Information processing device

Abstract

Problem: To provide an information processing device that can effectively reduce the risk of pest damage to agricultural products.Solution: An information processing device 10 predicts the risk of pest damage to agricultural products, and includes: a prediction unit that predicts the effect of reducing the risk of pest damage for each of a plurality of candidate countermeasures that change at least one impact parameter that affects the risk of pest damage, and a selection unit that selects a countermeasure from the plurality of candidate countermeasures by prioritizing a countermeasure with a greater effect of reducing the risk of pest damage based on the prediction result provided by the prediction unit regarding the effect of reducing the risk of pest damage.

Description

[Technical field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] Agricultural techniques have been proposed to protect produce from pests (i.e., disease damage, which is damage to produce caused by disease, or insect damage, which is damage to produce caused by insects). Such techniques include predicting pest risk, which is the risk of pests occurring (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2004-185222(A) Summary of the Invention [Problem to be solved by the invention]

[0004] The predicted pest risk is notified to a user (i.e., a farmer) and used to protect agricultural products from pests. However, there are a wide variety of actions that a user can take in response to the notification of the predicted pest risk. Therefore, it may be difficult for a user to select an action to effectively reduce the pest risk. As a result, it may be difficult to effectively reduce the pest risk to agricultural products.

[0005] In view of this problem, an object of the present invention is to provide an information processing device capable of effectively reducing the risk of pests and diseases in agricultural products. [Means for solving the problem]

[0006] In order to solve the above problem, an information processing device predicts the risk of pests and disease for agricultural products, and the information processing device includes a prediction unit that predicts the effect of reducing the risk of pests and disease for each of a plurality of candidate countermeasures that change at least one impact parameter that affects the risk of pests and disease, and a selection unit that selects a countermeasure from the plurality of candidate countermeasures by prioritizing a countermeasure having a greater effect in reducing the risk of pests and disease based on the prediction result provided by the prediction unit regarding the effect of reducing the risk of pests and disease. Effect of the Invention

[0007] According to the present invention, the risk of pests and diseases on agricultural products can be effectively reduced. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing a general configuration of an information processing system according to an embodiment of the present invention.

[0009] [Diagram 2] FIG. 2 is a block diagram showing an example of a functional configuration of an information processing server according to an embodiment of the present invention.

[0010] [Diagram 3] FIG. 3 is a flowchart showing an example of a processing flow related to pest and disease risk prediction performed by an information processing server according to an embodiment of the present invention.

[0011] [Figure 4] FIG. 4 is a schematic diagram showing an example of a prediction result provided by a prediction unit according to an embodiment of the present invention with respect to the effect of reducing a pest risk. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The dimensions, materials, and other specific values ​​shown in this embodiment are merely examples to aid in understanding the present invention, and do not limit the present invention unless otherwise specified. Please note that in this specification and drawings, components having substantially the same functions or configurations are given the same reference numerals, and duplicated descriptions thereof are omitted, and further, components not directly related to the present invention are omitted from the drawings.

[0013] Information Processing System Configuration The configuration of an information processing system 1 according to an embodiment of the present invention will be described with reference to FIG.

[0014] FIG. 1 is a schematic diagram showing a schematic configuration of an information processing system 1. As shown in FIG.

[0015] As shown in Fig. 1, the information processing system 1 includes an information processing server 10, a user terminal 20, a sensor device 30, and a weather information server 40. The information processing server 10, the user terminal 20, the sensor device 30, and the weather information server 40 can communicate with each other via a wireless communication network. The information processing system 1 is a system for supporting cultivation of agricultural products at a cultivation site C1 by a user U1 who is an agricultural worker. Although Fig. 1 shows an example in which the cultivation site C1 is a vinyl greenhouse, it should be noted that the cultivation site C1 may also be a cultivation site other than a vinyl greenhouse (for example, an open-field cultivation site without a roof, etc.).

[0016] Although the following description is related to a case where the information processing server 10 corresponds to an example of an information processing device according to the present invention, it should be noted that the information processing device according to the present invention may also be another device (e.g., a user terminal 20) other than the information processing server 10. Furthermore, the functions of the information processing device according to the present invention may also be implemented by a plurality of devices (e.g., the information processing server 10 and the user terminal 20). Similarly, the functions of the information processing device according to the present invention may also be implemented using, for example, cloud computing.

[0017] Using the information acquired from each device, i.e., using the information acquired from the user terminal 20, the sensor device 30, and the weather information server 40, the information processing server 10 transmits valuable information regarding the cultivation of agricultural products at the cultivation site C1 to the user terminal 20. The information transmitted from the information processing server 10 is displayed on the user terminal 20, thereby notifying the user U1.

[0018] Specifically, the information processing server 10 predicts the risk of pests and diseases on agricultural products by using the information acquired from each device, and transmits the predicted result of the risk of pests and diseases to the user terminal 20. The risk of pests and diseases means the risk of pests and diseases occurring. Furthermore, the information processing server 10 may also transmit information acquired from the sensor device 30 (specifically, various types of detection data related to the cultivation site C1 detected by the sensor device 30) to the user terminal 20. Please note that the detailed configuration of the information processing server 10 will be described later.

[0019] The user terminal 20 is an information processing terminal (specifically, a smartphone) used by the user U1. Although Fig. 1 shows an example in which the user terminal 20 is a smartphone, it should be noted that the user terminal 20 may also be an information processing terminal other than a smartphone (for example, a tablet terminal, a stationary personal computer, etc.).

[0020] The user terminal 20 has a function of receiving an input operation performed by the user U1, and transmits input information input by the user U1 to the information processing server 10. The input information from the user U1 includes information indicating, for example, the address of the cultivation site C1, the size of the cultivation site C1, the type of agricultural produce, the planting density of the agricultural produce, the start time of cultivation, the harvest time, or the spraying history of chemical substances (e.g., pesticides), etc. Furthermore, the user terminal 20 has a function of visually displaying information, and displays the information received from the information processing server 10.

[0021] The sensor device 30 is installed in the cultivation site C1 and includes a plurality of sensors. For example, the sensor device 30 includes a humidity sensor for detecting the humidity in the cultivation site C1, a temperature sensor for detecting the air temperature in the cultivation site C1, a carbon dioxide concentration sensor for detecting the carbon dioxide concentration in the cultivation site C1, or a solar radiation sensor for detecting the amount of solar radiation in the cultivation site C1.

[0022] The sensor device 30 transmits detection data from a sensor provided in the sensor device 30 to the information processing server 10. For example, the sensor detects various physical quantities at detection times separated by preset time intervals, and the sensor device 30 transmits the detection data from the sensor to the information processing server 10 at the detection times.

[0023] The weather information server 40 provides weather information to an external device. Specifically, the weather information server 40 transmits weather information for the area including the cultivation site C1 to the information processing server 10 in response to a request from the information processing server 10. The weather information is information related to the weather, and includes information indicating, for example, the temperature of the outside air (i.e., the outside air temperature), the humidity of the outside air, the amount of solar radiation, or the amount of rainfall.

[0024] Information processing server configuration The configuration of the information processing server 10 according to an embodiment of the present invention will be described with reference to FIG.

[0025] The information processing server 10 includes, for example, a CPU (Central Processing Unit) which is an arithmetic processing device, a ROM (Read Only Memory) which is a memory element for storing programs and arithmetic parameters used by the CPU, and a RAM (Random Access Memory) which is a memory element for temporarily storing parameters etc. which change appropriately when implemented by the CPU etc.

[0026] FIG. 2 is a block diagram showing an example of a functional configuration of the information processing server 10. As shown in FIG.

[0027] 2, the information processing server 10 includes, for example, a communication unit 11, a control unit 12, and a memory unit 13. It should be noted that the communication unit 11 corresponds to an example of an output unit according to the present invention.

[0028] The communication unit 11 communicates with each device in the information processing system 1. Specifically, the communication unit 11 receives information transmitted from those devices, i.e., information transmitted from the user terminal 20, the sensor device 30, and the weather information server 40, and outputs the acquired information to the control unit 12 and the memory unit 13. Furthermore, the communication unit 11 transmits information generated by the control unit 12 to the user terminal 20.

[0029] The control unit 12 executes various types of processing for generating information to be transmitted to the user terminal 20. As shown in Fig. 2, the control unit 12 includes, for example, a prediction unit 12a and a selection unit 12b that work in cooperation with a program.

[0030] The prediction unit 12a predicts the risk of pest damage to agricultural products. Specifically, the prediction unit 12a predicts the risk of pest damage to agricultural products by using a prediction model that has been learned in advance. The prediction model outputs the risk of pest damage to agricultural products when information transmitted from each device is input, that is, when information transmitted from the user terminal 20, the sensor device 30, and the weather information server 40 is input. The prediction model may be constructed according to an existing algorithm such as a support vector machine, or may be a time series model.

[0031] In cooperation with the prediction unit 12a, the selection unit 12b executes a process for making the user take an action to effectively reduce the pest risk. Specifically, the process executed by the prediction unit 12a for making the user take an action to effectively reduce the pest risk includes predicting a pest risk reduction effect for each of a plurality of candidate countermeasures that change at least one impact parameter that affects the pest risk. The selection unit 12b then selects a countermeasure from among the plurality of candidate countermeasures by prioritizing a countermeasure with a greater pest risk reduction effect based on the prediction result provided by the prediction unit 12a regarding the pest risk reduction effect. This allows the user U1 to take an action to cause a change in the impact parameter to effectively reduce the pest risk (in other words, an action that effectively reduces the pest risk). Therefore, the pest risk to the agricultural product can be effectively reduced. Details of the process related to the pest risk prediction executed by the information processing server 10 will be described later.

[0032] The memory unit 13 stores information used in the processing executed by the control unit 12. Specifically, the memory unit 13 stores information transmitted from each device, that is, information transmitted from the user terminal 20, the sensor device 30, and the weather information server 40.

[0033] Operation of the information processing server The operation of the information processing server 10 according to an embodiment of the present invention will be described with reference to FIGS.

[0034] Fig. 3 is a flowchart showing an example of a processing flow related to pest risk prediction executed by the information processing server 10. The processing flow shown in Fig. 3 is started, for example, at a preset time interval by the information processing server 10. Steps S101 and S109 in Fig. 3 correspond to the start and end of the processing flow shown in Fig. 3, respectively.

[0035] When the process flow shown in FIG. 3 is started, first, in step S102, the prediction unit 12a predicts the risk of pests or diseases on agricultural products.

[0036] In step S102, the prediction unit 12a predicts the risk of pests or diseases to the agricultural products (e.g., a numerical value indicating the possibility of pests or diseases occurring one or two days later) by using input information from the user U1 obtained from the user terminal 20 (e.g., information indicating the address of the cultivation site C1, the size of the cultivation site C1, the type of agricultural product, the planting density of the agricultural product, the start of cultivation, the harvest time, or the history of chemical spraying, etc.), sensor information obtained from the sensor device 30 (e.g., information indicating the humidity in the cultivation site C1, the temperature in the cultivation site C1, the carbon dioxide concentration in the cultivation site C1, or the amount of solar radiation in the cultivation site C1, etc.), and weather information obtained from the weather information server 40 (e.g., information indicating the outside air temperature, outside humidity, the amount of solar radiation, or the amount of rainfall, etc.), and by using a prediction model. Please note that the prediction unit 12a may also predict the risk of pests and diseases to agricultural products, taking into account the control automatically performed by each piece of equipment (e.g., heating equipment, etc.) that may affect the environment within the cultivation site C1.

[0037] In step S103 following step S102, the control unit 12 judges whether the pest risk is greater than the standard. If the pest risk is considered to be greater than the standard (step S103 / Yes), the process flow proceeds to step S104, and a process is executed to have the user U1 take an action to effectively reduce the pest risk (specifically, steps S104 to S107). If the pest risk is considered to be less than the standard (step S103 / No), the process flow proceeds to step S108, and the communication unit 11 transmits the prediction result regarding the pest risk to the user terminal 20, and the process flow shown in FIG. 3 is terminated.

[0038] The criterion in step S103 is used to determine whether or not it is necessary to have the user U1 take action to protect the agricultural produce from the risk of pests and diseases. In other words, if the risk of pests and diseases is considered to be greater than the criterion, it can be determined that it is necessary to have the user U1 take action to protect the agricultural produce from the risk of pests and diseases. For example, if the numerical value of the predicted risk of pests and diseases is greater than the criterion value, the control unit 12 determines that the risk of pests and diseases is greater than the criterion.

[0039] If the determination in step S103 is yes, in step S104, the prediction unit 12a determines a number of candidate measures for changing at least one impact parameter that affects the risk of pest damage.

[0040] A countermeasure in this context governs which influence parameters are changed and in what manner they are changed. Moreover, the prediction unit 12a may also determine countermeasures that change multiple influence parameters as candidates.

[0041] The influence parameters may include, for example, environmental parameters related to the environment in the cultivation site C1 of the agricultural products. For example, the humidity in the cultivation site C1, the temperature in the cultivation site C1, the carbon dioxide concentration in the cultivation site C1, or the amount of solar radiation in the cultivation site C1 may be used as the environmental parameters. Moreover, the environmental parameters may be the target of detection by the sensor device 30, or may be parameters that are not the target of detection by the sensor device 30 (for example, the soil moisture in the cultivation site C1, etc.).

[0042] Examples of measures to change the humidity within the cultivation site C1 include measures to increase the humidity by 5%, measures to increase the humidity by 10%, measures to decrease the humidity by 5%, measures to decrease the humidity by 10%, etc.

[0043] Examples of measures to change the temperature within the cultivation site C1 include measures to raise the temperature by 1°C, measures to raise the temperature by 2°C, measures to lower the temperature by 1°C, measures to lower the temperature by 2°C, etc.

[0044] Examples of measures to change the carbon dioxide concentration in the cultivation site C1 include measures to increase the carbon dioxide concentration by 5%, measures to increase the carbon dioxide concentration by 10%, measures to decrease the carbon dioxide concentration by 5%, measures to decrease the carbon dioxide concentration by 10%, etc.

[0045] Solar radiation is 1MJ / m 2 The measure is to increase the amount of solar radiation to 2MJ / m 2 The measure is to increase the amount of solar radiation by 1MJ / m 2 Measures to reduce the amount of solar radiation to 2MJ / m 2 Examples of measures for changing the amount of solar radiation in the cultivation site C1 include measures for decreasing the amount of solar radiation.

[0046] Furthermore, the impact parameters may also include, for example, chemical application parameters related to chemical application in the cultivation field C1 of the agricultural produce. For example, the chemical application time in the cultivation field C1, the amount of chemical application in the cultivation field C1, or the type of chemical applied in the cultivation field C1 may be used as the chemical application parameters.

[0047] Examples of measures that change the spraying time within the cultivation site C1 include measures to advance the spraying time, measures to delay the spraying time, etc.

[0048] Spray volume of 1000m 2 The measure is to increase the spray volume by 100L per 1000m 2 A measure to reduce the amount of chemicals sprayed in the cultivation site C1 by 100 L per cultivation is an example of a measure to change the amount of chemicals sprayed in the cultivation site C1.

[0049] An example of a measure to change the type of chemical being sprayed within the cultivation site C1 is to change the chemical being used to a chemical having an effect different from that of the chemical currently being used.

[0050] As mentioned above, the prediction unit 12a may also determine candidate countermeasures that govern the changes of multiple influencing parameters. Examples of such countermeasures include increasing the humidity in the cultivation site C1 by 5% and increasing the carbon dioxide concentration in the cultivation site C1 by 5%, or increasing the temperature in the cultivation site C1 by 1° C. and advancing the spraying time.

[0051] Here, weather conditions such as changes in outdoor temperature or humidity are factors that affect the risk of pest damage. Therefore, the prediction unit 12a preferably determines a plurality of candidate countermeasures based on weather information (e.g., weather information for the current day, one day later, and two days later) from the viewpoint of appropriately determining candidates of countermeasures for effectively reducing the risk of pest damage.

[0052] In step S105 following step S104, the prediction unit 12a predicts the effect of reducing the risk of pest damage for each of the multiple countermeasure candidates.

[0053] The disease and pest risk reduction effect means the degree to which the disease and pest risk when a countermeasure is implemented (i.e., when there is a change in the impact parameter governed by the countermeasure) is reduced compared to the disease and pest risk when the countermeasure is not implemented (i.e., the disease and pest risk predicted in step S102). In other words, the greater the disease and pest risk reduction effect, the more the disease and pest risk will be reduced as a result of implementing the countermeasure.

[0054] Specifically, the prediction unit 12a predicts the risk of pest damage to agricultural products for each candidate under conditions assuming that there has been a change in the impact parameters governed by the countermeasure, in the same manner as in step S102. For example, when a countermeasure to increase the humidity in the cultivation site C1 by 5% is determined as a candidate, the prediction unit 12a predicts the risk of pest damage to agricultural products for that candidate under conditions in which the humidity in the cultivation site C1 is increased by 5% from the current humidity.

[0055] In step S106 following step S105, the selection unit 12b selects a countermeasure from among the multiple countermeasure candidates based on the prediction result provided by the prediction unit 12a regarding the pest risk reduction effect. Specifically, the selection unit 12b performs the selection by prioritizing a countermeasure having a larger pest risk reduction effect.

[0056] 4 is a schematic diagram showing an example of a prediction result provided by the prediction unit 12a regarding the effect of reducing the risk of pest damage. The horizontal axis A1 in FIG. 4 is an axis for classifying each of the countermeasure candidates, and the vertical axis A2 represents the effect of reducing the risk of pest damage.

[0057] 4 shows five candidate measures, namely candidate M1, candidate M2, candidate M3, candidate M4, and candidate M5. Each candidate has a different effect of reducing the risk of pest damage, and the effect increases in the order of candidate M1, candidate M4, candidate M3, candidate M5, and candidate M2. In other words, candidate M2 has the greatest effect of reducing the risk of pest damage, and candidate M1 has the least effect of reducing the risk of pest damage.

[0058] The selection unit 12b selects, for example, a candidate countermeasure having the greatest effect of reducing the risk of pest damage from among a plurality of candidate countermeasures. In the example shown in Fig. 4, for example, the selection unit 12b selects, from among candidates M1, M2, M3, M4, and M5, a countermeasure corresponding to candidate M2 having the greatest effect of reducing the risk of pest damage.

[0059] Here, the selection unit 12b preferably performs the selection by giving priority to a measure having a greater pest risk reduction effect, and further by giving priority to a measure that can be implemented with less effort from the viewpoint of reducing the pest risk to the agricultural product with less effort. In the example shown in Fig. 4, for example, the selection unit 12b may select a measure corresponding to a candidate that can be implemented with the least effort from among candidates M2, M3, and M5, which are candidates having a pest risk reduction effect greater than the threshold value TH. It should be noted that a candidate other than the candidate M2 having the greatest pest risk reduction effect (i.e., candidate M3 or candidate M5) is sometimes selected as a result.

[0060] Furthermore, the selection unit 12b preferably performs the selection by giving priority to a measure having a greater pest risk reduction effect, and further by giving priority to a measure that can be implemented at a lower cost from the viewpoint of reducing the pest risk to the agricultural product at a lower cost. In the example shown in Fig. 4, for example, the selection unit 12b may select a measure corresponding to a candidate that can be implemented at the lowest cost from among candidates M2, M3, and M5, which are candidates having a pest risk reduction effect greater than the threshold value TH. It should be noted that a candidate other than the candidate M2 having the greatest pest risk reduction effect (i.e., candidate M3 or candidate M5) is sometimes selected as a result.

[0061] Moreover, the selection unit 12b may also select a countermeasure from among a plurality of candidate countermeasures while considering both the effort and the cost. For example, the selection unit 12b may perform selection while considering both the effort and the cost by using a first score that increases as the effort decreases and a second score that increases as the cost decreases. In the example shown in FIG. 4, for example, the selection unit 12b may determine a first score and a second score for each of the candidates M2, M3, and M5, which are candidates whose pest risk reduction effect is greater than the threshold value TH, and then select the candidate whose first score and the second score are the smallest from among the candidates M2, M3, and M5. In another example, the selection unit 12b may determine a first score and a second score for each of the candidates M2, M3, and M5, which are candidates whose pest risk reduction effect is greater than the threshold value TH, and then select the candidate whose first score and the second score are the largest from among the candidates M2, M3, and M5.

[0062] In step S107 following step S106, the communication unit 11 transmits the disease / pest risk prediction result and measure information related to the measure selected by the selection unit 12b to the user terminal 20, and then ends the processing flow shown in FIG.

[0063] The countermeasure information may include, for example, information indicating a change in the influence parameter due to the countermeasure selected by the selection unit 12b. In this case, the information indicating a change in the influence parameter due to the countermeasure selected by the selection unit 12b is transmitted from the information processing server 10 to the user terminal 20 and displayed on the user terminal 20, thereby notifying the user U1. For example, when the selection unit 12b selects the countermeasure of increasing the humidity in the cultivation site C1 by 5%, the information indicating that the humidity in the cultivation site C1 is increased by 5% is transmitted from the information processing server 10 to the user terminal 20, thereby notifying the user U1.

[0064] It should be noted that multiple influence parameters may change in conjunction with each other. For example, when the temperature in the cultivation site C1 rises, the humidity in the cultivation site C1 generally tends to decrease. Therefore, when the measure selected by the selection unit 12b governs the change of multiple influence parameters that change in conjunction with each other (for example, the temperature in the cultivation site C1 and the humidity in the cultivation site C1), one influence parameter can be changed, and thus influence parameters other than the change target can also be changed. Therefore, in this case, the communication unit 11 may transmit information indicating only the change of one influence parameter to the user terminal 20 as the measure information. As a result, it is possible to prevent the amount of information notified to the user U1 from becoming excessively large, and therefore the user U1 can easily grasp the effect caused by the change in the influence parameter due to the measure selected by the selection unit 12b.

[0065] Furthermore, the countermeasure information may include information indicating an action that the user U1 should take to cause a change in the impact parameter due to the countermeasure selected by the selection unit 12b. In this case, the information indicating the action that the user U1 should take to cause a change in the impact parameter due to the countermeasure selected by the selection unit 12b is transmitted from the information processing server 10 to the user terminal 20 and displayed on the user terminal 20, thereby notifying the user U1. For example, when the selection unit 12b selects a countermeasure of increasing the humidity in the cultivation site C1 by 5%, information indicating an action of watering the cultivation site C1 for a predetermined time (for example, 10 minutes) is transmitted from the information processing server 10 to the user terminal 20, thereby notifying the user U1.

[0066] Here, the correspondence between each countermeasure and the action that the user U1 takes to cause a change in the impact parameter due to the countermeasure may be set in various ways as follows. It should be noted that the information processing server 10 may specifically execute a process related to prediction of pest and disease risk for multiple users, and that this correspondence may differ depending on the user.

[0067] For example, the measure of changing (ie, increasing or decreasing) the humidity in the cultivation site C1 may correspond to an action of opening or closing a window in the cultivation site C1.

[0068] Furthermore, the measure of changing (i.e., raising or lowering) the temperature in the cultivation site C1 may correspond to, for example, the operation of operating a heating device in the cultivation site C1, or the operation of opening or closing a window in the cultivation site C1. Furthermore, the measure of lowering the temperature in the cultivation site C1 may correspond to the operation of sprinkling water during heating operation in the cultivation site C1.

[0069] Furthermore, the measure to change (i.e., increase or decrease) the carbon dioxide concentration in the cultivation site C1 may correspond to, for example, an operation to open or close a window in the cultivation site C1. Furthermore, the measure to increase the carbon dioxide concentration in the cultivation site C1 may correspond to an operation to operate a carbon dioxide generation device in the cultivation site C1.

[0070] Furthermore, the measure of changing (i.e., increasing or decreasing) the amount of solar radiation in the cultivation site C1 may correspond to, for example, an operation of opening or closing a curtain installed on a window in the cultivation site C1. Furthermore, the measure of changing (i.e., advancing or delaying) the spraying time in the cultivation site C1 may correspond to, for example, an operation of spraying a chemical substance at a changed spraying time.

[0071] Furthermore, the measure of varying (ie increasing or decreasing) the amount of spraying of a chemical substance in the cultivation site C1 may for example correspond to the action of spraying the chemical substance at the varied amount of spraying of the chemical substance.

[0072] Furthermore, the measure of changing the type of chemical substance sprayed within the cultivation site C1 may correspond to the action of spraying a chemical substance of a changed type.

[0073] In the above description, an example of predicting the pest risk reduction effect for all of the multiple countermeasure candidates determined in step S104 has been described with reference to the flowchart in FIG. 3. However, it should be noted that the prediction unit 12a may omit the prediction of the pest risk reduction effect for some of the multiple countermeasure candidates determined in step S104 by using an existing optimization algorithm or a similar one. Specifically, the prediction unit 12a may omit the prediction of the pest risk reduction effect for countermeasure candidates that are expected to have a relatively small pest risk reduction effect by using the prediction result of the pest risk reduction effect for some of the multiple countermeasure candidates. This can reduce the processing load of the information processing server 10 (for example, the load of the prediction process in step S105).

[0074] Furthermore, in the above description, the process flow related to pest risk prediction is described on the assumption that one of the countermeasures is selected from a plurality of countermeasure candidates, but it is also possible to select no countermeasure from a plurality of countermeasure candidates. For example, when none of the candidates has a pest risk reduction effect that satisfies a threshold value (for example, the threshold value TH shown in FIG. 4), the selection unit 12b may determine that none of the candidates has a sufficient pest risk reduction effect, and may not select a countermeasure from a plurality of countermeasure candidates. In that case, the communication unit 11 may transmit, for example, to the user terminal 20, a display indicating that none of the candidates has a sufficient pest risk reduction effect, or a display indicating that there is no countermeasure for effectively reducing the pest risk. This can prompt the user U1 to consider another plan for effectively reducing the pest risk.

[0075] Furthermore, although an example of selecting one countermeasure from a plurality of candidate countermeasures has been described above, the selection unit 12b may similarly select two or more countermeasures (specifically, a number of countermeasures less than the number of candidates) from a plurality of candidate countermeasures. In the example shown in FIG. 4, for example, the selection unit 12b may select three countermeasures corresponding to candidates M2, M3, and M5, which are candidates having a pest risk reduction effect greater than the threshold value TH. In this case, too, the options of actions to be taken by the user U1 can be limited, and this allows the user U1 to take an action to effectively reduce the pest risk. Therefore, the pest risk to agricultural products can be effectively reduced. The selected plurality of countermeasures may be a plurality of countermeasures that can be implemented simultaneously (for example, a measure to increase the humidity in the cultivation site C1 by 5% and a measure to increase the amount of solar radiation by 1 MJ / m 2 It should be noted that the measures may be one measure to increase the humidity in the cultivation site C1 by 5%, or may be multiple measures that cannot be implemented simultaneously (e.g., a measure to increase the humidity in the cultivation site C1 by 5% and a measure to increase the humidity in the cultivation site C1 by 10%).

[0076] Advantages of information processing servers The advantages of the information processing server 10 according to an embodiment of the present invention will be described below.

[0077] In the information processing server 10 according to this embodiment, the prediction unit 12a predicts the pest risk reduction effect for each of a plurality of candidate countermeasures that change at least one impact parameter that affects the pest risk. Then, the selection unit 12b selects a countermeasure from the plurality of candidate countermeasures by prioritizing a countermeasure with a larger pest risk reduction effect based on the prediction result provided by the prediction unit 12a regarding the pest risk reduction effect. This allows the user U1 to take an action that causes a change in the impact parameter to effectively reduce the pest risk (in other words, an action for effectively reducing the pest risk). Therefore, the pest risk to agricultural products can be effectively reduced.

[0078] Furthermore, in the information processing server 10 according to this embodiment, the selection unit 12b preferably performs the selection by prioritizing a measure having a greater effect of reducing the pest risk, and further by prioritizing a measure that can be implemented with less effort. This allows the user U1 to perform an action that causes a change in the impact parameter in order to effectively reduce the pest risk with less effort (in other words, an action for effectively reducing the pest risk with less effort). Therefore, the pest risk to agricultural products can be reduced with less effort.

[0079] Furthermore, in the information processing server 10 according to this embodiment, the selection unit 12b preferably performs the selection by prioritizing a measure having a greater effect of reducing the pest risk, and further by prioritizing a measure that can be implemented at a lower cost. This allows the user U1 to perform an action that causes a change in the impact parameter in order to effectively reduce the pest risk at a lower cost (in other words, an action for effectively reducing the pest risk at a lower cost). Therefore, the pest risk to agricultural products can be reduced at a lower cost.

[0080] Furthermore, in the information processing server 10 according to this embodiment, an output unit (e.g., communication unit 11) preferably outputs countermeasure information related to the countermeasure selected by the selection unit 12b. This makes it possible to notify the user U1 of the countermeasure information related to the countermeasure selected by the selection unit 12b. Therefore, it is possible to appropriately cause the user U1 to take an action to effectively reduce the risk of pest damage.

[0081] Although an example in which the communication unit 11 of the information processing server 10 functions as an output unit has been described above, it should be noted that when the functions of the information processing device according to the present invention are realized by the user terminal 20, for example, a display control unit (a functional unit that controls the operation of a display device) of the user terminal 20 may correspond to the output unit. In this case, the display control unit of the user terminal 20 causes, for example, a display device to display countermeasure information.

[0082] Furthermore, in the information processing server 10 according to this embodiment, the countermeasure information preferably includes information indicating a change in the impact parameter due to the countermeasure selected by the selection unit 12b. This makes it possible to notify the user U1 of the information indicating the change in the impact parameter due to the countermeasure selected by the selection unit 12b. This makes it possible to more appropriately cause the user U1 to take an action to effectively reduce the risk of pest damage.

[0083] Furthermore, in the information processing server 10 according to this embodiment, the countermeasure information preferably includes information indicating an action that the user U1 should take to cause a change in the impact parameter due to the countermeasure selected by the selection unit 12b. This makes it possible to notify the user U1 of information indicating an action that the user U1 should take to cause a change in the impact parameter due to the countermeasure selected by the selection unit 12b. Therefore, the user U1 can more intuitively grasp the action for effectively reducing the risk of pest damage, and this makes it possible to more appropriately cause the user U1 to take the action for effectively reducing the risk of pest damage.

[0084] Furthermore, in the information processing server 10 according to this embodiment, the prediction unit 12a preferably determines a plurality of candidate countermeasures based on weather information (e.g., weather information for the current day, one day later, and two days later). This allows the candidate countermeasures to be determined while taking into consideration the weather that affects the risk of pest damage (e.g., changes in outdoor temperature or humidity, etc.). Therefore, countermeasures for effectively reducing the risk of pest damage can be appropriately determined as candidates.

[0085] Furthermore, in the information processing server 10 according to this embodiment, the influence parameters preferably include environmental parameters related to the environment in the cultivation site C1 of the agricultural produce. As a result, the risk of pest damage can be effectively reduced by having the user U1 perform an action that will cause a change in the environmental parameters. Therefore, it is possible to effectively and appropriately reduce the risk of pest damage to the agricultural produce. Furthermore, in the information processing server 10 according to this embodiment, the influence parameters preferably include chemical substance spraying parameters related to chemical substance spraying in the cultivation site C1 of the agricultural produce. As a result, the risk of pest damage can be effectively reduced by having the user U1 perform an action that will cause a change in the chemical substance spraying parameters. Therefore, it is possible to effectively and appropriately reduce the risk of pest damage to the agricultural produce.

[0086] Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, it goes without saying that the present invention is not limited to the above-described embodiments, and various modifications and alterations within the scope of the claims also fall within the technical scope of the present invention.

[0087] For example, the processes described herein with reference to flowcharts need not necessarily be implemented in the order shown in the flowcharts, multiple process steps may be implemented in parallel, and additional process steps may be employed or some process steps may be omitted.

[0088] Furthermore, for example, the series of control processes brought about by the above-mentioned information processing server 10 can be implemented using any of software, hardware, or a combination of software and hardware. The programs constituting the software are stored in advance in a recording medium provided inside or outside the information processing device. [Explanation of symbols]

[0089] 1. Information Processing Systems 10 Information processing server (information processing device) 11 Communication unit (output unit) 12 Control unit 12a Prediction Unit 12b Elective Unit 13 Memory Unit 20 User terminal 30 Sensor Devices 40 Weather Information Server C1 Cultivation site U1 User

Claims

1. An information processing device (10) for predicting a risk of pests or diseases to agricultural products, a prediction unit (12a) for predicting a reduction effect of the pest risk for each of a plurality of candidate measures for changing at least one impact parameter that affects the pest risk; a selection unit (12b) for selecting countermeasures from the plurality of candidate countermeasures by prioritizing countermeasures having a greater effect of reducing the pest risk based on the prediction result provided by the prediction unit (12a) regarding the effect of reducing the pest risk; An information processing device (10).

2. 2 . The information processing device according to claim 1 , wherein the selection unit (12 b) makes the selection by giving priority to measures that have a greater effect in reducing the pest risk and further by giving priority to measures that can be implemented with less effort.

3. 3. The information processing device according to claim 1, wherein the selection unit (12b) makes the selection by giving priority to measures having a greater effect in reducing the risk of pests and diseases and further by giving priority to measures that can be implemented at a lower cost.

4. 4. The information processing apparatus according to claim 1, further comprising an output unit (11) for outputting countermeasure information related to the countermeasure selected by the selection unit (12b).

5. The information processing apparatus according to claim 4 , wherein the countermeasure information includes information indicating a change in the influence parameter caused by the countermeasure selected by the selection unit (12b).

6. The information processing apparatus according to claim 4 or 5, wherein the countermeasure information includes information indicating an action to be taken by a user (U1) to cause a change in the influence parameter due to the countermeasure selected by the selection unit (12b).

7. The information processing apparatus according to any one of claims 1 to 6, wherein the prediction unit (12a) determines the plurality of candidate countermeasures based on meteorological information.

8. The information processing device according to any one of claims 1 to 7, wherein the influence parameters include environmental parameters related to an environment in a cultivation site (C1) of the agricultural product.

9. The information processing apparatus according to any one of claims 1 to 8, wherein the influence parameters include chemical substance application parameters related to the application of chemical substances at a cultivation site (C1) of the agricultural produce.

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