Control device, work machine and non-transitory computer readable medium storing program

A control device includes: an image acquiring section that acquires image data of an image of a work target of the work machine; a feature recognizing section that recognizes a feature about at least one of (i) a type of the work target of the work machine, (ii) a number or density of the work target, (iii) a shape of the work target and (iv) an appearance of the work target after work, based on the image data acquired by the image acquiring section; and a control parameter deciding section that decides at least either (i) a parameter for controlling travel of the work machine or (ii) a parameter for controlling work of the work machine, based on the feature recognized by the feature recognizing section.

BACKGROUND

1. Technical Field

The present invention relates to a control device, a work machine and a program.

2. Related Art

In recent years, lawn mowers, cleaners and the like that run autonomously to work have been developed (please see Patent Document 1 or 2, for example).

PRIOR ART DOCUMENTS

Patent Document

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, (some) embodiment(s) of the present invention will be described. The embodiment(s) do(es) not limit the invention according to the claims, and all the combinations of the features described in the embodiment(s) are not necessarily essential to means provided by aspects of the invention. Identical or similar portions in figures are given identical reference numbers, and the same explanation is omitted in some cases.

[Outline of Control Device100]FIG. 1schematically shows one example of the internal configuration of a control device100. In the present embodiment, the control device100for example includes an image acquiring section110, a feature recognizing section120and a control parameter deciding section130.

In the present embodiment, the control device100controls the work machine102. In the present embodiment, the work machine102executes various types of works. Examples of the works may include pruning, lawn mowing, grass mowing, watering, fertilization, cleaning, transportation, monitoring, security, guard and the like. The work machine102may be a travelling body having an autonomous travel function. Thereby, the work machine102for example can travel by automatic operation by a computer mounted on the work machine102. The work machine102may travel by remote manipulation by a user. The work machine102may be a travelling body to run, a travelling body to fly, or a travelling body to travel in or on water.

The work machine102may include a thrust generating section (not illustrated in figures) such as a wheel or a propeller, and a driving section (not illustrated in figures) that drives the thrust generating section. Examples of the driving section may include an engine, a motor, a prime mover and the like. The work machine102may include a positional information acquiring section (not illustrated in figures) that acquires positional information indicating a position of the work machine102. Examples of the positional information acquiring section may include a GPS signal receiver, a beacon signal receiver, a radio field intensity measuring machine, a millimeter wave sensor, a magnetic sensor, a camera, an infrared camera, a microphone, an ultrasonic wave sensor and the like.

In the present embodiment, the image acquiring section110acquires image data of an image of a work target104of the work machine102. The image may be a moving image or still image. The image acquiring section110for example acquires image data of a plant. The plant may be one example of the work target104. The plant may be one example of an object of an image. The image acquiring section110may acquire information in which image data of a plant, and positional information indicating a position where the image of the plant was captured are associated with each other.

The image acquiring section110may be an image-capturing device that captures an image, may be a data processing device that processes image data of an image captured by an image-capturing device, may be a storage device that stores image data of an image captured by an image-capturing device, or may be a communication interface that receives image data of an image captured by an image-capturing device. The above-mentioned image-capturing device may be an image-capturing device mounted on a travelling body that runs autonomously or flies autonomously.

In the present embodiment, the feature recognizing section120recognizes a feature of the work target104. For example, the feature recognizing section120recognizes a feature about at least one of (i) the type of the work target104of the work machine102, (ii) the number or density of the work target104, (iii) the shape of the work target104and (iv) the appearance of the work target104after work, based on the image data acquired by the image acquiring section110. The feature recognizing section120may transmit a feature of the work target104to the control parameter deciding section130. The feature recognizing section120may transmit, to the control parameter deciding section130and in association with each other, information indicating a feature of the work target104and positional information of a position where an image of the work target104was captured.

A feature about the shape of the work target104may be a feature about end portions of the work target. A feature about end portions of the work target104may be a feature about (i) at least one of the shape, hue and luster of end portions of the work target104, (ii) a matter attaching to end portions of the work target104, (iii) a leakage from end portions of the work target104, and the like. A feature about end portions of the work target104may be a difference between a feature of the end portions of the work target104and a feature of other portions of the work target104.

A feature about the appearance of the work target104may be a feature about (i) at least one of the shape, hue and luster of the work target104, (ii) a matter attaching to the work target104, (iii) a leakage from the work target104, and the like. A feature about the appearance of the work target104may be a feature about end portions of the above-mentioned work target. In a feature recognition process of the feature recognizing section120, a known image recognition technology may be utilized, or an image recognition technology to be newly developed in the future may be utilized. In an image recognition process, a machine-learning technique or deep-learning technique may be utilized.

In information processing of the feature recognizing section120, a known image recognition technology may be utilized, or an image recognition technology to be newly developed in the future may be utilized. In an image recognition process, an image recognition technology utilizing machine learning may be utilized. In information processing of the feature recognizing section120, machine learning may be utilized. The machine learning may be supervised learning, unsupervised learning, or reinforcement learning. In the learning process, learning techniques using a neural network technology, deep-learning technology or the like may be used.

In the present embodiment, the control parameter deciding section130decides at least either (i) a parameter for controlling travel of the work machine102or (ii) a parameter for controlling a work of the work machine102, based on a feature recognized by the feature recognizing section120. The parameter for controlling travel of the work machine102may be a parameter for controlling at least one of the travel speed, travel direction and travel route of the work machine102. The parameter for controlling a work of the work machine102may be a parameter for controlling at least one of the type of a work, strength of a work and schedule of a work of the work machine102.

The control parameter deciding section130may (i) decide a control parameter based on a predetermined determination criterion or (ii) decide a control parameter utilizing a learning model obtained through machine learning. The above-mentioned determination criterion may be information in which one or more factors (which may be sometimes referred to as factors to consider), conditions about respective factors to consider, and control parameters are associated with each other.

Based on what kind of determination criterion a control parameter is decided may be decided by a user or administrator, or may be decided through machine learning. Based on what kind of determination criterion a control parameter is decided may be decided for each type of the work target104or decided for each specification of the work machine102.

A threshold for deciding whether or not a decision target of a control parameter matches a condition about each factor to consider may be decided by a user or administrator, or may be decided through machine learning. The above-mentioned threshold may be decided for each type of the work target104or decided for each specification of the work machine102.

In information processing of the control parameter deciding section130, a known image recognition technology may be utilized, or an image recognition technology to be newly developed in the future may be utilized. In an image recognition process, an image recognition technology utilizing machine learning may be utilized. In information processing of the control parameter deciding section130, machine learning may be utilized. The machine learning may be supervised learning, unsupervised learning, or reinforcement learning. In the learning process, learning techniques using a neural network technology, deep-learning technology or the like may be used.

The control parameter deciding section130may transmit a decided control parameter to the work machine102. The control parameter deciding section130may transmit, to the work machine102and in association with each other, a control parameter and positional information indicating a position at which the control parameter is applied. A work area of the work machine102may be managed, while being classified into a plurality of subareas. In this case, the control parameter deciding section130may transmit, to the work machine102and in association with each other, identification information of a subarea and a control parameter to be applied at the subarea.

Upon receiving a control parameter from the control device100, the work machine102operates in accordance with the control parameter. Thereby, at least either the work quality or work efficiency of the work machine102can be improved. For example, if the work target104is lawn grasses, and the work machine102is a lawn mower, the travel speed of the work machine102can be adjusted, the work strength of the work machine102can be adjusted, and so on corresponding to the position of the work machine102and the density of lawn grasses at the position. For example the work strength can be increased or reduced by increasing or reducing the rotational speed of a cutting blade. Also, the work efficiency or work quality of the work machine102can be judged based on the state of lawn grasses after work, and based on a result of the judgment, the travel speed of the work machine102can be adjusted, the work strength of the work machine102can be adjusted, and so on.

[Specific Configuration of Each Section of Control device100] Each section of the control device100may be realized by hardware, software, or hardware and software. Each section of the control device100may be, at least partially, realized by a single server or a plurality of servers. Each section of the control device100may be, at least partially, realized on a virtual server or cloud system. Each section of the control device100may be, at least partially, realized by a personal computer or mobile terminal. Examples of the mobile terminal may include a mobile phone, a smartphone, a PDA, a tablet, a notebook computer or laptop computer, a wearable computer and the like. The control device100may store information utilizing a distributed ledger technology or distributed network such as a blockchain.

If at least some of components constituting the control device100are realized by software, the components realized by the software may be realized by activating, in an information processing device having a general configuration, software or a program stipulating operations about the components.

The above-mentioned information processing device may include: (i) a data processing device having processors such as a CPU or a GPU, a ROM, a RAM, a communication interface and the like, (ii) input devices such as a keyboard, touch panel, camera, microphone, various types of sensors or GPS receiver, (iii) output devices such as a display device, a speaker or a vibration device, and (iv) storage devices (including external storage devices) such as a memory or a HDD. In the above-mentioned information processing device, the above-mentioned data processing device or storage devices may store the above-mentioned software or program. Upon being executed by a processor, the above-mentioned software or program causes the above-mentioned information processing device to execute operations stipulated by the software or program. The above-mentioned software or program may be stored in a non-transitory computer-readable recording medium.

The above-mentioned software or program may be a program for causing a computer to function as the control device100. The above-mentioned software or program may be a control program for controlling the work machine102. The control program may be a control program for causing a computer to execute: an image acquisition procedure of acquiring an image of a work target of the work machine; a feature recognition procedure of recognizing a feature about at least one of (i) a type of the work target, (ii) a number or density of the work target, (iii) a shape of the work target and (iv) an appearance of the work target after work, based on the image acquired in the image acquisition procedure; and a control parameter decision procedure of deciding at least either (i) a parameter for controlling travel of the work machine or (ii) a parameter for controlling work of the work machine, based on the feature recognized in the feature recognition procedure. The above-mentioned computer may be a computer mounted on the work machine102or may be a computer that controls the work machine102via a communication network.

[Outline of Garden Managing System200]FIG. 2schematically shows one example of the system configuration of a garden managing system200. The garden managing system200manages a garden30. For example, the garden managing system200manages the vegetation state of the garden30. The garden managing system200may manage growth of plants cultivated in the garden30. In the present embodiment, the garden managing system200includes a managing server210, a monitoring camera220, a lawn mower230and a sprinkling device240. The sprinkling device240for example has a sprinkler242and a water-supply control section244.

The garden managing system200may be one example of a water-supply system, information processing system or control device. The managing server210may be one example of a water-supply system, information processing device or control device. The monitoring camera220may be one example of an image-capturing section or image acquiring section. The lawn mower230may be one example of a work machine, water-supply system, information processing system, control device, image-capturing section or image acquiring section. The sprinkling device240may be one example of a water-supply section. The sprinkler242may be one example of a water-supply section. The water-supply control section244may be one example of a water-supply section.

In the present embodiment, each section of the garden managing system200can transmit and receive information to and from each other via a communication network10. Each section of the garden managing system200may transmit and receive information to and from a user terminal20via the communication network10. In the present embodiment, the monitoring camera220, the lawn mower230and the sprinkling device240are disposed inside or around the garden30.

In the present embodiment, the communication network10may be a wired communication transmission path, a wireless communication transmission path, or a combination of a wireless communication transmission path and a wired communication transmission path. The communication network10may include a wireless packet communication network, the Internet, a P2P network, a private line, a VPN, an electric power line communication line and the like. The communication network10: (i) may include a mobile communication network such as a mobile phone line network; and (ii) may include a wireless communication network such as a wireless MAN (for example, WiMAX (registered trademark)), a wireless LAN (for example, WiFi (registered trademark)), Bluetooth (registered trademark), Zigbee (registered trademark) or NFC (Near Field Communication).

In the present embodiment, the user terminal20is a communication terminal that a user of the garden30, the garden managing system200or the lawn mower230utilizes, and details thereof are not particularly limited. Examples of the user terminal20may include a personal computer, mobile terminal and the like. Examples of the mobile terminal may include a mobile phone, a smartphone, a PDA, a tablet, a notebook computer or laptop computer, a wearable computer and the like.

In the present embodiment, the managing server210manages the monitoring camera220, the lawn mower230and the sprinkling device240. For example, the managing server210collects information about the garden30from at least one of the monitoring camera220, the lawn mower230and the sprinkling device240. The managing server210may generate information indicating a geographical distribution (which may be sometimes referred to as map information) of features of the garden30. The managing server210may manage the state of at least one of the monitoring camera220, lawn mower230and sprinkling device240. The managing server210may control operation of at least one of the monitoring camera220, the lawn mower230and the sprinkling device240.

In the present embodiment, the monitoring camera220monitors the garden30. For example, the monitoring camera220captures an image of a work area of the lawn mower230. The monitoring camera220may capture an image of the lawn mower230while it is working. The monitoring camera220may capture an image of a work target of the lawn mower230. The monitoring camera220may capture an image of lawn grasses present around the lawn mower230while it is working. The monitoring camera220may capture an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230. The monitoring camera220may capture an image of lawn grasses present in a region that the lawn mower230passed through. Lawn grasses may be one example of a work target of the lawn mower230. Lawn grasses may be one example of an object of image data.

In the present embodiment, the lawn mower230has an autonomous travel function. The lawn mower230cuts lawn grasses while it is running autonomously in the garden30. To cut lawn grasses (which may be sometimes referred to as lawn mowing) may be one example of a work of the lawn mower230, and lawn grasses may be one example of a work target of the lawn mower230. Lawn grasses may be one example of plants growing in the garden30.

In one embodiment, the lawn mower230has a communication function. The lawn mower230for example transmits and receives information to and from at least one of the managing server210, the monitoring camera220and the sprinkling device240via the communication network10. For example, the lawn mower230travels in the garden30, performs lawn mowing, and so on based on an instruction from the managing server210. Lawn mower230may collect information about the garden30while it is travelling or working, and transmit the information to the managing server210. The information about the garden30may be information about an ecological system in the garden30. The information about the garden30may be information about vegetation of the garden30. The information about the garden30may be information about the state of lawn grasses.

In another embodiment, the lawn mower230may have an image-capturing device mounted thereon. The lawn mower230may capture an image of lawn grasses present around the lawn mower230using the image-capturing device. The lawn mower230may capture an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230. Thereby, close-up image-capturing of lawn grasses that the lawn mower230is about to cut at the moment becomes possible. The lawn mower230may capture an image of lawn grasses present in a region that the lawn mower230passed through. Thereby, close-up image-capturing of lawn grasses cut by the lawn mower230becomes possible.

The lawn mower230may execute various types of judgment processes based on an image of lawn grasses captured by an image-capturing device. For example, the lawn mower230judges the state of the lawn mower230based on an image of lawn grasses captured by the image-capturing device. The lawn mower230may control at least either travel or work of the lawn mower230based on an image of lawn grasses captured by the image-capturing device. The lawn mower230may judge whether or not water has been supplied to the garden30based on an image of lawn grasses captured by the image-capturing device.

In the present embodiment, the sprinkling device240supplies water to plants growing in the garden30. The sprinkling device240may supply water to plants based on a decision by the managing server210or lawn mower230. The sprinkling device240may be installed in the garden30or may be mounted on the lawn mower230.

In the present embodiment, the sprinkler242sprinkles water. The sprinkler242may sprinkle water containing fertilizer components. In the present embodiment, the water-supply control section244controls the amount of water to be supplied to the sprinkler242. For example, the water-supply control section244receives, from the managing server210or lawn mower230, an instruction about water-supply. The water-supply control section244controls the start or stop of water-supply based on the above-mentioned instruction. The water-supply control section244may adjust the amount of water-supply based on the above-mentioned instruction.

[Specific Configuration of Each Section of Garden Managing System200] Each section of the garden managing system200may be realized by hardware, software, or hardware and software. Each section of the garden managing system200may be, at least partially, realized by a single server or a plurality of servers. Each section of the garden managing system200may be, at least partially, realized on a virtual server or cloud system. Each section of the garden managing system200may be, at least partially, realized by a personal computer or mobile terminal. Examples of the mobile terminal may include a mobile phone, a smartphone, a PDA, a tablet, a notebook computer or laptop computer, a wearable computer and the like. The garden managing system200may store information utilizing a distributed ledger technology or distributed network such as a blockchain.

If at least some of components constituting the garden managing system200are realized by software, the components realized by the software may be realized by activating, in an information processing device having a general configuration, software or a program stipulating operations about the components.

The above-mentioned information processing device may include: (i) a data processing device having processors such as a CPU or a GPU, a ROM, a RAM, a communication interface and the like, (ii) input devices such as a keyboard, touch panel, camera, microphone, various types of sensors or GPS receiver, (iii) output devices such as a display device, a speaker or a vibration device, and (iv) storage devices (including external storage devices) such as a memory or a HDD. In the above-mentioned information processing device, the above-mentioned data processing device or storage devices may store the above-mentioned software or program. Upon being executed by a processor, the above-mentioned software or program causes the above-mentioned information processing device to execute operations stipulated by the software or program. The above-mentioned software or program may be stored in a non-transitory computer-readable recording medium.

FIG. 3schematically shows one example of the internal configuration of the managing server210. In the present embodiment, the managing server210includes a receiving section310, an image analyzing section320, an information storage section322, an instruction generating section330and a transmitting section340. Each section of the managing server210may transmit and receive information to and from each other, in directions not limited by arrows in the figure.

The receiving section310may be one example of an image acquiring section. The image analyzing section320may be one example of an information processing device or control device. The image analyzing section320may be one example of an image acquiring section, positional information acquiring section, judging section, form recognizing section, deciding section or control parameter deciding section. The instruction generating section330may be one example of a control section, travel control section, work control section, deciding section or control parameter deciding section. The transmitting section340may be one example of a notifying section.

In the present embodiment, the receiving section310acquires information transmitted by at least one of the user terminal20, the monitoring camera220, the lawn mower230and the sprinkling device240. For example, the receiving section310receives image data from at least either the monitoring camera220or the lawn mower230. Thereby, the managing server210can acquire data of an image captured by the monitoring camera or data of an image captured by an image-capturing device mounted on the lawn mower230. The above-mentioned image may be a still image or moving image. The above-mentioned image data may be image data of a work target (for example, plants such as lawn grasses or weeds) of the lawn mower230. The receiving section310may receive positional information associated with the above-mentioned image data. The receiving section310transmits the above-mentioned image data to the image analyzing section320. If positional information is associated with the above-mentioned image data, the receiving section310may transmit the image data and the positional information to the image analyzing section320.

In the present embodiment, the image analyzing section320analyzes image data. The image analyzing section320may analyze image data utilizing an image recognition technique. The above-mentioned image recognition technique may be a known image recognition technique or may be an image recognition technique to be newly developed in the future. In the above-mentioned image recognition technique, a machine-learning technique or deep-learning technique may be utilized.

For example, the image analyzing section320acquires, from the receiving section310, image data of an image to be a target of analysis. The image analyzing section320analyzes the above-mentioned image data, and generates at least either (i) various types of parameters about the lawn mower230or garden30, or (ii) various types of map information about the garden30. Examples of the map information may include a geographical distribution of various types of parameters in the garden30, a vegetation distribution in the garden30, and the like.

Examples of the various types of parameters may include (i) a parameter indicating the state of the lawn mower230(which may be sometimes referred to as a state parameter), (ii) a parameter for controlling the lawn mower230(which may be sometimes referred to as a control parameter), (iii) a parameter about whether water-supply to plants in the garden30is necessary or not, or the level of water content in a medium of the plants (which may be sometimes referred to as a water-supply parameter), and the like. The state parameter may be a parameter indicating the state of a blade to cut lawn grasses. The blade may be one example of a cutting section.

The parameter about the level of water content in a medium may be the water content in the medium. Examples of the parameter about whether water-supply is necessary or not may include information indicating that water-supply is necessary, information indicating that water-supply is unnecessary, information indicating the amount of water that should be supplied to a medium (which may be sometimes referred to as the amount of water-supply), and the like. The amount of water-supply may be a water supply amount per time or a water supply amount per area, volume or weight of a medium. If the amount of water-supply is 0 or if the amount of water-supply is smaller than a predetermined value, information indicating that water-supply is unnecessary may be generated. If the amount of water-supply exceeds the predetermined value, information indicating that water-supply is necessary may be generated.

The image analyzing section320may output a result of analysis of image data to the instruction generating section330or transmitting section340. In one embodiment, the image analyzing section320transmits at least one of the state parameter, the control parameter and the water-supply parameter to the instruction generating section330. The image analyzing section320transmits the above-mentioned parameter to the instruction generating section330in a map information format. In another embodiment, if at least one of the state parameter, the control parameter and the water-supply parameter satisfies a predetermined condition, the image analyzing section320generates a message to notify the user terminal20of such a fact. The image analyzing section320outputs the above-mentioned message to the transmitting section340.

In the present embodiment, the information storage section322stores various types of information. The information storage section322may store information to be utilized in image analysis processing at the image analyzing section320. The information storage section322may store a result of analysis by the image analyzing section320. For example, the information storage section322stores learning data for machine learning of the image analyzing section320. Also, the information storage section322stores a learning model constructed through machine learning of the image analyzing section320. The information storage section322may store image data acquired by the receiving section310, various types of parameters and various types of maps generated by the image analyzing section320, and the like.

In the present embodiment, the instruction generating section330generates an instruction to at least either the lawn mower230or the sprinkling device240. For example, the instruction generating section330receives information indicating a result of analysis by the image analyzing section320from the image analyzing section320, and generates an instruction to at least either the lawn mower230or the sprinkling device240based on the result of analysis. The instruction generating section330may generate an instruction based on at least one parameter, may generate an instruction based on at least one piece of map information, and may generate an instruction based on at least one parameter and at least one piece of map information.

According to one embodiment, if a state parameter included in the above-mentioned result of analysis satisfies a predetermined condition, the instruction generating section330generates an instruction for displaying, on a user interface of the lawn mower230, a message corresponding to the above-mentioned condition. For example, if the state parameter indicates that maintenance of or a check on a blade is necessary, the instruction generating section330generates an instruction for displaying, on the user interface of the lawn mower230, a message indicating that maintenance of or a check on the blade is recommended.

According to another embodiment, if the above-mentioned result of analysis includes a control parameter or if a control parameter included in the above-mentioned result of analysis satisfies a predetermined condition, the instruction generating section330generates an instruction for controlling the lawn mower230. According to still another embodiment, if a water-supply parameter included in the above-mentioned result of analysis satisfies a predetermined condition, the instruction generating section330generates an instruction for controlling the sprinkling device240.

In the present embodiment, the transmitting section340transmits information to at least one of the user terminal20, the lawn mower230and the sprinkling device240. According to one embodiment, the transmitting section340transmits a message generated by the image analyzing section320to at least one of the user terminal20, the lawn mower230and the sprinkling device240. According to another embodiment, the transmitting section340transmits an instruction generated by the instruction generating section330to at least one of the user terminal20, the lawn mower230and the sprinkling device240. Thereby, a result of analysis by the image analyzing section320can be notified to a user, transmitted to the lawn mower230or sprinkling device240, and so on.

[Configuration of Image Analyzing section320]FIG. 4schematically shows one example of the internal configuration of the image analyzing section320. The image analyzing section320includes a learning processing section410, a position calculating section420, a lawn recognizing section430and a judgment processing section440. The judgment processing section440for example has a lawn state judging section442, a blade state judging section444, a parameter generating section446and a map generating section448. Each section of the image analyzing section320may transmit and receive information to and from each other, in directions not limited by arrows in the figure.

The position calculating section420may be one example of a positional information acquiring section. The lawn recognizing section430may be one example of a form recognizing section. The judgment processing section440may be one example of an information processing device or control device. The judgment processing section440may be one example of an image acquiring section, positional information acquiring section, specification information acquiring section, judging section, form recognizing section, deciding section or control parameter deciding section. The lawn state judging section442may be one example of a feature recognizing section. The blade state judging section444may be one example of a judging section. The parameter generating section446may be one example of a deciding section or control parameter deciding section.

In the present embodiment, through machine learning, the learning processing section410constructs various types of learning models to be utilized in the image analyzing section320. The learning processing section410may construct a learning model utilizing a deep-learning technique. For example, the learning processing section410constructs a learning model utilizing learning data stored in the information storage section322. The learning processing section410may store the constructed learning model in the information storage section322. Thereby, the lawn recognizing section430or judgment processing section440can execute an image recognition process utilizing the learning model constructed by the learning processing section410.

The learning processing section410may separate a work area of the lawn mower230into a plurality of subareas, and construct various types of learning models for the respective subareas. For example, the learning processing section410constructs a learning model of each subarea utilizing image data of lawn grasses captured in each subarea. The learning processing section410may construct a learning model of each subarea utilizing supervisor data prepared for each subarea. The learning processing section410may construct a learning model of each subarea utilizing a feature about the shapes of lawn grasses extracted by the lawn recognizing section430from image data of lawn grasses an image of which has been captured in each subarea.

In the present embodiment, the position calculating section420acquires, from the receiving section310, positional information indicating a position where an image to be a target of analysis by the image analyzing section320was captured. Based on the above-mentioned positional information, the position calculating section420calculates a position of an object of the above-mentioned image. For example, the position calculating section420calculates a position of lawn grasses in a work area based on positional information associated with image data of an image capturing the lawn grasses. Examples of the above-mentioned positional information may include positional information indicating a position of the monitoring camera220that captured the above-mentioned image, positional information indicating a position of the lawn mower230at the time when it captured the above-mentioned image, and the like. The position calculating section420may transmit positional information indicating a position of an object of an image to the lawn state judging section442.

In one embodiment, the position calculating section420calculates a positional relationship between an image-capturing device that captured an image to be a target of analysis and an object in the image. The position calculating section420calculates a position of an object based on a position of the image-capturing device and the above-mentioned positional relationship. For example, the position calculating section420calculates a position of an object in an image based on positional information associated with image data, an image-capturing condition of the image, and the geometrical arrangement of an image-capturing device in the lawn mower230or garden30. Examples of the image-capturing condition may include (i) an angle of view, (ii) at least one of a pan angle, a tilt angle and a roll angle, (iii) a zoom factor and the like. Thereby, a position of an object can be calculated highly precisely.

In another embodiment, the position calculating section420judges whether or not an image to be a target of analysis includes an object or region the position and size of which are known. If an image to be a target of analysis includes an object or region the position and size of which are known, together with an object to be a target of judgment by the judgment processing section440, the position calculating section420calculates a positional relationship between the above-mentioned object and the above-mentioned object or region. The position calculating section420calculates a position of an object based on the position of the above-mentioned object or region and the above-mentioned positional relationship. Thereby, a position of the object can be calculated highly precisely.

In the present embodiment, the lawn recognizing section430acquires, from the receiving section310, image data of an image to be a target of analysis by the image analyzing section320. The lawn recognizing section430may acquire, from the receiving section310, positional information indicating a position where the above-mentioned image was captured. The lawn recognizing section430may acquire, from the position calculating section420, positional information indicating a position of an object in the above-mentioned image. In the present embodiment, utilizing an image recognition technique, the lawn recognizing section430determines whether or not an image to be a target of analysis includes a target of judgment by the judgment processing section440. The target of judgment may be an object in an image or a background in an image. There may be one or more targets of judgment.

If an image to be a target of analysis includes a target of judgment by the judgment processing section440, the lawn recognizing section430recognizes the target of judgment by the judgment processing section440, and extracts, from the image to be the target of analysis, at least one of (i) an image of the target of judgment, (ii) an outline or shape of the target of judgment and (iii) a feature of the target of judgment. The lawn recognizing section430transmits, to the lawn state judging section442, information about an image, outline, shape, feature and the like of the target of judgment by the judgment processing section440. The lawn recognizing section430may transmit, to the lawn state judging section442and in association with each other, (i) information about an image, outline, shape, feature and the like of the target of judgment by the judgment processing section440and (ii) positional information indicating a position where the image to be the target of analysis was captured or a position of an object in the image.

The lawn recognizing section430may store, in the information storage section322, image data acquired from the receiving section310. The lawn recognizing section430may store, in the information storage section322, information about an image, outline, shape, feature and the like of the target of judgment by the judgment processing section440. The lawn recognizing section430may store, in the information storage section322and in association with each other, the above-mentioned data image or above-mentioned information, and positional information indicating a position where the image was captured or a position of an object in the image. The lawn recognizing section430may store, in the information storage section322, the above-mentioned data image or above-mentioned information as learning data of the learning processing section410.

In the present embodiment, the lawn recognizing section430recognizes the form of lawn grasses present in an image (which may be sometimes referred to as lawn grasses included in an image). The lawn recognizing section430recognizes the form of at least one lawn grass among one or more lawn grasses present in an image. The lawn grasses may be one example of a target of judgment by the judgment processing section440. For example, the lawn recognizing section430analyzes an image acquired from the receiving section310, and recognizes at least either (i) the shapes of lawn grasses or (ii) end portions of lawn grasses that are included in the image.

As explained usingFIG. 1, in one embodiment, the lawn recognizing section430recognizes the respective shapes of a plurality of lawn grasses. The lawn recognizing section430may treat the entire image as a target, and recognize the respective shapes of a plurality of lawn grasses included in the image. The lawn recognizing section430may treat a partial region of an image as a target, and recognize the respective shapes of one or more lawn grasses included in the region. Examples of the above-mentioned region may include a focused region, a region that satisfies a condition about colors and the like. The lawn recognizing section430may recognize end portions of recognized lawn grasses based on the shapes of the lawn grasses.

In another embodiment, the lawn recognizing section430extracts, from within an image, a region that is likely to include many end portions of a plurality of lawn grasses. For example, the lawn recognizing section430extracts, as a region that is likely to include many end portions of a plurality of lawn grasses, one of a plurality of images that are obtained by dividing, in the vertical direction, an image laterally capturing the plurality of lawn grasses. Thereby, the lawn recognizing section430can recognize end portions of a plurality of lawn grasses without recognizing the respective shapes of the lawn grasses. As a result, depending on images, time required to recognize end portions of lawn grasses can be shortened significantly.

The lawn recognizing section430(i) may recognize the form of lawn grasses based on a predetermined determination criterion or algorithm, or (ii) may recognize the form of lawn grasses utilizing a learning model obtained through machine learning. The above-mentioned determination criterion may be a general criterion for extracting an outline of an object, or information indicating a condition about each among one or more factors to consider to be used for extracting the shapes or end portions of lawn grasses.

Based on what kind of determination criterion the form of lawn grasses is judged may be decided by a user or administrator, or may be decided through machine learning. Based on what kind of determination criterion the form of lawn grasses is judged may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230. A threshold about the above-mentioned determination criterion may be decided by a user or administrator, or may be decided through machine learning. The above-mentioned threshold may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230.

For example, the lawn recognizing section430recognizes the form of lawn grasses utilizing a learning model constructed by the learning processing section410. The lawn recognizing section430may acquire specification information of the lawn mower230, and select a learning model matching a specification of the lawn mower230. The lawn recognizing section430may select a learning model matching the type of lawn grasses. For example, the lawn recognizing section430acquires map information about vegetation of the garden30, and estimates the type of lawn grasses captured. The lawn recognizing section430may estimate the type of lawn grasses captured, based on positional information indicating a position where an image to be a target of analysis was captured or a position of an object in the image, and the above-mentioned map information.

The lawn recognizing section430may execute the above-mentioned process for each piece among a plurality of pieces of image data received by the receiving section310. The lawn recognizing section430may separate a work area of the lawn mower230into a plurality of subareas, and execute the above-mentioned recognition process for each subarea. The lawn recognizing section430may execute the above-mentioned recognition process for a predetermined number of pieces of image data for each subarea.

[Outline of Judgment Processing section440] In the present embodiment, the judgment processing section440executes various types of judgment processes. The judgment processing section440may execute the judgment processes utilizing information indicating the form of lawn grasses recognized by the lawn recognizing section430. Thereby, judgment precision can be improved. In one embodiment, the judgment processing section440executes the judgment processes based on a predetermined determination criterion. In another embodiment, the judgment processing section440executes the judgment processes utilizing a learning model constructed by the learning processing section410. The judgment processing section440may transmit a result of judgment to the instruction generating section330or transmitting section340.

According to one embodiment, the judgment processing section440first judges the state of lawn grasses. Next, the judgment processing section440judges the state of the lawn mower230based on a result of judgment about the lawn grasses. The judgment processing section440may generate a state parameter indicating the state of the lawn mower230. According to another embodiment, the judgment processing section440first judges the state of lawn grasses. Next, the judgment processing section440generates a control parameter based on a result of judgment about lawn grasses. According to still another embodiment, the judgment processing section440first judges the state of lawn grasses. Next, the judgment processing section440generates a water-supply parameter based on a result of judgment about lawn grasses. According to still another embodiment, the judgment processing section440generates map information about various types of parameters. The judgment processing section440may generate map information about vegetation in the garden30.

[Outline of Lawn State Judging section442] In the present embodiment, the lawn state judging section442judges the state of lawn grasses based on image data of the lawn grasses acquired by the receiving section310. For example, the lawn state judging section442acquires, from the lawn recognizing section430, information about the form of lawn grasses recognized by the lawn recognizing section430. The lawn state judging section442judges the state of lawn grasses based on the information about the form of the lawn grasses.

The lawn state judging section442(i) may judge the state of lawn grasses based on a predetermined determination criterion, or (ii) may judge the state of lawn grasses utilizing a learning model obtained through machine learning. The above-mentioned determination criterion may be information in which one or more factors to consider, conditions about respective factors to consider and the state of lawn grasses are associated with each other. Examples of the factors to consider for judging the state of lawn grasses may include (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, (iv) the appearance of end portions of lawn grasses, (v) a specification of the lawn mower230, and the like.

Based on what kind of determination criterion the state of lawn grasses is judged may be decided by a user or administrator, or may be decided through machine learning. Based on what kind of determination criterion the state of lawn grasses is judged may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230. A threshold for deciding whether or not a target of judgment matches a condition about each factor to consider may be decided by a user or administrator, or may be decided through machine learning. The above-mentioned threshold may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230.

The state of lawn grasses may be evaluated by consecutive numerical values, or may be evaluated stepwise using a plurality of steps. Examples of the state of lawn grasses may include the cut state of lawn grasses, the growth state of lawn grasses and the like. The cut state of lawn grasses may be the state of cut surfaces. Examples of the growth state of lawn grasses may include the type of lawn grasses, the density of lawn grasses, whether the growth is good or bad, sufficiency or insufficiency of lawn mowing, sufficiency or insufficiency of water, sufficiency or insufficiency of nutriment and the like. Examples of a parameter indicating sufficiency or insufficiency of water may include at least one of (a) the level of water content in a medium of lawn grasses, (b) whether water-supply to lawn grasses is necessary or not and (c) the amount of water-supply to lawn grasses.

The lawn state judging section442transmits a result of judgment about the state of lawn grasses for example to at least either the blade state judging section444or the parameter generating section446. The lawn state judging section442may transmit, to at least either the blade state judging section444or the parameter generating section446and in association with each other, a result of judgment about the state of lawn grasses and the positional information of the lawn grasses.

In one embodiment, the lawn state judging section442receives an input of information indicating the form of lawn grasses, and outputs the information indicating the state of the lawn grasses. In another embodiment, the lawn state judging section442recognizes a feature of lawn grasses based on the form of the lawn grasses, and judges the state of the lawn grasses based on the feature. In still another embodiment, the lawn state judging section442recognizes a feature of end portions of lawn grasses based on the form of the lawn grasses, and judges the state of the lawn grasses based on the feature.

The lawn state judging section442(i) may recognize a feature of lawn grasses or a feature of end portions of the lawn grasses based on a predetermined determination criterion, or (ii) may recognize a feature of lawn grasses or a feature of end portions of the lawn grasses utilizing a learning model obtained through machine learning. The above-mentioned determination criterion may be information in which one or more factors to consider, a condition about respective factors to consider and features of lawn grasses or particular features of end portions of the lawn grasses are associated with each other. Examples of the factors to consider for judging a feature of lawn grasses or a feature of end portions of the lawn grasses may include (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, (iv) the appearance of end portions of lawn grasses, (v) a specification of the lawn mower230, and the like.

Based on what kind of determination criterion a feature of lawn grasses or a feature of end portions of the lawn grasses is judged may be decided by a user or administrator, or may be decided through machine learning. Based on what kind of determination criterion a feature of lawn grasses or a feature of end portions of the lawn grasses is judged may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230. A threshold for deciding whether or not a target of judgment matches a condition about each factor to consider may be decided by a user or administrator, or may be decided through machine learning. The above-mentioned threshold may be decided for each type of lawn grasses. The above-mentioned threshold may be decided for each specification of the lawn mower230.

Examples of features of lawn grasses may include at least one of (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, and (iv) inclination of lawn grasses to a medium. Examples of features of end portions of lawn grasses may include (i) at least one of the shape, hue and luster of end portions of lawn grasses, (ii) a difference between end portions of lawn grasses and another portion of the lawn grasses, and the like. If lawn grasses are cut, a feature of end portions of the lawn grasses may be a feature of cut portions. Examples of features of cut portions may include (i) at least one of the shape, hue and luster of cut surfaces, (ii) presence or absence, or degree of burrs, (iii) presence or absence, or degree of liquid droplets, and the like.

The lawn state judging section442may analyze image data of lawn grasses present in the forward direction in terms of a course of the lawn mower230, and recognizes a feature of the lawn grasses. The lawn state judging section442may analyze image data of lawn grasses present in a region that the lawn mower230passed through, and recognize a feature of the lawn grasses. Also, the lawn state judging section442may analyze image data of lawn grasses present in a region that the lawn mower230passed through, and recognize a feature of cut portions.

In still another embodiment, the lawn state judging section442may acquire, from the lawn mower230, information about an electric current value of a motor to rotate a blade. The lawn state judging section442may recognize a feature of lawn grasses based on an electric current value of a motor to rotate a blade. If the lawn mower230cuts a hard material, an electric current value of a motor to rotate a blade increases. Also, the hardness of lawn grasses varies depending on the types of lawn grasses. Because of this, an electric current value of a motor can be a factor to consider for judging the type of lawn grasses. The lawn state judging section442may recognize a feature of lawn grasses based on image data of the lawn grasses present in the forward direction in terms of a course of the lawn mower230and an electric current value of a motor to rotate a blade. For example, the lawn state judging section442decides the density of lawn grasses based on a result of image analysis, and decides the hardness of the lawn grasses based on the density of the lawn grasses and an electric current value of a motor.

[Outline of Process at Lawn State Judging section442] For example, the lawn state judging section442first receives, from the lawn recognizing section430, information indicating the form of lawn grasses recognized by the lawn recognizing section430. The lawn state judging section442may receive, from the lawn recognizing section430, image data of an image to be a target of analysis. The lawn state judging section442may acquire, from the position calculating section420or lawn recognizing section430, positional information indicating a position where an image of lawn grasses was captured or a position of lawn grasses (which may be sometimes referred to as lawn grass positional information).

Next, the lawn state judging section442recognizes a feature of lawn grasses or a feature of end portions of the lawn grasses based on information indicating the form of the lawn grasses. A feature of lawn grasses that should be recognized may be any feature as long as it is utilized in a judgment process at the lawn state judging section442, and specific details are not particularly limited. The lawn state judging section442for example recognizes a feature of lawn grasses or a feature of end portions of the lawn grasses utilizing a learning model constructed by the learning processing section410.

In one embodiment, the lawn state judging section442recognizes a feature of lawn grasses or a feature of end portions of the lawn grasses utilizing information indicating the shapes of the lawn grasses. For example, utilizing information indicating the shapes of lawn grasses, the lawn state judging section442extracts an image of each lawn grass from an image capturing a plurality of lawn grasses. Then, the lawn state judging section442recognizes, about at least one lawn grass: presence or absence of a shape that is unique to each type of lawn grasses; thickness; curvature; inclination angle to a medium; shape of an end portion; color of an end portion; luster of an end portion; whether or not there is variation in colors between end portions and other portions, and details of the variation; and the like.

In another embodiment, the lawn state judging section442recognizes a feature of end portions of lawn grasses utilizing information indicating the end portions of the lawn grasses. For example, the lawn state judging section442acquires, from the lawn recognizing section430and as information indicating end portions of lawn grasses, an image that is likely to include many end portions of lawn grasses. The lawn state judging section442may recognize a feature of the image as a feature of end portions of lawn grasses. The lawn state judging section442may acquire, from the lawn recognizing section430and as an reference image, an image that is likely to include many root portions or middle portions of lawn grasses. The lawn state judging section442may recognize, as a feature of end portions of lawn grasses, a difference between a feature of an image that is likely to include many end portions of lawn grasses and a feature of the reference image.

Next, the lawn state judging section442judges the state of lawn grasses. For example, the lawn state judging section442judges the cut state of lawn grasses based on a feature of end portions of the lawn grasses. The lawn state judging section442may judge the growth state of lawn grasses based on at least either a feature of the lawn grasses or a feature of end portions of the lawn grasses. Judgment processes about at least either the cut state or growth state of lawn grasses may be executed utilizing a learning model constructed by the learning processing section410.

Next, the lawn state judging section442outputs a result of judgment about the state of lawn grasses. The lawn state judging section442may output, in association with each other, a result of judgment about the state of lawn grasses and positional information of the lawn grasses. In one embodiment, the lawn state judging section442transmits, to the blade state judging section444, a result of judgment about the cut state of lawn grasses. Thereby, the blade state judging section444can judge the state of a blade utilizing a result of judgment about the cut state of lawn grasses.

In another embodiment, the lawn state judging section442transmits, to the parameter generating section446, a result of judgment about the growth state of lawn grasses. Thereby, the parameter generating section446can generate at least either a control parameter or a water-supply parameter utilizing a result of judgment about the growth state of lawn grasses. As mentioned below, the parameter generating section446may generate a control parameter utilizing a result of judgment about the state of a blade.

The lawn state judging section442may execute the above-mentioned process for each piece among a plurality of pieces of image data received by the receiving section310. The lawn state judging section442may separate a work area of the lawn mower230into a plurality of subareas, and execute the above-mentioned judgment process for each subarea. The lawn state judging section442may execute the above-mentioned judgment process for a predetermined number of pieces of image data for each subarea.

[Outline of Blade State Judging section444] In the present embodiment, the blade state judging section444judges the state of a blade of the lawn mower230.

Examples of the state of a blade may include (i) the cutting performance of the blade, (ii) whether maintenance of or a check on the blade is necessary or not, (iii) recommended timing of maintenance of or a check on the blade, or time left until the timing, and the like. The state of the blade may be evaluated by consecutive numerical values, or may be evaluated stepwise using a plurality of steps.

In the present embodiment, the blade state judging section444judges the state of the blade of the lawn mower230based on image data acquired by the receiving section310. For example, the blade state judging section444receives a result of judgment about the cut state of lawn grasses from the lawn state judging section442, and judges the state of the blade of the lawn mower230based on the result of judgment. The blade state judging section444transmits the result of judgment about the state of the blade for example to at least either the parameter generating section446or the map generating section448. The blade state judging section444may transmit, in association with each other, the result of judgment about the state of the blade and positional information of the lawn grasses utilized for the judgment to at least either the parameter generating section446or the map generating section448.

The blade state judging section444(i) may judge the state of the blade based on a predetermined determination criterion, or (ii) may judge the state of the blade utilizing a learning model obtained through machine learning. The above-mentioned determination criterion may be information in which one or more factors to consider, conditions about respective factors to consider and the state of lawn grasses are associated with each other. Examples of the factors to consider for judging the state of a blade may include (i) the type of lawn grasses, (ii) at least one of the shape, hue and luster of cut surfaces of lawn grasses, (iii) a specification of the blade and the like.

Based on what kind of determination criterion the state of the blade is judged may be decided by a user or administrator, or may be decided through machine learning. Based on what kind of determination criterion the state of the blade is judged may be decided for each type of lawn grasses, or may be decided for each specification of the lawn mower230. A threshold for deciding whether or not a target of judgment matches a condition about each factor to consider may be decided by a user or administrator, or may be decided through machine learning. The above-mentioned threshold may be decided for each type of lawn grasses. The above-mentioned threshold may be decided for each specification of the blade.

For example, the blade state judging section444acquires information about a specification of the blade, and the blade state judging section444judges the state of the blade of the lawn mower230based on a result of judgment about the cut state of lawn grasses and the information about the specification of the blade. The information about the specification of a blade is stored for example in a storage device of the lawn mower230, the information storage section322or the like. Examples of a specification of the blade may include the type of the blade, the quality of the material of the blade, the size of the blade and the like. Examples of the type of a blade may include a chip saw, a nylon cutter, a metal blade and the like.

According to one embodiment, the blade state judging section444judges whether maintenance of or a check on the blade is necessary or not based on a result of judgment about the cut state of lawn grasses. According to another embodiment, it judges whether maintenance of or a check on the blade is necessary or not based on a result of judgment about the cut state of lawn grasses and information about a specification of the blade. According to still another embodiment, the blade state judging section444may judge whether maintenance of or a check on the blade is necessary or not based on a result of judgment about the state of the blade.

In the present embodiment explained, the blade state judging section444receives a result of judgment about the cut state of lawn grasses from the lawn state judging section442. However, the blade state judging section444is not limited to the present embodiment. In another embodiment, the blade state judging section444may receive information indicating a feature of cut portions of lawn grasses from the lawn state judging section442. The feature of cut portions of the lawn grasses is obtained for example by the lawn state judging section442extracting it from image data. In this case, the blade state judging section444may judge the state of a blade based on a feature of cut portions of lawn grasses.

[Outline of Parameter Generating section446] In the present embodiment, the parameter generating section446generates various types of parameters. The parameter generating section446generates at least one of a state parameter, a control parameter and a water-supply parameter based on a result of judgment by at least either the lawn state judging section442or the blade state judging section444. For example, the parameter generating section446transmits a generated parameter to the map generating section448. The parameter generating section446may output the generated parameter to the instruction generating section330or transmitting section340. The parameter generating section446may output, in association with each other, the parameter and positional information indicating a position at which the parameter is applied.

[State Parameter] In the present embodiment, for example, the parameter generating section446receives a result of judgment about the state of the blade from the blade state judging section444. Then, the parameter generating section446generates a state parameter indicating the state of the blade based on a result of judgment about the state of the blade. According to the present embodiment, the state parameter is generated for example based on a feature about at least one of (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, and (iv) the appearance of cut lawn grasses. The appearance of cut lawn grasses may be one example of a feature of cut portions of lawn grasses. The state parameter may be generated based on a specification of the lawn mower230, an electric current value of a motor to rotate the blade and the like.

[Control Parameter] In one embodiment, the parameter generating section446receives a result of judgment about the growth state of lawn grasses from the lawn state judging section442. Then, the parameter generating section446generates a control parameter based on a result of judgment about the growth state of lawn grasses. In another embodiment, the parameter generating section446receives a result of judgment about the cut state of lawn grasses from the lawn state judging section442. Then, the parameter generating section446generates a control parameter based on a result of judgment about the cut state of lawn grasses. According to these embodiments, the control parameter is generated for example based on a feature about at least one of (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, and (iv) the appearance of cut lawn grasses. The appearance of cut lawn grasses may be one example of a feature of cut portions of lawn grasses. The control parameter may be generated based on a specification of the lawn mower230, an electric current value of a motor to rotate a blade and the like. For example, the parameter generating section446generates a control parameter about a number of revolution of a motor of the lawn mower230to rotate the blade, a travel speed of the lawn mower230, a travel direction of the lawn mower230and the like based on an electric current value of the motor.

In still another embodiment, the parameter generating section446receives a result of judgment about the state of the blade from the blade state judging section444. The parameter generating section446generates a control parameter based on a result of judgment about the state of the blade. For example, if the cutting performance of the blade does not satisfy a predetermined condition, the parameter generating section446decides the control parameter such that (i) a travel speed of the lawn mower230becomes lower or (ii) a rotational speed of the blade becomes higher, as compared with a case where the cutting performance of the blade satisfies the predetermined condition. The parameter generating section446may generate the control parameter based on a given combination of a result of judgment about the growth state of lawn grasses, a result of judgment about the cut state of lawn grasses and a result of judgment about the state of the blade.

[Water-Supply Parameter] For example, the parameter generating section446receives a result of judgment about the growth state of lawn grasses from the lawn state judging section442. The parameter generating section446generates a water-supply parameter based on a result of judgment about the growth state of lawn grasses. According to the present embodiment, the water-supply parameter is generated for example based on a feature about at least one of (i) the type of lawn grasses, (ii) the number or density of lawn grasses, (iii) the shapes of lawn grasses, and (iv) the appearance of cut lawn grasses. The appearance of cut lawn grasses may be one example of a feature of cut portions of lawn grasses. Thereby, for example, it can decide at least one of (a) the level of water content in a medium of lawn grasses, (b) whether water-supply to lawn grasses is necessary or not, and (c) the amount of water-supply to lawn grasses based on a feature of at least either the shapes of lawn grasses or end portions of lawn grasses. The water-supply parameter may be generated based on a specification of the lawn mower230, an electric current value of a motor to rotate a blade, and the like.

[Outline of Map Generating section448] The map generating section448generates various types of map information. Map information of each parameter may be one example of the parameter. The map generating section448outputs the map information for example to the instruction generating section330or transmitting section340.

In one embodiment, the map generating section448receives, from the parameter generating section446, various types of parameters, and information indicating a position at which the parameters are applied. The map generating section448generates map information of each parameter by associating each parameter and information indicating a position at which the parameter is applied. The map generating section448may generate the map information utilizing parameters satisfying a predetermined condition.

In another embodiment, the map generating section448may acquire, from at least either the lawn state judging section442or the blade state judging section444, (i) positional information indicating a position where an image to be a target of judgment was captured or positional information indicating a position of an object in the image and (ii) information indicating a result of judgment about the target of judgment. The map generating section448may generate map information by associating the above-mentioned positional information and information indicating the above-mentioned result of judgment. The map generating section448may generate map information utilizing a result of judgment satisfying a predetermined condition. At least either the lawn state judging section442or the blade state judging section444may output the above-mentioned information to448if a result of judgment satisfies a predetermined condition.

In still another embodiment, the map generating section448acquires, from the lawn state judging section442, (i) positional information indicating a position where an image to be a target of judgment was captured, or positional information indicating a position of an object in the image, and (ii) information about at least one of a plant, an animal, a microorganism, soil and waste that are included in each image. The information about at least one of a plant, an animal, a microorganism, soil and waste may be information indicating the type of at least one of a plant, an animal, a microorganism, soil and waste. The map generating section448may generate map information by associating the above-mentioned positional information and information indicating the above-mentioned result of judgment. Soil may be one example of a medium of a plant.

Processes at each section in the image analyzing section320are not limited to the embodiment explained usingFIG. 4. In another embodiment, at least part of information processing at a particular member of the image analyzing section320may be executed at another member. For example, in the present embodiment explained, the lawn recognizing section430recognizes the form of lawn grasses, and the lawn state judging section442judges the state of the lawn grasses based on the form of the lawn grasses. However, the judgment processing section440is not limited to the present embodiment. In another embodiment, at least part of information processing at the lawn recognizing section430may be executed at the lawn state judging section442.

Also, in the present embodiment explained, the parameter generating section446generates various types of parameters. However, the judgment processing section440is not limited to the present embodiment. In another embodiment, at least either the lawn state judging section442or the blade state judging section444may generate parameters. For example, in the present embodiment explained, the lawn state judging section442judges the growth state of lawn grasses based on the form of the lawn grasses, and the parameter generating section446generates a water-supply parameter based on a result of judgment about the growth state of the lawn grasses. However, the judgment processing section440is not limited to the present embodiment. In another embodiment, the lawn state judging section442may generate a water-supply parameter based on the form of lawn grasses. For example, based on the form of lawn grasses, the lawn state judging section442decides at least one of (a) the level of water content in a medium of lawn grasses, (b) whether water-supply to lawn grasses is necessary or not, and (c) the amount of water-supply to lawn grasses.

FIG. 5schematically shows one example of the internal configuration of the information storage section322. In the present embodiment, the information storage section322includes a work machine information storage section510, a learning data storage section520and a learning model storage section530. The learning data storage section520may be one example of a shape information storage section.

The work machine information storage section510stores information about a specification of the lawn mower230. The learning data storage section520stores learning data of the learning processing section410. The learning model storage section530stores learning data corresponding to various conditions. The learning data storage section520may store, in association with each other, (i) positional information acquired by the position calculating section420and (ii) information about the shapes of lawn grasses recognized by the lawn recognizing section430. The learning model storage section530stores a learning model constructed by the learning processing section410. The learning model storage section530may store learning models corresponding to various conditions.

[Outline of Lawn Mower230] The outline of the lawn mower230is explained usingFIG. 6,FIG. 7andFIG. 8.FIG. 6schematically shows one example of the internal configuration of the lawn mower230. In the present embodiment, the lawn mower230includes a housing602. In the present embodiment, the lawn mower230includes, under the housing602, a pair of front wheels612and a pair of rear wheels614. The lawn mower230may include a pair of motors for run616that respectively drive the pair of rear wheels614.

In the present embodiment, the lawn mower230includes a work unit620. The work unit620for example has a blade disk622, a cutter blade624, a motor for work626and a shaft628. The lawn mower230may include a position adjusting section630that adjusts a position of the work unit620. The work unit620may be one example of a cutting section. The blade disk622and the cutter blade624may be one example of a rotor for cutting a work target.

The blade disk622is coupled with the motor for work626via the shaft628. The cutter blade624may be a cutting blade for cutting lawn grasses. The cutter blade624is attached to the blade disk622and rotates together with the blade disk622. The motor for work626rotates the blade disk622.

In the present embodiment, inside the housing602or above the housing602, the lawn mower230includes a battery unit640, a user interface650, an image-capturing unit660, a sensor unit670and a control unit680. The image-capturing unit660may be one example of an image-capturing section or image acquiring section. The control unit680may be one example of a judging section, information processing device or control device.

In the present embodiment, the battery unit640supplies electric power to each section of the lawn mower230. In the present embodiment, the user interface650receives a user input. The user interface650outputs information to a user. Examples of the user interface650may include a keyboard, a pointing device, a microphone, a touch panel, a display, a speaker and the like.

In the present embodiment, the image-capturing unit660captures an image of the space around the lawn mower230. The image-capturing unit660may capture an image of lawn grasses to be a work target of the lawn mower230. The image-capturing unit660may capture an image of lawn grasses cut by the lawn mower230. The image-capturing unit660may acquire a still image of an object or acquire a moving image of an object. The image-capturing unit660may have a plurality of image sensors. The image-capturing unit660may be a 360-degree angle camera.

In the present embodiment, the sensor unit670includes various types of sensors. The sensor unit670transmits outputs of various types of sensors to the control unit680. Examples of the sensors may include a GPS signal receiver, a beacon receiver, a radio field intensity measuring machine, an acceleration sensor, an angular speed sensor, a wheel speed sensor, a contact sensor, a magnetic sensor, a temperature sensor, a humidity sensor, a soil water sensor and the like.

In the present embodiment, the control unit680controls operation of the lawn mower230. According to one embodiment, the control unit680controls the pair of motors for run616to control travel of the lawn mower230. According to another embodiment, the control unit680controls the motor for work626to control work of the lawn mower230.

The control unit680may control the lawn mower230based on a result of a judgment process at the image analyzing section320of the managing server210. For example, the control unit680controls the lawn mower230in accordance with an instruction generated by the instruction generating section330of the managing server210.

In another embodiment, the control unit680may execute various types of judgment processes. The control unit680may execute at least one of judgment processes at the judgment processing section440. In one embodiment, the control unit680may control the lawn mower230based on a result the above-mentioned judgment processes. For example, the control unit680judges the state of the work unit620based on image data of an image captured by the image-capturing unit660. The state of the work unit620may be the cutting performance of the cutter blade624.

In another embodiment, the control unit680may control the sprinkling device240based on a result of the above-mentioned judgment processes. For example, the control unit680recognizes the shapes of lawn grasses based on image data of an image captured by the image-capturing unit660. The control unit680decides a water-supply parameter based on the shapes of lawn grasses. The control unit680transmits a water-supply parameter to the sprinkling device240to control the amount of water-supply to a particular position in the garden30.

FIG. 7schematically shows one example of the internal configuration of the control unit680. In the present embodiment, the control unit680includes a communication control section710, a running control section720, a work unit control section730and an input-output control section740. The communication control section710may be one example of a notifying section or image acquiring section. The running control section720may be one example of a travel control section. The work unit control section730may be one example of a work control section.

In the present embodiment, the communication control section710controls communication with an instrument located outside the lawn mower230. The communication control section710may be a communication interface compatible with one or more communication systems. Examples of the instrument located outside may include the user terminal20, the managing server210, the sprinkling device240and the like. The communication control section710as necessary may acquire, from the monitoring camera220, image data of an image captured by the monitoring camera220.

In the present embodiment, the running control section720controls the motors for run616to control travel of the lawn mower230. The running control section720controls autonomous run of the lawn mower230. For example, the running control section720controls at least one of a travel speed, travel direction and travel route of the lawn mower230.

The running control section720may control the motors for run616based on a result of judgment at the image analyzing section320of the managing server210. In another embodiment, the running control section720may control the motors for run616based on a result of a judgment process at the control unit680.

In the present embodiment, the work unit control section730controls the work unit620. The work unit control section730may control at least one of the type of work, strength of work and schedule of work of the work unit620. For example, the work unit control section730controls the motor for work626to control the strength of work of the work unit620. The work unit control section730may control the position adjusting section630to control the strength of work of the work unit620.

The work unit control section730may control at least either the motor for work626or the position adjusting section630based on a result of judgment at the image analyzing section320of the managing server210. In another embodiment, the work unit control section730may control at least either the motor for work626or the position adjusting section630based on a result of a judgment process at the control unit680. In still another embodiment, the work unit control section730may monitor an electric current value of the motor for work626. The work unit control section730may transmit, to the image analyzing section320, information indicating an electric current value of the motor for work626.

In the present embodiment, the input-output control section740receives an input from at least one of the user interface650, the image-capturing unit660and the sensor unit670. The input-output control section740outputs information to the user interface650. The input-output control section740may control at least one of the user interface650, the image-capturing unit660and the sensor unit670. For example, the input-output control section740controls at least one instrument among the user interface650, the image-capturing unit660and the sensor unit670by adjusting setting of the instrument.

FIG. 8schematically shows another example of the internal configuration of the control unit680. It was explained usingFIG. 7that the managing server210has the image analyzing section320, the information storage section322and the instruction generating section330, and various types of judgment processes are executed at the managing server210. In the embodiment ofFIG. 7, the control unit680controls the lawn mower230based on a result of a judgment process at the image analyzing section320.

The embodiment of theFIG. 8is different from the embodiment ofFIG. 7in that the image analyzing section320, the information storage section322and the instruction generating section330are disposed in the control unit680. In the present embodiment, the running control section720and the work unit control section730control at least one of the motors for run616, the motor for work626and the position adjusting section630based on an instruction generated by the instruction generating section330. In other respects, it may have a configuration similar to the embodiment ofFIG. 7.

In the present embodiment explained, the control unit680has the image analyzing section320, the information storage section322and the instruction generating section330. However, the control unit680is not limited to the present embodiment. In another embodiment, one or two among the image analyzing section320, the information storage section322and the instruction generating section330may be disposed in the control unit680, and the remaining sections may be disposed in the managing server210. For example, the control unit680may not have the information storage section322. In this case, the image analyzing section320disposed in the control unit680as necessary executes image analysis processing by accessing the information storage section322disposed in the managing server210.

In still another embodiment, some configurations among a plurality of configurations included in the image analyzing section320may be disposed in the control unit680and the remaining configurations may be disposed in the managing server210. For example, among a plurality of configurations included in the image analyzing section320, the judgment processing section440is disposed in the control unit680, and the learning processing section410, the position calculating section420and the lawn recognizing section430are disposed in the managing server210. Also, the parameter generating section446may be disposed in the control unit680, and the remaining configurations of the image analyzing section320may be disposed in the managing server210.

FIG. 9schematically shows one example of the system configuration of the sprinkling device240. In the present embodiment, the sprinkling device240includes the sprinkler242and the water-supply control section244. The sprinkling device240may include a hydrant902and a water-supply line904. The water-supply line904may be a pipe or water-supply facility that transfers water supplied from the hydrant902to the sprinkler242. One end of the water-supply line904may be connected to the hydrant902, and the other end of the water-supply line904may be connected to the sprinkler242.

In the present embodiment, the water-supply control section244is disposed between the sprinkler242and the hydrant902in the water-supply line904, and adjusts the amount of water-supply to the sprinkler242. In the present embodiment, the water-supply control section244has an automatic valve910, a communication control section920and an open-close control section930.

In the present embodiment, the automatic valve910adjusts the amount of water to flow through the water-supply line904. The automatic valve910may adjust the amount of water to flow through the water-supply line904based on a control signal from the open-close control section930. The automatic valve910may be an electrically operated valve.

In the present embodiment, the communication control section920controls communication with the managing server210or the lawn mower230. The communication control section920receives, from the managing server210or lawn mower230, a water-supply parameter or water-supply instruction. The communication control section920transmits the water-supply parameter or water-supply instruction to the open-close control section930. In the present embodiment, the open-close control section930controls operation of the automatic valve910. For example, the open-close control section930controls operation of the automatic valve910based on a water-supply parameter or water-supply instruction.

FIG. 10schematically shows another example of the system configuration of the sprinkling device240. In the present embodiment, the sprinkling device240includes the sprinkler242and the water-supply control section244. The sprinkling device240may include the water-supply line904and a water tank1002. In the present embodiment, the water-supply control section244has a pump1010, the communication control section920and a pump control section1030. The sprinkling device240may be mounted on the lawn mower230.

In the present embodiment, one end of the water-supply line904is connected to the water tank1002, and the other end of the water-supply line904is connected to the sprinkler242. The pump1010transfers water inside the water tank1002. The pump control section1030may adjust the amount of water to be transferred, based on a control signal from the pump control section1030. The pump control section1030controls operation of the pump1010. For example, the pump control section1030controls operation of the pump control section1030based on a water-supply parameter or water-supply instruction received by the communication control section920.

FIG. 11schematically shows one example of information processing at the image analyzing section320. According to the present embodiment, at Step1102(Step may be sometimes abbreviated to S), the lawn recognizing section430acquires, from the receiving section310, image data of an image to be a target of analysis. At S1104, the lawn recognizing section430determines whether or not the image acquired from the receiving section310is an image after lawn mowing or an image before lawn mowing. For example, if the image acquired from the receiving section310is an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230, the lawn recognizing section430determines that the image is an image before lawn mowing. If the image acquired from the receiving section310is an image of lawn grasses present in a region that the lawn mower230passed through, the lawn recognizing section430determines that the image is an image after lawn mowing.

In one embodiment, the lawn recognizing section430may acquire information about at least either an installation position or an image-capturing condition of an image-capturing device that captured the image, and based on the information, determine whether the image acquired from the receiving section310is an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230or an image of lawn grasses present in a region that the lawn mower230passed through. For example, if the image-capturing device is mounted at a front portion of the lawn mower230, an image-capturing device is set to capture an image of the forward direction of the lawn mower230, and so on, the lawn recognizing section430determines that an image acquired from the receiving section310is an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230.

In another embodiment, the lawn recognizing section430acquires, from the position calculating section420, positional information indicating a position of lawn grasses in an image. The position calculating section420decides a position of lawn grasses in an image for example based on at least either an installation position or an image-capturing condition of an image-capturing device that captured the image. The lawn recognizing section430acquires information indicating the current position of the lawn mower230. The lawn recognizing section430may determine whether an image acquired from the receiving section310is an image of lawn grasses present in the forward direction in terms of a course of the lawn mower230or an image of lawn grasses present in a region that the lawn mower230passed through, based on a position of the lawn grasses and a position of the lawn mower230.

If it is determined that the image acquired from the receiving section310is an image after lawn mowing (if YES at S1104), at S1112, the lawn recognizing section430recognizes end portions of one or more lawn grasses present in the image. After recognizing the shape of at least one lawn grass, the lawn recognizing section430may recognize an end portion of the lawn grass based on the shape of the lawn grass. Also, the lawn state judging section442judges the cut state of the lawn grass based on a feature of the end portion of the lawn grass.

Next, at S1114, the lawn state judging section442transmits, to the blade state judging section444, information indicating the cut state of the lawn grass. Then, the blade state judging section444judges the state of a blade based on the cut state of the lawn grass. Also, at S1116, the lawn state judging section442judges the growth state of the lawn grass based on a feature of the end portion of the lawn grass. In this case, the lawn recognizing section430may recognize the shape of lawn grass, and the lawn state judging section442may judge the growth state of the lawn grass based on the shape of the lawn grass.

Thereafter, at S1122, the parameter generating section446generates various types of parameters based on at least one of the cut state of the lawn grass, the growth state of the lawn grass and the state of a blade. In this case, the map generating section448may generate map information utilizing the parameters generated by the parameter generating section446.

On the other hand, if it is determined that the image acquired from the receiving section310is an image before lawn mowing (if NO at S1104), at S1116, the lawn state judging section442judges the growth state of lawn grasses by a procedure similar to the above-mentioned one, and at S1122, the parameter generating section446generates various types of parameters by a procedure similar to the above-mentioned one. The map generating section448may generate map information utilizing parameters generated by the parameter generating section446.

The various types of parameters generated at S1122are transmitted to the instruction generating section330. The various types of parameters may be transmitted to the instruction generating section330in a map information format. Thereby, processes at the image analyzing section320end. The image analyzing section320(i) may execute a series of processes every time it acquires image data from the receiving section310or (ii) may execute a series of processes for each subarea and for a predetermined number of pieces of image data.

FIG. 12schematically shows one example of a data table1200. The data table1200may be one example of a determination criterion for judging the type of lawn grasses. In the present embodiment, the data table1200utilizes a color of lawn grasses1202and a hardness of lawn grasses1204as factors to consider for judging the type of lawn grasses. The data table1200stores, in association with each other, a condition about the color of lawn grasses1202and a condition about the hardness of lawn grasses1204, and a result of judgment1206.

In the present embodiment, the condition about the color of lawn grasses1202is evaluated using evaluation categories consisting of four steps, “reddish brown”, “yellowish green”, “green” and “dark green”. The color of lawn grasses may be evaluated based on an image to be a target of analysis by the image analyzing section320. In the present embodiment, the condition about the hardness of lawn grasses1204is evaluated using evaluation categories consisting of three steps, “hard”, “normal” and “soft”. The hardness of lawn grasses1204may be evaluated for example based on an electric current value of the motor for work626. Thresholds for classification into respective evaluation categories may be decided by a user or administrator, or may be decided through machine learning.

FIG. 13schematically shows one example of a data table1300. The data table1300may be one example of a determination criterion for deciding a determination criterion to be utilized in various types of judgment processes. In the present embodiment, the data table1300stores, in association with each other, a type of lawn grasses1302, a determination criterion1304to be utilized in a process of judging the state of lawn grasses, and a determination criterion1306to be utilized in a process of judging the state of a blade.

FIG. 14schematically shows one example of a data table1400. The data table1400may be one example of a determination criterion to be utilized in a process of judging the state of lawn grasses. In the present embodiment, the data table1400utilizes, as factors to consider for judging the state of lawn grasses, shapes of cut portions1402and a color of cut portions1404. The data table1400stores, in association with each other, a condition about the shapes of cut portions1402and a condition about a color of cut portions1404, and a result of judgment1406.

While the embodiments of the present invention have been described, the technical scope of the invention is not limited to the above described embodiments. It is apparent to persons skilled in the art that various alterations and improvements can be added to the above-described embodiments. Also, matters explained with reference to a particular embodiment can be applied to other embodiments as long as such application does not cause a technical contradiction. For example, matters explained about the embodiment ofFIG. 1can be applied to the embodiments ofFIG. 2toFIG. 14. It is also apparent from the scope of the claims that the embodiments added with such alterations or improvements can be included in the technical scope of the invention.

For example, the following matters are described in the present specification.

[Item A-1] A work machine having an autonomous travel function, comprising:

a cutting section that cuts a work target of the work machine;

an image-capturing section that captures an image of the work target cut by the cutting section; and

a judging section that judges a state of the cutting section based on the image captured by the image-capturing section.

[Item A-2] The work machine according to Item A-1, wherein the judging section judges whether maintenance of or a check on the cutting section is necessary or not based on a result of judgment about the state of the cutting section.

[Item A-3] The work machine according to Item A-1 or Item A-2, further comprising a specification information acquiring section that acquires specification information about a specification of the cutting section, wherein

the judging section judges the state of the cutting section based on the specification information acquired by the specification information acquiring section and the image captured by the image-capturing section.

[Item A-4] The work machine according to any one of Item A-1 to Item A-3, further comprising a notifying section that notifies a result of judgment by the judging section to a user of the work machine.

[Item A-5] The work machine according to any one of Item A-1 to Item A-4, further comprising a positional information acquiring section that acquires positional information indicating a position where the image-capturing section has captured the image, wherein

the judging section outputs, in association with each other, the positional information acquired by the positional information acquiring section and information indicating a result of judgment at the position indicated by the positional information.

[Item A-6] The work machine according to Item A-5, wherein if a result of judgment by the judging section satisfies a predetermined condition, the judging section outputs, in association with each other, the positional information acquired by the positional information acquiring section and information indicating a result of judgment by the judging section at the position indicated by the positional information.
[Item A-7] The work machine according to any one of Item A-1 to Item A-6, further comprising a travel control section that controls travel of the work machine based on a result of judgment by the judging section.
[Item A-8] The work machine according to any one of Item A-1 to Item A-7, further comprising a work control section that controls operation of the cutting section based on a result of judgment by the judging section.
[Item A-9] A control device that controls a work machine having an autonomous travel function, wherein

the work machine has:

a cutting section that cuts a work target of the work machine; and

an image-capturing section that captures an image of the work target cut by the cutting section, and

the control device comprises:

a judging section that judges a state of the cutting section based on the image captured by the image-capturing section; and

a control section that controls the work machine based on a result of judgment by the judging section.

[Item A-10] A control program for controlling a work machine having an autonomous travel function, wherein

the work machine has:

a cutting section that cuts a work target of the work machine; and

an image-capturing section that captures an image of the work target cut by the cutting section, and

the control program is a program for causing a computer to execute:

a judgment procedure of judging a state of the cutting section based on the image captured by the image-capturing section; and

a control procedure of controlling the work machine based on a result of judgment by in judgment procedure.

[Item A-11] The control program according to Item A-10, wherein

the work machine further has a processor, and

the computer is the processor of the work machine.

[Item B-1] An information processing device comprising:

an image acquiring section that acquires image data of an image of a plant;

a form recognizing section that recognizes at least either (i) a shape of the plant or (ii) an end portion of the plant, based on the image data acquired by the image acquiring section; and

a deciding section that decides at least one of (a) a level of water content in a medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) an amount of water-supply to the plant, based on a feature of at least either the shape of the plant or the end portion of the plant that is recognized by the form recognizing section.

[Item B-2] The information processing device according to Item B-1, wherein

the deciding section:

recognizes a feature of at least either the shape of the plant or the end portion of the plant, based on a result of recognition by the form recognizing section; and

decides at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant, based on the feature recognized by the feature recognizing section.

[Item B-3] The information processing device according to Item B-2, further comprising:

a positional information acquiring section that acquires positional information indicating a position where the image has been captured; and

a form information storage section that stores, in association with each other, (i) the positional information acquired by the positional information acquiring section, and (ii) information about at least either the shape of the plant or the end portion of the plant recognized by the form recognizing section, wherein

the deciding section recognizes a feature of at least either the shape of the plant or the end portion of the plant, utilizing, as learning data, information stored in the form information storage section.

[Item B-4] The information processing device according to Item B-1 or Item B-2, further comprising a positional information acquiring section that acquires positional information indicating a position where the image has been captured, wherein

the deciding section outputs, in association with each other, the positional information acquired by the positional information acquiring section and at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant at the position indicated by the positional information.

[Item B-5] The information processing device according to Item B-4, wherein if at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant satisfies a predetermined condition, the deciding section outputs, in association with each other, the positional information acquired by the positional information acquiring section and at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant at the position indicated by the positional information.
[Item B-6] The information processing device according to any one of Item B-1 to Item B-5, wherein the deciding section decides at least either (i) whether water-supply to the plant is necessary or not or (ii) the amount of water-supply, based on the level of water content in the medium of the plant.
[Item B-7] A water-supply system comprising:

the information processing device according to Item B-6; and

a water-supply section that supplies water to the plant based on a decision by the deciding section.

[Item B-8] The water-supply system according to Item B-7, further comprising a work machine having an autonomous travel function, wherein

the work machine has an image-capturing section that captures an image of the plant, and

an image acquiring section of the information processing device acquires image data of the image of the plant captured by the image-capturing section of the work machine.

[Item B-9] The water-supply system according to Item B-7, further comprising a work machine having an autonomous travel function, wherein

the water-supply section is disposed in the work machine.

[Item B-10] The water-supply system according to Item B-7, further comprising a work machine having an autonomous travel function, wherein

the work machine has a cutting section that cuts the plant, and

the deciding section of the information processing device decides at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant, based on a feature of a cut portion of the plant cut by the cutting section.

[Item B-11] An information processing system comprising:

the information processing device according to any one of Item B-1 to Item B-6; and

a work machine having an autonomous travel function, wherein

the work machine has an image-capturing section that captures an image of the plant, and

an image acquiring section of the information processing device acquires image data of the image of the plant captured by the image-capturing section of the work machine.

[Item B-12] An information processing system comprising:

the information processing device according to any one of Item B-1 to Item B-6; and

a work machine having an autonomous travel function, wherein

the work machine has a cutting section that cuts the plant, and

the deciding section of the information processing device decides at least one of (a) the level of water content in the medium of the plant, (b) whether water-supply to the plant is necessary or not, and (c) the amount of water-supply to the plant, based on a feature of a cut portion of the plant cut by the cutting section.

[Item B-13] A program for causing a computer to function as the information processing device according to any one of Item B-1 to Item B-6.

[Item C-1] A control device that controls a work machine having an autonomous travel function, the control device comprising:

an image acquiring section that acquires image data of an image of a work target of the work machine;

a feature recognizing section that recognizes a feature about at least one of (i) a type of the work target of the work machine, (ii) a number or density of the work target, (iii) a shape of the work target and (iv) an appearance of the work target after work, based on the image data acquired by the image acquiring section; and

a control parameter deciding section that decides at least either (i) a parameter for controlling travel of the work machine or (ii) a parameter for controlling work of the work machine, based on the feature recognized by the feature recognizing section.

[Item C-2] The control device according to Item C-1, further comprising a transmitting section that transmits, to the work machine, the parameter decided by the control parameter deciding section.

[Item C-3] The control device according to Item C-1 or Item C-2, wherein

the work machine has a cutting section that cuts the work target,

the feature recognizing section recognizes a feature of a cut portion of the work target cut by the cutting section, and

the control device further comprises a judging section that judges a state of the cutting section based on the feature of the cut portion of the work target recognized by the feature recognizing section.

[Item C-4] The control device according to Item C-3, wherein the control parameter deciding section decides the parameter based on a result of judgment by the judging section.

[Item C-5] The control device according to Item C-3 or Item C-4, wherein

the cutting section has a rotor for cutting the work target,

the judging section judges a cutting performance of the cutting section, and

if the cutting performance of the cutting section judged by the judging section does not satisfy a predetermined condition, the control parameter deciding section decides the parameter such that (i) a travel speed of the work machine becomes lower or (ii) a rotational speed of the rotor becomes higher, as compared with a case where the cutting performance of the cutting section satisfies the predetermined condition.

[Item C-6] The control device any one of Item C-1 to Item C-5, wherein

the work machine has an image-capturing section that captures an image of the work target, and

the image acquiring section acquires image data of the image of the work target captured by the image-capturing section of the work machine.

[Item C-7] A work machine having an autonomous travel function, the work machine comprising;

the control device according to any one of Item C-1 to Item C-5; and

an image-capturing section that captures an image of the work target, wherein the image acquiring section of the control device acquires image data of the image of the work target captured by the image-capturing section of the work machine.

[Item C-8] A program for causing a computer to function as the control device according to any one of Item C-1 to Item C-6.