Pest control system

Abstract

To provide a pest control system that can suppress the increase in insect populations. [Solution] The pest control system 7 of the present invention includes a control unit 71 that determines whether there is a risk of an increase in the number of captured insect species based on the identification result of the captured insect and environmental information about the environment, and outputs a preventive method to prevent an increase in the number of said insect species if there is a risk of an increase in said insect species.

Description

Technical Field

[0001] The present invention relates to a control system.

Background Art

[0002] Conventionally, as a captured insect identification system for identifying captured insects, the system described in Patent Document 1 is known. The captured insect identification system includes image reading means for reading an image of an adhesive sheet to which insects are adhered, and an analysis device for analyzing the read image to identify the captured insects. The analysis device includes preprocessing means for performing image processing to distinguish between an insect area, which is an area where insects are shown, and the background, on the read image, feature extraction means for acquiring Merkmal data of insects from the preprocessed image, data storage means for storing standard Merkmal data obtained by standardizing the Merkmal of the morphological features of insects, and identification means for identifying insects by comparing the Merkmal data extracted by the feature extraction means with the standard Merkmal data stored in the data storage means.

[0003] According to the captured insect identification system as described above, it is said that an objective and consistent identification result can be obtained as compared with the case of manually identifying insects.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] An object of the present invention is to provide a control system capable of suppressing an increase in insects.

Means for Solving the Problems

[0006] The pest control system of the present invention includes a control unit that determines whether there is a risk of an increase in the number of captured insect species based on the identification results of the captured insects and environmental information, and outputs a preventive method to prevent an increase in the number of said insect species if there is a risk of an increase in said insect species.

[0007] Furthermore, the control unit may be configured to determine at least one of the intrusion route information relating to the intrusion route of the insect of the said type and the source information relating to the source of the insect of the said type, and to output the prevention method based on at least one of the determined intrusion route information and source information.

[0008] Furthermore, the control unit may be configured to extract factors that include at least one of the temperature, air temperature, and humidity that increase the number of the aforementioned type of insect, and to determine whether or not there is a risk of an increase in the aforementioned type of insect based on the extracted factors and the environmental information.

[0009] Furthermore, the control unit may be configured to output at least one of the following treatment methods as the prevention method: a treatment method using physical control and a treatment method using chemical control. [Effects of the Invention]

[0010] According to the present invention, a pest control system that can suppress the increase of insects can be obtained. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram showing an imaging device for capturing pre-processing image data in a method for generating insect identification images according to one embodiment of the present invention. [Figure 2] This is a flowchart showing the processing steps involved in generating images for identifying the same insect. [Figure 3] This is a flowchart showing the processing steps for the part extraction step in the method for generating images for identifying the same insect. [Figure 4] This figure shows an image corresponding to the pre-processed image data to which frame information has been added, in the method for generating images for identifying the same insect. [Figure 5] This figure shows insects corresponding to insect data for which the part extraction process has been completed in the method for generating images for insect identification. (a) shows insects corresponding to insect data for which part determination is possible, and (b) shows insects corresponding to insect data for which part determination is not possible. [Figure 6] This figure shows insects corresponding to insect data to which point information and sequential information have been added in the method for generating images for insect identification. (a) shows insects corresponding to insect data where the boundaries of the body parts are recognizable, and (b) shows insects corresponding to insect data where the boundaries of the body parts are not recognizable. [Figure 7] This figure shows an overview of an insect identification system according to one embodiment of the present invention. [Figure 8] This is a block of data from the insect identification system. [Figure 9] This is a block diagram of a pest control system that implements response control. [Figure 10] This flowchart shows the processing steps for determining insect increase, source, and treatment method in a pest control system that implements response control. [Figure 11] This is a flowchart showing the process for determining the chemical treatment method in a pest control system that performs response control. [Figure 12] This is a flowchart showing the process for determining whether or not to implement a control measure in a pest control system. [Figure 13] This is a data table used to make various treatment decisions in a pest control system that performs response control. [Figure 14] This is a control system for implementing preventive control. [Figure 15] This is a flowchart showing the processing flow in a pest control system that implements preventive control. [Figure 16] This is a data table used to determine the environment for insect proliferation in a pest control system that implements preventive control. [Modes for carrying out the invention]

[0012] A method for generating image data for insect identification and an insect identification system 6 according to an embodiment of the present invention will be described with reference to FIGS. 1 to 8.

[0013] First, the pre-processing image data processed by the method for generating image data for insect identification of the present embodiment will be described with reference to FIG. 1. The pre-processing image data is generated by an imaging device 3 capable of imaging the capture surface 2a of a captured sample 2 having a capture surface 2a capable of capturing an insect 1, on which the insect 1 has been captured. The captured sample 2 of the present embodiment is a sample configured such that the insect 1 can adhere to the capture surface 2a and is captured in a state where a plurality of insects 1 adhere to the capture surface 2a. Specifically, it is an insect-catching paper on which the insect 1 has been captured. Here, the "insect" includes all small animals other than mammals, birds, and fish and shellfish, and in the present embodiment, it refers to arthropods (particularly insects and spiders). Further, the capture surface 2a has a flat shape whose surface direction extends in one direction (in the present embodiment, the direction penetrating the paper surface of FIG. 1). Furthermore, the capture surface 2a is plain, and in the present embodiment, it is a yellow surface as a whole and has no ruled lines or the like.

[0014] A plurality of insects 1 adhere to the capture surface 2a of the captured sample 2. As the captured sample 2 of the present embodiment, a sample created by sprinkling and adhering the corpses of insects 1 on the capture surface 2a, a sample created by attracting insects 1 with light or odor and adhering them to the capture surface 2a, etc. are adopted, but it is not limited thereto.

[0015] The imaging device 3 is a device that images the captured sample 2 to generate pre-processing image data. Specifically, the imaging device 3 includes a box-shaped housing 31 capable of blocking external light, a holding means (not shown) provided inside the housing 31 and capable of holding the captured sample 2 in a state where the surface direction of the capture surface 2a extends in one direction (in the present embodiment, the direction penetrating the paper surface of FIG. 1), an imaging means 32 provided inside the housing 31 and capable of imaging the capture surface 2a of the captured sample 2, an irradiation device 33 provided inside the housing 31 and capable of irradiating light onto the capture surface 2a of the captured sample 2, and an imaging processing unit (not shown) that processes the image captured by the imaging means 32 to generate pre-processing image data.

[0016] The imaging means 32 includes a camera 34 positioned at a distance from the capture surface 2a of the captured sample 2 held by the holding means to image the capture surface 2a of the captured sample 2 held by the holding means, and a traveling means (not shown) for moving the camera 34 along the planar direction of the capture surface 2a of the captured sample 2 held by the holding means. The camera 34 is movable (travels) along the planar direction of the capture surface 2a by the traveling means, and can image the capture surface 2a at multiple positions along the planar direction of the capture surface 2a while moving along the planar direction of the capture surface 2a. In addition, the camera 34 in this embodiment is positioned opposite the capture surface 2a. Furthermore, the camera 34 has a focal length such that only the portion of the capture surface 2a opposite the camera 34 is in focus.

[0017] The illumination device 33 is configured to illuminate the capture surface 2a with light from both sides of the travel area on which the camera 34 travels. Specifically, the illumination device 33 has illumination units 35 on one side and the other side that straddle the travel area, and each illumination unit 35 illuminates the capture surface 2a with light. The illumination units 35 are positioned to illuminate the capture surface 2a from a position approximately 45 degrees upward from the center in the width direction (direction perpendicular to the surface direction). In this embodiment, the illumination device 33 is configured so that multiple illumination units 35 are arranged in a line along the travel area, making it possible to illuminate the entire area in one direction of the travel area with light. In addition, the illumination device 33 in this embodiment illuminates the capture surface 2a with white light.

[0018] The imaging processing unit generates pre-processed image data by stitching together multiple images captured by the imaging means 32. Specifically, the imaging processing unit generates pre-processed image data by stitching together the in-focus portions of the multiple images captured by the camera 34. In this embodiment, the imaging processing unit generates pre-processed image data by stitching together the images of the portion of the capture surface 2a facing the camera 34 from the multiple images captured by the camera 34.

[0019] The pre-processing image data generated as described above is, for example, data representing an image of an insect 1 on the capture surface 2a, as shown in Figure 4. In this embodiment, the pre-processing image data is image data representing an image with the capture surface 2a as the background and multiple insects 1 photographed on the background, and the pre-processing image data includes multiple insect data D related to insect 1. Insect data D is the image data of the portion of the pre-processing image data that shows insect 1.

[0020] Next, a method for generating insect identification image data by processing pre-processed image data will be described with reference to Figures 2 to 6. The insect identification image data generation method of this embodiment is a method for generating insect identification image data to be used as training data for machine learning.

[0021] As shown in Figure 2, the method for generating insect identification image data in this embodiment is a method for generating insect identification image data by processing pre-processed image data that contains insect 1 in the image, as described above. Specifically, the method for generating insect identification image data comprises an insect extraction step S1 for extracting insect data D related to insect 1 from pre-processed image data, a part extraction step S2 for extracting parts of insect 1 included in the extracted insect data D, a point information assignment step S3 for assigning point information 4 to the extracted insect data D, and a sequence information assignment step S4 for assigning sequence information 5 related to the order to the point information 4. In the method for generating insect identification image data in this embodiment, each of the above steps is performed for all insect data D before the next step is performed. In this embodiment, each step is performed by a person.

[0022] The insect extraction process S1 is a process that performs an insect discovery process S11 to find insect 1 contained in the image corresponding to the pre-processing image data, and an insect identification process S12 to identify the discovered insect 1. In other words, in the insect extraction process S1, insect 1 is discovered in the insect discovery process S11, and in the insect identification process S12, data with information added to identify the discovered insect 1 is extracted as insect data D.

[0023] The insect detection step S11 is a step of processing the pre-processing image data to find insects 1 included in the image corresponding to the pre-processing image data. In the insect detection step S11 of this embodiment, the step is to search for parts that have the characteristics of insect 1 from the image corresponding to the pre-processing image data. Specifically, if a part that is a different color from the background has an outline of a predetermined shape, the part enclosed by the outline is determined to be one insect 1.

[0024] As shown in Figure 4, the insect identification step S12 is a step that performs a frame information assignment step, which is a step of assigning frame information, which is information about the frame F surrounding the insect 1 discovered in the insect discovery step S11; an extraction step, which is a step of extracting insect data based on the frame information assigned in the frame information assignment step; and a species information assignment step, which is a step of assigning species information, which is information about the type of insect 1 discovered. In other words, in the insect identification step S12 of this embodiment, information about the range of insect 1 included in the image corresponding to the pre-processing image data and the type of insect 1 is identified, and insect data D related to the identified insect 1 is extracted.

[0025] The frame information assignment step involves identifying the area occupied by the discovered insect 1 in the pre-processing image data and assigning frame information relating to the frame F that encloses the area occupied by the insect 1. Specifically, the frame information assignment step involves identifying the outer edge of the insect 1 discovered in the insect discovery step S11 and assigning information relating to the frame F that encloses that outer edge. The frame F corresponding to the frame information assigned in this embodiment is square-shaped, particularly rectangular, and for example, the coordinates of two points located diagonally opposite each other within the rectangular frame F are assigned as frame information.

[0026] The extraction process involves extracting insect data D based on frame information. Specifically, in the extraction process, the data enclosed by frame F within the area where insect 1, discovered in the insect discovery process S11, is located is extracted as insect data D. In other words, insect data D is image data extracted from the pre-processing image data by enclosing the area where insect 1 is located with frame F.

[0027] The species information assignment process identifies the species of the discovered insect 1 (specifically, the species in the classification necessary to identify the source of insect 1, as described later, for example, the family in the biological classification rank to which insect 1 belongs) and assigns information about the species of insect 1 to insect data D. Here, the species information to be assigned is information that can identify the species of insect 1, for example, the species name of insect 1 or an identifier related to the species of insect 1. In the species information assignment process, information about the species name of insect 1, enclosed in frame F corresponding to the frame information, is assigned to insect data D.

[0028] As shown in Figures 2 and 3, the part extraction step S2 is a step of extracting the parts of insect 1 corresponding to each extracted insect data D. In addition, in the part extraction step S2 of this embodiment, it is determined whether or not it is possible to identify the parts of insect 1 corresponding to each insect data D, and if it is possible to identify the parts, the parts of insect 1 corresponding to each insect data D are identified and extracted. Specifically, the part extraction step S2 is a step of executing a selection step S21 to select insect 1 corresponding to the insect data D to be processed, a feasibility determination step S22 to determine whether it is possible to identify the parts of insect 1 corresponding to the insect data D selected in the selection step S21, an identification step S23 to identify and extract the parts of insect 1 corresponding to insect data D for which the parts can be identified, and a non-specification processing step S24 to specify that the parts should not be identified for insect 1 corresponding to insect data D for which the parts cannot be identified. In the part extraction step S2 of this embodiment, it is determined whether or not there is any unprocessed insect data D, and if there is any unprocessed insect data D, an iterative step S25 is executed to repeat each process in the part extraction step S2.

[0029] Here, the body structure of insect 1 will be described with reference to Figures 4 to 6. Insect 1 comprises a body 11 having a head 13 and a torso 14 connected to the head 13, including at least an abdomen 16, and an accessory part 12 extending outward from the body 11. The accessory part 12 includes, for example, antennae, legs, wings, etc. The structure of the body 11 varies depending on the insect 1. For example, the body 11 of an insect comprises a head 13 and a torso 14 consisting of a thorax 15 and an abdomen 16. The body 11 of a spider comprises a cephalothorax as the head 13 and an abdomen 16 as the torso 14. In this embodiment, the term "part of insect 1" refers to each part of the body 11 of insect 1.

[0030] As shown in Figure 3, the selection step S21 is the process of selecting one insect 1 from the insect 1 corresponding to the multiple insect data D extracted, to be processed. In addition, the selection step S21 selects an insect 1 corresponding to an unprocessed insect data D that has not been processed in the part extraction step S2.

[0031] The feasibility determination step S22 is a step in determining whether the parts of insect 1 corresponding to the selected insect data D can be identified. In the feasibility determination step S22 of this embodiment, it is determined whether each part is visible to a degree that is discernible in the image of insect 1 corresponding to the selected insect data D, and if each part is visible to a degree that is discernible, it is determined that the parts of insect 1 corresponding to the selected insect data D can be identified. In the feasibility determination step S22 of this embodiment, it is determined that the parts can be identified if at least the head 13 and the body 14 can be distinguished. For example, as shown in Figure 5(a), it is determined that the parts of insect 1 can be identified if the boundaries of each part (head 13 and body 14) on the body 11 of insect 1 corresponding to insect data D are recognizable. On the other hand, as shown in Figure 5(b), if the body 11 of insect 1 corresponding to insect data D is hidden by an attachment 12 (specifically wings), another insect 1, dust, etc., and the boundary between the head 13 and the body 14 is indistinguishable, it is determined that the parts cannot be identified. Furthermore, in the feasibility determination step S22, it is determined that part identification is not possible if the insect 1 is crushed or damaged on the capture surface 2a, causing part of it to be lost or separated from the main body 11, or if the part of the insect 1 corresponding to the insect data D cannot be identified. In this embodiment, even if part of the insect 1 corresponding to the insect data D is crushed or hidden, it is determined that the part of the insect 1 can be identified if the main body 11 can be recognized to a degree that allows for the determination of the part of the insect 1.

[0032] The identification step S23 is a step in which, for insect 1 that has been determined in the feasibility determination step S22 to be able to identify the parts of insect 1 corresponding to insect data D, each part is identified and extracted. Specifically, the identification step S23 identifies and extracts the range in which each part is located from the insect 1 image data corresponding to insect data D. For example, as shown in Figure 5(a), if the entire body 11 of insect 1 corresponding to insect data D is recognizable and the boundaries of the head 13, thorax 15, and abdomen 16 are recognizable in the identification step S23, the range in which the head 13 is located, the range in which the thorax 15 is located, and the range in which the abdomen 16 is located are identified and extracted from the body 11 of insect 1, and information corresponding to each part is added to each extracted range. Furthermore, in the specific step S23, if the boundary between the head 13 and thorax 15 of insect 1 corresponding to insect data D is recognizable, but the boundary between the thorax 15 and abdomen 16 is not recognizable, or if the body 14 does not have at least one of the thorax 15 and abdomen 16 (for example, a spider) (for example, as shown in Figure 6(b)), then at least two or more parts, such as the area where the head 13 is located and the area where the body 14 is located, are identified and extracted, and information corresponding to each part is assigned to each extracted area.

[0033] The non-specification processing step S24 is a step in which, for insect 1 corresponding to insect data D that was determined in the feasibility determination step S22 to be unable to determine the part, the part is not specified. As a result of the non-specification processing step S24, information that the part cannot be determined is added to insect 1 corresponding to insect data D that was determined to be unable to determine the part, for example, as shown in Figure 5(b).

[0034] The repeating step S25 determines whether there is an insect 1 corresponding to an unprocessed insect data D that has not been processed in the part extraction step S2, and if there is an unprocessed insect 1, it repeats each process in the part extraction step S2. Specifically, in the repeating step S25, if the multiple insect data D included in the pre-processing image data includes insect data D that has not been processed in the specific step S23 or the unspecified processing step S24, it is determined that there is an insect 1 corresponding to the unprocessed insect data D, and the process proceeds to the selection step S21. Furthermore, in the repeating step S25, if the processing in the specific step S23 or the unspecified processing step S24 has been completed for all insect data D included in the pre-processing image data, the part extraction step S22 is terminated.

[0035] As shown in Figure 2, the point information assignment step S3 is a step of assigning point information 4 to insect data D by specifying at least three points. In addition, in the point information assignment step S3, point information 4 is assigned to three points that lie on the surface of insect 1 corresponding to insect data D, and specifically, point information 4 is assigned to three points that are spaced apart in the overall length direction of the insect's body 11 (the direction connecting the head and abdomen of insect 1). That is, the point information assignment step S3 is a step of assigning at least three points of point information 4 to the pre-processed image data within the range where insect 1 corresponding to insect data D is located, and specifically, point information 4 is assigned to at least three points within the range where each part of insect 1 extracted in the part extraction step S2 is located. In the point information assignment step S3 of this embodiment, point information 4 is assigned such that three points correspond to two or more parts among the multiple parts that constitute the insect's body 11 corresponding to insect data D, for example, point information 4 is assigned such that three points correspond to the head 13 and the body 14. As shown in Figure 6, in the point information assignment step S3 of this embodiment, point information 4 is assigned to each insect 1 corresponding to the insect data D, at least to the position corresponding to the head 13 of the insect 1 and the position corresponding to the body 14 of the insect 1. Specifically, point information 4 is assigned to three points: a head-corresponding point 41 corresponding to the head 13 of the insect 1, an abdominal-corresponding point 42 corresponding to the abdomen 16 of the insect 1, and an intermediate point 43 located between the head-corresponding point 41 and the abdominal-corresponding point 42. Thus, in the point information assignment step S3 of this embodiment, point information 4 is assigned based on the part of the insect 1 corresponding to the insect data D. Therefore, in the part extraction step S2, point information 4 is assigned to the insect data D from which a part has been extracted, but point information 4 is not assigned to insect data D that is determined to be unable to be determined as a part in the part extraction step S2.

[0036] The head-corresponding point 41 is a point designated at the tip of the head 13 (the head end in the overall length direction of the body 11) and is also a point designated approximately in the center in the width direction of the body 11 (in the direction perpendicular to the overall length direction of the body 11 in the image of insect 1 corresponding to insect data D). That is, the head-corresponding point 41 is a point designated at the tip of the body 11 of insect 1 and approximately in the center in the width direction of the body 11. The abdomen-corresponding point 42 is a point designated at the rear end of the abdomen 16 (the ventral end in the overall length direction of the body 11) and is also a point designated approximately in the center in the width direction of the body 11. That is, the abdomen-corresponding point 42 is a point designated at the rear end of the torso 14 (the rear end of the body 11 of insect 1) and approximately in the center in the width direction of the body 11.

[0037] The midpoint 43 is a point designated as being in the middle of the insect's body 11 in the overall length direction. Furthermore, the midpoint 43 is a point designated as being approximately in the center in the width direction of the insect 1, and in this embodiment, it is located in the trunk 14. In addition, the midpoint 43 is a point located at a characteristic point in the insect's body 11. A characteristic point is a point that has external characteristics of the insect 1, such as the boundary between parts, the base of the wings or the pattern of the insect 1, or the midpoint between the tip and posterior end of each part of the insect 1, or a position that can be identified based on a characteristic point within a part. The midpoint between the tip and posterior end of each part of the insect 1 is, for example, the midpoint between the tip and posterior end of the abdomen or the midpoint between the tip and posterior end of the trunk. For example, in the case of the insect 1 shown in Figure 6(a), the midpoint 43 is designated as a characteristic point, being the midpoint in the width direction of the boundary between the thorax 15 and the abdomen 16. Furthermore, in the case of insect 1 (for example, a beetle) shown in Figure 6(b), the position of the intermediate point 43 is specified with the base of the wings of the body 14 as a feature point. In the point information assignment step S3 of this embodiment, if the boundaries between parts of the body 11 of insect 1 corresponding to insect data D are recognizable, the boundaries between parts are treated as feature points. If the boundaries between parts of the body 11 of insect 1 corresponding to insect data D are not recognizable, and a point with a feature within the part is recognizable, the point with a feature within the part is treated as a feature point.

[0038] The sequential information assignment step S4 is a step in which sequential information 5 is assigned to the three point information 4 assigned in the point information assignment step S3. The sequential information 5 is information about the order assigned to the point information 4 in order to identify the posture of the insect 1 corresponding to the insect data D, and specifically, it is information about the order in which the head corresponding point 41, the intermediate point 43, and the abdomen corresponding point 42 are arranged in a predetermined order. Furthermore, the sequential information 5 is information about the order in which the points are arranged along the entire length of the insect's body 11, and in this embodiment, as shown in Figure 6, it is information about the order from the tip side (head side) to the rear end side (ventral side). That is, in the sequential information assignment step S4, sequential information 5 is assigned to the point information 4 based on the order in which the parts of the insect's body 11 are arranged, and in this embodiment, sequential information 5 is assigned regarding the order in which the head corresponding point 41, the intermediate point 43, and the abdomen corresponding point 42 are arranged. Furthermore, in the sequential information assignment step S4 of this embodiment, sequential information 5 is assigned to point information 4. Therefore, sequential information 5 is not assigned to insect data D that were not assigned point information 4 in the point information assignment step S3 (insect data D that were determined to be unable to be determined as a part in the part extraction step S2).

[0039] As described above, in the method for generating insect identification image data of this embodiment, the image data for insect identification is generated by performing each of the above steps on the image data before processing.

[0040] Next, the insect identification system 6 will be explained with reference to Figures 7 and 8.

[0041] As shown in Figure 7, the insect identification system 6 is a system that identifies insects 1 contained in image data transmitted from a client terminal C (imaging device 3 in this embodiment) via the Internet I. Specifically, the insect identification system 6 processes target image data captured by the client from an insect-captured sample, identifies the insects 1 contained in the target image data, and outputs the results. In this embodiment, the target image data is captured by the aforementioned imaging device 3 and transmitted to the insect identification system 6 using communication means built into the imaging device 3.

[0042] As shown in Figure 8, the insect identification system 6 comprises a communication unit 61 for communicating with the outside, an image data acquisition unit 62 for acquiring target image data from the outside, an insect identification unit 63 for identifying the type of insect 1 included in the target image data, and an output unit 64 for outputting the identification results of the insect identification unit 63. The image data acquisition unit 62 acquires target image data from a client terminal C (imaging device 3) via the communication unit 61 and the Internet I. The output unit 64 outputs the identification results to the client terminal C (specifically, the client's computer P or tablet terminal, etc.) via the communication unit 61 and the Internet I. Furthermore, such an insect identification system 6 is implemented by a computer. Specifically, the insect identification system 6 of this embodiment is implemented by configuring a computer to function as the communication unit 61 for communicating with the outside, the image data acquisition unit 62 for acquiring target image data from the outside, the insect identification unit 63 for identifying the type of insect 1 included in the target image data, and the output unit 64 for outputting the identification results of the insect identification unit 63.

[0043] The insect identification unit 63 is configured to identify the type of insect 1 contained in the target image using a trained model 65 constructed by machine learning using training data. The trained model 65 is constructed by machine learning using the insect identification image data generated by the insect identification image data generation method described above as training data.

[0044] The training data for the trained model 65 includes pre-processed image data as training image data containing insect 1 in the image, pose information including point information 4 assigned to insect data D relating to insect 1 contained in the training image data, specifying at least three points, and sequence information 5 relating to the order assigned to the point information 4, and type information relating to the type of insect 1 contained in the training image data. The point information 4 is the same as the point information 4 assigned in the point information assignment step S3, the sequence information 5 is the same as the sequence information 5 assigned in the sequence information assignment step S4, and the type information is the same as the type information assigned in the type information assignment step. Furthermore, the training data of this embodiment is data to which frame information relating to the frame F surrounding insect 1 contained in the training image data is assigned. The frame information is the same as the frame information assigned in the frame information assignment step.

[0045] The trained model 65 is constructed using so-called supervised learning, with the insect identification image data generated by the insect identification image data generation method described above as training data. In other words, the trained model 65 is a model constructed by providing a machine learning algorithm with a large number of insect identification image data to which species information has been added.

[0046] Such an insect identification unit 63 processes the target image data to find insects 1 contained in the target image data and identifies the type of insect 1 found. The insect identification unit 63 in this embodiment adds type information of the found insects 1 to the target image data and outputs the number of insects 1 for each type as the identification result. In this embodiment, classification at the family level is used as the type of insect 1.

[0047] According to the method for generating insect identification image data with the above configuration, point information 4 and sequential information 5 are assigned to the extracted insect 1. Based on the point information 4 and sequential information 5, the posture of the insect 1 can be determined, and even if the insect 1 is bent, for example, the type of insect 1 can be appropriately identified. Therefore, the accuracy of identifying insect 1 included in the image data can be improved.

[0048] Furthermore, since frame information is assigned to the extracted insect 1, it is easier to properly identify insect 1 even when multiple insect 1s overlap.

[0049] Furthermore, by assigning point information 4 to two or more body parts and assigning point information 4 based on the order in which the body parts are arranged, the posture of insect 1 can be appropriately determined even when insect 1 is bent.

[0050] Furthermore, since the posture of the insect 1 can be determined based on at least three points, including the head corresponding point 41, the abdomen corresponding point 42, and the midpoint point 43, the posture of the insect 1 can be appropriately determined even if the insect 1 is bent.

[0051] Furthermore, since point information 4 is assigned to the feature point as an intermediate point 43, point information 4 can be assigned to approximately the same position on insect 1 for the same type of insect 1.

[0052] Furthermore, since the imaging device 3 illuminates the travel area from both sides, it can suppress the shadowing of the insect 1, and since it generates pre-processing image data by stitching together multiple images, it can suppress blurring of the insect 1 in the pre-processing image data.

[0053] Furthermore, since the process includes a step to add species information about the type of insect 1, even if the insect 1 included in the image data is bent, the image data including the bent insect 1 can be used as training data.

[0054] Furthermore, the process includes a part extraction step S2 that extracts parts of insect 1 corresponding to the insect data D from the insect data D extracted before the point information assignment step S3, and the part extraction step S2 extracts at least the head 13 and abdomen 16 of insect 1. Therefore, in the point information assignment step S3, point information 4 can be appropriately assigned to parts of the insect 1's body 11.

[0055] Furthermore, the part extraction step S2 includes a feasibility determination step S22 that determines whether or not part extraction is possible for the extracted insect data D. Part extraction is performed only for the insect data D that is determined to be extractable in the feasibility determination step S22, and in the point information assignment step S3, point information 4 is assigned only to the insect data D from which part extraction has been performed. Therefore, point information 4 can be reliably assigned to the part of insect 1 corresponding to the insect data D.

[0056] Furthermore, in the point information assignment step S3, point information 4 is assigned to at least three points on the main body 11 of the insect 1. Therefore, the posture of the insect 1 can be reliably determined regardless of the state of the appendages 12, such as the appendages of the insect.

[0057] Furthermore, in the forward information assignment step S4, forward information 5 is assigned in the order of head corresponding point 41, midpoint 43, abdomen corresponding point 42, or abdomen corresponding point 42, midpoint 43, head corresponding point 41. Therefore, since forward information 5 is assigned along the entire length direction of the body 11, the posture of the insect 1 can be reliably determined.

[0058] Furthermore, with the insect identification system 6 configured as described above, the trained model 65 is constructed by machine learning using training data that includes point information 4 and pose information 5, which includes point information 4 and forward information 5, for at least three points specified for the insect data D included in the training image data. Therefore, even if the insect 1 is bent, for example, the type of insect 1 can be appropriately identified.

[0059] The inventors compared the insect identification system 6 using the pre-trained model 65 described above with a conventional insect identification system using a conventional pre-trained model constructed as training data in which only information about the insect species was attached to an image of an insect, without including information about the insect's posture such as the point information 4 and forward information 5. The results are described below. When insect 1 was identified using the same insect-captured sample, the identification accuracy of the conventional insect identification system was approximately 60.0%, while the identification accuracy of the insect identification system 6 using the pre-trained model 65 according to the present invention was approximately 79.1%. Furthermore, in the conventional insect identification system, when insect 1 was densely clustered in the pre-processing image, it was sometimes unable to identify insect 1 as insect 1, resulting in a decrease in the accuracy of detecting insect 1 (specifically, from 90.0% or more to 80.0% or less). However, the insect identification system 6 using the pre-trained model 65 was able to maintain the accuracy of detecting insect 1 (generally 90.0% or more) even when insect 1 was densely clustered.

[0060] Although embodiments of the present invention have been described above with reference to one example, the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention.

[0061] For example, we have described a case where image data is created as training data using the method for generating insect identification image data. However, this method is not limited to such cases. For example, it can also be used as preprocessing in the process of identifying insects 1 contained in image data by a person or machine. Specifically, the method for generating insect identification image data according to the present invention can be applied to sample imaging data obtained by imaging a captured sample 2, and the sample imaging data as preprocessed image data can be used as identification data for insect identification during the identification process.

[0062] Furthermore, while we have described a case where a person adds information to pre-processing image data to create image data for insect identification, the process is not limited to this case. For example, it is also possible to configure the system so that a machine adds information to the pre-processing image data in all or some of the steps. When the system is configured so that a machine adds information to the pre-processing image data in all steps, the system can be configured as an insect identification image generation method in which a computer performs an insect extraction step S1, a part extraction step S2, a point information addition step S3, and a sequential information addition step S4 to generate an image for insect identification.

[0063] Furthermore, while we have described the case where each step is performed for all insects before proceeding to the next step, the system is not limited to this case. For example, it can be configured to perform all steps for one insect before performing all steps for the next insect. It is also possible to perform each step independently for multiple insects.

[0064] Furthermore, although we have described a case in which a species information assignment step is included in which species information about the type of insect 1 is assigned to the insect data D extracted in the insect extraction step S1, the process is not limited to this case. For example, when the method for generating image data for insect identification is used as a preprocessing step in the process of identifying insect 1, the system can be configured not to assign species information about insect 1.

[0065] Furthermore, in the insect extraction step S1, we have described a case where frame information is added for a rectangular frame F surrounding the insect 1 in the image as a frame information addition step. However, the process is not limited to this configuration. For example, frame information can be added for frames other than rectangular ones, such as circular frames or frames that follow the outline of the insect 1. It is also possible to not add frame F, that is, to omit the frame information addition step.

[0066] Furthermore, in the part extraction step S2, we have described a case where it is determined whether or not part extraction is possible for insect 1 corresponding to insect data D, and parts of insect 1 are extracted only for insect data D for which part extraction is deemed possible. However, the configuration is not limited to this, and it is also possible to configure it to extract parts of insect 1 for all insect data D.

[0067] Furthermore, although the case with a part extraction step S2 has been described, a configuration without a part extraction step S2 is also possible. If the part extraction step S2 is not included, for example, in the point information assignment step S3, point information 4 can be assigned to the insect data D at three points: one end, the other end, and the position between both ends of the body 11, and in the sequential information assignment step S4, sequential information 5 can be assigned to the point information 4 sequentially from one end to the other end. In this case, in effect, point information 4 is assigned so that at least three points correspond to two or more parts of the insect 1. In the above configuration, for example, one end of the body 11 can be determined as the location of the insect 1's antennae. Also, in the case of an insect 1 with a small head 13 and a large body 14, three or more points of point information 4 can be assigned to the body 14, and sequential information 5 can be assigned sequentially from the head 13 side, for example. Furthermore, although the case with point information 4 being assigned only to the body 11 has been described, the configuration is not limited to this, and point information 4 can also be assigned to appendages 12 such as antennae, legs, and wings. For example, one or more point information 4 can be assigned to the tips of the antennae, the main body 11, and the legs, and sequential information 5 can be assigned sequentially starting from the antennae. In any case, by assigning three or more point information 4 and sequential information 5 at intervals sufficient to estimate the posture of the insect 1, the posture of the insect 1 can be determined, and the accuracy of insect identification can be improved. Furthermore, when four or more point information 4 are assigned, the posture can be determined with high accuracy.

[0068] Furthermore, the system can be configured to add line information by connecting point information 4 according to the forward information 5 added to insect data D in the forward information addition step S4. When line information is added, the posture of insect 1 can be easily determined by the added line information. In addition, information regarding the area enclosed by the lines connecting each point added in the point information addition step S3 can also be added to insect data D. When area information is added, for example, if the area enclosed by the three point information 4 added in the point information addition step S3 is large, it can be determined that insect 1 is bent significantly.

[0069] Furthermore, although the image data acquisition unit 62 in the insect identification system 6 was described as acquiring image data via the Internet I, it is not limited to this configuration, and can also be configured to acquire image data directly from a device that has captured images of insect traps or the like.

[0070] Furthermore, while we have described the case where the target image data is acquired using the same method as the training image data, the configuration is not limited to this, and the image data can also be acquired using a different method than the training image data.

[0071] Furthermore, while we have explained the case where the "family" level in biological classification is used to define a type of insect, it is not limited to this case. Other levels such as "order," "genus," and "species" can also be used, and classifications that are not based on taxonomic ranks (for example, classifications based on appearance such as "crane flies") can also be adopted.

[0072] Next, the pest control system 7 will be described with reference to Figures 9 to 16. The pest control system 7 is a system that performs processing to suppress adverse effects caused by insects when the number of insects increases or when there is a high possibility of an increase in insects. In this embodiment, it is a system for controlling insects inside a building (for example, a food factory) and / or insects that invade the building. The pest control system 7 of this embodiment uses the insect identification system 6 described above.

[0073] The pest control system 7 performs two types of control: response control (feedback control), which takes action to reduce the number of insects inside and / or entering the building in response to an increase in insects; and preventive control (feedforward control), which takes action to prevent an increase in insects when there is a possibility of an increase in the number of insects inside and / or entering the building. First, let's explain the response control.

[0074] As shown in Figure 9, the pest control system 7, which performs response control, includes a control unit 71 that receives and processes information regarding the identification results from the insect identification system 6, and a storage means 72 that stores various information, and outputs the processing results of the control unit 71 to the processing unit 8. The insect identification system 6 that outputs information regarding the identification results to the pest control system 7 in this embodiment outputs the number of each type of insect included in the target image data as an identification result to the control unit 71, based on the target image data generated (specifically, imaging and image processing) by the imaging device 3, which is installed inside and outside the building and is set up for fixed-point observation. The imaging device 3 generates target image data by imaging insects caught in a plurality of insect trapping devices (e.g., insect traps) installed inside and outside the building. That is, the insect identification system 6 in this embodiment outputs identification results regarding the number of each type of insect caught in the insect trapping devices. In addition, in this embodiment, the insect identification system 6 is configured to output identification results at predetermined intervals.

[0075] The pest control system 7 comprises a control unit 71 that performs predetermined processing and a storage means 72 that stores information regarding the identification results received from the insect identification system 6. The pest control system 7 of this embodiment is configured to identify a type of insect whose number has increased above a threshold based on the information regarding the identification results received from the insect identification system 6, and to output a processing method D4 corresponding to the identified type of insect. Furthermore, the pest control system 7 of this embodiment is implemented by cloud computing, but is not limited to this.

[0076] The storage means 72 stores identification results received from the insect identification system 6, at least the identification results from the previous reception. The storage means 72 can also transmit the previously received identification results it has stored to the control unit 71. Furthermore, the storage means 72 stores a database (for example, table T1 shown in Figure 13) used by the control unit 71 for various determinations, and transmits it to the control unit 71 upon request. As shown in Figure 13, the database stored by the storage means 72 in this embodiment is a database in which the type of insect (type information) is associated with the invasion route information D2, the source information D3, and the processing method D4.

[0077] The control unit 71 includes an increase determination unit 711 that compares the identification results received from the insect identification system 6 with the previously received identification results stored in the storage means 72 to determine whether the number of insects of each insect species has increased by more than a threshold; an infestation source determination unit 712 that determines, for each insect species that the increase determination unit 711 has determined to have increased by more than a threshold, infestation route information D2 regarding the infestation route of that insect species and infestation source information D3 regarding the source of the infestation; and a processing method determination unit 713 that determines a processing method D4 for reducing the number of insects in the building based on the determination of the infestation source determination unit 712.

[0078] The increase determination unit 711 receives identification results from the insect identification system 6 and the previously received identification results from the storage means 72, compares the previously received identification results with the currently received identification results, and determines whether the number of insects has increased by a threshold or more for each insect species. In other words, the increase determination unit 711 in this embodiment determines, for each insect species, whether the number of insects newly captured by the insect trap is above a threshold between the time the insect identification system 6 outputs the previous identification results and the time the current identification results are output. With such an increase determination unit 711, it is possible to determine, for each insect species, whether the number of insects newly captured by the insect trap is above a threshold or more within a predetermined time (the interval at which the insect identification system 6 outputs identification results), so that if there are many of a particular type of insect in the environment where the insect trap is installed, it is possible to determine that there are many of that type of insect. Note that the threshold in this embodiment is a preset numerical value, but the configuration is not limited to this, and for example, twice the normal increase amount can be used as the threshold.

[0079] The source determination unit 712 determines source information D3 regarding the source of an insect species for which the increase determination unit 711 has determined that the number has increased by a threshold or more. That is, the source determination unit 712 identifies the source of an insect species when there is a large number of a particular species in the environment where the insect trapping device is installed. In this embodiment, the source determination unit 712 determines intrusion route information D2 regarding the intrusion route of the insect species that has been determined to have increased, and source information D3 regarding the source of the insect. Intrusion route information D2 is information that distinguishes whether the insect species is an external intrusion type that originates outside the building and invades the building, or an internal intrusion type that originates inside the building. In this embodiment, the source determination unit 712 identifies the intrusion route information D2 and source information D3 of the increased insect species based on a pre-prepared database of intrusion route information D2 and source information D3 for each insect species.

[0080] As shown in Figure 13, the source information D3 is information for identifying the source of that type of insect within the building. For externally invasive insects, it is information for identifying how the insect enters the building. For internally invasive insects, it is information for identifying the environment within the building in which they breed. Specifically, for externally invasive insects, the source information D3 includes flying insects that enter the building, such as large flying flies (e.g., houseflies, flesh flies, blowflies, etc.), and other flying insects that enter the building other than large flies (e.g., flies other than large flies, thrips, moths, caddisflies, ants, wasps, planthoppers, etc.). This information concerns which of the following four categories the insect belongs to: leafhoppers, aphids, etc.; wandering / creeping insects that enter buildings by roaming or crawling (for example, ants, earwigs, crickets, springtails, centipedes, millipedes, house centipedes, woodlice, mites, spiders, ground beetles, stink bugs, etc.); or other insects that do not fall into the above categories (for example, rodents). Furthermore, source information D3 includes, for internally occurring insects, drainage / water system insects that originate from water systems such as drains (e.g., drain flies, fruit flies, phorid flies, false flies, gnats, etc.), fungivorous insects that originate from fungi (e.g., winged booklice, powdery booklice, small leafhoppers, rove beetles, etc.), and indoor dust / food system insects that originate from indoor dust and food (e.g., booklice, silverfish, etc.) This information concerns which of the following six categories an insect belongs to: carpet beetles, dust mites, cigarette beetles, flat beetles, leaf beetles, moths, etc.; insect predators that feed on insects (for example, spiders of the order Araneae such as the families Chilean spiders, cellar spiders, and jumping spiders); heat source / crevice systems that emerge from heat sources or crevices (for example, cockroaches, etc.); or other internally-emerging insects that do not belong to the above categories (for example, bed bugs, etc.).

[0081] In this embodiment, the source determination unit 712 refers to a table T1 as shown in Figure 13 to determine the invasion route information D2 and source information D3 for the types that the increase determination unit 711 has determined to have increased. Specifically, it refers to the type information D1 in table T1 to obtain the invasion route information D2 and source information D3 corresponding to the types that have been determined to have increased. Specifically, if the type that has been determined to have increased is one of a1, a2, or a3, the source determination unit 712 determines that the increasing insects are externally invading and the source is a type of large flying fly. Furthermore, if the type of insects related to the source information D3 is the same, the source determination unit 712 outputs only one type of insect that has increased. For example, if the type that has been determined to have increased is multiple of a4, a5, a6, a7, or a8, the source determination unit 712 determines that the increasing insects are externally invading and the source is a flying or other type.

[0082] The treatment method determination unit 713 determines and outputs a treatment method D4 to reduce insects based on the source information D3 determined by the source determination unit 712. In this embodiment, the treatment method determination unit 713 outputs both a treatment method D4 using physical control D41 and a treatment method D4 using chemical control D42 (chemical treatment) as treatment methods D4 to reduce insects. Furthermore, the treatment method determination unit 713 in this embodiment determines the treatment method D4 by referring to a database related to treatment methods D4, specifically by referring to a table T1 as shown in Figure 13. For example, if the source determination unit 712 determines that the type of source of the increasing species is a flying large fly, it outputs warning instructions such as checking for animal carcasses that may be sources, installing insect nets, checking openings, and shutter opening times as physical control D41 in the treatment method D4 corresponding to the type of flying large fly, and outputs chemical spraying of chemicals at entry points as chemical control D42. Furthermore, if the source determination unit 712 determines that the insect is of the external invasion type and that source information D3 indicates an increase in other types of insects, the treatment method determination unit 713 outputs confirmation of potential external entry points as physical control D41 in the treatment method D4 corresponding to other types. In this way, the treatment method determination unit 713 outputs treatment methods D4 for each type in the source information D3, divided into physical control D41 and chemical control D42.

[0083] Furthermore, the treatment method determination unit 713 determines the type and quantity of chemical agents to be used for chemical control D42, as well as the method and location of chemical treatment, and decides whether or not to immediately carry out the chemical treatment according to the determined method. The specific treatment flow will be described later.

[0084] The processing unit 8 is configured to perform predetermined processing according to the output of the processing method determination unit 713. The processing unit 8 in this embodiment includes a notification means 81 configured to notify the building manager or the like of the processing method D4 related to physical control D41 and chemical control D42 output by the processing method determination unit 713, and a chemical processing means 82 which executes chemical processing related to chemical control D42 output by the processing method determination unit 713.

[0085] The processing flow in the pest control system 7 with the above configuration will be explained with reference to Figures 10 to 12.

[0086] As shown in Figure 10, the pest control system 7 repeatedly performs the following steps: current result acquisition step S51, previous result acquisition step S52, increase determination step S53, source determination step S54, treatment method determination step S55, and treatment output step S56.

[0087] In this embodiment, the result acquisition step S51 is a step in which the identification results output by the insect identification system 6 are acquired and the number of insects of each type included in the identification results is acquired. In this embodiment, the result acquisition step S51 is executed by the increase determination unit 711, and the insect identification system 6 outputs the identification results at predetermined intervals, so that the identification results can be acquired at predetermined intervals. In addition, in the result acquisition step S51, the acquired identification results are stored in the storage means 72.

[0088] The previous result acquisition step S52 is a step in which the insect identification system 6 acquires the identification results output in the previous instance and acquires the number of insects of each species included in the previous identification results. In this embodiment, the previous result acquisition step S52 is performed by the increase determination unit 711, which acquires the previous identification results from the storage means 72.

[0089] The increase determination step S53 is a step in which the insect identification system 6 compares the number of insects of each species included in the identification results output this time with the number of insects of each species included in the identification results output last time, determines whether the number of insects has increased by a threshold or more for each species, and outputs the species for which the number of insects has increased by a threshold or more. If it is determined in the increase determination step S53 that there are no species for which the number of insects has increased by a threshold or more (NO in the increase determination step S53), the process proceeds to the current result acquisition step S51. If it is determined that there is one or more species for which the number of insects has increased by a threshold or more (YES in the increase determination step S53), the process proceeds to the source determination step S54. In this embodiment, the increase determination step S53 is executed by the increase determination unit 711.

[0090] The source determination step S54 is a step in which the invasion route information D2 and source information D3 are determined for insects whose number has increased above a threshold in the increase determination step S53. Specifically, in the source determination step S54, for the insect species whose number has increased above a threshold in the increase determination step S53, the invasion route information D2 and source information D3 are referred to the database (table T1 shown in Figure 13 in this embodiment) to identify the source of the increasing species. For example, in the increase determination step S53, if one or more of a1, a2, and a3 types are increasing, it is determined that the type of externally invasive flying large fly is increasing; if one or more of a4, a5, a6, a7, and a8 types are increasing, it is determined that the type of externally invasive flying and other types is increasing; if one or more of b1, b2, b3, b4, and b5 types are increasing, it is determined that the type of externally invasive wandering and crawling types is increasing; and if c1 or c2 is increasing, it is determined that the type of externally invasive other types is increasing. Furthermore, in the source determination step S54, if one or more of d1, d2, d3, d4, d5 are increasing, it is determined that the internally generated drainage / water-related type is increasing; if one or more of e1, e2, e3, e4, e5 are increasing, it is determined that the internally generated fungivorous type is increasing; if one or more of f1, f2, f3, f4, f5 are increasing, it is determined that the internally generated indoor dust / food type is increasing; if one or more of g1, g2, g3 are increasing, it is determined that the internally generated insect predation type is increasing; if h1 is increasing, it is determined that the internally generated heat source / gap type is increasing; and if i1 is increasing, it is determined that other internally generated types are increasing. In the source determination step S54, the source is determined for all types that were determined to have increased by a threshold or more in the increase determination step S53. The source determination step S54 is performed by the source determination unit 712.

[0091] The treatment method determination step S55 is a step in determining a treatment method D4 corresponding to the source of an increasing type of insect. Specifically, it is a step in determining a treatment to reduce the increasing number of insects based on the invasion route information D2 and source information D3 determined in the source determination step S54. In this embodiment, the treatment method determination step S55 outputs the treatment method D4 corresponding to the source information D3 by referring to a database (for example, table T1 shown in Figure 13). In addition, the treatment method determination step S55 outputs physical control D41 and chemical control D42 separately for treatment method D4. For example, in the treatment method determination step S55, if the type of large flying fly is increasing, the physical control D41 outputs instructions to check for animal carcasses that may be sources, install insect nets, check openings, and take precautions such as shutter opening time, and the chemical control D42 outputs instructions for spraying chemicals into the entry point and / or spraying chemicals on the source. If the number of flying or other types of insects is increasing, physical control D41 will output instructions for installing insect nets, checking openings, providing cautionary instructions regarding shutter opening times, and specifying the placement of attractant lights around the building and switching LED lights. Chemical control D42 will output instructions for spraying insecticide into the entry points and / or spraying insecticide at the source. Furthermore, if the number of wandering or crawling types is increasing, physical control D41 will output instructions for installing sticky traps, and chemical control D42 will output instructions for spraying insecticide around the building and preventive insecticide treatment of entry points. If the number of other types of externally entering insects is increasing, physical control D41 will support checking passages that could be external entry points. In addition, in the treatment method determination step S55, if the number of internally occurring types in the entry route information D2 is increasing, physical control D41 will output instructions for cleaning the location corresponding to the source information D3, and chemical control D42 will output instructions for insecticide treatment to exterminate the insects and preventive insecticide treatment at the source.Specifically, in the treatment method determination step S55, as physical control D41, if the number of pests in the drainage / water system is increasing, the system outputs removal of the source of the pest in the drainage / water system, etc.; if the number of pests in the fungus system is increasing, the system outputs cleaning and removal of mold, etc., which may be a source of pests; if the number of pests in the indoor dust / food system is increasing, the system outputs cleaning and removal of dust, etc., which may be a source of pests; if the number of pests in the insect predation system is increasing, the system outputs checking for the occurrence of insects that may be a source of pests or attractants; and if the number of pests in the heat source / crevice system is increasing, the system outputs cleaning and removal of food residue, etc., which may be a source of pests. Furthermore, as chemical control D42, if the number of pests in the drainage / water system, fungus system, indoor dust / food system, or heat source / crevice system is increasing, the system outputs chemical treatment for controlling the pests and preventive chemical treatment for the source of the pests; and if the number of pests in the insect predation system is increasing, the system outputs chemical treatment for controlling the pests and preventive chemical treatment for attracting / source insects. Furthermore, if other types of internally occurring insects (specifically bed bugs) are increasing, a notification of bed bug detection is issued as a physical control measure D41, and chemical control measures D42 are output, including chemical treatment for controlling the infested insects and preventive chemical treatment. The treatment method determination step S55 is performed by the treatment method determination unit 713.

[0092] As described above, in the treatment method determination step S55, if the number of externally invading insects is increasing, excluding other types, a method for preventing the invasion of the increasing insects is output as physical control D41, and chemical treatment of at least one of the invading part and the source is output as chemical control D42. Furthermore, if the number of internally occurring insects is increasing, excluding other types, cleaning and removal of the source is output as physical control D41, and chemical treatment for exterminating the insects and preventive chemical treatment of the source are output as chemical control D42.

[0093] Furthermore, in the treatment method determination step S55, the type and quantity of chemical agent to be used, as well as the method and location of the chemical treatment, are determined for chemical control D42. Specifically, as shown in Figure 11, the treatment method determination step S57 is performed to determine which type of chemical agent to use and how to perform the treatment, the treatment quantity determination step S58 is performed to determine the amount of chemical agent to be used in the treatment, and the feasibility determination step S59 is performed to determine whether or not to perform the chemical treatment immediately.

[0094] In the treatment method determination step S57, it is determined that the pesticide selected in accordance with the source of the infestation should be sprayed at the source and the location where the insects were detected. For example, based on a database (not shown), the method of pesticide treatment corresponding to the source information D3 is determined.

[0095] In the processing amount determination step S58, the amount of the chemical selected in the processing method determination step S57 to be used for processing is determined. The amount of chemical used for processing can be determined according to the chemical processing method and the number of insects that have appeared. For example, the system can be configured to derive and determine the amount of chemical to be used corresponding to the processing method D4 and the number of insects based on a database (not shown).

[0096] The feasibility determination step S59 is a step in which a decision is made as to whether or not to carry out the chemical treatment. Specifically, in the feasibility determination step S59, a decision is made as to whether or not to carry out the chemical treatment depending on the condition of the area where the chemical treatment will be carried out. In this embodiment, as shown in Figure 12, the feasibility determination step S599 carries out a person determination step S591 which determines whether or not there are people at the place where the chemical treatment will be carried out, a time determination step S592 which determines whether a predetermined amount of time has passed since the previous chemical treatment at the place where the chemical treatment will be carried out, and a processing-possible date and time determination step S593 which determines whether or not it is the date and time when the spraying of the chemical is permitted. In the person determination step S591, for example, information from a motion sensor or camera installed at the place where the chemical treatment will be carried out is acquired to determine whether or not there are people at the place where the chemical treatment will be carried out. In addition, the processing-possible date and time determination step S593 is a step in which a decision is made as to whether or not it is the date and time when chemical treatment is permitted, for example, the date and time when chemical treatment is permitted is set based on the time and day of the week. In this embodiment, if it is determined in the person determination step S591 that there are no people (YES in the person determination step S591), and in the time determination step S592 that a predetermined time has elapsed since the previous drug treatment (YES in the time determination step S592), and in the processing availability date and time determination step S593 that it is a processing availability date and time (YES in the processing availability date and time determination step S593), then it is determined that drug treatment can be performed, and the processing unit 8 is instructed to perform drug treatment (drug treatment execution instruction step S594). Furthermore, if it is determined in the person determination step S591 that there are people (NO in the person determination step S591), or in the time determination step S592 that a predetermined time has not elapsed since the previous drug treatment (NO in the time determination step S592), and in the processing availability date and time determination step S593 that it is not a processing availability date and time (NO in the processing availability date and time determination step S593), then it is determined that drug treatment should not be performed immediately, and the skip process S595 is executed. In skip processing S595, the notification means 81 of the processing unit 8 notifies the administrator of the need for drug processing and prompts the administrator to manually perform the drug processing or to specify a date and time for the administrator to perform the drug processing.

[0097] In the processing output step S56, various instructions are given to the processing unit 8 based on the content output in the processing method determination step S55. Specifically, if the processing method determination step S55 outputs information related to physical control D41, the output content is notified to the building manager, etc., via the notification means 81, prompting the manager, etc., to carry out physical control D41. If the output is related to chemical control D42, the chemical treatment means 82 is controlled to carry out chemical treatment, or the skip process S595 is executed. Furthermore, when the notification means 81 is used to notify, it can also be indicated that it is a notification related to countermeasure control.

[0098] As described above, according to the pest control system 7 of this embodiment, based on the identification results output by the insect identification system 6, it is determined whether or not there are any species of insects that have increased above a threshold, and if there are any species of insects that have increased above a threshold, a processing method D4 corresponding to the increased species is output, thereby suppressing adverse effects caused by insects.

[0099] Furthermore, for each increased species, the source information D3 is determined, and a processing method D4 corresponding to the source information D3 is output. This prevents the output of duplicate processing methods D4, for example, when multiple types of insects originate from the same source.

[0100] While an example of the pest control system 7 that performs response control has been described, the configuration is not limited to this example.

[0101] For example, while we have described a case where the identification results output by the insect identification system 6 are used as the identification result, the configuration is not limited to this, and it is also possible to configure it to use, for example, the identification results output by a conventional insect identification system 6 or the identification results determined by a person. Furthermore, it is not limited to using the identification results of insects captured by the insect trapping device, but it is also possible to employ an insect identification system 6 that detects insects moving around inside a building, identifies the type of insect, and outputs identification results for each type (for example, an insect identification system 6 that outputs the type and number of insects as identification results based on an odor sensor).

[0102] Furthermore, while the pest control system 7 has been described in terms of determining and outputting a treatment method D4, it is not limited to this configuration. For example, it can also be configured to output intrusion route information D2 and source information D3 and notify building managers, etc. With such a configuration, building managers, etc., can take the necessary pest control work based on the notified intrusion route information D2 and source information D3.

[0103] Furthermore, although we have described a case where the source determination unit 712 determines the source information D3 and the processing method determination unit 713 outputs a processing method D4 corresponding to the source information D3, the configuration is not limited to this. For example, the processing method determination unit 713 can also be configured to output a processing method D4 corresponding to the type in which the number output by the increase determination unit 711 has increased, without relying on the source information D3.

[0104] Furthermore, although the case where the treatment method determination unit 713 outputs both physical control D41 and chemical control D42 as control methods has been described, it is also possible to configure it to output only one of either physical control D41 or chemical control D42. In such a configuration, the treatment method determination unit 713 can also be configured to select and output an appropriate treatment method D4 from physical control D41 and chemical control D42 based on information such as the number of insects.

[0105] Furthermore, although the processing method D4 for physical pest control D41 has been described as a case where it is notified to the building manager, etc., by the notification means 81, the configuration is not limited to this, and physical pest control D41 can also be configured to be executed automatically. For example, the processing unit 8 can be configured to close the opening when an output indicating that an opening has been confirmed is generated.

[0106] Furthermore, although the method described for determining the increase in step S53 is to use the previous identification results, the configuration is not limited to this. For example, it is also possible to determine whether the current number has increased by a threshold or more relative to the average number of insects in the past several identification results for each insect species.

[0107] Furthermore, although we have described a case where the processing method D4 is determined based on the source information D3, the configuration is not limited to this, and for example, it can also be configured to be determined based on type information D1, or based on intrusion route information D2.

[0108] Next, the pest control system 7 that implements preventive control will be described with reference to Figures 14 to 16.

[0109] As shown in Figure 14, the pest control system 7 that performs preventive control includes a control unit 71 that determines whether there is a risk of an increase in insects based on the identification results received from the insect identification system 6 and environmental information about the environment inside and outside the building received from the factor sensor unit 9 that monitors the environment inside and outside the building, and outputs a preventive method to prevent an increase in insects if there is a risk of an increase in insects, and a storage means 72 that stores a database (for example, tables T1 and T2 shown in Figures 13 and 16) necessary for the various determinations of the control unit 71 and environmental information received from the factor sensor. The configuration of the insect identification system 6 and the configuration of the processing unit 8 are the same as the configuration of the pest control system 7 that performs countermeasure control.

[0110] The factor sensor unit 9 is a sensor that monitors the environment inside and outside the building, and specifically, it is a sensor that monitors at least humidity and temperature. In this embodiment, the factor sensor unit 9 is installed to monitor the environment inside the building where internally-breeding insects may occur (specifically, the locations indicated by the source information D3 for internally-breeding insects shown in Figure 13) and the environment outside the building (specifically, the area around the building). The factor sensor unit 9 also outputs environmental information about the environment obtained through monitoring to the pest control system 7.

[0111] The storage means 72 stores environmental information received from the factor sensor unit 9 for a predetermined period (for example, one year). The storage means 72 also stores databases (for example, tables T1 and T2 shown in Figures 13 and 16) used by the control unit 71 for various determinations, and transmits them to the control unit 71 upon request. Specifically, the storage means 72 stores a database, as shown in Figure 16, that associates insect species with factors that increase their numbers (prevention conditions D7), and a database, as shown in Figure 13, that associates insect species (species information D1) with information on invasion routes D2, sources of occurrence D3, and treatment methods D4. In addition, the database shown in Figure 16 associates insect species with the developmental zero point D5 and the effective accumulated temperature D6 required for occurrence.

[0112] The control unit 71 includes an insect species extraction unit 714 that extracts insect species that exist in numbers exceeding a threshold from the identification results received from the insect identification system 6; a factor extraction unit 715 that extracts factors (prevention conditions D7) that increase the number of existing species extracted by the insect species extraction unit 714; a condition determination unit 716 that determines whether or not the factors (prevention conditions D7) that increase extracted by the factor extraction unit 715 are met; and a prevention method determination unit 717 that determines a method to prevent the increase of species for which the condition determination unit 716 has determined that the factors (prevention conditions D7) are met, and outputs this to the processing unit 8.

[0113] The insect species extraction unit 714 refers to the number of each insect species output as identification results from the insect identification system 6 and extracts species whose number is equal to or greater than a threshold. In this embodiment, the insect species extraction unit 714 is configured to extract species in which there is one or more individuals from the identification results.

[0114] The factor extraction unit 715 extracts factors (prevention conditions D7) that increase the types of insects extracted by the insect species extraction unit 714. Specifically, the factor extraction unit 715 refers to the database stored in the storage means 72 (specifically, table T2 shown in Figure 16) to extract factors (prevention conditions D7) that increase the types of insects extracted by the insect species extraction unit 714. The factor extraction unit 715 extracts the conditions recorded as prevention conditions D7 in table T2 shown in Figure 16 as factors, and specifically extracts one or more of the following conditions as factors: effective accumulated temperature D6 (a numerical value obtained by accumulating the temperature above the development zero point D5 over days; in this embodiment, diurnal degrees), average temperature (in this embodiment, the average temperature for one day), and relative humidity. For example, the factor extraction unit 715 extracts the following factors for type a1: an effective accumulated temperature higher than 250 day degrees as an increasing factor (preventive condition D7); for type a4: an effective accumulated temperature higher than 1300 day degrees or a daily average temperature higher than 25 degrees as an increasing factor (preventive condition D7); for type d2: an effective accumulated temperature higher than 300 day degrees and a daily average temperature lower than 20 degrees as an increasing factor (preventive condition D7); and for type f1: an effective accumulated temperature higher than 50 day degrees and a relative humidity of less than 90% as an increasing factor (preventive condition D7). Furthermore, the factor extraction unit 715 extracts information (specifically, developmental zero points D5) from the database to determine whether each of the above factors is met.

[0115] The condition determination unit 716 determines, for each type of insect, whether the factors that increase insect populations (prevention conditions D7) are met, based on the environmental information output by the factor sensor unit 9 and the factors extracted by the factor extraction unit 715. Specifically, the condition determination unit 716 derives average temperature, relative humidity, effective accumulated temperature, etc., based on the environmental information output by the factor sensor unit 9 and stored in the storage means 72, and information for determining whether each factor extracted by the factor extraction unit 715 is met (for example, development zero point D5), and determines whether the factors extracted by the factor extraction unit 715 are met based on the derived results. Furthermore, for externally invading insects, the condition determination unit 716 determines whether the factors are met based on the environmental information outside the building, and for internally originating insects, it determines whether the factors are met based on the environmental information inside the building (more specifically, environmental information of places inside the building where internally originating insects may originate).

[0116] The prevention method determination unit 717 determines a method to prevent the increase of insects for the species that the condition determination unit 716 has determined to satisfy the conditions. The prevention method determination unit 717 refers to a table T1 as shown in Figure 13 and determines a prevention method to prevent the increase of insects for the species that the condition determination unit 716 has determined to satisfy the conditions. In this embodiment, the prevention method determination unit 717 determines invasion route information D2 and source information D3 corresponding to the species that has been determined to satisfy the conditions. Specifically, it refers to the species information D1 in table T1 and obtains invasion route information D2 and source information D3 corresponding to the species that has been determined to have increased. Furthermore, the prevention method determination unit 717 determines a prevention method to prevent the increase of insects based on the obtained source information D3 and outputs it to the processing unit 8.

[0117] The prevention method determination unit 717 of this embodiment outputs a treatment method D4 as a prevention method, which is the same as the treatment method D4 output by the treatment method determination unit 713 described above. However, the configuration is not limited to this, and it is also possible to configure it to output different methods for treatment method D4 and prevention method. For example, in the prevention method, it is possible to output only physical control D41 and not chemical control D42, or to output adjusting temperature and humidity (environment), or in chemical control D42, it is possible to output chemical treatment using a slow-acting agent with lower repellency than the agent used in treatment method D4.

[0118] The processing flow in the pest control system 7 described above will be explained with reference to Figure 15.

[0119] As shown in Figure 15, the pest control system 7 repeatedly performs the following steps: identification result acquisition step S61, type information extraction step S62, increase factor extraction step S63, factor information acquisition step S64, factor determination step S65, source information acquisition step S66, prevention method acquisition step S67, and processing output step S56.

[0120] The identification result acquisition step S61 is a step of acquiring the identification result output by the insect identification system 6. In this embodiment, the identification result acquisition step S61 is executed at predetermined intervals. In addition, the identification result acquisition step S61 in this embodiment is executed when the insect identification system 6 outputs the identification result at predetermined intervals. The identification result acquisition step S61 is executed by the insect species extraction unit 714.

[0121] The species information extraction step S62 is a step in which species whose number exceeds a threshold are extracted from the identification results obtained in the identification result acquisition step S61. In this embodiment, it is a step in which insect species whose identification results include one or more individuals are extracted. The species information extraction step S62 is performed by the insect species extraction unit 714.

[0122] The increase factor extraction step S63 is a step in which the factors that increase (prevention conditions D7) for the species extracted in the species information extraction step S62 are extracted from the database. Specifically, in the increase factor extraction step S63, the database stored in the storage means 72 is referenced to extract the factors that increase (prevention conditions D7) for the insect species extracted in the species information extraction step S62 and information (specifically, developmental zero points D5) for determining whether the factors that increase (prevention conditions D7) are met. The increase factor extraction step S63 is performed by the factor extraction unit 715.

[0123] The factor information acquisition step S64 is a step in which environmental information related to the factors (prevention conditions D7) that cause an increase in insects, which were extracted in the increase factor extraction step S63, is acquired. Specifically, in the factor information acquisition step S64, environmental information stored in the storage means 72 is acquired, and factor information such as average temperature, relative humidity, and effective accumulated temperature is derived based on the acquired environmental information and information for determining whether the increase factors (prevention conditions D7) extracted in the increase factor extraction step S63 are met. The factor information acquisition step S64 is performed by the factor extraction unit 715.

[0124] The factor determination step S65 is a step in which the factor information derived in the factor information acquisition step S64 determines whether the factor (prevention condition D7) that caused the increase in insects extracted in the increase factor extraction step S63 is satisfied by the factor information. If, for all species extracted in the species information extraction step S62, the factor determination step S65 determines that the prevention condition D7 is not satisfied by the factor information (NO in the factor determination step S65), the process proceeds to the identification result acquisition step S61. If, for at least one species extracted in the species information extraction step S62, the factor determination step S65 determines that the prevention condition D7 is satisfied by the factor information (YES in the factor determination step S65), the process proceeds to the source information acquisition step S66. The factor determination step S65 is performed by the condition determination unit 716.

[0125] The source information acquisition step S66 is a step in which source information D3 and invasion route information D2 are identified for species that meet the factors (prevention conditions D7) that cause an increase in insects. Specifically, in the source information acquisition step S66, for species that have been determined to meet the conditions for an increase in insects (prevention conditions D7), the invasion route information D2 and source information D3 are referred to a database (table T1 shown in Figure 13 in this embodiment) to identify the source of the increasing species. The specific processing in the source information acquisition step S66 is the same as the source determination step S54 in the pest control system 7 that performs countermeasure control. The source information acquisition step S66 is executed by the prevention method determination unit 717.

[0126] The prevention method acquisition step S67 is a step in determining a treatment method D4 corresponding to the source of an insect species that has been determined to satisfy the factors for increasing. Specifically, it is a step in determining a treatment to suppress the increase of an increasing insect based on the invasion route information D2 and source information D3 determined in the source information acquisition step S66. In the treatment method determination step S55 of this embodiment, the treatment method D4 corresponding to the source information D3 is output by referring to a database (for example, table T1 shown in Figure 13). The prevention method acquisition step S67 of this embodiment is the same as the treatment method determination step S55 in the pest control system 7 that performs countermeasure control. That is, the prevention method acquisition step S67 includes a step in determining the specific method of chemical treatment in chemical control D42 and whether or not to carry out chemical treatment.

[0127] The processing output step S68 issues various instructions to the processing unit 8 based on the content output in the prevention method acquisition step S67. The processing output step S68 in this embodiment is the same as the processing output step S56 in the pest control system 7 that performs countermeasure control. Specifically, if the prevention method acquisition step S67 outputs information regarding physical control D41, the notification means 81 notifies the building manager, etc., of the output information and prompts the manager, etc., to carry out physical control D41. If the output is regarding chemical control D42, the chemical treatment means 82 is controlled to carry out chemical treatment, or the skip process S595 is executed.

[0128] According to the pest control system 7 configured as described above, for species in which the number of insects exceeds a threshold, it is possible to determine whether the conditions for an increase in that species of insect are met. If the conditions for an increase are met, preventive treatment can be applied, thereby suppressing the increase in insects.

[0129] Furthermore, for species that satisfy the conditions for increase, the source information D3 is determined, and a processing method D4 corresponding to the source information D3 is output. For example, if multiple types of insects satisfy the conditions for increase from the same source, it is possible to suppress the output of duplicate processing methods D4.

[0130] In the above-described pest control system 7, the case in which environmental information is acquired from the factor sensor unit 9 has been explained. However, the configuration is not limited to this, and for example, environmental information regarding the environment outside the building can be acquired from weather information, etc., without using sensors.

[0131] Furthermore, while we have explained that effective accumulated temperature D6, average temperature, and relative humidity are factors that increase insect populations, the analysis is not limited to these configurations. For example, information regarding season, weather, and sunshine duration can also be used.

[0132] Furthermore, although the pest control system 7 that performs reactive control and the pest control system 7 that performs preventive control have been described separately, the configuration is not limited to this, and it is also possible to configure a single system to perform both reactive and preventive control. In such a configuration, for example, when the insect identification system 6 outputs the identification result, both reactive and preventive control can be executed, and both the processing required for reactive control and the processing required for preventive control can be output to the processing unit 8.

[0133] The above embodiment is summarized below.

[0134] The method for generating insect identification image data according to the above embodiment is a method for generating insect identification image data by processing pre-processed image data containing insects in the image, comprising: an insect extraction step of extracting insect data relating to insects from the pre-processed image data; a point information assignment step of assigning point information to at least three points specified in the extracted insect data; and a sequence information assignment step of assigning sequence information relating to the order of the at least three points specified in the point information assignment step.

[0135] With this configuration, point information and sequential information are assigned to the extracted insects, allowing the insect's posture to be determined based on the point information and sequential information. For example, even if the insect is bent, the species can be appropriately identified. Therefore, the accuracy of insect identification included in the image data can be improved.

[0136] Furthermore, the insect extraction step may also include a frame information assignment step that assigns frame information relating to a frame surrounding the insects contained in the image, and the insect data may be extracted based on the frame information assigned in the frame information assignment step.

[0137] With this configuration, frame information is assigned to the extracted insects, making it easier to properly identify insects even when multiple insects overlap. Therefore, the accuracy of insect identification included in image data can be improved.

[0138] Furthermore, the point information assignment step is a step of assigning point information such that at least three points correspond to two or more of the multiple parts that the insect has, and the sequence information assignment step may be configured to assign the sequence information to the point information based on the order in which the two or more parts are arranged.

[0139] With this configuration, point information is assigned to two or more body parts, and the point information is assigned based on the order in which the body parts are arranged. This allows for an accurate determination of the insect's posture even when the insect is bent or folded. Therefore, the accuracy of insect identification included in the image data can be improved.

[0140] Furthermore, the point information assignment step may also be configured to assign point information to the insect data by specifying at least three points: a head-corresponding point corresponding to the head of the insect corresponding to the insect data, an abdominal-corresponding point corresponding to the abdomen of the insect, and an intermediate point located between the head-corresponding point and the abdominal-corresponding point.

[0141] With this configuration, the insect's posture can be determined based on at least three points, including the head-corresponding point, the abdomen-corresponding point, and the midpoint. Therefore, even if the insect is bent, its posture can be appropriately determined. Thus, the accuracy of insect identification included in image data can be improved.

[0142] Furthermore, the point information assignment step may also be configured to assign point information to a feature point located between the head-corresponding point and the abdomen-corresponding point, as the intermediate point.

[0143] With this configuration, since point information is assigned to feature points as intermediate points, point information can be assigned to approximately the same location on the insect for the same species. Therefore, the accuracy of insect identification included in image data can be improved.

[0144] Furthermore, the pre-processing image data is generated by an imaging device comprising a camera positioned at a distance from the capture surface of a captured sample in which insects have been captured on the capture surface, and which travels along the surface direction of the capture surface, and an illumination device that irradiates the capture surface with light from both sides of the travel area in which the camera travels. The imaging device can also be configured to capture multiple images by moving the camera and to generate pre-processing image data by stitching together the captured multiple images.

[0145] With this configuration, the imaging device illuminates the travel area from both sides, suppressing shadows on the insects. Furthermore, by stitching together multiple images to generate pre-processed image data, blurring of the insects in the pre-processed image data is suppressed. Therefore, the accuracy of insect identification in the image data can be improved.

[0146] Furthermore, a method for generating insect identification image data to be used as training data for machine learning can also be configured to include a step of adding species information to the extracted insect data, in which species information relating to the type of insect is added.

[0147] With this configuration, since it includes a step of adding species information about the type of insect, even if the insect included in the image data is bent, the image data including the bent insect can be used as training data.

[0148] Furthermore, the insect identification system of the above embodiment comprises an image data acquisition unit that acquires target image data containing an insect to be identified in the image, and an insect identification unit that identifies the type of insect contained in the target image data. The insect identification unit is configured to identify the type of insect contained in the target image data acquired by the image data acquisition unit using a trained model constructed by machine learning using training data. The trained model is constructed by machine learning using training data that includes training image data containing an insect in the image, posture information including point information assigned to insect data relating to the insect contained in the training image data, and order information relating to the order in which the points are assigned, and type information relating to the type of insect contained in the training image data.

[0149] With this configuration, the trained model is constructed by machine learning using training data that includes point information and pose information, including forward information, for at least three points specified for the insect data included in the training image data. Therefore, for example, it can appropriately identify the type of insect even if the insect is bent.

[0150] Furthermore, the method for generating insect identification image data according to the above embodiment is a method for generating insect identification image data by processing pre-processed image data containing insects, comprising: a feasibility determination step of determining whether the parts of the insect contained in the pre-processed image data can be identified; and a specification step of identifying and extracting each part of the insect for which it has been determined in the feasibility determination step that the parts can be identified, and assigning information corresponding to each part to the extracted range.

[0151] Furthermore, the system can be configured so that information indicating that an insect is present is added to insects that are determined to be in an unidentifiable part in the feasibility determination process.

[0152] Furthermore, the system may also include an insect extraction step for extracting insect data related to insects from the pre-processed image data, and the feasibility determination step may be configured to determine whether or not it is possible to identify a specific part of an insect corresponding to the insect data extracted in the insect extraction step.

[0153] Furthermore, the insect extraction process may also be configured to include an insect discovery step of discovering insects included in the pre-processing image data, and an insect identification step of assigning frame information relating to a frame surrounding the insect discovered in the insect discovery step, and extracting the insect data based on the assigned frame information.

[0154] Furthermore, the system can also be configured to include a point information assignment step for assigning point information to each part of the insect identified in the specific step. [Explanation of Symbols]

[0155] 1...Insect, 11...Body, 12...Attachments, 13...Head, 14...Torso, 15...Thorax, 16...Abdomen, 2...Captured sample, 2a...Capture surface, 3...Imaging device, 31...Housing, 32...Imaging means, 33...Irradiation device, 34...Camera, 35...Illumination unit, 4...Point information, 41...Head corresponding point, 42...Abdomen corresponding point, 43...Intermediate point, 5...Sequential information, 6...Insect identification system, 61...Communication unit, 62...Image data acquisition unit, 63...Insect identification unit, 64... Output unit, 65...Trained model, 7...Pest control system, 71...Control unit, 711...Increase determination unit, 712...Source determination unit, 713...Treatment method determination unit, 714...Insect species extraction unit, 715...Factor extraction unit, 716...Condition determination unit, 717...Prevention method determination unit, 72...Storage means, 8...Processing unit, 81...Notification means, 82...Chemical treatment means, C...Client terminal, D...Insect data, F...Frame, I...Internet, P...Computer

Claims

1. A pest control system comprising a control unit that determines whether there is a risk of an increase in the number of captured insect species based on the identification results of the captured insects and environmental information, and outputs a preventive method to prevent an increase in the number of said insect species if there is a risk of an increase in said insect species.

2. The control unit determines at least one of the intrusion route information relating to the intrusion route of the insect of the type and the source information relating to the source of the insect of the type, and outputs the prevention method based on at least one of the determined intrusion route information and source information, according to claim 1.

3. The control unit extracts factors including at least one of temperature, air temperature, and humidity that increase the number of the said type of insect, and determines whether or not there is a risk of an increase in the said type of insect based on the extracted factors and the environmental information, as described in claim 1.

4. The pest control system according to any one of claims 1 to 3, wherein the control unit outputs at least one of a physical control method and a chemical control method as the prevention method.

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