Adhesion state determination system, adhesion state determination method, adhesion state determination program, and system for predicting suitable time for pest control

Abstract

In the present invention, the state of adhesion of insects on crops is determined with high accuracy. This adhesion state determination system comprises: a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration patterns of a crop subject to determination which grows in a field; an adhesion determination unit that determines, on the basis of the vibration pattern information, the adhesion state of a prescribed insect with respect to the crop subject to determination; and an output unit that outputs information pertaining to the determined adhesion state.

Description

Adhesion status determination system, adhesion status determination method, adhesion status determination program, and pest control optimum timing prediction system CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on Japanese Patent Application No. 2023-149767, filed on September 15, 2023, the contents of which are incorporated herein by reference.

[0002] The present disclosure relates to an adhesion state determination system, an adhesion state determination method, an adhesion state determination program, and a pest control optimum timing prediction system.

[0003] Conventionally, methods utilizing information processing technology for managing farm fields, particularly for predicting pest infestations, have been known. For example, Patent Document 1 (Patent Document 1) describes a pest infestation prediction system that includes a terminal device and a server device connected to the terminal device via a network. The terminal device includes a camera that photographs an insect trapping sheet (a sheet coated with an adhesive to trap pests) to acquire image data, a location information acquisition unit that acquires location information for the insect trapping sheet, and a control unit that creates insect trapping sheet information including the image data and location information and transmits the insect trapping sheet information to the server device. The server device includes an image analysis unit that receives the insect trapping sheet information from the terminal device, analyzes the image data included in the insect trapping sheet information for each insect trapping sheet, counts the number of pests for each insect trapping sheet, and creates basic information including the number of pests and location information, and a prediction unit that predicts pest infestations based on the basic information for each insect trapping sheet.

[0004] JP 2015-154732 A

[0005] However, the capture status of the insect trapping sheet can be affected not only by the growth status of the pest insects, but also by factors such as the installation position of the insect trapping sheet and the adhesive strength of the sheet. Therefore, the capture status of the insect trapping sheet may not match the growth status of the insects in the field, and in such cases, the system cannot accurately predict the occurrence of pest insects.

[0006] Therefore, an object of the present disclosure is to determine the insect infestation status of crops with high accuracy.

[0007] An attachment status determination system according to one aspect of the present disclosure includes a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration pattern of a target crop that inhabits a field, an attachment determination unit that determines the attachment status of a specified insect on the target crop based on the vibration pattern information, and an output unit that outputs information related to the determined attachment status.

[0008] In addition, a system for predicting the optimal time for pest control according to one embodiment of the present disclosure includes a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration pattern of a target crop that inhabits a field, an attachment determination unit that determines the attachment status of a specified insect in the target crop based on the vibration pattern information, a habitat prediction unit that predicts the future habitat status of the specified insect in the field based on the determined attachment status, and an optimal pest control time prediction unit that predicts the optimal timing for pest control treatment of the specified insect in the field based on the prediction.

[0009] According to the present disclosure, it is possible to determine the insect infestation status of crops with high accuracy.

[0010] FIG. 1 is a diagram showing an overview of processing in an adhesion status determination system 100 according to an embodiment of the present disclosure. FIG. 2 is a diagram showing a configuration of an adhesion status determination system 100 according to an embodiment of the present disclosure. FIG. 3 is a diagram showing an example of vibration pattern information. FIG. 4 is a diagram showing an example of vibration pattern information. FIG. 5 is a diagram showing an example of a result of analysis processing by a frequency analysis unit 150. FIG. 6 is a diagram showing an example of a result of analysis processing by a frequency analysis unit 150. FIG. 7 is a diagram showing an example of growth prediction information stored in a storage unit 110. FIG. 8 is a flowchart showing an example of processing in the adhesion status determination system 100. FIG. 9 is a diagram showing a configuration of a pest control optimum timing prediction system 101 according to an embodiment of the present disclosure. FIG. 10 is a diagram showing an example of pest control optimum timing prediction information stored in a storage unit 110. FIG. 11 is a flowchart showing an example of processing in the pest control optimum timing prediction system 101. FIG. 12 is a diagram showing an example of the hardware configuration of a computer 1000.

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which: Fig. 1 is a diagram illustrating an outline of processing in an adhesion state determination system 100 according to an embodiment of the present disclosure;

[0012] The infestation status determination system 100 is an information processing system implemented by an infestation status determination program. The infestation status determination system 100 determines the infestation status of a target crop of insects based on vibration patterns of the target crop living in a field, and predicts the future growth status of the target insects.

[0013] First, the adhesion state determination system 100 acquires vibration pattern information indicating the vibration pattern of the target crop inhabiting the field (S101). The adhesion state determination system 100 may acquire the vibration pattern information through a predetermined device (e.g., a vibration pattern acquisition device).

[0014] The attachment status determination system 100 determines the attachment status of a predetermined insect in the target crop based on the vibration pattern information and generates attachment status information (S102). At this time, the attachment status determination system 100 may determine the communication status of the predetermined insect based on the vibration pattern information to generate communication status information, and determine the attachment status of the predetermined insect based on the communication status information.

[0015] The attachment status determination system 100 predicts the future inhabitation status of the specified insect in the field based on the attachment status information and generates growth prediction information (S103).The attachment status determination system 100 then outputs at least one of the attachment status information and the growth prediction information to the user device 200 (S104).

[0016] Here, the target crop is not particularly limited, but may be, for example, rice. The predetermined insect is not particularly limited, but may be, for example, a pest that attaches to rice, and in particular may be, for example, a planthopper.

[0017] Furthermore, "communication" in this embodiment may include not only mutual communication between multiple specified insects, but also one-way communication (in other words, sending or transmitting, or in other words, calling for communication) by one specified insect to another specified insect.

[0018] More specifically, consider a case where a given insect communicates reciprocally between males and females, but not between insects of the same sex. When both males and females of the given insect are contained within a given space, the given insect will communicate reciprocally between males and females. Furthermore, when only insects of the same sex are contained within a given space, the given insect may communicate unidirectionally with other given insects (e.g., of the opposite sex). In other words, "communication" in this embodiment includes reciprocal communication between males and females (hereinafter simply referred to as "intersexual communication") that occurs when both males and females are contained within a given space, and unidirectional communication with other given insects (e.g., of the opposite sex) that occurs when only insects of the same sex are contained within a given space (hereinafter simply referred to as "intersexual communication"). The other given insects may be unspecified or an unspecified number of insects.

[0019] 2 is a diagram showing the configuration of an adhesion state determination system 100 according to an embodiment of the present disclosure. The adhesion state determination system 100 is communicably connected to a user device 200 via a network such as the Internet. Details of the adhesion state determination system 100 will be described later.

[0020] The user device 200 is an information processing device used by a user of the adhesion state determination system 100, and is a computer, a smartphone, a tablet terminal, a personal computer, or the like.

[0021] The user uses the user device 200 to acquire information (e.g., communication status information, adhesion status information, or growth prediction information) output by the adhesion status determination system 100. The user refers to the information acquired from the adhesion status determination system 100 to, for example, manage the farm field, in particular, manage the growth status of the crops and perform pest control treatment on the crops.

[0022] Although one user device 200 is shown in FIG. 2, a plurality of user devices 200 may be used.

[0023] Next, details of the adhesion state determination system 100 will be described. The adhesion state determination system 100 includes a storage unit 110, a vibration pattern acquisition unit 120, a noise vibration pattern acquisition unit 130, a noise removal unit 140, a frequency analysis unit 150, a communication determination unit 160, an adhesion determination unit 170, a growth prediction unit 180, and an output unit 190. Each unit shown in Fig. 2 can be realized, for example, by using a storage area or by a processor executing a program stored in the storage area.

[0024] The storage unit 110 stores information processed in the adhesion state determination system 100. The storage unit 110 can store, for example, vibration pattern information, communication state information, adhesion state information, and growth prediction information, which will be described later.

[0025] The vibration pattern acquisition unit 120 acquires vibration pattern information indicating the vibration pattern of the target crop that grows in the field, and stores the vibration pattern information in the storage unit 110 .

[0026] The vibration pattern acquisition unit 120 can acquire vibration pattern information from, for example, an external device. In this case, the external device may be, for example, a predetermined device that measures the vibration pattern of the target crop, or an information processing system that acquires vibration pattern information through the predetermined device.

[0027] The vibration pattern information is, for example, information indicating the vibration pattern of a predetermined part (e.g., leaf, stem) of the target crop within a predetermined time (e.g., several tens of seconds). The vibration pattern information may be, for example, information associating a measurement time with the vibration speed at the measurement time.

[0028] The vibration indicated by the vibration pattern information may be, for example, a vibration measured by a predetermined device (for example, a laser Doppler vibrometer).

[0029] The vibration pattern information may be, for example, vibration pattern information of a target crop in a predetermined section of a field. Here, the predetermined section may be, for example, a corner or a center of the field, or may be a section specially provided for obtaining the vibration pattern information.

[0030] Certain insects (e.g., planthoppers) communicate between males and females. Furthermore, certain insects (e.g., planthoppers) may also communicate with the opposite sex (one-sided communication) even in a space where only insects of the same sex are present. Therefore, when certain insects communicate with each other between opposite sexes or the same sex, the crop to which the certain insects are attached vibrates. Therefore, the attachment status determination system 100 acquires the vibration pattern of the target crop and determines the communication status and attachment status of the certain insects based on the vibration pattern.

[0031] 3A and 3B are diagrams showing examples of vibration pattern information. The vibration pattern information shown in Fig. 3A and Fig. 3B shows an example in which the target crop is rice and the specified insect is a planthopper. The vibration pattern information shown in Fig. 3A shows vibration pattern information in the case where both male and female planthoppers are attached to rice. Fig. 3B shows vibration pattern information in the case where only male planthoppers are attached to rice.

[0032] 3A and 3B, the vibration pattern information is, for example, information that associates measurement times with vibration speeds. It can also be seen that there is a difference in the vibration pattern between when both male and female planthoppers are attached to rice plants and when only male planthoppers are attached to rice plants.

[0033] The noise vibration pattern acquisition unit 130 acquires noise vibration pattern information indicating a pattern of vibration of the target crop that is caused by noise factors other than the predetermined insects, which is measured in advance, and stores the information in the storage unit 110 .

[0034] The noise vibration pattern acquisition unit 130 can acquire the noise vibration pattern information from, for example, an external device. In this case, the external device may be, for example, a predetermined device that measures the vibration pattern of the target crop, or may be an information processing system that acquires the vibration pattern information through the predetermined device.

[0035] Noise factors other than the specified insect may include, for example, wind. That is, the noise vibration may include, for example, vibration caused by wind. This allows the insect adhesion status determination system 100 to determine the insect adhesion status while eliminating the influence of vibration patterns caused by wind.

[0036] Furthermore, noise factors other than the specified insects may include, for example, the ground. That is, the noise vibration may include, for example, vibration caused by ground vibration. The ground vibration may be, for example, ground vibration caused by the movement of a vehicle passing near the crops. In this way, the infestation status determination system 100 can determine the infestation status of the specified insects while eliminating the influence of vibration patterns originating from the ground.

[0037] The noise vibration pattern information may be, for example, information indicating the vibration pattern of a target crop to be evaluated that is not infested with a specific insect. More specifically, the noise vibration pattern information may be, for example, information indicating at least one of the vibration patterns of vibrations caused by wind and vibrations caused by ground vibrations in a target crop to be evaluated that is not infested with a specific insect. This allows the insect infestation status determination system 100 to determine the insect infestation status by taking into account the influence of the presence of the specific insect.

[0038] The noise vibration pattern information may be, for example, noise vibration pattern information of a crop to be evaluated in a specified section of a field, or may be noise vibration pattern information of a crop to be evaluated in a specified section of a location other than a field (for example, within a specified facility).

[0039] The noise vibration pattern information is, for example, information indicating a vibration pattern of a predetermined part (e.g., leaf, stem) of the target crop within a predetermined time (e.g., several tens of seconds) that is caused by a noise factor other than a predetermined insect. The noise vibration pattern information may be, for example, information that associates a measurement time with a vibration velocity at the measurement time.

[0040] The vibration indicated by the noise vibration pattern information may be, for example, a vibration measured by a predetermined device (for example, a laser Doppler vibrometer).

[0041] Furthermore, the crop to be determined in the vibration pattern information and the crop to be determined in the noise vibration pattern information may be, for example, the same or different strains, or the same or different species of crops.

[0042] The noise removal unit 140 removes the influence of vibrations resulting from noise factors in the vibration pattern information based on the vibration pattern information and the noise vibration pattern information.

[0043] The noise removal unit 140 may remove the influence of vibrations resulting from noise factors by, for example, calculating the difference between the vibration pattern information and the noise vibration pattern information.

[0044] The vibration pattern indicated by the vibration pattern information may include vibrations caused by noise other than the communication of the specified insects, in addition to vibrations caused by the communication of the specified insects. Therefore, the noise removal unit 140 can generate vibration pattern information of vibrations caused by the communication of the specified insects by removing the influence of vibrations caused by noise factors.

[0045] The frequency analysis unit 150 analyzes the frequency components of the vibration based on the vibration pattern information.

[0046] The frequency analysis section 150 may analyze the frequency components of the vibration based on the vibration pattern information from which the influence of vibrations resulting from noise factors has been removed by the noise removal section 140 .

[0047] The frequency analysis unit 150 may analyze the frequency components of the vibration using, for example, a Fourier transform.

[0048] 4A and 4B are diagrams showing examples of the results of analysis processing by the frequency analysis unit 150. Fig. 4A and Fig. 4B show examples of the results of analysis based on the vibration pattern information of Fig. 3A and Fig. 3B, respectively.

[0049] 4A and 4B, the frequency analysis unit 150 analyzes the power for each frequency at each measurement time by analyzing the time-frequency characteristics of the power spectrum based on the vibration pattern information. It can also be seen that there is a difference between the power for each frequency of the vibration pattern when both male and female planthoppers are attached to rice plants and the power for each frequency of the vibration pattern when only male planthoppers are attached to rice plants.

[0050] The communication determination unit 160 determines the communication status of a predetermined insect in the target crop based on the vibration pattern information, generates communication status information indicating the result of the determination, and stores it in the storage unit 110.

[0051] The communication status information output by the communication determination unit 160 may include, for example, information in which section information indicating the section in the field to be determined is associated with the determination result of the communication status. The determination result of the communication status may include, for example, at least any of the following: whether or not predetermined insects are communicating with each other in the determination target crop, whether or not male and female predetermined insects are communicating with each other, whether or not male predetermined insects are communicating with each other, whether or not female predetermined insects are communicating with each other, and the number of predetermined insects that are communicating with each other.

[0052] Furthermore, the communication determination unit 160 can determine the communication status based on the pattern of frequency components obtained by analyzing the vibration pattern information.

[0053] The communication determination unit 160 can determine the communication status of a specified insect in the crop being evaluated, for example, based on first correspondence information that indicates the correspondence between the vibration pattern (or frequency component pattern) and the communication status of a specified insect.

[0054] Here, the first correspondence information may be, for example, information indicating the correspondence between a predetermined communication situation of a predetermined insect, measured in advance in a field or a predetermined facility, and a vibration pattern (or a pattern of frequency components) of a target crop having that communication situation. Furthermore, the first correspondence information may be, for example, a learning model that is trained based on a predetermined communication situation of a predetermined insect, measured in advance in a field or a predetermined facility, and a vibration pattern (or a pattern of frequency components) of a target crop having that communication situation. That is, the first correspondence information may be, for example, a learning model that outputs the presence or absence of a communication situation of a predetermined insect when vibration pattern information (or a pattern of frequency components) is input.

[0055] The first correspondence information may be generated, for example, by acquiring a vibration pattern (or a pattern of frequency components) of a target crop on which a predetermined ratio of male to female insects is attached.

[0056] The predetermined communication status of the predetermined insect in the first correspondence information may include, for example, the communication status of the male and female of the predetermined insect. That is, in this case, the predetermined communication status of the predetermined insect may include, for example, a communication status in which both male and female insects are communicating, a communication status in which only female insects are communicating, a communication status in which only male insects are communicating, and a communication status in which the predetermined insects are not communicating. This allows the attachment status determination system 100 to determine the communication status of the male and female of the predetermined insect.

[0057] Furthermore, the communication status of a predetermined insect in the first correspondence information may include, for example, the communication status of a predetermined number of predetermined insects. That is, in this case, the attachment status of a predetermined number of predetermined insects may include, for example, a communication status in which zero predetermined insects are communicating (a communication status in which no insects are communicating), a communication status in which five predetermined insects are communicating, or a communication status in which ten predetermined insects are communicating. This allows the attachment status determination system 100 to determine the number of attached predetermined insects.

[0058] Furthermore, the communication status of a predetermined insect in the first correspondence information may include, for example, the communication status of males and females of a predetermined number of predetermined insects.

[0059] The crop to be determined that has been measured in advance in the first correspondence information and the crop to be determined in the vibration pattern information may be, for example, the same or different strains, or the same or different species of crop.

[0060] The attachment determination unit 170 determines the attachment status of a predetermined insect in the target crop based on the vibration pattern information, generates attachment status information indicating the result of the determination, and stores it in the storage unit 110.

[0061] Furthermore, the adhesion determining unit 170 can determine the adhesion state based on the pattern of frequency components obtained by analyzing the vibration pattern information.

[0062] The adhesion determination unit 170 can determine the adhesion status of a specified insect in the crop being evaluated, for example, based on second correspondence information that indicates the correspondence between the vibration pattern (or frequency component pattern) and the adhesion status of a specified insect.

[0063] Here, the second correspondence information may be, for example, information indicating the correspondence between a predetermined attachment state of a predetermined insect, measured in advance in a field or a predetermined laboratory, and the vibration pattern (or frequency component pattern) of a target crop having that attachment state. Furthermore, the second correspondence information may be, for example, a learning model that is trained based on a predetermined attachment state of a predetermined insect, measured in advance in a field or a predetermined laboratory, and the vibration pattern (or frequency component pattern) of a target crop having that attachment state. That is, the second correspondence information may be, for example, a learning model that outputs the presence or absence of a predetermined insect attachment state when vibration pattern information (or frequency component pattern) is input.

[0064] The predetermined attachment status of the predetermined insect in the second correspondence information may include, for example, the attachment status of the male and female of the predetermined insect. That is, in this case, the predetermined attachment status of the predetermined insect may include, for example, an attachment status where both male and female insects are attached, an attachment status where only female insects are attached, an attachment status where only male insects are attached, and an attachment status where no predetermined insects are attached. This allows the attachment status determination system 100 to determine the attachment status of the male and female of the predetermined insect.

[0065] Furthermore, the attachment status of a predetermined insect in the second correspondence information may include, for example, the attachment status of a predetermined number of predetermined insects. That is, in this case, the attachment status of a predetermined number of predetermined insects may include, for example, an attachment status where no predetermined insects are attached (an attachment status where no insects are attached), an attachment status where five predetermined insects are attached, or an attachment status where ten predetermined insects are attached. This allows the attachment status determination system 100 to determine the number of attached predetermined insects.

[0066] Furthermore, the attachment status of the predetermined insects in the second correspondence information may include, for example, the attachment status of males and females of a predetermined number of predetermined insects.

[0067] The adhesion determining unit 170 may also determine the adhesion state based on vibration pattern information from which the influence of vibrations resulting from noise factors has been removed by the noise removing unit 140 .

[0068] The adhesion determining unit 170 may also determine the adhesion state based on the determined communication state.

[0069] At this time, if the communication determination unit 160 determines that "male and female insects of a specified type are communicating," the adhesion determination unit 170 can determine that male and female insects of a specified type are attached to the crop being evaluated.

[0070] The adhesion status information generated by the adhesion determination unit 170 may include, for example, information in which plot information indicating a target plot in the field is associated with a determination result of the adhesion status. The determination result of the adhesion status may include, for example, at least any of the following: whether or not a predetermined insect is attached to the target crop, whether or not both male and female predetermined insects are attached, whether or not male predetermined insects are attached, whether or not female predetermined insects are attached, and the number of predetermined insects attached.

[0071] In this way, the attachment determination unit 170 can determine the attachment status of specific insects that are actually attached to the target crop based on the vibration pattern information. In this regard, for example, when using a trap to capture specific insects and determining the attachment status of specific insects based on the number of specific insects captured in the trap, factors other than the attachment status of the specific insects, such as the installation location of the trap and the trap's capture ability (e.g., adhesive strength), may affect the determination result. However, the attachment status determination system 100 determines the attachment status of specific insects that are actually attached to the target crop based on vibration pattern information resulting from communication between specific insects that are actually attached to the target crop. Therefore, the attachment status determination system 100 can determine the attachment status of specific insects with higher accuracy.

[0072] The growth prediction unit 180 predicts the future inhabitation status of the specified insect in the field based on the determined attachment status, generates growth prediction information indicating the results of the prediction, and stores it in the memory unit 110.

[0073] The growth prediction unit 180 can predict the future inhabitation status of a specific insect in a farm field, for example, based on a growth prediction model that is set in advance.

[0074] The growth prediction model may be, for example, a model that outputs the future inhabitation status of a predetermined insect in a field when the attachment status of the predetermined insect is input. The growth prediction model may be, for example, a model that predicts that the number of growth of the predetermined insect will increase in the future as the number of males and females of the predetermined insect attaches, or a model that predicts that the number of growth of the predetermined insect will not increase in the future if only males of the predetermined insect attach.

[0075] The growth prediction information generated by the growth prediction unit 180 is, for example, information indicating the growth status of a predetermined insect in a field after a certain period of time. The growth status of a predetermined insect in a field after a certain period of time may be, for example, information indicating the total number of predetermined insects growing in the field or the number or ratio of males and females of the predetermined insects growing in the field after a certain period of time.

[0076] 5 is a diagram showing an example of growth prediction information stored in the storage unit 110. The growth prediction information stored in the storage unit 110 includes, for example, a growth prediction ID, plot information, and growth prediction content information. The growth prediction ID is information that identifies the growth prediction information stored in the storage unit 110. The growth prediction content information is information that indicates the content of the prediction result by the growth prediction unit 180, and is, for example, information that indicates the growth status of a specific insect in a field after a certain period of time.

[0077] The output unit 190 outputs information relating to the determined adhesion state. More specifically, the output unit 190 can output adhesion state information generated by the adhesion determination unit 170, for example.

[0078] Furthermore, the output unit 190 can output, for example, communication determination information generated by the communication determination unit 160 and growth prediction information generated by the growth prediction unit 180 .

[0079] The output unit 190 can output information about the determined adhesion status to, for example, the user device 200. At this time, the output unit 190 may output the information directly to the user device 200, or may output the information through a predetermined information processing device that provides a predetermined information processing service to the user device 200. The predetermined information processing device may be, for example, an information processing device that provides a service for managing the growth status of crops in a field to the user device 200.

[0080] FIG. 6 is a flowchart showing an example of processing in the adhesion state determination system 100.

[0081] First, the noise vibration pattern acquisition unit 130 acquires noise vibration pattern information (S601). The vibration pattern acquisition unit 120 acquires vibration pattern information (S602). The noise removal unit 140 removes the influence of vibrations caused by noise factors from the vibration pattern information based on the vibration pattern information and the noise vibration pattern information (S603). The frequency analysis unit 150 analyzes the frequency components of the noise-removed vibration pattern information (S604).

[0082] The communication determination unit 160 determines the communication status of the predetermined insect based on the noise-removed vibration pattern information or the analyzed frequency component pattern (S605). The attachment determination unit 170 determines the attachment status of the predetermined insect based on the communication status of the predetermined insect (S606).

[0083] The growth prediction unit 180 predicts the future growth state based on the adhesion state determination result (S607). The output unit 190 outputs at least one of the communication determination information, adhesion state information, and growth prediction result information (S608).

[0084] Next, a pest control optimum timing prediction system 101, which is a modified example of the adhesion state determination system 100, will be described.

[0085] The system for predicting the optimal timing for pest control 101 is an information processing system that predicts the optimal timing for pest control treatment of a specified insect in a field based on the future growth status of the specified insect, which is predicted based on the attachment status of the specified insect on the crop being evaluated in the field.

[0086] FIG. 7 is a diagram showing the configuration of a pest control optimum timing prediction system 101 according to one embodiment of the present disclosure.

[0087] Similar to the adhesion state determination system 100, the pest control optimum timing prediction system 101 is communicably connected to the user device 200 via a network such as the Internet. Similar to the adhesion state determination system 100, the pest control optimum timing prediction system 101 includes a memory unit 110, a vibration pattern acquisition unit 120, a noise vibration pattern acquisition unit 130, a noise removal unit 140, a frequency analysis unit 150, a communication determination unit 160, an adhesion determination unit 170, a growth prediction unit 180, and an output unit 190. Unless otherwise specified, the processing of each unit included in the pest control optimum timing prediction system 101 is the same as the processing of the corresponding unit in the adhesion state determination system 100.

[0088] The memory unit 110 stores information processed in the pest control optimum timing prediction system 101. The memory unit 110 can store, for example, vibration pattern information, communication status information, adhesion status information, growth prediction information, and pest control optimum timing prediction information, which will be described later.

[0089] The pest control optimum timing prediction system 101 further includes a pest control optimum timing prediction unit 185.

[0090] The optimum control timing prediction unit 185 predicts the optimum timing for pest control treatment of a specified insect in the field based on the prediction by the growth prediction unit 180, generates optimum control timing prediction information indicating the results of the prediction, and stores it in the memory unit 110.

[0091] Based on the prediction by the growth prediction unit 180, the control optimum timing prediction unit 185 can predict, for example, the timing (e.g., date) when the growth number (total number, number of females, or number of males) of a specified insect exceeds a specified threshold as an appropriate timing for control treatment of the specified insect.

[0092] 8 is a diagram showing an example of pest control optimum time prediction information stored in the storage unit 110. The pest control optimum time prediction information stored in the storage unit 110 includes, for example, an optimum time prediction ID, section information, and optimum time prediction content information. The optimum time prediction ID is information that identifies the pest control optimum time prediction information stored in the storage unit 110.

[0093] The optimum timing prediction content information is information indicating the content of the prediction result by the optimum pest control timing prediction unit 185, and is, for example, information indicating the optimum timing for pest control treatment. Note that the optimum timing for pest control treatment may be, for example, information indicating a specific date such as "July 10th," or may be information indicating the relative optimum timing for pest control, such as "three days later."

[0094] The output unit 190 can output information on the prediction of the optimum time for pest control.

[0095] The user device 200 acquires information output by the system for predicting the optimum pest-control time 101 (for example, communication status information, adhesion status information, growth prediction information, or optimum pest-control time prediction information).

[0096] 9 is a flowchart showing an example of processing in the pest-control optimum timing prediction system 101. The processing in S901 to S907 is the same as S601 to S607 in FIG. 6. Next, the pest-control optimum timing prediction unit 185 predicts the optimum pest-control timing based on the predicted growth status (S908). Then, the output unit 190 outputs at least one of communication determination information, adhesion status information, growth prediction result information, and optimum pest-control timing prediction information (S909).

[0097] Next, an example of a hardware configuration in which the adhesion state determination system 100 is realized by a computer 1000 will be described with reference to Fig. 10. Fig. 10 is a diagram showing an example of the hardware configuration of the computer 1000.

[0098] As shown in FIG. 10, a computer 1000 includes, for example, a processor 1001, a memory 1002, a storage device 1003, an input I / F unit 1004, a data I / F unit 1005, a communication I / F unit 1006, and a display device 1007.

[0099] The computer 1000 may be, for example, a server computer, a personal computer (e.g., desktop, laptop, tablet, etc.), a media computing platform (e.g., cable, satellite set-top box, digital video recorder, etc.), a handheld computing device (e.g., PDA, email client, etc.), or any other type of computer or communications platform.

[0100] The processor 1001 is a control unit that controls various processes in the computer 1000 by executing programs stored in the memory 1002 .

[0101] The memory 1002 is a storage medium such as a RAM (Random Access Memory), etc. The memory 1002 temporarily stores the program code of the program executed by the processor 1001 and data required when the program is executed.

[0102] The storage device 1003 is a non-volatile storage medium such as a hard disk drive (HDD), flash memory, etc. The storage device 1003 stores an operating system and various programs for realizing the above-mentioned components.

[0103] The input I / F unit 1004 is a device for receiving input from a user. The input I / F unit 1004 is, for example, a keyboard, a mouse, a touch panel, various sensors, a wearable device, etc. The input I / F unit 1004 may be connected to the computer 1000 via an interface such as a USB (Universal Serial Bus).

[0104] The data I / F unit 1005 is a device for inputting data from outside the computer 1000. The data I / F unit 1005 is, for example, a drive device for reading data stored in various storage media. The data I / F unit 1005 may be provided outside the computer 1000. When the data I / F unit 1005 is provided outside the computer 1000, the data I / F unit 1005 is connected to the computer 1000 via an interface such as a USB.

[0105] The communication I / F unit 1006 is a device for performing data communication via a network such as the Internet, either wired or wirelessly, with devices external to the computer 1000. The communication I / F unit 1006 may be provided external to the computer 1000. When the communication I / F unit 1006 is provided external to the computer 1000, the communication I / F unit 1006 is connected to the computer 1000 via an interface such as a USB.

[0106] The display device 1007 is a device for displaying various types of information. The display device 1007 is, for example, a liquid crystal display, an organic EL (Electro-Luminescence) display, a display of a wearable device, or the like. The display device 1007 may be provided outside the computer 1000. When the display device 1007 is provided outside the computer 1000, the display device 1007 is connected to the computer 1000 via, for example, a display cable. Furthermore, when a touch panel is used as the input I / F unit 1004, the display device 1007 may be configured as an integral part of the input I / F unit 1004.

[0107] The above describes one embodiment of the present disclosure. The infestation condition determination system 100 acquires vibration pattern information, determines the infestation condition of a predetermined insect based on the vibration pattern information, and outputs information related to the determined infestation condition. This allows the infestation condition determination system 100 to determine the infestation condition of an insect on a crop with high accuracy.

[0108] Furthermore, the attachment status determination system 100 can determine the communication status of a predetermined insect based on the vibration pattern information, and determine the attachment status of the predetermined insect based on the determined communication status. This allows the attachment status determination system 100 to determine the communication status of a predetermined insect and determine the attachment status of the predetermined insect with high accuracy.

[0109] Furthermore, the adhesion status determination system 100 can predict the future growth status of a specific insect based on the determined adhesion status. This allows the adhesion status determination system 100 to predict the future growth status of a specific insect with high accuracy.

[0110] Furthermore, the adhesion state determination system 100 can analyze the frequency components of the vibration based on the vibration pattern information and determine the adhesion state based on the pattern of the analyzed frequency components. This allows for evaluating differences in the frequency components of the vibration pattern information according to differences in adhesion state, and for determining the adhesion state with high accuracy.

[0111] Furthermore, the adhesion state determination system 100 can analyze the frequency components of vibrations based on the vibration pattern information and determine the communication state based on the pattern of the analyzed frequency components. This allows the communication state to be determined with high accuracy by evaluating the difference in the pattern of the frequency components of the vibration pattern information according to the difference in adhesion state.

[0112] Furthermore, the adhesion state determination system 100 can acquire noise vibration pattern information, remove the influence of vibrations resulting from noise factors in the vibration pattern information, and determine the adhesion state based on the vibration pattern information from which the influence of noise factors has been removed. This allows the adhesion state determination system 100 to remove the influence resulting from noise factors and determine the adhesion state with high accuracy.

[0113] Furthermore, the system for predicting the optimum pest control timing 101 can acquire vibration pattern information, determine the adhesion state based on the vibration pattern information, predict the future growth state based on the determined adhesion state, and predict the optimum timing for pest control treatment of a specified insect in a field based on the predicted growth state. As a result, the system for predicting the optimum pest control timing 101 can predict the optimum timing for pest control treatment with high accuracy, and minimize the number of pest control treatments and the amount of chemical used in the pest control treatments.

[0114] It should be noted that the present embodiment is provided to facilitate understanding of the present disclosure and is not intended to limit the present disclosure. The present disclosure may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present disclosure.

[0115] In addition, in this disclosure, a "unit" does not simply mean a physical means, but also includes cases where the functions of the "unit" are realized by software. Furthermore, the functions of one "unit" or device may be realized by two or more physical means, devices, or software, and the functions of two or more "units" or devices may be realized by one physical means, device, or software.

[0116] 100 Adhesion status determination system, 101 Pest control optimum timing prediction system, 110 Memory unit, 120 Vibration pattern acquisition unit, 130 Noise vibration pattern acquisition unit, 140 Noise removal unit, 150 Frequency analysis unit, 160 Communication determination unit, 170 Adhesion determination unit, 180 Growth prediction unit, 185 Pest control optimum timing prediction unit, 190 Output unit, 200 User device

Claims

1. An attachment status determination system comprising: a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration pattern of a target crop inhabiting a field; an attachment determination unit that determines the attachment status of a specified insect in the target crop based on the vibration pattern information; and an output unit that outputs information related to the determined attachment status.

2. The adhesion status determination system of claim 1, further comprising a communication determination unit that determines the communication status of the specified insect in the target crop based on the vibration pattern information, and the adhesion determination unit determines the adhesion status based on the determined communication status.

3. An attachment status determination system as described in claim 1 or 2, further comprising a habitat prediction unit that predicts the future habitat status of the specified insect in the field based on the determined attachment status.

4. The adhesion status determination system according to claim 1, further comprising a frequency analysis unit that analyzes frequency components of the vibration based on the vibration pattern information, wherein the adhesion determination unit determines the adhesion status based on a pattern of the frequency components.

5. The adhesion status determination system according to claim 2, further comprising a frequency analysis unit that analyzes the frequency components of the vibration based on the vibration pattern information, wherein the communication determination unit determines the communication status based on the frequency components.

6. An adhesion status determination system as described in claim 1 or 2, further comprising: a noise vibration pattern acquisition unit that acquires noise vibration pattern information indicating a vibration pattern of the crop to be determined that is caused by noise factors other than the specified insect, which is measured in advance; and a noise removal unit that removes the influence of vibrations caused by the noise factors in the vibration pattern information based on the vibration pattern information and the noise vibration pattern information, wherein the adhesion determination unit determines the adhesion status based on the vibration pattern information after the removal.

7. The attachment status determination system according to claim 1 or 2, wherein the target crop is rice, and the specified insect is a pest that attaches to rice.

8. A pest control optimum timing prediction system comprising: a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration pattern of a target crop inhabiting a field; an attachment determination unit that determines the attachment status of a specified insect in the target crop based on the vibration pattern information; a habitat prediction unit that predicts the future habitat status of the specified insect in the field based on the determined attachment status; and a pest control optimum timing prediction unit that predicts an optimum timing for pest control treatment of the specified insect in the field based on the prediction.

9. A method for determining an attachment status, comprising: a computer acquiring vibration pattern information indicating a vibration pattern of a target crop inhabiting a field; determining an attachment status of a specified insect in the target crop based on the vibration pattern information; and outputting information relating to the determined attachment status.

10. An attachment status determination program for implementing in a computer: a vibration pattern acquisition unit that acquires vibration pattern information indicating the vibration pattern of a target crop inhabiting a field; an attachment determination unit that determines the attachment status of a specified insect in the target crop based on the vibration pattern information; and an output unit that outputs information related to the determined attachment status.

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