Patent Publication Number: US-2022225583-A1

Title: Management device for cultivation of fruit vegetable plants and fruit trees, learning device, management method for cultivation of fruit vegetable plants and fruit trees, learning model generation method, management program for cultivation of fruit vegetable plants and fruit trees, and learning model generation program

Description:
TECHNICAL FIELD 
     The invention relates to a cultivation and management device for fruit vegetable plants and fruit trees, a learning device, a cultivation and management method for fruit vegetable plants and fruit trees, a learning model generation method, a cultivation and management program for fruit vegetable plants and fruit trees, and a learning model generation program. 
     BACKGROUND ART 
     Conventionally, as a method for managing plant cultivation, Patent Document 1 proposes the next technique. First, field conditions are calculated based on future weather conditions, a work plan registered by a user in a work plan table, and a formula for calculating field conditions. Then, a growth state of prediction target cropping on a prediction target date is calculated using a prediction model based on the field conditions in a period from a prediction start date to the prediction target date. Here, it is determined whether or not a predicted growth range, which is a possible range of an index for evaluating the predicted growth state obtained as described above, is included in a manageable range, which is a criterion for determining whether or not the growth of crop is successful. This determination indicates whether or not the prediction target cropping will achieve the growth state desired by the user. 
     However, the conventional technique as described above shows a result whether or not the growth state desired by the user will be achieved when cultivation is carried out based on the work plan table registered by the user. However, the technique does not give instructions for how to change the work plan when the growth state desired by the user will not be achieved. 
     In addition, in the cultivation of fruit vegetable plants including fruits of herbaceous plants such as tomatoes, strawberries, and melons, it is not enough to simply grow plants large. Their cultivation is difficult due to complicated parameters required for producing nutritious and tasteful fruits, and the like in addition to the number of fruits harvested. In order to grow such fruit vegetable plants as desired by the user, more complicated cultivation is required. However, the prior art does not target such complicated cultivation. Therefore, even when a work is performed according to the work plan, the growth state desired by the user may have not been achieved. 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     
         
         Patent Document 1: Japanese Patent No. 5756374 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     The invention has been made in view of the above problems, and therefore an object of the invention is to provide a technique capable of giving an appropriate work instruction. 
     Means for Solving the Problem 
     A cultivation and management device for fruit vegetable plants and fruit trees of the invention includes: 
     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 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 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; and 
     an output unit configured to output the work including the shape change work for the fruit vegetable plant or the fruit tree. 
     In the invention, the learning model is trained on the cultivation evaluation index of the cultivated fruit vegetable plant or fruit tree, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the work history of the work for the fruit vegetable plant or the fruit tree, the work including the shape change work for changing the shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, so as to determine and output the work including the 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. Therefore, according to the cultivation and management device for fruit vegetable plants or fruit trees of the invention, the learning model determines and outputs the work including the shape change work, taking into account the history of work including the shape change work for the fruit vegetable plant or the fruit tree. Accordingly, appropriate work instructions can be given. 
     Here, the environment state information is information on the state of environment in which the cultivation target plant is placed. The environment state information may include, but is not limited to, temperature, humidity, and illuminance. The work history information is information including a history of work including a work whose amount of work cannot be expressed as a continuous value, such as a shape change work. The shape change work is a work of modifying a shape of the cultivated fruit vegetable plant or fruit tree, or modifying both shape and element or tissue of the fruit vegetable plant or the fruit tree, including removal such as bud removal, foliage removal, flower picking, topping, fruit thinning, stem mutilation, and weeding, and plant training. The cultivation evaluation index is an index for evaluating a cultivation result of the fruit vegetable plant or the fruit tree, and includes a yield, which is information on yield obtained as a result of cultivating the fruit vegetable plant or the fruit tree. As the cultivation evaluation index, a sugar content, a sugar acid ratio, a single fruit weight, a leaf area, a stem thickness, and the like can be used, but are not limited thereto. 
     Further, a cultivation and management device for fruit vegetable plants and fruit trees of the invention includes: 
     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 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 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; and 
     an output unit configured to output the predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree. 
     In the invention, the learning model is trained on the cultivation evaluation index of the cultivated fruit vegetable plant or fruit tree, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the work history of the 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, so as to output the 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. Therefore, according to the cultivation and management device for fruit vegetable plants and fruit trees of the invention, the learning model outputs the predicted cultivation evaluation index, taking into account the history of work including the shape change work for the fruit vegetable plant or the fruit tree. Accordingly, a cultivation evaluation index can be predicted with high accuracy. 
     Further, a learning device of the invention includes: 
     a training environment state information acquisition unit 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 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 configured to acquire training cultivation evaluation index information on a cultivation evaluation index of the fruit vegetable plant or the fruit tree; and 
     a learning process unit configured to generate a learning model 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. 
     In the invention, the learning model is generated by being trained on the training data including the training environment state information, training work history information, and the training cultivation evaluation index information. Therefore, the learning device of the invention can generate the learning model that takes into account human intervention in the cultivation of the fruit vegetable plant or the fruit tree, including the shape change work. 
     Here, the training environment state information is environment information used as the training data for training the learning model. The training work history information is work history information used as the training data for training the learning model. The training cultivation evaluation index information is cultivation evaluation index information used as the training data for training the learning model. 
     Further, a cultivation and management method for fruit vegetable plants and fruit trees of the invention executes on a computer the steps of: 
     acquiring environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated; 
     acquiring planned cultivation evaluation index information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree; and 
     determining a work including a shape change work for the fruit vegetable plant or the fruit tree based on the environment state information and the planned cultivation evaluation index information acquired, using a learning model trained on the cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, the environment state 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 the shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree. 
     In the invention, the work including the shape change work for the fruit vegetable plant or the fruit tree is determined with respect to inputs of the environment state information and the planned cultivation evaluation index information, using the learning model trained on the cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the history of work for the fruit vegetable plant or the fruit tree, the work including the shape change work for changing the shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree. Therefore, according to the cultivation and management method for fruit vegetable plants and fruit trees, the learning model determines and outputs the work including the shape change work, taking into account the history of work including the shape change work for the fruit vegetable plant or the fruit tree. Accordingly, appropriate work instructions can be given. 
     Here, the order of acquiring the environment state information and the planned cultivation evaluation index information is not limited to the above. 
     Further, a cultivation and management method for fruit vegetable plants and fruit trees of the invention executes on a computer the steps of: 
     acquiring environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated; 
     acquiring work history information on a history of 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; and 
     outputting a predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree based on the environment state information and the work history information acquired, using a learning model 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. 
     In the invention, the learning model is trained on the cultivation evaluation index of the cultivated fruit vegetable plant or fruit tree, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the work history of the 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, so as to output the 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. Therefore, according to the cultivation and management method for fruit vegetable plants and fruit trees of the invention, the learning model outputs the predicted cultivation evaluation index, taking into account the history of work including the shape change work for cultivation of the fruit vegetable plant or the fruit tree. Accordingly, a cultivation evaluation index can be predicted with high accuracy. 
     Here, the order of acquiring the environment state information and the work history information is not limited to the above. 
     Further, a learning model generation method of the invention executes on a computer the steps of: 
     acquiring training cultivation evaluation index information on a cultivation evaluation index when a fruit vegetable plant or a fruit tree is cultivated; 
     acquiring training environment state information on an environment state of the fruit vegetable plant or the fruit tree cultivated; 
     acquiring 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 cultivated; and 
     generating a learning model configured to determine a work including the shape change work for the fruit vegetable plant or the fruit tree based on environment state information on an environment state of the fruit vegetable plant or the fruit tree and planned cultivation evaluation index, which is information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree, the learning model being trained on training data including at least the training cultivation evaluation index information, the training environment state information, and the training work history information. 
     In the invention, the learning model is generated by being trained on the training data including at least the training work history information in addition to the training cultivation evaluation index and the training environment state information, so as to determine the work including the shape change work for the fruit vegetable plant or the fruit tree. Therefore, the learning model generation method of the invention can generate the learning model that takes into account human intervention in the cultivation of the fruit vegetable plant or the fruit tree, including the shape change work. 
     Here, the order of acquiring the training cultivation evaluation index, the training environment state information, and the training work history information is not limited to the above. 
     In addition, the step of generating the learning model may include the step of generating the learning model by reinforcement learning in which the environment state information is set as an environment state, the work including the shape change work for the fruit vegetable plant or the fruit tree is set as an action on the environment, and the cultivation evaluation index is set as a reward. 
     Further, a cultivation and management program for fruit vegetable plants and fruit trees of the invention executes on a computer the steps of: 
     acquiring environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated; 
     acquiring planned cultivation evaluation index information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree; and 
     determining a work including a shape change work for the fruit vegetable plant or the fruit tree based on the environment state information and the planned cultivation evaluation index information acquired, using a learning model trained on the cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, the environment state 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. 
     In the invention, the work including the shape change work for the fruit vegetable plant or the fruit tree is determined with respect to inputs of the environment state information and the planned cultivation evaluation index information, using the learning model trained on the cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the history of work for the fruit vegetable plant or the fruit tree, the work including the shape change work for changing the shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree. Therefore, according to the cultivation and management program for fruit vegetable plants and fruit trees of the invention, the learning model determines and outputs the work including the shape change work, taking into account the history of work including the shape change work for the fruit vegetable plant or the fruit tree. Accordingly, appropriate work instructions can be given. 
     Here, the order of acquiring the environment state information and the planned cultivation evaluation index information is not limited to the above. 
     Further, a cultivation and management program for fruit vegetable plants and fruit trees of the invention executes on a computer the steps of: 
     acquiring environment state information on an environment state of a fruit vegetable plant or a fruit tree to be cultivated; 
     acquiring 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; and 
     outputting a predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree based on the environment state information and the work history information acquired, using a learning model 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. 
     In the invention, the learning model is trained on the cultivation evaluation index of the cultivated fruit vegetable plant or fruit tree, the environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and the work history of the 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, so as to output the 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. Therefore, according to the cultivation and management program for fruit vegetable plants and fruit trees of the invention, the learning model outputs the predicted cultivation evaluation index, taking into account the history of work including the shape change work for the fruit vegetable plant or the fruit tree. Accordingly, a cultivation evaluation index can be predicted with high accuracy. 
     Here, the order of acquiring the environment state information and the work history information is not limited to the above. 
     Further, a learning model generation program of the invention executes on a computer the steps of: 
     acquiring training cultivation evaluation index information on a cultivation evaluation index when a fruit vegetable plant or a fruit tree is cultivated; 
     acquiring training environment state information on an environment state of the fruit vegetable plant or the fruit tree cultivated; 
     acquiring 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 cultivated; and 
     generating a learning model configured to determine a work including the shape change work for the fruit vegetable plant or the fruit tree based on environment state information on an environment state of the fruit vegetable plant or the fruit tree and planned cultivation evaluation index, which is information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree, the learning model being trained on training data including at least the training cultivation evaluation index information, the training environment state information, and the training work history information. 
     In the invention, the learning model that determines the work including the shape change work for the fruit vegetable plant or the fruit tree is generated by being trained on the training data including at least the training work history information in addition to the training cultivation evaluation index and the training environment state information. Therefore, the learning model generation program of the invention can generate the learning model that takes into account human intervention in the cultivation of the fruit vegetable plant or the fruit tree, including the shape change work. 
     Here, the order of acquiring the training cultivation evaluation index, the training environment state information, and the training work history information is not limited to the above. 
     Effect of the Invention 
     According to the invention, it is possible to provide a technique capable of giving an appropriate work instruction. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a hardware configuration diagram of a learning device according to a first embodiment of the invention. 
         FIG. 2  is a functional block diagram of the learning device according to the first embodiment of the invention. 
         FIG. 3  is a flowchart showing process steps of a learning model generation method according to the first embodiment of the invention. 
         FIGS. 4(A) and 4(B)  are diagrams showing examples of a configuration of a learner according to the first embodiment of the invention. 
         FIG. 5  is a schematic configuration diagram of a cultivation and management system according to the first embodiment of the invention. 
         FIG. 6  is a hardware configuration diagram of a cultivation and management device according to the first embodiment of the invention. 
         FIGS. 7(A) and 7(B)  are functional block diagrams of the cultivation and management device according to the first embodiment of the invention. 
         FIGS. 8(A) and 8(B)  are flowcharts showing process steps of a cultivation and management method according to the first embodiment of the invention. 
         FIG. 9  is a functional block diagram of a learning device according to a second embodiment of the invention. 
         FIG. 10  is a flowchart showing process steps of a learning model generation method according to the second embodiment of the invention. 
         FIG. 11  is a schematic configuration diagram of a cultivation and management system according to the second embodiment of the invention. 
         FIGS. 12(A) and 12(B)  are functional block diagrams of a cultivation and management device according to the second embodiment of the invention. 
         FIGS. 13(A) and 13(B)  are flowcharts showing process steps of a cultivation and management method according to the second embodiment of the invention. 
         FIG. 14  is a schematic configuration diagram of a cultivation and management system according to a third embodiment of the invention. 
         FIGS. 15(A) and 15(B)  are functional block diagrams of a cultivation and management device according to the third embodiment of the invention. 
         FIGS. 16(A) and 16(B)  are flowcharts showing process steps of a cultivation and management method according to the third embodiment of the invention. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Application Examples 
     Hereinafter, application examples of the invention will be described with reference to the drawings. 
       FIG. 5  shows a cultivation and management system  1  including cultivation and management devices  200  and  300  to which the invention is applied.  FIGS. 7(A) and 7(B)  show functional block diagrams of the cultivation and management device  200 . Here, the case where the cultivation and management device  200  outputs work instructions for fruit vegetable plants or fruit trees will be described. However, as described later, the cultivation and management device  200  can also be configured to output a yield. 
     A learner  25  that outputs work instructions is generated in a learning device  100 . In the learning device  100  (see  FIG. 2 ), a learning process unit  24  trains and generates the learner  25 , using training environment state information, training work history information, and training yield information as training data, so that the learner  25  outputs work instructions including a shape change work when environment state information and planned yield are input. Since the learner  25  is 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 learner  25  can output an appropriate work instruction. 
     In the cultivation and management device  200 , work instructions including the shape change work are output by inputting to the learner  25  generated 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 learner  25  generated 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 device  200  can output an appropriate work instruction. 
     Further, by configuring the cultivation and management system with the learning device  100  and the cultivation and management devices  200  and  300 , training data can be collected from the cultivation and management device  200  or  300  to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices  200  and  300 , 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. 
     &lt;Device Configuration&gt; 
       FIG. 1  is a hardware configuration diagram of a learning device  100  according to the embodiment. 
     The learning device  100  is a computer device including a processor  11 , a main storage unit  12 , an auxiliary storage unit  13 , an input unit  14 , an output unit  15 , an external interface  16 , a communication interface  17 , and a bus  18 . 
     The processor  11  is a CPU, a DSP, or the like. 
     The main storage unit  12  is configured with a read only memory (ROM), a random access memory (RAM), and the like. 
     The auxiliary storage unit  13  includes 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 unit  13  stores an operating system (OS), various programs, various tables, and the like. The processor  11  executes the stored programs loaded into a work area of the main storage unit  12  to 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 device  100  does 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 unit  12  and the processor  11  that executes a predetermined program loaded into the work area of the main storage unit  12  are also collectively referred to as a control unit  10 . 
     The input unit  14  includes a keyboard, a mouse, a microphone, and the like, and accepts input operations from the user. 
     The output unit  15  includes a display, a speaker, and the like, and provides information to the user. 
     The external interface (indicated as I/F in the drawings)  16  is an interface for connection to various external devices. 
     The communication interface  17  is an interface for connecting the learning device  100  to a network. The communication interface  17  can adopt an appropriate configuration depending on a connection system with the network. 
     The bus  18  is a signal transmission line connecting each part of the learning device  100 . 
       FIG. 2  is a functional block diagram of the learning device  100 . 
     The learning device  100  includes a training environment state information acquisition unit  21 , a training work history information acquisition unit  22 , a training yield acquisition unit  23 , a learning process unit  24 , and a learner  25 . 
     The training environment state information acquisition unit  21  is 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 unit  22  is 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 unit  23  is 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 unit  23  corresponds 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 unit  10  may acquire the training environment state information, the training work history information, and the training yield information input by the user via the input unit  14 . In this case, the input unit  14  and the control unit  10  configure the training environment state information acquisition unit  21 , the training work history information acquisition unit  22 , and the training yield acquisition unit  23 . The user may also directly input the training work history information via the input unit  14 . Further, the control unit  10  may convert the information input by the user via the input unit  14  to acquire the training work history information. Here, the control unit  10  functions 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&#39;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 unit  15 , and selects a work amount from a work amount pull-down menu displayed according to the selected work type. For example, in the learning device  100 , 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 unit  10  may control these sensors through the external interface  16  to acquire the training environment state information. In this case, the external interface  16  and the control unit  10  connected to each of the sensors configure the training environment state information acquisition unit  21 . 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 unit  10  may acquire the information from the external server via the network such as the Internet through the communication interface  17 . In this case, the communication interface  17  and the control unit  10  configure the training environment state information acquisition unit  21 . 
     The training data storage unit  131 , which is a predetermined area of the auxiliary storage unit  13 , stores the training environment state information, the training work history information, and the training yield information acquired through the training environment state information acquisition unit  21 , the training work history information acquisition unit  22 , and the training yield acquisition unit  23 . 
     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 unit  24  performs 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 unit  131  are input. The learning process unit  24  performs machine learning by reading and executing by the control unit  10  a learning model generation program stored in a predetermined area of the auxiliary storage unit  13 . 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 unit  24  using a large amount of training data, a trained learner  25  is obtained. The trained learner  25  obtained in this way is stored in the learning result data storage unit  132 , which is a predetermined area of the auxiliary storage unit  13 . Here, the learner  25  corresponds to a learning model of the invention. 
     &lt;Learning Model Generation Method&gt; 
       FIG. 3  is 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 device  100 . 
     First, the control unit  10  acquires the training environment state information (step S 101 ). 
     Next, the control unit  10  acquires the training work history information (step S 102 ). 
     Next, the control unit  10  acquires the training yield information (step S 103 ). 
     Then, the learning process unit  24  trains 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 S 104 ). 
     Here, the case where the learner  25  is configured with the neural network will be described as an example. As shown in  FIG. 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 S 101  to step S 104  fora large amount of training data, the trained learner  25  is output from the learning process unit  24 , and the control unit  10  acquires and stores the trained learner  25  in the learning result data storage unit  132  (step S 105 ). 
     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 learner  25  configured with the neural network has been described, but the learning model generation method is not limited to the neural network. As shown in  FIG. 4(B) , the learner  25  may 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 greenhouses  410  and  420  in which plants  411  and  421  are cultivated, and the learning device  100  or the cultivation and management devices  200  and  300  correspond 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 learner  25  is trained with respect to the action An that maximizes the reward Rw under the state St in the learner  25 . In other words, the learner  25  learns 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 in  FIG. 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 learner  25  that 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. 
     &lt;Cultivation and Management System&gt; 
       FIG. 5  is a schematic diagram showing a schematic configuration of the cultivation and management system  1  including the learning device  100  and cultivation and management devices  200  and  300  according to the embodiment. 
     The learning device  100 , the cultivation and management device  200 , and the cultivation and management device  300  are connected via the network  4 . The cultivation and management device  200  is connected to a controller  412  of the greenhouse  410  in which the plant  411  is cultivated. The cultivation and management device  300  is also connected to a controller  422  of the greenhouse  420  in which the plant  421  is similarly cultivated. Hereinafter, the cultivation and management device  200  will be described, but the same applies to the cultivation and management device  300 . Further, the cultivation and management device  200  is not limited to a case where it is connected to the controller  412  of one greenhouse  410 , and may be connected to controllers of a plurality of greenhouses. The learner  25  generated in the learning device  100  is transmitted to the cultivation and management devices  200  and  300  via the network  4 . Further, the cultivation and management devices  200  and  300  transmit the training environment state information, the training work history information, and the training yield information to the learning device  100  via the network  4 . By configuring the cultivation and management system with the learning device  100  and the cultivation and management devices  200  and  300 , training data can be collected from the cultivation and management devices  200  and  300  to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices  200  and  300 , more accurate cultivation becomes possible. 
     Here, a temperature/humidity sensor  413  is provided to measure a temperature and humidity of the greenhouse  410  with respect to the environment state of the cultivation target plant  411 . The temperature/humidity sensor  413  is connected to the controller  412 , and the temperature and humidity measured by the temperature/humidity sensor  413  are transmitted to the cultivation and management device  200  via the controller  412  and acquired by the cultivation and management device  200  as environment state information. The greenhouse  420  is also provided with a temperature/humidity sensor  423 . 
     &lt;Cultivation and Management Device&gt; 
       FIG. 6  is a hardware configuration diagram of the cultivation and management device  200 . 
     The cultivation and management device  200  is a computer device including a processor  211 , a main storage unit  212 , an auxiliary storage unit  213 , an input unit  214 , an output unit  215 , an external interface  216 , a communication interface  217 , and a bus  218 . 
     The processor  211 , the main storage unit  212 , the auxiliary storage unit  213 , the input unit  214 , the output unit  215 , the external interface  216 , the communication interface  217 , the bus  218 , and a control unit  210  are the same as those described for the learning device  100 , and thus detailed description will be omitted. 
     Here, the auxiliary storage unit  213  stores the trained learner  25  generated by the learning device  100 . 
       FIG. 7(A)  is a first functional block diagram of the cultivation and management device  200 . 
     The cultivation and management device  200  includes at least an environment state information acquisition unit  220 , a planned yield acquisition unit  221 , and a calculation unit  223 . 
     The environment state information acquisition unit  220  is 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 unit  210  may acquire the environment state information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the environment state information acquisition unit  220 . In addition, regarding the environment state information such as temperature, humidity, and illuminance inside the greenhouse  410  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 unit  210  may acquire the environment state information detected by the environment detection means such as the temperature/humidity sensor  413  installed in the greenhouse  410  from the controller  412  of the greenhouse  410  connected through the external interface  216 . In this case, in the example in  FIG. 4 , the temperature/humidity sensor  413 , the external interface  216  connected to the controller  412 , and the control unit  210  configure the environment state information acquisition unit  220 . 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 unit  210  may acquire the information from the external server via the network such as the Internet through the communication interface  217 . In this case, the communication interface  217  and the control unit  210  configure the environment state information acquisition unit  220 . 
     The planned yield acquisition unit  221  is a means for acquiring planned yield information that is information on a yield in a cultivation plan scheduled by the user. Here, the control unit  210  may acquire the planned yield information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the planned yield acquisition unit  221 . Further, the control unit  210  may 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 network  4  through the communication interface  217 . In this case, the communication interface  217  and the control unit  210  configure the environment state information acquisition unit  220  and the planned yield acquisition unit  221 . Further, the planned yield information previously input by the user via the input unit  214  may be stored in a predetermined area of the auxiliary storage unit  213 , and the control unit  210  may read and acquire the information. 
     When the environment state information acquired by the environment state information acquisition unit  220  and the planned yield information acquired by the planned yield acquisition unit  221  are input, the calculation unit  223  of the cultivation and management device  200 , using the above-mentioned trained leaner  25 , can output to the output unit  215  information on the work including the shape change work as work instruction information  224  required for achieving the planned yield under the environment state specified by the environment state information. For example, the work instruction information  224  is displayed on a display as the output unit  215 , or is printed out as a document from a printer as the output unit  215 . The work instruction information  224  may also be transmitted to an external device through the external interface  216  or the communication interface  217 . For example, a work detail (including quantity) may be transmitted through the communication interface  217  to a mobile terminal such as a worker&#39;s smartphone connected to the network. Here, the calculation unit  223  is configured with the control unit  210  that executes a calculation program. 
       FIG. 7(B)  is a second functional block diagram of the cultivation and management device  200 . 
     The cultivation and management device  200  includes at least the environment state information acquisition unit  220 , a work history information acquisition unit  222 , and the calculation unit  223 . The same symbols are used for the same configuration as the first block diagram shown in  FIG. 7(A)  to omit detailed description thereof. 
     The work history information acquisition unit  222  is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit  210  may acquire the work history information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the work history information acquisition unit  222 . Further, the control unit  210  may convert the information input by the user via the input unit  214  to acquire the work history information. Here, the control unit  210  functions 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&#39;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 unit  215 , 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 device  200 , same as in the learning device  100 , 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 unit  213 , and the control unit  210  may read and acquire the information from the area when the calculation unit  223  predicts a yield. Further, the control unit  210  may acquire the information from an external device through the external interface  216  or the communication interface  217 . In this case, the external interface  216  or the communication interface  217  and the control unit  210  configure the work history information acquisition unit  222 . 
     When the environment state information acquired by the environment state information acquisition unit  220  and the work history information acquired by the work history information acquisition unit  222  are input, the calculation unit  223  of the cultivation and management device  200 , using the above-mentioned trained learner  25 , can output to the output unit  215  a predicted yield as predicted yield information  225 , based on the environment state specified by the environment state information and the work history up to that point. The predicted yield information  225  is, for example, displayed on a display as the output unit  215  or printed out as a document from a printer as the output unit  215 . The work instruction information  224  may also be transmitted to an external device through the external interface  216  or the communication interface  217 . For example, the predicted yield information  225  may be transmitted through the communication interface  217  to a mobile terminal such as an administrator&#39;s smartphone connected to the network. Here, the calculation unit  223  is configured with the control unit  210  that executes a calculation program. 
     The learner  25  that outputs a predicted yield as the predicted yield information  225 , 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 device  100  shown in  FIGS. 1 and 2  and the learning model generation method shown in  FIG. 3 . However, the learner  25  that 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. 
     &lt;Cultivation and Management Method&gt; 
       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 device  200 . 
     First, the environment state information acquisition unit  220  acquires the environment state information (step S 201 ). 
     Next, the planned yield acquisition unit  221  acquires the planned yield information (step S 202 ). 
     Then, the calculation unit  223  inputs the environment state information and the planned yield information to the trained learner  25  to perform a calculation process (step S 203 ). 
     A cultivation model that is the trained learner generated by the learning device  100  in 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 S 201  and the planned yield information (x2) acquired in step S 202 . 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 unit  210  acquires (step S 204 ) and outputs the information to, for example, the output unit  215 . 
     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 in  FIG. 8(A)  to omit description thereof. Here, the trained learner  25  used 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 learner  25  outputs the predicted yield by inputting the environment state information and the work history information. 
     First, the environment state information acquisition unit  220  acquires the environment state information (step S 201 ). 
     Next, the work history information acquisition unit  222  acquires the work history information (step S 205 ). 
     Then, the calculation unit  223  inputs the environment state information and the work history information to the trained learner  25  to perform a calculation process (step S 203 ). Here, the predicted yield information  225  is 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 S 201  and the work history information (x3) acquired in step S 205 . 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 unit  210  acquires (step S 206 ) and outputs the information to, for example, the output unit  215 . 
     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 device  500  and cultivation and management devices  600  and  700  according to the second embodiment will be described. 
     First is described a process of generating, through training, a model used for cultivation management. 
     &lt;Device Configuration&gt; 
     Since a hardware configuration of the learning device  500  according to the second embodiment is the same as the hardware configuration of the learning device  100  according to the first embodiment shown in  FIG. 1 , the description thereof will be omitted. 
       FIG. 9  is a functional block diagram of the learning device  500 . The same symbols are used for the configurations common to the learning device  100  shown in  FIG. 1  to omit description thereof. 
     The learning device  500  includes a training environment state information acquisition unit  521 , a training work history information acquisition unit  522 , a training yield acquisition unit  523 , and a training growth state information acquisition unit  524 . 
     The training growth state information acquisition unit  524  is 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 unit  10  may control these growth state detection means through the external interface  16  to acquire the growth state information. In this case, the external interface  16  and the control unit  10  connected to the growth state detecting means configure the training growth state information acquisition unit  524 . 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 interface  16  or the communication interface  17  connected to the external device configures the training growth state information acquisition unit  524 . Further, the processor  11  that executes a predetermined program may acquire the training growth state information input by the user via the input unit  14 . In this case, the input unit  14  and the control unit  10  configure the training growth state information acquisition unit  524 . 
     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 unit  131  that is a predetermined area of the auxiliary storage unit  13 . 
     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 unit  525  performs 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 unit  131  are input. The learning process unit  525  performs machine learning by executing by the control unit  10  a learning model generation program stored in a predetermined area of the auxiliary storage unit  13 . 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 unit  525  using a large amount of training data, a trained learner  526  is obtained. The trained learner  526  obtained in this way is stored in the learning result data storage unit  132 , which is a predetermined area of the auxiliary storage unit  13 . 
     &lt;Learning Model Generation Method&gt; 
       FIG. 10  is 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 device  500 . 
     First, the training environment state information acquisition unit  521  acquires the training environment state information (step S 301 ). 
     Next, the training growth state information acquisition unit  524  acquires the training growth state information (step S 302 ). 
     Next, the training work history information acquisition unit  522  acquires the training work history information (step S 303 ). 
     Next, the training yield acquisition unit  523  acquires the training yield information (step S 304 ). 
     Then, the learning process unit  525  trains 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 S 305 ). 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 S 301  to step S 305  for a large amount of training data, the trained learner  526  is output from the learning process unit  525 . The control unit  10  acquires and stores the trained learner  526  in the learning result data storage unit  132  (step S 306 ). 
     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. 
     &lt;Cultivation and Management System&gt; 
       FIG. 11  is a schematic diagram showing a schematic configuration of a cultivation and management system  2  including the learning device  500  and the cultivation and management devices  600  and  700  according to the second embodiment. 
     The learning device  500 , the cultivation and management device  600 , and the cultivation and management device  700  are connected via a network. The cultivation and management device  600  is connected to the controller  412  of the greenhouse  410  in which plants are cultivated. The cultivation and management device  700  is also connected to the controller  422  of the greenhouse  420  in which plants are cultivated similarly. Hereinafter, the cultivation and management device  600  will be described, but the same applies to the cultivation and management device  700 . Further, the cultivation and management device  600  is not limited to a case where it is connected to the controller  412  of one greenhouse  410 , and may be connected to controllers of a plurality of greenhouses. The learner  526  generated in the learning device  500  is transmitted to the cultivation and management devices  600  and  700  via the network  4 . Further, the cultivation and management devices  600  and  700  transmit the training environment state information, the training work history information, the training yield information, and the training growth state information to the learning device  500  via the network  4 . By configuring the cultivation and management system with the learning device  500  and the cultivation and management devices  600  and  700 , training data can be collected from the cultivation and management devices  600  and  700  to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices  600  and  700 , more accurate cultivation becomes possible. 
     Here, a sap flow sensor  414  is provided as the growth state detecting means for measuring a sap flow rate of the plant  411  to be cultivated. The sap flow sensor  414  is connected to the controller  412 , and the sap flow rate measured by the sap flow sensor  414  is transmitted to the cultivation and management device  600  via the controller  412  and acquired as the growth state information. The plant  421  in the greenhouse  420  is also provided with a sap flow sensor  424  connected to the controller  422 . 
     Since a hardware configuration of the cultivation and management device  600  according to the second embodiment is the same as that of the cultivation and management device  200  according to the first embodiment shown in  FIG. 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 device  600  according to the second embodiment. 
     The cultivation and management device  600  includes at least an environment state information acquisition unit  620 , a growth state information acquisition unit  621 , a planned yield acquisition unit  622 , and a calculation unit  624 . 
     The environment state information acquisition unit  620  is a means for acquiring environment state information that is information on the environment under which the cultivation target plant is placed. Here, the control unit  210  may acquire the environment state information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the environment state information acquisition unit  620 . 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 unit  210  may acquire the environment state information detected by the environment detection means such as the temperature/humidity sensor  413  installed in the greenhouse  410  from the controller  412  of the greenhouse  410  connected through the external interface  216 . In this case, in the example in  FIG. 11 , the temperature/humidity sensor  413  and the external interface  216  connected to the controller  412  configure the environment state information acquisition unit  620 . 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 unit  210  may acquire the information from the external server via the network such as the Internet through the communication interface  217 . In this case, the communication interface  217  and the control unit  210  configure the environment state information acquisition unit  620 . 
     The growth state information acquisition unit  621  is 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 unit  210  may control these growth state detection means through the external interface  216  to acquire the growth state information. In this case, the external interface  216  connected to the growth state detecting means configures the growth state information acquisition unit  621 . 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 interface  216  or the communication interface  217  connected to the external device configures the growth state information acquisition unit  621 . The control unit  210  may also acquire the growth state information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the growth state information acquisition unit  621 . 
     The work history information acquisition unit  623  is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit  210  may acquire the work history information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the work history information acquisition unit  623 . Further, the control unit  210  may convert the information input by the user via the input unit  214  to acquire the work history information. Here, the control unit  210  functions 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&#39;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 unit  215 , 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 device  600 , same as in the learning device  500 , 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 unit  213 , and the control unit  210  may read and acquire the information from the area when the calculation unit  624  predicts a yield. Further, the control unit  210  may acquire the information from an external device through the external interface  216  or the communication interface  217 . In this case, the external interface  216  or the communication interface  217  and the control unit  210  configure the work history information acquisition unit  623 . 
     The planned yield acquisition unit  622  is 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 unit  210  may acquire the planned yield information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the planned yield acquisition unit  622 . Further, the planned yield information previously input by the user via the input unit  214  may be stored in a predetermined area of the auxiliary storage unit  213 , and the control unit  210  may read the information. 
     When the environment state information acquired by the environment state information acquisition unit  620 , the growth state information acquired by the growth state information acquisition unit  621 , and the planned yield information acquired by the planned yield acquisition unit  622  are input to, the calculation unit  624  of the cultivation and management device  600 , using the above-mentioned learner  526 , the calculation unit  624  can output to the output unit  215  work instruction information  625  on 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 information  625  is, for example, displayed on a display as the output unit  215 , or is printed out as a document from a printer as the output unit  215 . The work instruction information  224  may also be transmitted to an external device through the external interface  216  or the communication interface  217 . Here, the calculation unit  624  is configured with the control unit  210  that executes a calculation program. 
       FIG. 12(B)  is a second functional block diagram of the cultivation and management device  600  according to the second embodiment. 
     The cultivation and management device  600  includes at least the environment state information acquisition unit  620 , the growth state information acquisition unit  621 , the work history information acquisition unit  623 , and the calculation unit  624 . The same symbols are used for the same configurations as the first functional block diagram shown in  FIG. 12(A)  to omit detailed description thereof. 
     The work history information acquisition unit  623  is a means for acquiring work history information that quantifies a history of work performed by human on the cultivation target plant. Here, the control unit  210  may acquire the work history information input by the user via the input unit  214 . In this case, the input unit  214  and the control unit  210  configure the work history information acquisition unit  623 . Further, the control unit  210  may convert the information input by the user via the input unit  214  to acquire the work history information. Here, the control unit  210  functions 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&#39;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 unit  215 , 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 device  600 , same as in the learning device  500 , 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 unit  213 , and the control unit  210  may read and acquire the information from the area when the calculation unit  624  predicts a yield. Further, the control unit  210  may acquire the information from an external device through the external interface  216  or the communication interface  217 . In this case, the external interface  216  or the communication interface  217  and the control unit  210  configure the work history information acquisition unit  623 . 
     When the environment state information acquired by the environment state information acquisition unit  620 , the growth state information acquired by the growth state information acquisition unit  621 , and the work history information acquired by the work history information acquisition unit  623  are input to the above-mentioned learner  526 , the calculation unit  624  of the cultivation and management device  600  can output to the output unit  215  predicted yield information  626  based 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 information  626  is, for example, displayed on a display as the output unit  215  or printed out as a document from a printer as the output unit  215 . The work instruction information  224  may also be transmitted to an external device through the external interface  216  or the communication interface  217 . Here, the calculation unit  624  is configured with the control unit  210  that executes a calculation program. 
     &lt;Cultivation and Management Method&gt; 
       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 device  600 . 
     First, the environment state information acquisition unit  620  acquires the environment state information (step S 401 ). 
     Next, the growth state information acquisition unit  621  acquires the growth state information (step S 402 ). 
     Next, the planned yield acquisition unit  622  acquires the planned yield information (step S 403 ). 
     Then, the calculation unit  624  inputs the environment state information, the growth state information, and the planned yield information to the trained learner  526  to perform the calculation process (step S 404 ). 
     Then, the calculation unit  624  outputs, through the calculation process, the work instruction information  625  that 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 unit  210  acquires and outputs the information to, for example, the output unit  215  (step S 405 ). Since the calculation process of the work instruction information  625  is 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 in  FIG. 13(A)  to omit description thereof. 
     First, the environment state information acquisition unit  620  acquires the environment state information (step S 401 ). 
     Next, the growth state information acquisition unit  621  acquires the growth state information (step S 402 ). 
     Next, the work history information acquisition unit  623  acquires the work history information (step S 406 ). 
     Then, the calculation unit  624  inputs the environment state information, the growth state information, and the work history information to the trained learner  526  to perform the calculation process (step S 404 ). 
     Then, the calculation unit  624  outputs, through the calculation process, the predicted yield information  626  based 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 unit  210  acquires and outputs the information to, for example, the output unit  215  (step S 407 ). 
     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 device  800  and cultivation and management devices  900  and  1000  according to the third embodiment will be described. 
     Since a hardware configuration, functional blocks, and learning model generation method of the learning device  800  according 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. 
     &lt;Cultivation and Management System&gt; 
       FIG. 14  is a schematic diagram showing a schematic configuration of a cultivation and management system  3  including the learning device  800  and the cultivation and management devices  900  and  1000  according to the third embodiment. 
     The learning device  800 , the cultivation and management device  900 , and the cultivation and management device  1000  are connected via the network  4 . The cultivation and management device  900  is connected to the controller  412  of the greenhouse  410  in which the plant  411  will be cultivated. The cultivation and management device  1000  is also connected to the controller  422  of the greenhouse  420  in which the plant  421  is cultivated. Hereinafter, the cultivation and management device  900  will be described, but the same applies to the cultivation and management device  1000 . Further, the cultivation and management device  900  is not limited to a case where it is connected to the controller  412  of one greenhouse  410 , and may be connected to controllers of a plurality of greenhouses. The learner  526  generated in the learning device  800  is transmitted to the cultivation and management devices  900  and  1000  via the network  4 . Further, the cultivation and management devices  900  and  1000  transmit the training environment state information, the training work history information, and the training yield information to the learning device  800  via the network  4 . By configuring the cultivation and management system with the learning device  800  and the cultivation and management devices  900  and  1000 , training data can be collected from the cultivation and management devices  900  and  1000  to further train the learner to improve its performance. Then, by providing an updated learner to the cultivation and management devices  900  and  1000 , more accurate cultivation becomes possible. 
     Here, a light-shielding curtain  415  is 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 curtain  415  is connected to the controller  412 , and the opening degree is controlled based on state control instruction information transmitted from the cultivation and management device  900 . Accordingly, the amount of solar radiation directed to the plant is controlled. The greenhouse  420  is also provided with a light-shielding curtain  425  connected to the controller  422 . 
     Since a hardware configuration of the cultivation and management device  900  according to the third embodiment is the same as that of the cultivation and management device  200  according to the first embodiment shown in  FIG. 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 device  900 . 
     The cultivation and management device  900  includes at least an environment state information acquisition unit  920 , a growth state information acquisition unit  921 , a planned yield acquisition unit  922 , a calculation unit  924 , and a state control instruction unit  925 . 
     The calculation unit  924  inputs to the trained learner  526  the environment state information acquired by the environment state information acquisition unit  920 , the growth state information acquired by the growth state information acquisition unit  921 , and the planned yield acquired by the planned yield acquisition unit  922 , and outputs to the output unit  215  work instruction information  926  and also outputs to the state control instruction unit  925  state control instruction information  927 . 
     The control unit  210  acquires the state control instruction information  927 , and the state control instruction unit  925  transmits to the controller  412  connected through the external interface  216  information for giving an instruction for the control of a state control means (for example, light-shielding curtain  415 ) that controls the environment state and the growth state in the greenhouse  410 . 
     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 device  900 . 
     First, the environment state information acquisition unit  920  acquires the environment state information (step S 501 ). 
     Next, the growth state information acquisition unit  921  acquires the growth state information (step S 502 ). 
     Next, the planned yield acquisition unit  922  acquires the planned yield information (step S 503 ). 
     Then, the calculation unit  924  inputs the environment state information, the growth state information, and the planned yield information to the trained learner  526  to perform the calculation process (step S 504 ). 
     Then, the calculation unit  924  outputs, through the calculation process in step S 505 , the work instruction information  926  (step S 505 ) that gives an instruction for the work including the shape change work necessary for achieving the planned yield and the state control instruction information  927  (step S 506 ) 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 unit  210  acquires the work instruction information  926  and outputs it, for example, to the output unit  215 , and also acquires the state control instruction information  927  and outputs it, for example, to the state control instruction unit  925 . 
     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 device  900 . 
     The cultivation and management device  900  includes at least the environment state information acquisition unit  920 , the growth state information acquisition unit  921 , a work history information acquisition unit  923 , and the calculation unit  924 . The same symbols are used for the same configurations as those in the first block diagram shown in  FIG. 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 in  FIG. 16(A)  to omit description thereof. 
     First, the environment state information acquisition unit  920  acquires the environment state information (step S 501 ). 
     Next, the growth state information acquisition unit  921  acquires the growth state information (step S 502 ). 
     Next, the work history information acquisition unit  923  acquires the work history (step S 507 ). 
     Then, the calculation unit  924  inputs the environment state information, the growth state information, and the work history information to the trained learner  526  to perform the calculation process (step S 504 ). 
     Then, the calculation unit  924  outputs, through the calculation process in step S 704 , 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 unit  210  acquires the planned yield information  928  and outputs it to, for example, the output unit  215 . 
     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. 
     &lt;Invention 1&gt; 
     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; and 
     an output unit configured to output the work including the shape change work for the fruit vegetable plant or the fruit tree. 
     &lt;Invention 2&gt; 
     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; and 
     an output unit ( 215 ) configured to output the predicted cultivation evaluation index of the fruit vegetable plant or the fruit tree. 
     &lt;Invention 3&gt; 
     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; and 
     a 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 
     
         
         
           
               100  learning device 
               21  training environment state information acquisition unit 
               22  training work history information acquisition unit 
               23  training yield acquisition unit 
               24  learning process unit 
               25  learner 
               500  learning device 
               521  training environment state information acquisition unit 
               522  training work history information acquisition unit 
               523  training yield acquisition unit 
               524  training growth state information acquisition unit 
               525  learning process unit 
               526  learner 
               200  cultivation and management device 
               220  environment state information acquisition unit 
               221  planned yield acquisition unit 
               223  calculation unit 
               224  work instruction information 
               600  cultivation and management device 
               620  environment state information acquisition unit 
               621  growth state information acquisition unit 
               622  planned yield acquisition unit 
               624  calculation unit 
               625  work instruction information