Patent ID: 12250910

MODE FOR CARRYING OUT THE INVENTION

Application Examples

Hereinafter, application examples of the invention will be described with reference to the drawings.

FIG.5shows a cultivation and management system1including cultivation and management devices200and300to which the invention is applied.FIGS.7(A) and7(B)show functional block diagrams of the cultivation and management device200. Here, the case where the cultivation and management device200outputs work instructions for fruit vegetable plants or fruit trees will be described. However, as described later, the cultivation and management device200can also be configured to output a yield.

A learner25that outputs work instructions is generated in a learning device100. In the learning device100(seeFIG.2), a learning process unit24trains and generates the learner25, using training environment state information, training work history information, and training yield information as training data, so that the learner25outputs work instructions including a shape change work when environment state information and planned yield are input. Since the learner25is trained using the training environment state information and the training yield information as input data and a work history including the shape change work as supervised data, the learner25can output an appropriate work instruction.

In the cultivation and management device200, work instructions including the shape change work are output by inputting to the learner25generated as described above the environment state information and the planned yield information related to cultivation target fruit vegetable plants or fruit trees. As described above, since the learner25generated is trained using the training environment state information and the training yield information as the input data, and the work history including the shape change work as the supervised data, the cultivation and management device200can output an appropriate work instruction.

Further, by configuring the cultivation and management system with the learning device100and the cultivation and management devices200and300, training data can be collected from the cultivation and management device200or300to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices200and300, more accurate cultivation becomes possible.

First Embodiment

Hereinafter, a learning device and a cultivation and management device according to the first embodiment of the invention will be described in more detail with reference to the drawings. Hereinafter, a cultivation and management device for fruit vegetable plants and fruit trees, a cultivation and management method for fruit vegetable plants and fruit trees, and a cultivation and management program for fruit vegetable plants and fruit trees are simply referred to as a cultivation and management device, a cultivation and management method, and a cultivation and management program, respectively.

First is described a process of generating, through training, a model used for cultivation management.

<Device Configuration>

FIG.1is a hardware configuration diagram of a learning device100according to the embodiment.

The learning device100is a computer device including a processor11, a main storage unit12, an auxiliary storage unit13, an input unit14, an output unit15, an external interface16, a communication interface17, and a bus18.

The processor11is a CPU, a DSP, or the like.

The main storage unit12is configured with a read only memory (ROM), a random access memory (RAM), and the like.

The auxiliary storage unit13includes an erasable programmable ROM (EPROM), a hard disk drive (HDD), a removable medium, and the like. The removable medium is, for example, a flash memory such as a USB memory or an SD card, or a disc recording medium such as a CD-ROM, a DVD disc, or a Blu-ray disc. The auxiliary storage unit13stores an operating system (OS), various programs, various tables, and the like. The processor11executes the stored programs loaded into a work area of the main storage unit12to control each component or the like through program execution and realize each functional unit that fulfills a predetermined purpose as described later. However, some or all of the functional units may be realized by a hardware circuit such as an application specific integrated circuit (ASIC) or field programmable gate array (FPGA). Note that the learning device100does not necessarily have to be realized by a single physical configuration, and may be configured with a plurality of computers linked to each other. Hereinafter, the main storage unit12and the processor11that executes a predetermined program loaded into the work area of the main storage unit12are also collectively referred to as a control unit10.

The input unit14includes a keyboard, a mouse, a microphone, and the like, and accepts input operations from the user.

The output unit15includes a display, a speaker, and the like, and provides information to the user.

The external interface (indicated as I/F in the drawings)16is an interface for connection to various external devices.

The communication interface17is an interface for connecting the learning device100to a network. The communication interface17can adopt an appropriate configuration depending on a connection system with the network.

The bus18is a signal transmission line connecting each part of the learning device100.

FIG.2is a functional block diagram of the learning device100.

The learning device100includes a training environment state information acquisition unit21, a training work history information acquisition unit22, a training yield acquisition unit23, a learning process unit24, and a learner25.

The training environment state information acquisition unit21is a means for acquiring environment state information that is information on the environment state in which a cultivation target fruit vegetable plant or fruit tree is placed. The environment state information may include, but is not limited to, a temperature, humidity, and illuminance. The environment state information used as training data is referred to as the training environment state information. In the following description, a fruit vegetable plant or a fruit tree will be referred to simply as a “plant”.

The training work history information acquisition unit22is a means for acquiring work history information that quantifies a history of work including a work whose amount of work cannot be expressed as a continuous value, such as a shape change work for the cultivation target plant. The work history information used as the training data is referred to as the training work history information.

The training yield acquisition unit23is a means for acquiring yield information that is information on a yield achieved as a result of cultivating the cultivation target plant. The yield information used as the training data is called the training yield information. The training yield acquisition unit23corresponds to a training cultivation evaluation index acquisition unit of the invention. Here, the yield is explained as an example of the cultivation evaluation index. However, the cultivation evaluation index is an index for evaluating a cultivation result of a fruit vegetable plant or a fruit tree, and may include, but is not limited to, a sugar content, a sugar acid ratio, a single fruit weight, a leaf area, and a stem thickness.

Here, the control unit10may acquire the training environment state information, the training work history information, and the training yield information input by the user via the input unit14. In this case, the input unit14and the control unit10configure the training environment state information acquisition unit21, the training work history information acquisition unit22, and the training yield acquisition unit23. The user may also directly input the training work history information via the input unit14. Further, the control unit10may convert the information input by the user via the input unit14to acquire the training work history information. Here, the control unit10functions as a quantification means. In this way, the user does not need to input the work history in a quantified format, reducing a burden of the user's input work. For example, to input the work history, the user selects a work type from a pull-down menu displayed on a display as the output unit15, and selects a work amount from a work amount pull-down menu displayed according to the selected work type. For example, in the learning device100, a work type flag corresponding to the selected work type (for example, when a work is flower picking, a flower picking flag) is set to on, and a numerical value (for example, 3) quantified based on the unit (here, quantity) specified according to the work type is acquired as the training work history information. Works in plant cultivation are an act that directly affects a plant or an act that indirectly affects a plant through the environment such as soil. Some works have an irreversible effect of changing a shape of plant, while others can be repeated without changing the shape of plant. Such works include outlining, pruning, soil preparation, cultivation management, and spraying. Pruning includes bud removal, foliage removal, flower picking, topping, fruit thinning, stem mutilation, and weeding. Soil preparation includes soil disinfection, ridging, plowing, and base fertilizer. Cultivation management includes sowing, planting, support erection, and plant training. Among these works, a work that will change the shape of plant is referred to as a shape change work. The shape change work includes thinning, picking, and removal as described above that modifies both a plant element or tissue and plant shape, and plant training that modifies only the plant shape. Spraying includes fertilization, irrigation, pesticide spraying, and natural enemy pesticide spraying. Bud removal, flower picking, topping, and fruit thinning can be quantified by quantity. Foliage removal can be quantified by the number of leaves, stem mutilation by the number of stems, and weeding by the number of weeds. Soil disinfection, plowing, and base fertilizer can be quantified by the number of times. Ridging can be quantified by spacing and depth. Sowing and planting can be quantified by quantity. Support erection and plant training can be quantified by the number of supports and the like. Fertilization, irrigation, pesticide spraying, and natural enemy pesticide spraying can be quantified by a spraying amount and the like.

Further, regarding environment state information such as a temperature, a humidity, and an illuminance that can be detected by detectors such as a temperature sensor, a humidity sensor, and an illuminance sensor; the control unit10may control these sensors through the external interface16to acquire the training environment state information. In this case, the external interface16and the control unit10connected to each of the sensors configure the training environment state information acquisition unit21. The training work history information and the training yield information may also be acquired from an external device without user input.

In addition, regarding outdoor temperature and humidity in the environment state information that are acquired from an external server via the network, the control unit10may acquire the information from the external server via the network such as the Internet through the communication interface17. In this case, the communication interface17and the control unit10configure the training environment state information acquisition unit21.

The training data storage unit131, which is a predetermined area of the auxiliary storage unit13, stores the training environment state information, the training work history information, and the training yield information acquired through the training environment state information acquisition unit21, the training work history information acquisition unit22, and the training yield acquisition unit23.

Here, the training environment state information, the training work history information, and the training yield information are associated in chronological order with respect to the cultivation target plant. In other words, these pieces of information are associated with each other in chronological order, with respect to a certain plant, as the environment state information on an environment state at a specific date and time, the work history information on a history of work performed at a specific date and time, and the yield information at a specific date and time. In addition, these pieces of information may be acquired for each stem of the cultivation target plant, or may be collectively acquired for a group of stems cultivated in a predetermined plot or a predetermined greenhouse. Corresponding to each case, the training environment state information, the training work history information, and the training yield information are associated with each other by information that identifies each stem or the group of stems.

The learning process unit24performs machine learning of the learner so as to output a work when the training environment state information and the training yield information stored in the training data storage unit131are input. The learning process unit24performs machine learning by reading and executing by the control unit10a learning model generation program stored in a predetermined area of the auxiliary storage unit13. Here, the learner is a model that predicts a work required for achieving a target yield by using the training environment state information and the training yield information as training data, and the training work history information as supervised data. For example, the program may calculate the model by a neural network, but is not limited to this.

By repeating machine learning of the learner in the learning process unit24using a large amount of training data, a trained learner25is obtained. The trained learner25obtained in this way is stored in the learning result data storage unit132, which is a predetermined area of the auxiliary storage unit13. Here, the learner25corresponds to a learning model of the invention.

<Learning Model Generation Method>

FIG.3is a flowchart showing process steps of the learning model generation method. This learning model generation method is executed as the learning model generation program in the learning device100.

First, the control unit10acquires the training environment state information (step S101).

Next, the control unit10acquires the training work history information (step S102).

Next, the control unit10acquires the training yield information (step S103).

Then, the learning process unit24trains the above-mentioned learner, using the training environment state information and the training yield information acquired as the input data and the training work history information as the supervised data (step S104).

Here, the case where the learner25is configured with the neural network will be described as an example. As shown inFIG.4(A), an input layer Li, an intermediate layer Lh, and an output layer Lo are provided in this order from the input. The number of intermediate layers Lh is not limited to one, and two or more intermediate layers Lh may be provided.

The input layer Li has multiple nodes. Each piece of information such as humidity, temperature, and illuminance for a specific plant at a specific date and time or time zone is input to each node of the input layer as the training environment state information. In addition, a yield of the specific plant at the specific date and time or time zone is input to each node of the input layer.

The number of nodes in the intermediate layer Lh can be set as appropriate. The number of nodes in the output layer Lo is set according to the data to be output.

Nodes in adjacent layers are appropriately connected, and weights are set for each connection. The node of the output layer includes a work including the shape change work, and calculation of the work including the shape change work is repeated while adjusting the weight to reduce a loss function indicating a difference from the training work history information, which is the supervised data.

By repeating the process from step S101to step S104fora large amount of training data, the trained learner25is output from the learning process unit24, and the control unit10acquires and stores the trained learner25in the learning result data storage unit132(step S105).

In this way, the learner capable of giving appropriate work instructions is generated, taking into account human intervention, including the shape change work, in cultivation of the target plant.

In the above-mentioned learning model generation method, the learner25configured with the neural network has been described, but the learning model generation method is not limited to the neural network. As shown inFIG.4(B), the learner25may be trained by so-called reinforcement learning.FIG.4(B)is a schematic diagram showing a general mechanism of reinforcement learning.

In the embodiment, an environment En corresponds to the environment including greenhouses410and420in which plants411and421are cultivated, and the learning device100or the cultivation and management devices200and300correspond to an agent Ag. In this case, the environment state information corresponds to a state St of the environment En, the work including the shape change work corresponds to an action An for the environment En by the agent Ag, and the yield information corresponds to a reward Rw. The learner25is trained with respect to the action An that maximizes the reward Rw under the state St in the learner25. In other words, the learner25learns the work including the shape change work that maximizes the yield under a certain environment state. In this reinforcement learning, the neural network as shown inFIG.4(A)may be used.

In this way, by performing reinforcement learning using the training environment state information, the training work history information, and the training yield information, it is possible to generate the learner25that outputs the work including the shape change work by inputting the environment state information and the yield information.

Next, a process of cultivation management based on inference using the model generated by training will be described below.

<Cultivation and Management System>

FIG.5is a schematic diagram showing a schematic configuration of the cultivation and management system1including the learning device100and cultivation and management devices200and300according to the embodiment.

The learning device100, the cultivation and management device200, and the cultivation and management device300are connected via the network4. The cultivation and management device200is connected to a controller412of the greenhouse410in which the plant411is cultivated. The cultivation and management device300is also connected to a controller422of the greenhouse420in which the plant421is similarly cultivated. Hereinafter, the cultivation and management device200will be described, but the same applies to the cultivation and management device300. Further, the cultivation and management device200is not limited to a case where it is connected to the controller412of one greenhouse410, and may be connected to controllers of a plurality of greenhouses. The learner25generated in the learning device100is transmitted to the cultivation and management devices200and300via the network4. Further, the cultivation and management devices200and300transmit the training environment state information, the training work history information, and the training yield information to the learning device100via the network4. By configuring the cultivation and management system with the learning device100and the cultivation and management devices200and300, training data can be collected from the cultivation and management devices200and300to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices200and300, more accurate cultivation becomes possible.

Here, a temperature/humidity sensor413is provided to measure a temperature and humidity of the greenhouse410with respect to the environment state of the cultivation target plant411. The temperature/humidity sensor413is connected to the controller412, and the temperature and humidity measured by the temperature/humidity sensor413are transmitted to the cultivation and management device200via the controller412and acquired by the cultivation and management device200as environment state information. The greenhouse420is also provided with a temperature/humidity sensor423.

<Cultivation and Management Device>

FIG.6is a hardware configuration diagram of the cultivation and management device200.

The cultivation and management device200is a computer device including a processor211, a main storage unit212, an auxiliary storage unit213, an input unit214, an output unit215, an external interface216, a communication interface217, and a bus218.

The processor211, the main storage unit212, the auxiliary storage unit213, the input unit214, the output unit215, the external interface216, the communication interface217, the bus218, and a control unit210are the same as those described for the learning device100, and thus detailed description will be omitted.

Here, the auxiliary storage unit213stores the trained learner25generated by the learning device100.

FIG.7(A)is a first functional block diagram of the cultivation and management device200.

The cultivation and management device200includes at least an environment state information acquisition unit220, a planned yield acquisition unit221, and a calculation unit223.

The environment state information acquisition unit220is a means for acquiring the environment state information that is information on the environment in which the cultivation target plant is placed. Here, the control unit210may acquire the environment state information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the environment state information acquisition unit220. In addition, regarding the environment state information such as temperature, humidity, and illuminance inside the greenhouse410that can be detected by environment detection means such as a temperature sensor, a humidity sensor, and an illuminance sensor installed in the greenhouse, the control unit210may acquire the environment state information detected by the environment detection means such as the temperature/humidity sensor413installed in the greenhouse410from the controller412of the greenhouse410connected through the external interface216. In this case, in the example inFIG.4, the temperature/humidity sensor413, the external interface216connected to the controller412, and the control unit210configure the environment state information acquisition unit220. Further, regarding the environment state information that can be acquired from an external server via the network, such as outside temperature and humidity, the control unit210may acquire the information from the external server via the network such as the Internet through the communication interface217. In this case, the communication interface217and the control unit210configure the environment state information acquisition unit220.

The planned yield acquisition unit221is a means for acquiring planned yield information that is information on a yield in a cultivation plan scheduled by the user. Here, the control unit210may acquire the planned yield information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the planned yield acquisition unit221. Further, the control unit210may acquire the environment state information and the planned yield information input by the user from a mobile terminal such as a smartphone connected via the network4through the communication interface217. In this case, the communication interface217and the control unit210configure the environment state information acquisition unit220and the planned yield acquisition unit221. Further, the planned yield information previously input by the user via the input unit214may be stored in a predetermined area of the auxiliary storage unit213, and the control unit210may read and acquire the information.

When the environment state information acquired by the environment state information acquisition unit220and the planned yield information acquired by the planned yield acquisition unit221are input, the calculation unit223of the cultivation and management device200, using the above-mentioned trained leaner25, can output to the output unit215information on the work including the shape change work as work instruction information224required for achieving the planned yield under the environment state specified by the environment state information. For example, the work instruction information224is displayed on a display as the output unit215, or is printed out as a document from a printer as the output unit215. The work instruction information224may also be transmitted to an external device through the external interface216or the communication interface217. For example, a work detail (including quantity) may be transmitted through the communication interface217to a mobile terminal such as a worker's smartphone connected to the network. Here, the calculation unit223is configured with the control unit210that executes a calculation program.

FIG.7(B)is a second functional block diagram of the cultivation and management device200.

The cultivation and management device200includes at least the environment state information acquisition unit220, a work history information acquisition unit222, and the calculation unit223. The same symbols are used for the same configuration as the first block diagram shown inFIG.7(A)to omit detailed description thereof.

The work history information acquisition unit222is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit210may acquire the work history information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the work history information acquisition unit222. Further, the control unit210may convert the information input by the user via the input unit214to acquire the work history information. Here, the control unit210functions as a quantification means. In this way, the user does not need to input the work history in the form of quantified work history information, and a burden of the user's input work is reduced. For example, the user selects a work type from a pull-down menu displayed on the display as the output unit215, and selects a work amount from the work amount pull-down menu displayed according to the selected work type to enter the work history. For example, in the cultivation and management device200, same as in the learning device100, a work type flag corresponding to the selected work type (for example, when a work is flower picking, a flower picking flag) is set to on, and a numerical value (for example, 3) quantified based on the unit (here, quantity) specified according to the work type is acquired as the work history information. The work history information input by the user may be sequentially stored in a predetermined area of the auxiliary storage unit213, and the control unit210may read and acquire the information from the area when the calculation unit223predicts a yield. Further, the control unit210may acquire the information from an external device through the external interface216or the communication interface217. In this case, the external interface216or the communication interface217and the control unit210configure the work history information acquisition unit222.

When the environment state information acquired by the environment state information acquisition unit220and the work history information acquired by the work history information acquisition unit222are input, the calculation unit223of the cultivation and management device200, using the above-mentioned trained learner25, can output to the output unit215a predicted yield as predicted yield information225, based on the environment state specified by the environment state information and the work history up to that point. The predicted yield information225is, for example, displayed on a display as the output unit215or printed out as a document from a printer as the output unit215. The work instruction information224may also be transmitted to an external device through the external interface216or the communication interface217. For example, the predicted yield information225may be transmitted through the communication interface217to a mobile terminal such as an administrator's smartphone connected to the network. Here, the calculation unit223is configured with the control unit210that executes a calculation program.

The learner25that outputs a predicted yield as the predicted yield information225, based on the environment state specified by the environment state information and the work history up to that point, can be generated by the same configuration and method as the learning device100shown inFIGS.1and2and the learning model generation method shown inFIG.3. However, the learner25that outputs the predicted yield is a learning model generated using the training environment state information and the training work history information as the training data and the training yield information as the supervised data.

<Cultivation and Management Method>

FIG.8(A)is a flowchart showing process steps of the cultivation and management method. The cultivation and management method is executed as a cultivation and management program in the cultivation and management device200.

First, the environment state information acquisition unit220acquires the environment state information (step S201).

Next, the planned yield acquisition unit221acquires the planned yield information (step S202).

Then, the calculation unit223inputs the environment state information and the planned yield information to the trained learner25to perform a calculation process (step S203).

A cultivation model that is the trained learner generated by the learning device100in advance is set as f (x1, x2). Here, x1 indicates the environment state information and x2 indicates the planned yield information (here, the input data is simplified for the sake of explanation).

Here, the work including the shape change work can be calculated by f (x1, x2), based on the environment state information (x1) acquired in step S201and the planned yield information (x2) acquired in step S202. Specifically, in the case of a neural network, a work f (x1, x2), which is output, can be calculated by calculating a weight set to each connection between the nodes of each layer including the input layer having x1 and x2. In addition, in a statistical method, the work f (x1, x2) can be calculated by calculating a weight coefficient in a regression equation including inputs x1 and x2.

In this way, the work instruction information giving an instruction for necessary work is output through the calculation process, and the control unit210acquires (step S204) and outputs the information to, for example, the output unit215.

In this way, the accuracy of cultivation work instruction is improved by using the learner that takes into account human intervention in the cultivation of the target plant. In addition, even when a worker has little knowledge and experience of the cultivation, the planned yield can be achieved by performing cultivation based on the work instruction.

FIG.8(B)is a flowchart showing the process steps of a yield prediction method as another cultivation and management method. The same symbols are used for the same processes as the cultivation and management method shown inFIG.8(A)to omit description thereof. Here, the trained learner25used is trained using the environment state information and the work history information as the training data and the yield information as the supervised data. A yield prediction model configured with the above-mentioned trained learner25outputs the predicted yield by inputting the environment state information and the work history information.

First, the environment state information acquisition unit220acquires the environment state information (step S201).

Next, the work history information acquisition unit222acquires the work history information (step S205).

Then, the calculation unit223inputs the environment state information and the work history information to the trained learner25to perform a calculation process (step S203). Here, the predicted yield information225is output by the calculation process based on the environment state specified by the environment state information and the work history up to that point. Here, the yield can be calculated by g (x1, x3), based on the environment state information (x1) acquired in step S201and the work history information (x3) acquired in step S205. Specifically, in the case of the neural network, a yield g (x1, x3), which is output, can be calculated by calculating a weight set to each connection between the nodes of each layer including the input layer having x1 and x2. In addition, in a statistical method, the yield g (x1, x3) can be calculated by calculating a weight coefficient in a regression equation including inputs x1 and x3.

In this way, the predicted yield information is output through the calculation process, and the control unit210acquires (step S206) and outputs the information to, for example, the output unit215.

In this way, the accuracy of yield prediction is improved by using the learner that takes into account human intervention in the cultivation of the target plant.

Second Embodiment

Hereinafter, a learning device500and cultivation and management devices600and700according to the second embodiment will be described.

First is described a process of generating, through training, a model used for cultivation management.

<Device Configuration>

Since a hardware configuration of the learning device500according to the second embodiment is the same as the hardware configuration of the learning device100according to the first embodiment shown inFIG.1, the description thereof will be omitted.

FIG.9is a functional block diagram of the learning device500. The same symbols are used for the configurations common to the learning device100shown inFIG.1to omit description thereof.

The learning device500includes a training environment state information acquisition unit521, a training work history information acquisition unit522, a training yield acquisition unit523, and a training growth state information acquisition unit524.

The training growth state information acquisition unit524is a means for acquiring growth state information that is information on a growth state of a cultivation target plant itself. The growth state information is, for example, a sap flow rate measured by a sap flow sensor, an amount of absorbed nutrients measured by an absorbed nutrient sensor, and the like, and is information on the growth state that can be measured and detected from the plant itself. In addition, the growth state information includes a tree vigor detected by analyzing, using an image analysis means, an image of the cultivation target plant taken by an imaging device such as a camera. Here, the sap flow sensor, the absorbed nutrient sensor, the imaging device, and the image analysis means configure a growth state detecting means. Further, the growth state information as training data is referred to as training growth state information.

As described above, regarding information that can be measured and detected by the growth state detection means such as the sap flow sensor and the absorbed nutrient sensor, the control unit10may control these growth state detection means through the external interface16to acquire the growth state information. In this case, the external interface16and the control unit10connected to the growth state detecting means configure the training growth state information acquisition unit524. The training growth state information that has already been measured, detected, and stored in the external device may also be acquired. In this case, the external interface16or the communication interface17connected to the external device configures the training growth state information acquisition unit524. Further, the processor11that executes a predetermined program may acquire the training growth state information input by the user via the input unit14. In this case, the input unit14and the control unit10configure the training growth state information acquisition unit524.

As described above, the training data according to the second embodiment includes the training growth state information in addition to the training environment state information, training work history information, and training yield information. These pieces of training data are stored in the training data storage unit131that is a predetermined area of the auxiliary storage unit13.

Here, the training growth state information is also associated in chronological order with respect to the cultivation target plant together with the training environment state information, the training work history information, and the training yield information. In addition, these pieces of information may be acquired for each stem of the cultivation target plant, or may be collectively acquired for a group of stems cultivated in a predetermined plot or a predetermined greenhouse. Corresponding to each case, the training environment state information, the training work history information, the training yield information, and the training growth state information are associated with each other by information that identifies each stem or the group of stems.

A learning process unit525performs machine learning of the learner so as to output an output value corresponding to work information when the training environment state information, the training growth state information, and the training yield information stored in the training data storage unit131are input. The learning process unit525performs machine learning by executing by the control unit10a learning model generation program stored in a predetermined area of the auxiliary storage unit13. Here, for example, the learner is a model that predicts a work required for achieving a target yield by using the training environment state information, the training growth state information, and the training yield information as training data, and the training work history information as supervised data. For example, the program may calculate the model by a neural network, but is not limited to this. For example, the model can be calculated by work including shape change work h (x1, x2, x4) based on training environment state information (x1), training yield information (x2), and training growth state information (x4). Specifically, in the case of the neural network, model h (x1, x2, x4) can be obtained by adjusting a weight of each connection between the nodes of each layer including the input layer having x1, x2, and x4 to minimize error function with work h (x1, x2, x4), which is output, and training work history information (x3). In addition, in a statistical method, the model h (x1, x2, x4) can be obtained by calculating a weight coefficient in a regression equation including inputs x1, x2, and x4 by the least-square method or the like.

By repeating machine learning of the learner in the learning process unit525using a large amount of training data, a trained learner526is obtained. The trained learner526obtained in this way is stored in the learning result data storage unit132, which is a predetermined area of the auxiliary storage unit13.

<Learning Model Generation Method>

FIG.10is a flowchart showing process steps of the learning model generation method. The learning model generation method is executed as a learning model generation program in the learning device500.

First, the training environment state information acquisition unit521acquires the training environment state information (step S301).

Next, the training growth state information acquisition unit524acquires the training growth state information (step S302).

Next, the training work history information acquisition unit522acquires the training work history information (step S303).

Next, the training yield acquisition unit523acquires the training yield information (step S304).

Then, the learning process unit525trains the above-mentioned learner by using the acquired training environment state information, training growth state information, and training yield information as the input data and training work history information as the supervised data (step S305). Since the learning process of the learner is the same as that of the first embodiment, detailed description thereof will be omitted.

By repeating the process from step S301to step S305for a large amount of training data, the trained learner526is output from the learning process unit525. The control unit10acquires and stores the trained learner526in the learning result data storage unit132(step S306).

In this way, the learner capable of giving appropriate work instructions is generated, taking into account human intervention in cultivation of the target plant.

Next, a process of cultivation management based on inference using the model generated by training will be described.

<Cultivation and Management System>

FIG.11is a schematic diagram showing a schematic configuration of a cultivation and management system2including the learning device500and the cultivation and management devices600and700according to the second embodiment.

The learning device500, the cultivation and management device600, and the cultivation and management device700are connected via a network. The cultivation and management device600is connected to the controller412of the greenhouse410in which plants are cultivated. The cultivation and management device700is also connected to the controller422of the greenhouse420in which plants are cultivated similarly. Hereinafter, the cultivation and management device600will be described, but the same applies to the cultivation and management device700. Further, the cultivation and management device600is not limited to a case where it is connected to the controller412of one greenhouse410, and may be connected to controllers of a plurality of greenhouses. The learner526generated in the learning device500is transmitted to the cultivation and management devices600and700via the network4. Further, the cultivation and management devices600and700transmit the training environment state information, the training work history information, the training yield information, and the training growth state information to the learning device500via the network4. By configuring the cultivation and management system with the learning device500and the cultivation and management devices600and700, training data can be collected from the cultivation and management devices600and700to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices600and700, more accurate cultivation becomes possible.

Here, a sap flow sensor414is provided as the growth state detecting means for measuring a sap flow rate of the plant411to be cultivated. The sap flow sensor414is connected to the controller412, and the sap flow rate measured by the sap flow sensor414is transmitted to the cultivation and management device600via the controller412and acquired as the growth state information. The plant421in the greenhouse420is also provided with a sap flow sensor424connected to the controller422.

Since a hardware configuration of the cultivation and management device600according to the second embodiment is the same as that of the cultivation and management device200according to the first embodiment shown inFIG.5, the same symbols are used to omit detailed description thereof.

FIG.12(A)is a first functional block diagram of the cultivation and management device600according to the second embodiment.

The cultivation and management device600includes at least an environment state information acquisition unit620, a growth state information acquisition unit621, a planned yield acquisition unit622, and a calculation unit624.

The environment state information acquisition unit620is a means for acquiring environment state information that is information on the environment under which the cultivation target plant is placed. Here, the control unit210may acquire the environment state information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the environment state information acquisition unit620. In addition, with respect to the environment state information such as temperature, humidity, and illuminance inside the greenhouse that can be detected by environment detection means such as a temperature sensor, a humidity sensor, and an illuminance sensor installed in the greenhouse, the control unit210may acquire the environment state information detected by the environment detection means such as the temperature/humidity sensor413installed in the greenhouse410from the controller412of the greenhouse410connected through the external interface216. In this case, in the example inFIG.11, the temperature/humidity sensor413and the external interface216connected to the controller412configure the environment state information acquisition unit620. Further, regarding the environment state information that can be acquired from an external server via the network, such as outside temperature and humidity, the control unit210may acquire the information from the external server via the network such as the Internet through the communication interface217. In this case, the communication interface217and the control unit210configure the environment state information acquisition unit620.

The growth state information acquisition unit621is a means for acquiring growth state information that is information on a growth state of the cultivation target plant itself. The growth state information is, for example, a sap flow rate measured by a sap flow sensor, an amount of absorbed nutrients measured by an absorbed nutrient sensor, and the like, and is information on the growth state that can be measured and detected from the plant itself. In addition, the growth state information includes a tree vigor detected by analyzing, using an image analysis means, an image of the cultivation target plant taken by an imaging device such as a camera. Here, the sap flow sensor, the absorbed nutrient sensor, the imaging device, and the image analysis means configure a growth state detecting means.

As described above, regarding information that can be measured and detected by the growth state detection means such as the sap flow sensor and the absorbed nutrient sensor, the control unit210may control these growth state detection means through the external interface216to acquire the growth state information. In this case, the external interface216connected to the growth state detecting means configures the growth state information acquisition unit621. The growth state information that has already been measured, detected, and stored in the external device may also be acquired. In this case, the external interface216or the communication interface217connected to the external device configures the growth state information acquisition unit621. The control unit210may also acquire the growth state information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the growth state information acquisition unit621.

The work history information acquisition unit623is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit210may acquire the work history information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the work history information acquisition unit623. Further, the control unit210may convert the information input by the user via the input unit214to acquire the work history information. Here, the control unit210functions as a quantification means. In this way, the user does not need to input the work history in the form of quantified work history information, and a burden of the user's input work is reduced. For example, the user selects a work type from a pull-down menu displayed on the display as the output unit215, and selects a work amount from the work amount pull-down menu displayed according to the selected work type to enter the work history. For example, in the cultivation and management device600, same as in the learning device500, a work type flag corresponding to the selected work type (for example, when a work is flower picking, a flower picking flag) is set to on, and a numerical value (for example, 3) quantified based on the unit (here, quantity) specified according to the work type is acquired as the training work history information. The work history information input by the user may be sequentially stored in a predetermined area of the auxiliary storage unit213, and the control unit210may read and acquire the information from the area when the calculation unit624predicts a yield. Further, the control unit210may acquire the information from an external device through the external interface216or the communication interface217. In this case, the external interface216or the communication interface217and the control unit210configure the work history information acquisition unit623.

The planned yield acquisition unit622is a means for acquiring the planned yield information that is information on a yield in a cultivation plan scheduled by the user. Here, the control unit210may acquire the planned yield information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the planned yield acquisition unit622. Further, the planned yield information previously input by the user via the input unit214may be stored in a predetermined area of the auxiliary storage unit213, and the control unit210may read the information.

When the environment state information acquired by the environment state information acquisition unit620, the growth state information acquired by the growth state information acquisition unit621, and the planned yield information acquired by the planned yield acquisition unit622are input to, the calculation unit624of the cultivation and management device600, using the above-mentioned learner526, the calculation unit624can output to the output unit215work instruction information625on the work including the shape change work required for achieving the planned yield under the environment state specified by the environment state information and the growth state specified by the growth state information. The work instruction information625is, for example, displayed on a display as the output unit215, or is printed out as a document from a printer as the output unit215. The work instruction information224may also be transmitted to an external device through the external interface216or the communication interface217. Here, the calculation unit624is configured with the control unit210that executes a calculation program.

FIG.12(B)is a second functional block diagram of the cultivation and management device600according to the second embodiment.

The cultivation and management device600includes at least the environment state information acquisition unit620, the growth state information acquisition unit621, the work history information acquisition unit623, and the calculation unit624. The same symbols are used for the same configurations as the first functional block diagram shown inFIG.12(A)to omit detailed description thereof.

The work history information acquisition unit623is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit210may acquire the work history information input by the user via the input unit214. In this case, the input unit214and the control unit210configure the work history information acquisition unit623. Further, the control unit210may convert the information input by the user via the input unit214to acquire the work history information. Here, the control unit210functions as a quantification means. In this way, the user does not need to input the work history in the form of quantified work history information, and a burden of the user's input work is reduced. For example, the user selects a work type from a pull-down menu displayed on the display as the output unit215, and selects a work amount from the work amount pull-down menu displayed according to the selected work type to enter the work history. For example, in the cultivation and management device600, same as in the learning device500, a work type flag corresponding to the selected work type (for example, when a work is flower picking, a flower picking flag) is set to on, and a numerical value (for example, 3) quantified based on the unit (here, quantity) specified according to the work type is acquired as the training work history information. The work history information input by the user may be sequentially stored in a predetermined area of the auxiliary storage unit213, and the control unit210may read and acquire the information from the area when the calculation unit624predicts a yield. Further, the control unit210may acquire the information from an external device through the external interface216or the communication interface217. In this case, the external interface216or the communication interface217and the control unit210configure the work history information acquisition unit623.

When the environment state information acquired by the environment state information acquisition unit620, the growth state information acquired by the growth state information acquisition unit621, and the work history information acquired by the work history information acquisition unit623are input to the above-mentioned learner526, the calculation unit624of the cultivation and management device600can output to the output unit215predicted yield information626based on the environment state specified by the environment state information, the growth state specified by the growth state information, and the work history up to that point. The predicted yield information626is, for example, displayed on a display as the output unit215or printed out as a document from a printer as the output unit215. The work instruction information224may also be transmitted to an external device through the external interface216or the communication interface217. Here, the calculation unit624is configured with the control unit210that executes a calculation program.

<Cultivation and Management Method>

FIG.13(A)is a flowchart showing process steps of the cultivation and management method. The cultivation and management method is executed as a cultivation and management program in the cultivation and management device600.

First, the environment state information acquisition unit620acquires the environment state information (step S401).

Next, the growth state information acquisition unit621acquires the growth state information (step S402).

Next, the planned yield acquisition unit622acquires the planned yield information (step S403).

Then, the calculation unit624inputs the environment state information, the growth state information, and the planned yield information to the trained learner526to perform the calculation process (step S404).

Then, the calculation unit624outputs, through the calculation process, the work instruction information625that gives an instruction for the work including the shape change work necessary for achieving the planned yield under the environment state and the growth state specified by the environment state information and the growth state information that have been input. The control unit210acquires and outputs the information to, for example, the output unit215(step S405). Since the calculation process of the work instruction information625is the same as that in the first embodiment, detailed description thereof will be omitted.

In this way, the accuracy of cultivation work instruction is improved by using the learner that takes into account human intervention in the cultivation of the target plant. In addition, even when a worker has little knowledge and experience of the cultivation, the planned yield can be achieved by performing cultivation based on the work instruction.

FIG.13(B)is a flowchart showing the process steps of the yield prediction method as another cultivation and management method. The same symbols are used for the same processes as the cultivation and management method shown inFIG.13(A)to omit description thereof.

First, the environment state information acquisition unit620acquires the environment state information (step S401).

Next, the growth state information acquisition unit621acquires the growth state information (step S402).

Next, the work history information acquisition unit623acquires the work history information (step S406).

Then, the calculation unit624inputs the environment state information, the growth state information, and the work history information to the trained learner526to perform the calculation process (step S404).

Then, the calculation unit624outputs, through the calculation process, the predicted yield information626based on the environment state and the growth state specified by the environment state information and the growth state information that have been input and the work history up to that point. The control unit210acquires and outputs the information to, for example, the output unit215(step S407).

In this way, the accuracy of yield prediction is improved by using the learner that takes into account human intervention in the cultivation of the target plant.

Third Embodiment

Hereinafter, a learning device800and cultivation and management devices900and1000according to the third embodiment will be described.

Since a hardware configuration, functional blocks, and learning model generation method of the learning device800according to the third embodiment are the same as those of the second embodiment, the same symbols are given to a process of generating a model used for cultivation management through training to omit detailed description thereof. Next, a process of cultivation management based on inference using the model generated by training will be described below.

<Cultivation and Management System>

FIG.14is a schematic diagram showing a schematic configuration of a cultivation and management system3including the learning device800and the cultivation and management devices900and1000according to the third embodiment.

The learning device800, the cultivation and management device900, and the cultivation and management device1000are connected via the network4. The cultivation and management device900is connected to the controller412of the greenhouse410in which the plant411will be cultivated. The cultivation and management device1000is also connected to the controller422of the greenhouse420in which the plant421is cultivated. Hereinafter, the cultivation and management device900will be described, but the same applies to the cultivation and management device1000. Further, the cultivation and management device900is not limited to a case where it is connected to the controller412of one greenhouse410, and may be connected to controllers of a plurality of greenhouses. The learner526generated in the learning device800is transmitted to the cultivation and management devices900and1000via the network4. Further, the cultivation and management devices900and1000transmit the training environment state information, the training work history information, and the training yield information to the learning device800via the network4. By configuring the cultivation and management system with the learning device800and the cultivation and management devices900and1000, training data can be collected from the cultivation and management devices900and1000to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices900and1000, more accurate cultivation becomes possible.

Here, a light-shielding curtain415is provided as an environment state control means that controls an amount of solar radiation to the cultivation target plant by adjusting an opening degree. The light-shielding curtain415is connected to the controller412, and the opening degree is controlled based on state control instruction information transmitted from the cultivation and management device900. Accordingly, the amount of solar radiation directed to the plant is controlled. The greenhouse420is also provided with a light-shielding curtain425connected to the controller422.

Since a hardware configuration of the cultivation and management device900according to the third embodiment is the same as that of the cultivation and management device200according to the first embodiment shown inFIG.6, the same symbols are given to omit detailed description thereof.

FIG.15(A)is a first functional block diagram of the cultivation and management device900.

The cultivation and management device900includes at least an environment state information acquisition unit920, a growth state information acquisition unit921, a planned yield acquisition unit922, a calculation unit924, and a state control instruction unit925.

The calculation unit924inputs to the trained learner526the environment state information acquired by the environment state information acquisition unit920, the growth state information acquired by the growth state information acquisition unit921, and the planned yield acquired by the planned yield acquisition unit922, and outputs to the output unit215work instruction information926and also outputs to the state control instruction unit925state control instruction information927.

The control unit210acquires the state control instruction information927, and the state control instruction unit925transmits to the controller412connected through the external interface216information for giving an instruction for the control of a state control means (for example, light-shielding curtain415) that controls the environment state and the growth state in the greenhouse410.

Here, the state control means includes environment state control means that controls the environment state such as a heater/humidifier that controls a temperature and humidity, which is the environment state under which the cultivation target plant is placed, a light-shielding curtain that controls the amount of solar radiation, and a carbon dioxide generator that controls an amount of carbon dioxide. In addition, the state control means includes growth state control means that controls the growth state such as a fertilizer that controls nutrients in the soil that affect an amount of absorbed nutrients of plants and an irrigation device that controls a water content in the soil that affects a sap flow rate.

FIG.16(A)is a flowchart showing process steps of the cultivation and management method. This cultivation and management method is executed as a cultivation and management program in the cultivation and management device900.

First, the environment state information acquisition unit920acquires the environment state information (step S501).

Next, the growth state information acquisition unit921acquires the growth state information (step S502).

Next, the planned yield acquisition unit922acquires the planned yield information (step S503).

Then, the calculation unit924inputs the environment state information, the growth state information, and the planned yield information to the trained learner526to perform the calculation process (step S504).

Then, the calculation unit924outputs, through the calculation process in step S505, the work instruction information926(step S505) that gives an instruction for the work including the shape change work necessary for achieving the planned yield and the state control instruction information927(step S506) based on the environment state and the growth state specified by the environment state information and the growth state information that have been input. Since the calculation process of the necessary work instruction information and the state control instruction information is the same as that in the first embodiment, detailed description thereof will be omitted. The control unit210acquires the work instruction information926and outputs it, for example, to the output unit215, and also acquires the state control instruction information927and outputs it, for example, to the state control instruction unit925.

In this way, by using a learning device that takes into account human intervention in the cultivation of the target plant, the accuracy of instructions for cultivation work is improved and the accuracy of instructions for controlling environmental conditions and growth conditions is improved. In addition, even when a worker has little knowledge and experience of the cultivation, the planned yield can be achieved by performing cultivation based on the work instruction.

FIG.15(B)is a second functional block diagram of the cultivation and management device900.

The cultivation and management device900includes at least the environment state information acquisition unit920, the growth state information acquisition unit921, a work history information acquisition unit923, and the calculation unit924. The same symbols are used for the same configurations as those in the first block diagram shown inFIG.15(A)to omit description thereof.

FIG.16(B)is a flowchart showing the process steps of a yield prediction method as another cultivation and management method. The same symbols are used for the same processes as the cultivation and management method shown inFIG.16(A)to omit description thereof.

First, the environment state information acquisition unit920acquires the environment state information (step S501).

Next, the growth state information acquisition unit921acquires the growth state information (step S502).

Next, the work history information acquisition unit923acquires the work history (step S507).

Then, the calculation unit924inputs the environment state information, the growth state information, and the work history information to the trained learner526to perform the calculation process (step S504).

Then, the calculation unit924outputs, through the calculation process in step S704, the predicted yield under the environment state and the growth state specified by the environment state information and the growth state information, and the work history up to that point that have been input. The control unit210acquires the planned yield information928and outputs it to, for example, the output unit215.

In this way, the accuracy of yield prediction is improved by using the learner that takes into account human intervention in the cultivation of the target plant.

In order to make it possible to compare required components of the invention with the configurations of the embodiments, the required components of the invention are described below with symbols used in the drawings.

<Invention 1>

A cultivation and management device for fruit vegetable plants and fruit trees (200) including:environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated;planned cultivation evaluation index information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree;a calculation unit (223) configured to determine and output a work including a shape change work for the fruit vegetable plant or the fruit tree with respect to inputs of the environment state information and the planned cultivation evaluation index information, the calculation unit using a learning model (25) trained on a cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, an environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and a work history of a work for the fruit vegetable plant or the fruit tree, the work including the shape change work for changing a shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree; andan output unit configured to output the work including the shape change work for the fruit vegetable plant or the fruit tree.
<Invention 2>

A cultivation and management device for fruit vegetable plants and fruit trees (200) including:environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated;work history information on a history of a work for the fruit vegetable plant or the fruit tree, the work including a shape change work for changing a shape of the fruit vegetable plant or the fruit tree;a calculation unit (223) configured to output a predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree with respect to inputs of the environment state information and the work history information, the calculation unit using a learning model (25) trained on a cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, an environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and a work history of a work including the shape change work for the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree; andan output unit (215) configured to output the predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree.
<Invention 3>

A learning device (100) including:a training environment state information acquisition unit (21) configured to acquire training environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated;a training work history information acquisition unit (22) configured to acquire training work history information on a history of a work for the fruit vegetable plant or the fruit tree, the work including a shape change work for changing a shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree;a training cultivation evaluation index acquisition unit (23) configured to acquire training cultivation evaluation index information on a cultivation evaluation index of the fruit vegetable plant or the fruit tree; anda learning process unit (24) configured to generate a learning model (25) configured to determine and output a work including the shape change work for the fruit vegetable plant or the fruit tree with respect to inputs of environment state information and preplanned cultivation evaluation index information of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, the learning model being trained on training data including at least the training environment state information, the training work history information, and the training cultivation evaluation index information.

DESCRIPTION OF SYMBOLS

100learning device21training environment state information acquisition unit22training work history information acquisition unit23training yield acquisition unit24learning process unit25learner500learning device521training environment state information acquisition unit522training work history information acquisition unit523training yield acquisition unit524training growth state information acquisition unit525learning process unit526learner200cultivation and management device220environment state information acquisition unit221planned yield acquisition unit223calculation unit224work instruction information600cultivation and management device620environment state information acquisition unit621growth state information acquisition unit622planned yield acquisition unit624calculation unit625work instruction information