of the plurality of work information constituting the work group, and (b) work indicated by each of the plurality of work information constituting the work group. The statistics of time etc. can be illustrated. The parameter relating to the work group may be (c) a feature of a periodic pattern of work intensity indicated by each of a plurality of work information constituting the work group. Examples of the characteristics of the periodic pattern include the period and amplitude of the pattern.

  The statistics of the work intensity include (i) a representative value of work intensity, (ii) an index indicating the degree of variation or distribution of work intensity, and (iii) variation or distribution of work intensity depending on the position where the work is performed. (Iv) An index indicating the degree of variation or distribution of work intensity due to a difference in time zone or period in which the work was performed, (v) Work intensity is predetermined in a predetermined period The number of times or frequency exceeding the threshold or range can be exemplified. The variation or distribution of the work intensity due to the difference in the position where the work is performed may be a variation or distribution of the work intensity plotted along the movement path of the worker 12. Examples of the representative value include an average value and a median value. Examples of the index indicating the degree of variation or distribution include standard deviation and coefficient of variation.

  Examples of the statistics of the work time include (i) a representative value of the time or time zone at which the work was carried out (ii) an index indicating the variation or distribution of the time or time zone at which the work was carried out, and the like. it can. Examples of the representative value include an average value and a median value. Examples of the index indicating the degree of variation or distribution include standard deviation and coefficient of variation.

  The analysis unit 232 may collect a plurality of pieces of work information related to a plurality of types of work and handle them as a single work group. In this case, the analysis unit 232 may execute multivariate analysis or sensitivity analysis using any parameter related to the work group as an explanatory variable. The analysis unit 232 may collect a plurality of pieces of work information related to a plurality of types of work performed at the same position or region and handle them as a single work group. The analysis unit 232 may collect a plurality of pieces of work information related to a plurality of types of work performed in the same time zone at the same position or region, and may handle them as a single work group. The analysis unit 232 may collect a plurality of pieces of work information related to a plurality of types of work performed at the same position or region for each predetermined period and handle them as a single work group. The parameters relating to the work group may be as described above.

  In one embodiment, when the plurality of work types constituting the work group A and the plurality of work types constituting the work group B are the same, the order of the plurality of works constituting the work group A; When the order of a plurality of tasks constituting the work group B is different, the work group A and the work group B may be handled as different work groups. In another embodiment, when the plurality of work types constituting the work group A and the plurality of work types constituting the work group B are the same, the order of the plurality of works constituting the work group A Even when the order of the plurality of tasks constituting the work group B is different, the work group A and the work group B may be handled as the same work group.

  In other embodiments, the data analysis unit 410 may evaluate the work indicated by each of the plurality of work information. The data analysis unit 410 may evaluate the work indicated by each of the plurality of work information based on one or more pieces of result information. For example, each of work information and result information includes information indicating a position. Therefore, the data analysis unit 410 extracts one or more pieces of work information corresponding to each of one or a plurality of pieces of work information from a plurality of pieces of work information using information indicating the position as a key. The data analysis unit 410 evaluates the extracted one or more pieces of work information according to corresponding result information.

  In the present embodiment, the condition determination unit 420 determines a condition for the learning data generation unit 430 to generate a learning data set. The condition determination unit 420 may determine the above condition based on information indicating the purpose or use of the learned model generated by the work support unit 234. For example, the condition determination unit 420 acquires information indicating the use purpose or application of the learned model from the input / output control unit 212.

  In one embodiment, the condition determination unit 420 generates a condition indicating whether or not data correction is necessary and a condition indicating a data correction process target, and transmits the condition to the correction unit 432. In another embodiment, the condition determination unit 420 generates a condition indicating whether or not data merging is necessary and a condition indicating the type of information to be merged, and transmits the condition to the merge unit 434. As the types of information, (i) the type of information to be included in the learning data for generating a learned model specific to the field, and (ii) the learned model specific to the type of the agricultural product 30 is generated. The type of information to be included in the learning data for, (iii) the type of information to be included in the learning data for generating a learned model specific to the field and the type of agricultural product 30 cultivated in the field, And (iv) The kind of information etc. which should be included in the data for learning for producing | generating a general-purpose learned model which does not depend on the field and the kind of the agricultural product 30 grown in the said field can be illustrated.

  In yet another embodiment, the condition determination unit 420 generates a condition for the extraction unit 436 to extract data, and transmits the condition to the extraction unit 436. For example, the purpose of learning is to input the current and past situation of the agricultural product 30 and its surrounding environment at a specific position and the target relating to the agricultural product 30, and output the desired work content at the position or the desired work content regarding the agricultural product. To build a trained model for: (i) work information about the experienced worker 12; (ii) work information when an excellent quality produce 30 is harvested; (iii) produce 30 The extraction conditions are determined so that the work information when the harvest amount is good and (iv) the work information other than when the harvest amount of the agricultural product 30 is poor are extracted.

  The conditions for extracting data may include a condition relating to the worker 12, a condition relating to the agricultural robot 120, a condition relating to the agricultural product 30, and a condition relating to the surrounding environment of the agricultural product 30. Examples of conditions relating to the worker 12 include conditions relating to experience or skill level of specific work, conditions relating to the produce 30 cultivated by the worker 12, and the like. Examples of conditions relating to the farm robot 120 include conditions relating to the type of attachment, conditions relating to movement speed, conditions relating to work speed, conditions relating to use frequency, and the like.

  The conditions relating to the agricultural product 30 were the conditions relating to the position of the agricultural product 30, the conditions relating to the cultivation method of the agricultural product 30 (organic cultivation, low agricultural chemical cultivation, etc. can be exemplified as the cultivation method), and the agricultural product 30. Conditions relating to work, conditions relating to the growth of the agricultural product 30 (for example, growth rate, growth status, etc.), conditions relating to the quality or mass of the agricultural product 30, and the like can be exemplified. Examples of conditions relating to the surrounding environment of the agricultural product 30 include conditions relating to soil in the field where the agricultural product 30 is cultivated, conditions relating to the weather during the growing period of the agricultural product 30, and the like.

  In the present embodiment, the learning data generation unit 430 generates a learning data set for machine learning or deep learning. The learning data generation unit 430 may generate a learning data set according to the purpose or use of the learned model generated by the work support unit 234. For example, the learning data generation unit 430 includes (i) a learning data set for generating a learned model specific to a farm field, and (ii) a learning model for generating a learned model specific to the type of the agricultural product 30. A data set, (iii) a learning data set for generating a learned model specific to the field and the type of the agricultural product 30 cultivated in the field, and (iv) an agricultural product 30 cultivated in the field and the field At least one learning data set for generating a general-purpose learned model independent of the type is generated.

  In the present embodiment, the correction unit 432 corrects information stored in the storage unit 240. For example, the correction unit 432 corrects the information in consideration of the influence due to the difference in the field condition, the influence due to the difference in the weather condition, the influence due to the difference in the growth state, and the like on the information to be corrected. The correction unit 432 may correct the information stored in the storage unit 240 based on the analysis result of the data analysis unit 410.

  In one embodiment, the correction unit 432 uses the information indicating the degree of influence or sensitivity calculated by the data analysis unit 410 to correct information indicating the results included in one or more result information. In another embodiment, the correction unit 432 corrects information indicating the growth state included in one or a plurality of growth information using information indicating the degree of influence or sensitivity calculated by the data analysis unit 410.

  The information in the correction unit 432 is an example in which the sensitivity when the parameter regarding soil characteristics is an explanatory variable and the parameter indicating the quality or mass of the harvest is a target variable is known by the analysis processing of the data analysis unit 410. An example of the process will be described more specifically. For example, the correction unit 432 generates, from the condition determination unit 420, result information indicating the yield when the ratio of humus is increased at a specific position based on the result information stored in the storage unit 240. The command is received.

  The correction unit 432 refers to the storage unit 240 and extracts result information and soil information at a specific position. In addition, the correction unit 432 acquires information regarding the sensitivity of the yield with respect to the soil composition from the data analysis unit 410. Based on the information regarding the composition of the soil at the specific position and the information regarding the sensitivity of the yield with respect to the composition of the soil, the amount of change in the yield when the soil composition is changed at the specific position is calculated.

  The correction unit 432 corrects the result information based on the calculation result. In one embodiment, the correction unit 432 rewrites the result information stored in the storage unit 240 with the corrected result information. In another embodiment, the correction unit 432 stores the corrected result information in the storage unit 240 as information different from the original result information. In still another embodiment, the correction unit 432 transmits the corrected result information to the merge unit 434.

  In the present embodiment, the merge unit 434 merges a plurality of information (may be referred to as merging). For example, the work information stored in the storage unit 240 (may include information related to the accompanying work), the result information, the soil information, the weather information, and the growth information include information indicating the position. Therefore, the merge unit 434 merges at least two of work information, result information, soil information, weather information, and growth information using information indicating the position as a key. The merge unit 434 may merge the information corrected by the correction unit 432 with other information.

  For example, the merge unit 434 determines which information is to be merged with which information according to the condition determined by the condition determining unit 420. In one embodiment, when the learning data generation unit 430 generates learning data for generating a learned model specific to a field, the merging unit 434 does not have to merge soil information. On the other hand, when the learning data generation unit 430 generates learning data for generating a learned model unique to agricultural products or a general-purpose learned model, the merging unit 434 merges soil information.

  In the present embodiment, the extraction unit 436 extracts merge information constituting the learning data set from a plurality of pieces of information (sometimes referred to as merge information) generated by the merge process of the merge unit 434. For example, the extraction unit 436 extracts merge information that matches the condition determined by the condition determination unit 420 from the plurality of merge information as merge information constituting the learning data set. The extraction unit 436 transmits the extracted one or more merged information to the shaping unit 438.

  In the present embodiment, the shaping unit 438 acquires one or a plurality of merged information extracted by the extraction unit 436. The shaping unit 438 arranges the data format of one or a plurality of merged information and outputs it as a learning data set. For example, the shaping unit 438 deletes unnecessary data from each of one or a plurality of merged information, and generates a learning data set.

  In the present embodiment, the merging unit 434 merges various types of data after the correction unit 432 corrects various types of data as necessary. Further, the extraction unit 436 uses the data merged by the merge unit 434 to extract data constituting the learning data set. However, the generation method of the learning data set in the learning data generation unit 430 is not limited to this embodiment.

  In other embodiments, the merging unit 434 may merge a specific type of data extracted by the extraction unit 436 with other types of data. For example, the extraction unit 436 first extracts result information indicating a specific result from a plurality of result information stored in the storage unit 240. Each of the one or more pieces of result information extracted by the extraction unit 436 includes information indicating a position. Next, the extraction unit 436 extracts work information corresponding to each piece of work information from the plurality of work information stored in the storage unit 240 using information indicating the position as a key. Next, with the information indicating the position as a key, the merge unit 434 uses the one or more pieces of work information extracted by the extraction unit 436, the information indicating the characteristics of the soil included in the corresponding soil information, and the corresponding weather information. Merge with included weather data. In addition, instead of extracting the result information indicating the specific result from the plurality of result information stored in the storage unit 240, the extraction unit 436, from among the plurality of growth information stored in the storage unit 240, You may extract the growth information which shows a specific growth state.

  In still another embodiment, the learning data generation unit 430 may generate the learning data set by repeating the merging by the merging unit 434 and the extraction by the extraction unit 436. In still another embodiment, the correction unit 432 may correct the data as necessary after the merge unit 434 has merged.

  FIG. 5 schematically shows an example of the internal configuration of the work support unit 234. In the present embodiment, the work support unit 234 includes a learning unit 510 and a work content determination unit 520. The work content determination unit 520 may be an example of an application amount determination unit.

  In the present embodiment, the learning unit 510 generates a learned model corresponding to a specific purpose by machine learning or deep learning. For example, the learning unit 510 receives the learning data set generated by the analysis unit 232 and outputs a learned model. The learning unit 510 may store the generated learned model in the model information storage unit 282.

  The learning unit 510 may generate a plurality of types of learned models having different usage purposes or uses according to information included in the learning data set input to the learning unit 510. The learned models include (i) a learned model specific to the field, (ii) a learned model specific to the farm product, (iii) a learned model specific to the field and the farm product, and (iv) independent of the field and the farm product. A general-purpose learned model can be exemplified.

  In the present embodiment, the work content determination unit 520 uses the learned model generated by the learning unit 510 to support information for supporting at least one of the worker 12 and the farm robot 120 (when referred to as support information). Is output. The worker 12 may be an example of a second worker.

  For example, when the input / output control unit 212 receives a support request for requesting support information from at least one of the communication terminal 22, the farm work robot 120, and the spraying unit 150, the work content determination unit 520 performs support information generation processing. Start. For example, the input / output control unit 212 may acquire a target related to a specific agricultural product 30 input to the communication terminal 22, the agricultural robot 120, or the spraying unit 150 by the operator 12. Examples of the target related to the agricultural product 30 include the growth state of the agricultural product 30 at a specific time, the quality of the harvested product, and the mass of the harvested product.

  For example, the work content determination unit 520 refers to the storage unit 240 and acquires work information related to a specific position. Thereby, the work content determination unit 520 can acquire information on the history of work performed on the specific agricultural product 30. For example, the work content determination unit 520 refers to the storage unit 240 and acquires soil information and weather information regarding a specific position. Thereby, the work content determination unit 520 can acquire information related to the surrounding environment of the specific agricultural product 30. For example, the work content determination unit 520 refers to the storage unit 240 and acquires growth information regarding a specific position. By this. The work content determination unit 520 can acquire information on the current growth state or growth history of the specific agricultural product 30.

  For example, the work content determination unit 520 refers to the model information storage unit 282 and acquires a learned model corresponding to the content of the support request. The work content determination unit 520 may acquire an appropriate learning model based on the type of input data. The work content determination unit 520 inputs the data input to the work content determination unit 520 to the learned model.

  In one embodiment, the work content determination unit 520 transmits the data output from the learned model as support information to the communication terminal 22, the farm robot 120, or the spraying unit 150 that has transmitted the support request. In another embodiment, the work content determination unit 520 corrects the data output by the learned model using information indicating the degree of influence or sensitivity calculated by the data analysis unit 410. The work content determination unit 520 transmits the corrected data as support information to the communication terminal 22, the agricultural robot 120, or the spraying unit 150 that has transmitted the support request. The communication terminal 22 or the farm work robot 120 that has received the support information may transfer the support information to the spraying unit 150.

  For example, when the worker 12 is performing the spraying work, the work information includes information for specifying at least one of the position of the worker 12 in the field and the position of the spraying unit 150 in the field. Further, in this case, the work information includes information for specifying the amount of the substance already sprayed at the above position. When work information including the above information is input to the learned model in the work content determination unit 520, the amount of a substance to be dispersed is output at a position specified by the work information. At least one of the worker 12, the farm work robot 120, and the spraying unit 150 sprays the substance based on the spraying amount of the substance determined by the work content determination unit 520. Thereby, the growth of the agricultural product 30 can be controlled.

  FIG. 6 schematically shows an example of the data format of various information stored in the storage unit 240. In the present embodiment, the work information 652 is information in which, for example, the farm field ID, the sub area ID, the work date and time, the worker ID, the work content, and the URI of the image data in which the state of the work is captured are associated with each other. It is. The farm field ID and the sub area ID may be an example of information indicating a position where the work is performed.

  In the present embodiment, the field ID may be identification information for identifying each of one or more fields. The sub area ID may be identification information for identifying each of one or more sub areas set in each field. In one embodiment, the sub-area ID may be identification information for identifying each of one or more strains cultivated in each field. In other embodiments, multiple strains may be grown within a single sub-area. The work information 652 may include information indicating the coordinates of the position where the work is performed instead of the sub area ID or together with the sub area ID.

  The worker ID may be identification information for identifying the worker who performed the work. The information indicating the work content includes, for example, information on the type of work, the work intensity, the work procedure, the type of material used in the work, the type of equipment used in the work, the work content of the accompanying work, and the like.

  In the present embodiment, the result information 654 includes, for example, a field ID, a sub-area ID, a harvest date and time, a harvest type, a harvest quality, a harvest mass (sometimes referred to as a harvest amount), and a harvest. This is information in which the URI of image data obtained by imaging the state of work is associated. In the result information 654, the quality of the harvest and the harvest amount for each quality of the harvest may be associated with each other. Examples of quality include color, shape, size, composition, content of specific components, and the like. The result information 654 may be an example of harvest information.

  In the present embodiment, the soil information 662 is information in which, for example, an agricultural field ID, a subarea ID, and soil characteristics are associated with each other. Examples of soil characteristics include soil composition, drainage, fertilizer retention, hardness, and specific component content. As said specific component, the substance which comprises soil, a fertilizer substance, an agrochemical substance, etc. can be illustrated.

  In the present embodiment, the weather information 664 is information in which, for example, an agricultural field ID, a sub area ID, a date and time, and weather data are associated. Examples of weather data include temperature, water temperature, precipitation, wind direction, wind speed, sunshine duration, temperature in the vicinity of the agricultural product 30, humidity in the vicinity of the agricultural product 30, illuminance in the vicinity of the agricultural product 30, and the like. The weather data may be past data or future forecast data. The date / time may be an observation date / time or a predicted date / time.

  In the present embodiment, the growth information 666 may be information indicating the growth state of the agricultural product 30. The growth information 666 includes, for example, the date and time when the agricultural product 30 is observed, the field ID and subarea ID where the agricultural product 30 is cultivated, the type of the agricultural product 30, the growth stage of the agricultural product 30, the growth status of the agricultural product 30, and the agricultural product 30. This is information in which the URI of the image data of the captured image is associated.

  In the present embodiment, the learning data 672 is, for example, information in which the type of the agricultural product 30, the growth stage of the agricultural product 30, the content of the work performed on the agricultural product 30, and the state of the surrounding environment of the agricultural product 30 are associated. is there. Examples of the state of the surrounding environment of the agricultural product 30 include weather data and soil characteristics.

[Description of each part of agricultural robot 120]
FIG. 7 schematically shows an example of the system configuration of the agricultural robot 120. As described with reference to FIG. 1, in this embodiment, the farm work robot 120 includes a base unit 130, a moving unit 132, an imaging unit 134, a sensor unit 136, and a farm work unit 138. In this embodiment, the farm robot 120 includes a control unit 720, a power supply unit 730, a vibration control unit 750, and a balance adjustment unit 780. The moving unit 132, the imaging unit 134, the sensor unit 136, the farm work unit 138, the vibration control unit 750, and the balance adjustment unit 780 may be an example of an attachment for farm work.

  In the present embodiment, the base unit 130 includes a control unit 720 and a power supply unit 730. The control unit 720 and the power supply unit 730 may be built in the base unit 130. At least one of the control unit 720 and the power supply unit 730 may be detachably disposed on the base unit 130. In one embodiment, at least one of the control unit 720 and the power supply unit 730 is detachably disposed inside the base unit 130. In another embodiment, at least one of the control unit 720 and the power supply unit 730 is detachably disposed outside the base unit 130.

  In this embodiment, the base unit 130 detachably holds at least one of the moving unit 132, the farm work unit 138, and the balance adjustment unit 780. The base unit 130 may hold at least one of the vibration control unit 750, the imaging unit 134, and the sensor unit 136 in a detachable manner.

  In the present embodiment, the control unit 720 controls the farm work robot 120. In one embodiment, the control unit 720 communicates with each unit and controls the operation of the unit according to the type of the unit. The control unit 720 may control the operation of one or a plurality of units according to a combination of a plurality of units.

  For example, the control unit 720 communicates with at least one of the movement unit 132, the farm work unit 138, and the balance adjustment unit 780. The communication method may be wired communication or wireless communication. When the communication method between the control unit 720 and the other unit is wired communication, the base unit 130 may be referred to as a communication path (bus) that transmits information between the control unit 720 and the other unit. .). The communication path may be an example of a communication unit.

  In another embodiment, the control unit 720 communicates with the management server 110 and transmits information regarding the farm robot 120 to the management server 110. For example, the control unit 720 acquires the position information of the farm robot 120 and transmits the position information to the management server 110. According to another example, the control unit 720 associates the position information of the farm robot 120 with the information acquired by the sensor unit 136 at the position indicated by the position information, and transmits the information to the management server 110.

  The position information may be information indicating the position of the farm robot 120, and the content of the position information and the acquisition method are not particularly limited. The control unit 720 may specify the position of the farm robot 120 by an arbitrary self-position estimation method. The control unit 720 may receive the GPS signal and specify the position of the farm robot 120 based on the GPS signal. The control unit 720 may receive a beacon signal from a beacon transmitter installed around the farmland and specify the position of the farm robot 120 based on the beacon signal. The position of the farm work robot 120 may be determined based on the radio field intensity of the radio wave. The control unit 720 analyzes the image around the farm robot 120 captured by the imaging unit 134, and identifies the position of the farm robot 120 from the angle formed by the plurality of landmarks whose positions are known and the farm robot 120. Also good.

  The control unit 720 may acquire information for controlling the farm robot 120 from the management server 110. The information for controlling the farm work robot 120 may be information on work that the farm work robot 120 should perform from now on. The control unit 720 may control the operation of the unit related to the information based on the information acquired from the management server 110.

  In the present embodiment, the power supply unit 730 supplies power for driving the unit to at least one of the moving unit 132, the farm work unit 138, and the balance adjustment unit 780. The base unit 130 power supply unit 730 may supply power to at least one of the vibration control unit 750, the imaging unit 134, and the sensor unit 136.

  In the present embodiment, the vibration control unit 750 controls vibration. For example, the vibration control unit 750 controls at least one vibration of the imaging unit 134, the sensor unit 136, and the farm work unit 138. The vibration control unit 750 may control vibration according to a command from the control unit 720. The vibration suppression unit 750 may include a power source that generates power for driving the vibration suppression unit 750 using the electric power supplied from the power supply unit 730. Examples of the power source include a motor and an actuator.

  In the present embodiment, the balance adjustment unit 780 has a weight. The balance adjustment unit 780 is detachably held on the base unit 130. Depending on the weight of the unit mounted on the base unit 130 and the position of its center of gravity, the weight balance of the farm robot 120 becomes poor, and the farm robot 120 becomes unstable. Therefore, by attaching the balance adjustment unit 780 to an appropriate position of the base unit 130, the weight balance of the farm robot 120 can be adjusted.

  The balance adjustment unit 780 may be a manipulator having a weight at the tip. When the manipulator changes the relative position between the weight and the base unit 130, the position of the center of gravity of the farm robot 120 can be changed. The manipulator may be an example of a power source.

  In the present embodiment, the moving unit 132 converts the electric power supplied from the power supply unit 730 into power and moves the farm robot 120. The mobile unit 132 may include a power source that generates power using the power supplied from the power supply unit 730. Examples of the power source include a motor and an actuator.

  In one embodiment, the mobile unit 132 includes wheels for moving on land and a power source that drives the wheels. In another embodiment, the moving unit 132 includes an endless track for moving on land, and a power source that drives the endless track. In still another embodiment, the moving unit 132 may include a screw for moving on or in water and a power source that drives the screw. The moving unit 132 may further include a buoyancy material for obtaining buoyancy. In still another embodiment, the moving unit 132 may include a propeller for moving in the air and a power source that drives the propeller. The mobile unit 132 may further include a balloon or air sac for floating in the air.

  In the present embodiment, the imaging unit 134 images the surroundings of the farm robot 120. Thereby, the environmental information regarding the environment around the agricultural robot 120 can be acquired. The imaging unit 134 may be disposed in front of the base unit 130 in the traveling direction, or may be disposed behind the base unit 130 in the traveling direction. The imaging unit 134 may be disposed on the side surface of the farm work robot 120. The imaging unit 134 may be disposed on the upper surface of the base unit 130 or may be disposed on the lower surface of the base unit 130. The farm robot 120 may include a plurality of imaging units 134. The imaging unit 134 may include a light that emits illumination light. The light may be an example of an illumination unit.

  The imaging unit 134 is preferably capable of capturing a moving image, and may be a visible light camera or an infrared camera. The imaging unit 134 may include a manipulator and an imaging device attached to the tip of the manipulator. The manipulator can point the imaging device in an arbitrary direction using the power supplied from the power supply unit 730. The manipulator may be an example of a power source.

  The sensor unit 136 acquires environmental information related to the environment around the agricultural robot 120. Thereby, the information regarding the growth environment of agricultural products can be acquired. Examples of environmental information include information on soil, information on the atmosphere, information on light intensity, and the like. As information about soil, pH, temperature, moisture content, particle size distribution, hardness, and components, bacteria and viruses contained in soil can be exemplified. Examples of components contained in the soil include organic substances (carbon), nitrogen, phosphorus, potassium, magnesium, calcium, sulfur, iron, manganese, boron, zinc, molybdenum, copper, chlorine, and the like. Examples of the air-related information include air temperature, atmospheric pressure, humidity, carbon dioxide concentration, nitrogen concentration, oxygen concentration, wind direction, and air volume.

  In one embodiment, the sensor unit 136 includes a soil sensor that acquires information regarding the state of the soil around the farm robot 120. The sensor unit 136 may have a plurality of soil sensors. As the soil sensor, (i) an optical sensor such as “N-sensor” manufactured by Yara (for example, a spectral sensor in the visible light region or near-infrared light region), (ii) inserted into the soil, and real time Various sensors for analyzing components contained in soil in real time (for example, electrical conductivity sensors), (iii) Extracting water-soluble components in the soil by adding water to the sampled soil Various sensors to be analyzed can be exemplified. Examples of analysis items of the above extract include pH, calcium ion concentration, magnesium ion concentration, potassium ion concentration, nitrate ion concentration, nitrite ion concentration, phosphate ion concentration, sulfate ion concentration, chloride ion concentration and the like. be able to. The soil sensor may include a depth meter, a soil cutting resistance sensor, and the like.

  In the present embodiment, the case where the imaging unit 134 and the sensor unit 136 are detachably arranged outside the base unit 130 has been described. However, the imaging unit 134 and the sensor unit 136 are not limited to the present embodiment. In other embodiments, the imaging unit 134 and the sensor unit 136 may be disposed on an attachment for farm work. In still other embodiments, the imaging unit 134 and the sensor unit 136 may be incorporated into the control unit 720.

  In this embodiment, the farm work unit 138 is a unit specialized for one or more farm works. Agricultural work unit 138 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 cutting, a manipulator having a jig for harvesting agricultural products, a mowing device (for example, a 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.

  In the present embodiment, the farm work unit 138 is detachably held on the base unit 130. The farm work unit 138 has a power source that generates power for driving the farm work unit 138 using the electric power supplied from the power supply unit 730. Examples of the power source include a motor and an actuator.

  FIG. 8 schematically shows an example of the spraying unit 150. In the present embodiment, the spray unit 150 includes a housing 810, a tank 812, a spray nozzle 814, and a pipe 816. In the present embodiment, the spraying unit 150 includes a transfer unit 822, a flow rate adjustment unit 824, a flow meter 826, and a controller 832. The controller 832 may be an example of a spray amount control unit.

  In the present embodiment, the tank 812 is disposed inside the housing 810 and stores a substance to be sprayed. In the present embodiment, the spray nozzle 814 is disposed outside the housing 810 and sprays a substance. The spray nozzle 814 is connected to the tank 812 via the pipe 816. In the present embodiment, a transfer unit 822, a flow rate adjustment unit 824, and a flow meter 826 are arranged on the pipe 816 that connects the tank 812 and the spray nozzle 814.

  The transfer unit 822 transfers the substance stored in the tank 812 toward the spray nozzle 814. Examples of the transfer unit 822 include a powder feeder, a blower, and a pump. The flow rate adjustment unit 824 adjusts the transfer amount of the substance. Examples of the flow rate adjustment unit 824 include an automatic valve, a flow rate adjustment valve, and a combination thereof. The flow meter 826 measures the amount of substance transferred.

  The controller 832 controls the transfer of the substance. The controller 832 may control the transfer of the substance based on the measurement value of the flow meter 826. The controller 832 may adjust the transfer amount of the substance based on the amount of the substance to be dispersed determined by the work content determination unit 520. Information indicating the amount of the substance to be dispersed may be included in support information from the management server 110, for example.

  More specifically, the controller 832 controls, for example, at least one of the transfer unit 822 and the flow rate adjustment unit 824 to adjust the transfer amount of the substance. Thereby, for example, even if the operator 12 appropriately shakes the spray nozzle, an appropriate amount of the substance according to the position is sprayed. Further, even an inexperienced worker 12 can ensure work quality equivalent to that of the experienced worker 12.

  The controller 832 may send and receive information to and from the management server 110 via the communication network 10. For example, the controller 832 transmits the measurement value of the flow meter 826 to the management server 110. When the spraying unit 150 includes an imaging device, the controller 832 may transmit data of an image captured by the imaging device to the management server 110. The imaging device is disposed, for example, in the housing 810 or the spray nozzle 814. The controller 832 may receive support information from the management server 110.

  FIG. 9 schematically shows an example of the accommodation unit 160. In the present embodiment, the storage unit 160 includes a housing 910, a storage portion 922, a storage portion 924, and a storage portion 926 that are partitioned by a partition wall 912 and a partition wall 914. The housing unit 160 includes a mass meter 932, a mass meter 934, and a mass meter 936. Each of the mass meter 932, the mass meter 934, and the mass meter 936 measures the mass of the contents accommodated in each of the accommodating portion 922, the accommodating portion 924, and the accommodating portion 926. Each of the mass meter 932, the mass meter 934, and the mass meter 936 may be an example of a quality information acquisition unit and a mass information acquisition unit.

  In the storage unit 922, the storage unit 924, and the storage unit 926, for example, the harvested agricultural product 30 is stored. In the present embodiment, each of the storage unit 922, the storage unit 924, and the storage unit 926 stores agricultural products 30 having different qualities. Thereby, by measuring the mass of each accommodation thing of the accommodating part 922, the accommodating part 924, and the accommodating part 926, it is possible to specify how much and how much the agricultural product 30 is harvested. Each of the mass meter 932, the mass meter 934, and the mass meter 936 may transmit a measurement result to the control unit 720. The control unit 720 may transmit the measurement results of the mass meter 932, the mass meter 934, and the mass meter 936 to the management server 110.

  In the present embodiment, the case where the worker 12 determines quality based on vision has been described. However, the quality determination method is not limited to this embodiment. In other embodiments, the worker 12 may determine the quality based on information of a sense other than vision (for example, touch, hearing, smell, etc.). In still another embodiment, the quality may be determined based on the measurement result of the measurement device.

  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. 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.

In this specification, for example, the following matters are described.
[Item 1-1]
(I) a position of a worker who wears a spraying device for spraying a substance and sprays the substance on a field where one or more agricultural products are grown, and (ii) the spraying which is worn by the worker An application position specifying unit for specifying at least one of the positions of the device in the field;
In the position specified by the spraying position specifying part, a spraying quantity specifying part for specifying the amount of the substance sprayed,
(A) Work information generation for generating work information associated with information indicating the position specified by the spraying position specifying unit and (b) information indicating the spraying amount of the substance specified by the spraying amount specifying unit And
An information processing system comprising:
[Item 1-2]
A spraying direction specifying part for specifying the spraying direction of the substance at the position specified by the spraying position specifying part,
Further comprising
The work information generation unit generates the work information in which (a) information indicating the position specified by the spraying position specifying unit and (c) information indicating the direction specified by the spraying direction specifying unit are associated with each other. To
The information processing system according to item 1-1.
[Item 1-3]
At the position specified by the spraying position specifying unit, the worker, accompanying the spraying of the substance, an accompanying work specifying part for specifying work performed before spraying the substance or after spraying the substance,
Further comprising
The work information generation unit includes (a) information indicating the position specified by the spray position specifying unit, and (d) the work information associated with information indicating the work specified by the accompanying work specifying unit. Generate,
The information processing system according to item 1-1 or item 1-2.
[Item 1-4]
A teacher information generating unit that generates teacher information related to the work of spraying the substance based on the work information related to one or more first workers generated by the work information generating unit;
Further comprising
The information processing system according to any one of items 1-1 to 1-3.
[Item 1-5]
Soil information acquisition unit for acquiring information indicating the position in the farm field, and soil information associated with information indicating the characteristics of the soil at the position,
Further comprising
The teacher information generation unit generates the teacher information based on the soil information acquired by the soil information acquisition unit and the work information of the one or more first workers.
The information processing system according to item 1-4.
[Item 1-6]
A growth information acquisition unit that acquires growth information associated with information indicating a position in the field and information indicating a growth state of the one or more agricultural products at the position;
Further comprising
The teacher information generation unit generates the teacher information based on the growth information acquired by the growth information acquisition unit and the work information of the one or more first workers.
The information processing system according to item 1-4 or item 1-5.
[Item 1-7]
Harvest information acquisition unit for acquiring information indicating the position in the field and information related to the agricultural products harvested at the position,
Further comprising
The teacher information generation unit generates the teacher information based on the harvest information acquired by the harvest information acquisition unit and the work information of the one or more first workers.
The information processing system according to any one of items 1-4 to 1-6.
[Item 1-8]
The spraying position specifying unit includes at least one of (i) a position of the second worker spraying the substance in the field, and (ii) a position of the spraying device attached to the second worker in the field. Identify
The application amount specifying unit specifies the amount of the substance already applied at the position specified by the application position specifying unit,
The information processing system
(I) The amount of the substance already sprayed specified by the spraying amount specifying unit, and (ii) the spray position specifying unit specified based on the teacher information generated by the teacher information generating unit At the position of the second worker, an application amount determining unit for determining the amount of the substance to be applied;
Further comprising
The information processing system according to any one of items 1-4 to 1-7.
[Item 1-9]
A spray amount control unit for controlling the spray device based on the amount of the substance to be sprayed determined by the spray amount determination unit;
The information processing system according to item 1-8.
[Item 1-10]
An image data acquisition unit that acquires data of an image of the state in which at least one of the worker and the spraying device is spraying the substance;
An imaging position acquisition unit that acquires information indicating a position when the imaging device that captured the image captures the image;
Further comprising
The spray position specifying part
Analyzing the data acquired by the image data acquisition unit to determine a relative positional relationship between the imaging device and at least one of the worker and the spraying device,
Based on the position of the imaging device indicated by the information acquired by the imaging position acquisition unit and the relative positional relationship, at least one of the position of the worker in the field and the position of the spraying device in the field Identify
The information processing system according to any one of items 1-1 to 1-9.
[Item 1-11]
A position specifying unit for specifying a position where farm work is performed in a field where one or more agricultural products are cultivated;
At the position specified by the position specifying unit, a content specifying unit for specifying the contents of the farm work;
(A) information indicating the position specified by the position specifying unit, and (b) a work information generating unit that generates work information associated with information indicating the contents of the farm work specified by the content specifying unit;
An information processing system comprising:
[Item 1-12]
A program for causing a computer to function as the information processing system according to any one of items 1-1 to 1-11.

In this specification, for example, the following matters are described.
[Item 2-1]
In a field where one or more agricultural products are cultivated, a harvesting position specifying unit for specifying a position where one agricultural product is harvested;
A quality information acquisition unit for acquiring quality information indicating the quality of the one agricultural product,
(A) information indicating the position specified by the harvest position specifying unit, and (b) a harvest information generating unit that generates harvest information associated with the quality information acquired by the quality information acquiring unit;
An information processing system comprising:
[Item 2-2]
A mass information acquisition unit that acquires mass information indicating the mass of the one agricultural product,
Further comprising
The harvest information generating unit includes (a) information indicating the position specified by the harvest position specifying unit, (b) the quality information acquired by the quality information acquiring unit, and (c) the mass information acquiring unit acquiring Generating harvest information associated with the mass information,
The information processing system according to item 2-1.
[Item 2-3]
A first image data acquisition unit that acquires data of the image of the one agricultural product imaged before being harvested;
Analyzing the data acquired by the first image data acquisition unit, (i) the characteristics of the harvested part of the one agricultural product, and (ii) the one of the ones existing around the harvested part A feature information generating unit that generates feature information indicating at least one of the features of the unharvested part of the agricultural product;
Further comprising
The information processing system according to item 2-1 or item 2-2.
[Item 2-4]
A training condition acquisition unit for acquiring work information indicating a work history related to the one agricultural product,
An evaluation unit that evaluates at least a part of work included in the work history based on the information acquired by the growing condition acquisition unit and the harvest information generated by the harvest information generation unit;
Further comprising
The information processing system according to any one of items 2-1 to 2-3.
[Item 2-5]
The growing condition acquisition unit further acquires weather information in at least a part of the growing period of the one agricultural product,
The information processing system according to item 2-4.
[Item 2-6]
A second image data acquisition unit for acquiring data of an image of the state in which the one agricultural product is harvested;
An imaging position acquisition unit that acquires information indicating a position when the imaging device that captured the image captures the image;
Further comprising
The harvest position specifying unit
Analyzing the data acquired by the second image data acquisition unit to determine the relative positional relationship between the imaging device and the one agricultural product,
Based on the position of the imaging device indicated by the information acquired by the imaging position acquisition unit and the relative positional relationship, the position where the one agricultural product is harvested in the field is specified.
The information processing system according to any one of items 2-1 to 2-5.
[Item 2-7]
The imaging device is mounted on a moving body that moves inside the field,
The moving body is
(A) a plurality of storage units each storing one or more harvested agricultural products;
(B) a quality determination unit that determines the quality of the one agricultural product according to the storage unit in which the one agricultural product is stored when the one agricultural product is stored in any of the plurality of storage units; ,
(C) a transmission unit that transmits the quality information indicating the quality of the one agricultural product determined by the quality determination unit to the quality information acquisition unit;
Have
The quality information acquisition unit acquires the quality information from the mobile body via a communication network.
The information processing system according to item 2-6.
[Item 2-8]
The moving body further includes (d) at least one mass measuring unit that measures the mass of the object accommodated in at least one of the plurality of accommodating units,
The transmission unit transmits the mass information indicating the mass of the one agricultural product measured by the at least one mass measurement unit to the mass information acquisition unit,
The mass information acquisition unit acquires the mass information from the mobile body via a communication network.
The information processing system according to item 2-7.
[Item 2-9]
The moving body moves so as to be located behind the worker in the traveling direction of the worker who harvests the one agricultural product.
The information processing system according to item 2-7 or item 2-8.
[Item 2-10]
The moving body moves so as to be positioned in front of the worker in the traveling direction of the worker who harvests the one agricultural product.
The information processing system according to item 2-7 or item 2-8.
[Item 2-11]
A program for causing a computer to function as the information processing system according to any one of items 2-1 to 2-10.
[Item 2-12]
A mobile object having an autonomous movement function,
(A) a plurality of storage units each storing one or more harvested agricultural products;
(B) when one agricultural product is accommodated in any of the plurality of accommodating units, a quality determining unit that determines the quality of the one agricultural product according to the accommodating unit in which the one agricultural product is accommodated;
(C) a transmission unit that transmits the quality information indicating the quality of the one agricultural product determined by the quality determination unit to an external information processing device;
A moving object comprising:

  DESCRIPTION OF SYMBOLS 10 Communication network, 12 Worker, 22 Communication terminal, 30 Farm products, 100 Farm work support system, 110 Management server, 120 Farm work robot, 130 Base unit, 132 Moving unit, 134 Imaging unit, 136 Sensor unit, 138 Farm work unit, 150 Scattering Unit, 160 accommodation unit, 212 input / output control unit, 222 work data collection unit, 224 field data collection unit, 232 analysis unit, 234 work support unit, 240 storage unit, 252 work information storage unit, 254 result information storage unit, 262 Soil information storage unit, 264 Weather information storage unit, 266 Growth information storage unit, 282 Model information storage unit, 310 Work position specifying unit, 312 Image data acquiring unit, 314 Imaging position specifying unit, 316 Relative position specifying unit, 318 Absolute position Fixed part, 322 Work content specifying part, 324 Accompanying work specifying part, 326 Result information acquiring part, 332 Work information generating part, 410 Data analyzing part, 420 Condition determining part, 430 Learning data generating part, 432 Correcting part, 434 Merge Part, 436 extraction part, 438 shaping part, 510 learning part, 520 work content determination part, 652 work information, 654 result information, 662 soil information, 664 weather information, 666 growth information, 672 learning data, 720 control unit, 730 Power supply unit, 750 Vibration suppression unit, 780 Balance adjustment unit, 810 housing, 812 tank, 814 Spray nozzle, 816 piping, 822 Transfer section, 824 Flow control section, 826 Flow meter, 832 Controller, 910 Housing, 912 Bulkhead, 914 Bulkhead , 922 accommodation section, 924 Housing unit, 926 Housing unit, 932 mass meter, 934 mass meter, 936 mass meter

Claims (10)

In a field where one or more agricultural products are cultivated, a harvesting position specifying unit for specifying a position where one agricultural product is harvested;
A quality information acquisition unit for acquiring quality information indicating the quality of the one agricultural product;
(A) information indicating the position specified by the harvest position specifying unit, and (b) a harvest information generating unit that generates harvest information associated with the quality information acquired by the quality information acquiring unit;
A second image data acquisition unit that acquires data of an image of the state in which the one agricultural product is being harvested;
An imaging position acquisition unit that acquires information indicating a position when the imaging device that captured the image captured the image;
Bei to give a,
The harvest position specifying unit
Analyzing the data acquired by the second image data acquisition unit to determine a relative positional relationship between the imaging device and the one agricultural product,
Based on the position of the imaging device indicated by the information acquired by the imaging position acquisition unit and the relative positional relationship, the position where the one agricultural product is harvested in the field is specified.
Information processing system. A mass information acquisition unit for acquiring mass information indicating the mass of the one agricultural product;
Further comprising
The harvest information generating unit includes (a) information indicating the position specified by the harvest position specifying unit, (b) the quality information acquired by the quality information acquiring unit, and (c) the mass information acquiring unit acquiring Generating harvest information associated with the mass information,
The information processing system according to claim 1. A first image data acquisition unit for acquiring image data of the one agricultural product imaged before being harvested;
Analyzing the data acquired by the first image data acquisition unit, (i) characteristics of a part of the one agricultural product to be harvested, and (ii) the one existing around the part to be harvested A feature information generating unit that generates feature information indicating at least one of the features of the unharvested part of the agricultural product;
Further comprising
The information processing system according to claim 1 or 2. A training condition acquisition unit that acquires work information indicating a work history related to the one agricultural product,
An evaluation unit that evaluates at least a part of the work included in the work history based on the information acquired by the growing condition acquisition unit and the harvest information generated by the harvest information generation unit;
Further comprising
The information processing system according to any one of claims 1 to 3. The growing condition acquisition unit further acquires weather information in at least a part of the growing period of the one agricultural product,
The information processing system according to claim 4. The imaging device is mounted on a moving body that moves inside the field,
The moving body is
(A) a plurality of storage units each storing one or more harvested agricultural products;
(B) when the one agricultural product is stored in any of the plurality of storage units, a quality determination unit that determines the quality of the one agricultural product according to the storage unit in which the one agricultural product is stored; ,
(C) a transmission unit that transmits the quality information indicating the quality of the one agricultural product determined by the quality determination unit to the quality information acquisition unit;
Have
The quality information acquisition unit acquires the quality information from the mobile body via a communication network.
The information processing system according to any one of claims 1 to 5 . The moving body further includes (d) at least one mass measuring unit that measures the mass of an object accommodated in at least one of the plurality of accommodating units,
The transmission unit transmits mass information indicating the mass of the one agricultural product measured by the at least one mass measurement unit to the information processing system,
The information processing system acquires the mass information from the moving body via a communication network.
The information processing system according to claim 6 . The moving body moves so as to be located behind the worker in the traveling direction of the worker who harvests the one agricultural product.
The information processing system according to claim 6 or 7 . The moving body moves so as to be positioned in front of the worker in the traveling direction of the worker who harvests the one agricultural product.
The information processing system according to claim 6 or 7 . A program for causing a computer to function as the information processing system according to any one of claims 1 to 9 .