JP2014142833A - Captured insect identification method and identification system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a captured insect identification method and a captured insect identification system capable of automatically and quickly performing insect identification work at relatively low costs, and obtaining an objective and consistent identification result.SOLUTION: A captured insect identification method includes the step of reading the image of the adhesive sheet of an insect capturing device which has captured insects, the step of obtaining an edge from the read image to obtain an insect candidate area, the step of deleting a background area other than the insect candidate area, the step of extracting an insect area from the insect candidate area, the step of extracting a body area, a leg area, and a back area to obtain merkmal data of morphological features from the extracted body, leg and back areas, and the identification step of collating the merkmal data with standard merkmal data set and stored beforehand for each insect to identify the insect.

Description

  The present invention relates to an identification method and an identification system for captured insects, and more specifically, it is necessary to prevent as much as possible the entry of insects and other small insects such as food factories and pharmaceutical factories. The present invention relates to an identification method and an identification system for captured insects in order to identify captured insects at a place and obtain materials to be used for subsequent insect control measures.

  In food factories and pharmaceutical factories, it is required to prevent as much as possible the entry of insects and other small insects, both harmful and harmless. For this purpose, various types of insect trapping devices including an attracting light for attracting insects and an adhesive means for capturing the gathered insects are deployed at important points in the factory building.

  By the way, there are many kinds of reptiles, and there are thousands of kinds even if known, and the wavelength range that is strongly attracted for each reptile differs. Therefore, in order to efficiently capture the insects to be captured, it is effective to use a light source that emits light having a wavelength that strongly attracts the insects to be captured for the attracting light of the insect capturing apparatus.

  In addition, as a premise, it is necessary to identify the types of worms that gather at each factory and the like. Conventionally, identification of these worms has been performed exclusively by human work. That is, conventionally, the adhesive sheet to which the insects of the insect trapping apparatus adhered periodically is collected from each factory and the type and number of insects are identified visually. The applicant actually performs identification work for about 9000 sheets per year, but the identification of about 3 sheets per person per hour is limited. As a result, human costs were being cut and reductions were required.

  In addition, in the case of human work by visual inspection, there is a problem in that an identification result with objectivity and consistency cannot be expected due to individual differences.

Japanese Patent No. 2694726 Japanese Patent No. 2599101

  As described above, in order to efficiently capture the insects to be captured, it is effective to use a light source that emits light having a wavelength that strongly attracts the insects to be captured for the attracting light of the insect capturing apparatus. For this reason, first, it is necessary to identify the worms that gather at each factory, etc., but since the identification work has been done exclusively manually, the work cost has been increased, and the reduction has been required. . Moreover, it is difficult to expect an objective and consistent identification result as well as time is required for the human work by visual inspection, and improvement thereof has been demanded.

  The present invention has been made to meet such a demand, and can automatically and promptly identify insects gathering at each factory etc. at a relatively low cost. It is an object of the present invention to provide an identification method and identification system for captured insects that can obtain consistent identification results.

  The invention according to claim 1 for solving the above-described problem is that an image reading step of reading an image of an adhesive sheet of an insect trapping device that captures reptiles by an image reading means, and acquiring an edge from the read image. An insect candidate area acquiring step for acquiring an insect candidate area; a background area deleting step for deleting a background area other than the acquired insect candidate area; an insect area extracting step for extracting an insect area from the insect candidate area; Extract at least a body region, a leg region, and a heel region from the insect region, calculate a body length, a body width, and a ratio of the body length to the body width from the extracted body region, and calculate a leg length, a leg length, and a body length from the leg region. A feature extraction step for obtaining a morphological feature Merckmar data by calculating a ratio of the heel area and a ratio of the heel area and the body area from the heel region; A method for identifying a captured insect, comprising: an identification step for identifying the insect by comparing the received Merckmar data with a standard Mercumar data set and stored in advance for each insect. .

  In one embodiment, the Merckmar further includes body color, tail length, ratio of tail length to body length, slaughtered part, heel angle, head direction, back leg condition, body roundness, eye characteristics At least one of the following.

  In one embodiment, the insect candidate region obtaining step includes obtaining an edge from the read image, dividing the image into blocks, obtaining a block including an edge, and labeling the background. The region removing step includes deleting blocks other than the block including the edge using HSV color information, and the insect region extracting step includes edge detection for the insect candidate region and a gradient direction. This includes setting a target pixel and sequentially integrating similar luminance pixels adjacent to the target pixel.

  In one embodiment, the identification step includes a classification step for classifying the identification target reptiles into three types, arrogant, sterile, and giant, and setting a Merckmar threshold width for each reptile, Identifying whether or not the Merckmar value of the identification target insect is included within the threshold width of any of the insects, and if included, a primary identification step of identifying the identification target insect as the insect, The threshold width is expanded, and it is identified whether or not the identification target insects not identified in the primary identification step are included in the expanded threshold width, and if included, the identification target insects Secondary identification process to identify the worm and the representative value for multiple types of Merckmar, calculate the Euclidean distance from the worm to be identified for the representative value, and identify the multiple types in order of increasing distance Decide candidates and Consisting of a tertiary identifying step for finally identified from among the identified candidates.

  Further, in one embodiment, the expansion of the threshold width in the secondary identification step is performed from an insect having a high capture probability, and finally, from among a plurality of types of identification candidates in the tertiary identification step. In the identification, a candidate rank is moved up on condition that the feature is satisfied for a reptile having a unique feature.

  The invention according to claim 9 for solving the above-described problems includes an insect trapping device that catches insects by an adhesive sheet, an image reading unit that reads an image of an adhesive sheet to which insects collected from the insect trapping device adhere, and the image reading device An analysis device for analyzing the image read by the means to identify the captured insects and an output means for outputting the result identified by the analysis device, the analysis device being read by the image reading means Preprocessing means for performing image processing for distinguishing the insect region from the background region, a feature extraction means for acquiring Merckal data of morphological features of the reptile from the preprocessed image, and an insect Data storage means for storing standard Merckmar data obtained by standardizing Merkmar of the morphological features of the class, and insects extracted by the feature extraction means An identification means for identifying the reptile by comparing the Merckmar data of the morphological features of the data and the standard Merckmar data stored in the data storage means. This is an insect identification system.

  In one embodiment, the Merckmar obtained by the feature extraction means includes at least body length, body width, body length to body width ratio, leg length, leg length to body length ratio, heel area, heel area, and body area. The ratio is included.

  The identification method and identification system for captured reptiles according to the present invention are as described above. The identification of captured reptiles is performed by acquiring Merckal data of morphological features from captured reptile images without human intervention. Because this can be done by image processing that compares with standard Merckmar data set for each insect in advance, identification of insects gathering at each factory etc. can be performed automatically and quickly at a relatively low cost. In addition, there is an effect that an objective and consistent identification result can be obtained.

  Further, in the method according to the present invention, instead of identifying the reptile from the silhouette of the entire reptile, first, each part of the body region, the leg region and the cocoon region is extracted, and the insect is identified from the characteristics of each part. Since this method is used, it is possible to minimize the influence of the difference in the direction of the insects that have been trapped. Furthermore, there is an effect that the problem that there exists a kind of insect having a similar color, shape, and size can be solved by increasing the number of features of each part.

It is a simplified block diagram of the identification system of the capture insects based on this invention. It is a figure which shows the example of 1 structure of the insect trap used in the identification system of the trap insect according to this invention. It is a flowchart which shows the rough flow of the identification method of the capture insects based on this invention. It is a figure which shows the insect candidate area | region acquisition method in the identification method of the capture insects based on this invention. It is a figure which shows the deletion method of the background area | region in the identification method of the capture insects based on this invention. It is a figure which shows the extraction method of the insect area | region in the identification method of the capture insects based on this invention. It is a flowchart which shows the flow of the identification step in the identification method of the capture insects which concerns on this invention. It is a figure for demonstrating the tertiary identification process of the identification step in the identification method of the capture insects based on this invention.

  Embodiments for carrying out the present invention will be described in detail with reference to the drawings. The identification method of the trapping insects according to the present invention includes an image reading step of reading an image of the adhesive sheet of the trapping device that has captured the insects by an image reading means, and an insect candidate region by acquiring an edge from the read image. Insect candidate area obtaining step for obtaining the insect candidate area, a background area deleting step for removing the background area other than the obtained insect candidate area, and an insect area extracting step for extracting the insect area from the insect candidate area.

  The method according to the present invention further extracts at least a body region, a leg region, and a heel region from the extracted insect region, and calculates a body length, a body width, and a ratio of the body length and the body width from the extracted body region. Calculating a leg length and a ratio of leg length to body length from the leg region, and calculating a heel area and a ratio of heel area to body area from the heel region to obtain a Merckmar data of morphological features; And an identification step of identifying the reptile by comparing the acquired Merckmar data with standard Merckmar data set and stored in advance for each insect.

  A trapping insect identification system according to the present invention is for carrying out the above method, and the identification system includes a trapping device 1 for trapping insects by an adhesive sheet, as shown in the simplified block diagram of FIG. An image reading means 2 for reading an image of a sticky adhesive sheet collected from the insect trapping apparatus 1, an analysis device 3 for analyzing the image read by the image reading means 2 and identifying the captured insects; And an output unit 8 for outputting the result identified by the analysis device 3.

  The analysis device 3 includes a preprocessing unit 4 that performs image processing for distinguishing the insect region and the background region on the image read by the image reading unit 2, and the morphological features of the insects from the preprocessed image. Feature extraction means 5 for extracting the Merckmar data, data storage means 6 for storing the standard Merckmar data obtained by standardizing the Merckmar data of the morphological features of the reptiles, the extracted Merckmar data and the data storage extracted by the feature extraction means 5 By means of collating with the standard Merckmar data stored in the means 6, the identification means 7 for identifying the worm is included.

  In addition, as the insect trapping device 1 for catching insects, for example, a frame-shaped main body case 11 having a front opening and a front opening 12 formed therein, and a light guide plate insertion opening 16 on the side surface of the main body case 11 are used. A light guide plate provided with the LED unit 13 and a light-transmitting pressure-sensitive adhesive sheet 15 that is superposed on the light guide plate and loaded in the main body case 11 is used ( (See FIG. 2). In that case, the adhesive sheet 15 is arranged so that the adhesive surface is directly exposed in the front opening 12 of the main body case 11 when the main body case 11 is loaded.

  In the present invention, the insects to be identified include chironomid mosquitoes, ganganbo, butterflies, flea flies, drosophila, black fly mushrooms, mosquito flies, wing flies, house flies, fly flies, leafhoppers, hornets, ants, moths, moths, cutlet worms , Kokunu Tomodoki, Garbushi, Hanakakushi, Aphid, Aokamushi, Marukamushi, Leafhopper, Leafhopper, Chatterbug, Thrips, and Mayfly.

Each of these reptiles has morphological features, but the morphological features can be classified to some extent. For example, the morphological characteristics of the house fly, gangabo, squid, chironomid, butterfly, flea fly, scallop fly, black-winged fly, and fake fly are as follows.
-Ganbo, squid, chironomid, black spring mushroom ... large body (10 mm or more).
-Giganbo, Musca, Chironomid, Flea, Tamabae ... Long legs and developed.
-Mushroom flies, fake flies ... The tactile sensation is thick and short and has a bumpy shape.
-Butterfly fly, Flyfly ... The wings are wide and symmetrical.
-Hyme flies, gangambo, fleas, black-winged flies, fake flies ... has characteristic veins.

  In view of these morphological characteristics, as the morphological characteristics of Merckmar, insect body length, body width, body length / body width ratio, body color, leg length, leg length / body length ratio, tail length, tail length / body length ratio, heel area, The area of the heel / body area, the body part (which part of the body), the angle of the heel, the direction of the head, the state of the back leg, the degree of roundness of the body, the state of the eye (position, aspect ratio), etc. Conceivable. Usually, in the present invention, at least the body length, body width, leg length, leg length / body length ratio, heel area, heel area / body area ratio are employed. In addition, the identification of the reptiles in the present invention is performed by observing the captured reptiles in a state of being adhered to the adhesive sheet, not in a normal state. It is also considered that there are cases where sticking is captured.

  Hereinafter, the system and method according to the present invention will be described in detail for each processing step with reference to the flowchart shown in FIG. Note that the flowchart of FIG. 3 shows steps from the processing by the image reading means 2, which is an insect identification operation, from analyzing the read image to identifying it and outputting the identification result.

Standard Merckmar data creation and storage step This step creates a standard Merckmar data (acceptable range) by setting the morphological features of the worms for each insect as a template prior to the identification of the reptiles. It is a step of storing in the means 6. This standard Merckmar data is created in advance through the same steps as an image reading step or a feature extraction step described later.

Image reading step (S11)
In this step, the image is read from the insect collecting device 1 installed in a factory or the like, and the adhesive sheet 15 to which the insects adhered to the film are wrapped as necessary is applied to the image reading device (image input device) 2 to read the image. It is. As the image reading device 2, for example, a 2480 × 3503 BMP image scanner, a camera, or the like is used.

Insect candidate region acquisition step (S12)
In the insect candidate area acquisition step, the edge 31 is acquired from the read image (hereinafter referred to as insect image) (FIG. 4A) (FIG. 4B), and the insect image is divided into blocks (for example, 1 The range of the block is 200 × 200 pixels), and a block including the edge 31 is acquired and labeling is performed (FIG. 4C). Since this insect candidate region 32 differs depending on each insect, in FIG. 4, it is assumed that four types of insects are captured, and for convenience, they are distinguished by attaching a circle, a star, a square, and a triangle. The edge 31 is acquired by a general edge detection method.

Background region deletion step (S13)
In order to extract the characteristics of the captured insects, it is necessary to extract the insect region from the insect candidate region 32 (see FIG. 5A), and in order to extract the insect region, the background region is extracted from the insect candidate region 32. Must be deleted. For this purpose, first, the HSV color information of the non-edge block area 33 not including the edge 31 is acquired. Next, the same color area as the HSV color information of the non-edge block area 33 is deleted from the edge block area including the edge 31, that is, the insect candidate area 32 and the non-edge block area 33 (FIG. 5B). ). For example, abs (insect candidate region V value−non-edge block region V value)> 20 (V value: 0 to 255) is used as a threshold value at that time. By removing the background from the insect image in this manner, the remaining area can be identified as an area other than the background, that is, an insect area.

  However, the insect area obtained by simply removing the background in this way includes the shadow of the insect in addition to the original insect area. Can't hope. Therefore, in the present invention, the insect region extracted as described above is not definitive, and is assumed to be a worm region 34 that is presumed to be a worm region. Image processing for confirmation is performed.

Insect region extraction step (S14)
In this step, first, edge detection is performed on the pseudoworm area 34 extracted using the color information by a general edge detection method (FIG. 6A). In FIG. 6, the dark portion indicated by reference numeral 35 is an edge. Next, the pixel of interest 36 in the gradient direction is extracted (positioned inside the edge 35 as shown in FIG. 6B), and for each pixel adjacent to each pixel of interest 36, four neighbors adjacent to it. If the difference is within a certain value (for example, abs (integrated pixel luminance value−target pixel luminance value) <15 (luminance value: 0 to 255)), the pixel is focused on. The luminance of the pixel 36 is integrated.

  Since the handling target of the method according to the present invention is a reptile and no perforated portion is generated, generally, the entire portion surrounded by the edge 35 is integrated into the target pixel 36 (FIG. 6C). Further, similar processing is performed on the pixels 37 adjacent to the background pixels with the background pixels, and similar luminance pixels are sequentially integrated. Thus, the final insect region 38 is obtained (FIG. 6C).

Morphological feature extraction step (S15)
After the insect region 38 is extracted as described above, the body region, the leg region, the heel region, and the tail region are first extracted based on the insect region 38. This morphological feature extraction step includes a body region, a leg region, a heel region, and a tail region extraction step, and a morphological feature Merckmar data acquisition step based thereon.

  The body region is a region other than the worm's legs, moths, and tails. To extract the body region, first, HSV color information of the insect region 38 and the background region 39 is acquired. Then, an area having a color different from that of the background area 39 is extracted as a body area. For example, abs (body region H value−background region H value)> 13, abs (body region H value−background region H value) ≦ 13 and abs (body region V value−background region V value)> 155, or , Abs (body region S value−background region S value)> 150, the body region and the background region are identified.

  Needless to say, the leg region is a much narrower and narrower region than the body region. Therefore, a process of fitting a 4 × 4 square block to the insect area is performed, and a narrow part sandwiched between the edges is extracted as a narrow area, and the 4 × 4 square block is not fitted, A portion extracted as a narrow region is set as a leg candidate region. The leg region is extracted by tracing the thin line from the end point of the thin line in the leg candidate region to the body region. That is, the thinning process is performed on the leg candidate region, and the leg region is defined from the end point of the thin line in the leg candidate region to the end point of the thin line in the body region.

  The cocoon region is extracted by labeling a region excluding the leg candidate region from the insect image, and setting the maximum region among them as a cocoon region.

  In the extraction of the tail region, as in the case of the extraction of the leg candidate region, a process of fitting a 4 × 4 square block to the body region is performed, and a narrow portion sandwiched between the edges is extracted as a narrow region. A portion where the square block of x4 does not fit and a portion where the portion extracted as the narrow region overlaps are defined as the tail region.

  Subsequently, Merckmar data of morphological features is acquired from the body region, leg region, heel region, and tail region extracted as described above. That is, the body length and body width are calculated from the body region, the leg length and the leg length to body length ratio are calculated from the leg region, and the heel area and the heel area to body area ratio are calculated from the heel region. Other morphological features of Merckmar include body color, tail length, tail length / length ratio, slaughtered part, heel angle, head direction, back leg condition, body roundness, eye position and aspect ratio, etc. Any of them can be used in combination. Of course, the morphological feature of Merckmar is not limited to these. Further, the feature extraction method is not limited to the method described below.

<Extraction of body length, body width and body length / body width ratio>
The body length is extracted by detecting the skeleton in the body region and measuring the length. The detection of the skeleton and the measurement of the length are performed by the following steps.
-Find the center of gravity of the body.
-Extract one straight line from the center of gravity of the body part to the most distal point.
-Extract one second straight line up to the most distal point at a different angle from the first straight line.
-Consider the first straight line and the second straight line as a skeleton, and measure their lengths.

  As for the body width, a plurality of straight lines orthogonal to the first straight line and the second straight line are extracted, and the length of the longest straight line among the plurality of orthogonal straight lines is defined as the body width. Then, the body length / body width ratio (body length / body width ratio) is extracted as one of the features from the body width value thus obtained and the body length value obtained as described above.

<Extraction of body color>
In extracting the characteristics of the body color, first, four color regions of black, red, yellow, and green are extracted from the body region using the HSV color system. In the extraction of the four-color region, first, for black, for example, when 0 ≦ V value <80 is regarded as a threshold value, for a portion not corresponding to this, for example, red: 0 <H value <40, yellow: 40 <H value <70, green: 70 <H value <100 as the respective threshold values, the ratio of each color, saturation average, and brightness average are acquired and extracted as body color features.

<Extraction of leg length and body length / leg length ratio>
The leg length can be obtained from the number of pixels of the thin line traced from the end point in the leg region to the end point in the body region. The length ratio between the body length and the leg length can be obtained from the leg length / body length.

<Extraction of tail length and body length / tail length ratio>
The tail length can be obtained from the length of the longest straight line that can be created in the tail region. The length ratio between the body length and the tail length can be obtained from the tail length / body length.

<Extraction of heel area and body area / heel area ratio>
The heel area can be obtained from the area of the heel region, and the ratio of the body area to the heel area can be obtained from the heel area / body area.

<Extraction of cocoon>
The extraction of the cocoon part is to detect from which part of the body part the cocoon comes out. For the extraction, the body part is divided into a front part, an intermediate part, and a rear part, and any part that is in contact with the heel is defined as a slaughtered part.

<Calculation of heel angle>
The heel angle is calculated as follows.
-Obtain a straight line from the center of the skeleton to the tip of the heel.
-Get two corners between a straight line and a skeleton.
-The smaller one of the two obtained angles is calculated as the angle of the eyelid.

<Estimation of insect head direction>
The head direction of the insect can be estimated based on the angle of the moth determined as described above. That is, when the angle of the heel is small, the direction opposite to the heel is the head direction, and when the angle of the heel is large, the thicker one (the body width is large) of the front part and the rear part of the body part Is the head direction.

<Extraction of back legs>
The back leg is the back leg with the leg extending from the direction opposite to the head direction.

<Feature extraction of body roundness>
The feature of the roundness of the body part can be extracted from an average value of slopes at both ends of the histogram by obtaining an approximate quadratic function from the histogram of the body width.

<Eye feature extraction>
In order to extract eye features, first, a black part that is a candidate for an eye is extracted by binarizing the body part. Then, the black portion is identified as an eye by a portion having both the requirement to be located at the distal end portion of the skeleton and the portion having a shape satisfying, for example, the aspect ratio <2.0.

Identification Step Next, an identification method using the feature data extracted by the above method will be described with reference to the flowchart shown in FIG. In the identification step, a classification process for classifying the identification target insects into three types, ie, arrogant, sterile, and giant (S21), an identification process ranging from primary to tertiary (S22, S24, S26), and an identification result is output. Result output step (S23, S25, S27).

The classification into the three types of 翅, 翅, and giant is done to improve the efficiency of processing. First, it is determined whether or not it is a giant reptile, and the insect identified as not a giant reptile Identify the presence or absence of a class. Whether or not it is huge is identified by, for example, whether or not the condition of the entire area> 10 mm ² is satisfied. Usually, gangambo and hiratabu are classified as giant reptiles. Next, for the reptiles that are not identified as giant reptiles, for example, it is identified whether or not the condition of the body / moth area ratio> 0.1 is satisfied. If it does not meet the criteria, then classify it as an insect-free species (for example, Knotnutomodoki, Katsuobushimushi, Hanekakushi, Shibanmushi).

In the primary identification step (S22), for each reptile, a numerical range (threshold value) of the Merckmar data is set, and whether or not the feature of the identification target insect corresponds to the threshold value of any reptile And, if applicable, a process of identifying the identification target insect as the insect. As an example of the threshold value, in the case of a trapped chironomid (small black) with a high capture probability, body length (mm) 0.50-2.00, body width (mm) 0.20-0.85), The body length / body width ratio is 0.90-1.60, the heel area (mm ² ) is 0.10-1.00, and the body / heel area ratio is 0.10-1.30. When identification is possible at the stage of the primary identification step (S22), the identification result (insect name) is output (S23), and when the identification is not possible, the process proceeds to the secondary identification step (S24).

  In the secondary identification step (S24), the threshold value is expanded, and it is identified whether the identification target insect that does not correspond in the primary identification step (S22) corresponds to the expanded threshold value. It is. In this case, it is preferable to extend the threshold from an insect having a high capture probability. If there is a corresponding item in the secondary identification step (S24), the identification target insect is identified as the insect, and the result is output (S25). Moreover, when there is no applicable thing, it transfers to a tertiary identification process (S26).

  The tertiary identification step (S26) is a final determination step of the insect name, and as sub-steps there are multiple types (for example, body length, body width, body length / body width ratio, leg length, leg length / body length ratio, heel area, heel area) -9 types of body area ratio, area, and body area), a step of setting a representative value, a step of calculating a Euclidean distance between the representative value and the insect to be identified, and three types in the order of the shortest distance Determining identification candidates. As the representative value, an average value of a plurality of samples is adopted.

  In the example shown in FIG. 8, the representative values are 1.5 mm in body length and 0.5 mm leg length in the case of the black fly, the body length is 3.0 mm and 0.0 mm in the case of Drosophila, and the body length is in the case of the fly fly. It is 1.0 mm, leg length is 1.5 mm, the chironomid case is set as 0.5 mm body length and leg length 0.5 mm, and the same is true when the body length of the insect to be identified is 1.5 mm and the leg length is 0.5 mm. This is a regular example. In this case, when the Euclidean distance between these representative values and the reptile to be identified is 0.70 for D. melanogaster, 1.58 for Drosophila, 1.11 for Drosophila, 1.00 for chironomid, In order from the first place, the order is Kurobane mushroom fly, chironomid, gall fly, and Drosophila.

The name of the insect is finally identified from the three types of identification candidates determined. Usually, the first candidate is used as the identification result (in the above example, the insect to be identified is identified as the black fly mushroom fly). In this case, however, it is possible to move up the candidate ranking for a reptile having a unique characteristic on the condition of the characteristic. For example, fleas can be raised to the first candidate when three or more of the following four characteristics are satisfied.
-The percentage of yellow for each body color is 40% or more-The rear leg is extracted-The cocoon part is the middle part-The black eye is extracted

  As described above, in the method according to the present invention, instead of identifying the reptile from the silhouette of the whole reptile, first, each part of the body region, the leg region and the cocoon region is extracted, and the insect is determined from the characteristics of each part. Is used. Thereby, it becomes possible to reduce the influence by the difference in the direction of the insect trapped by sticking as much as possible. In addition, the problem of the presence of similar types of insects with similar colors, shapes, and sizes can be solved by increasing the number of features in each part.

  Although the invention has been described in some detail in its most preferred embodiments, it will be appreciated that a wide variety of embodiments can be constructed without departing from the spirit and scope of the invention. For example, it is also possible to use an image transmitted from a remote location via a communication network as an image of the adhesive sheet read by the image reading means, and output the identification result to the remote location via the communication network. Accordingly, the invention is not limited to specific embodiments thereof except as limited in the appended claims.

DESCRIPTION OF SYMBOLS 1 Insect capture device 2 Image reading means 3 Analysis apparatus 4 Pre-processing means 5 Feature extraction means 6 Data storage means 7 Identification means 8 Output means 11 Main body case 12 Front opening part 13 LED unit 15 Translucent adhesive sheet 16 Light guide plate insertion port 31 Edge 32 Insect candidate area
33 non-edge block area 34 pseudoworm area 35 edge 36 pixel of interest 37 adjacent pixel 38 insect area 39 background area

Claims (11)

An image reading step of reading an image of the adhesive sheet of the insect trapping device that has captured the insects by an image reading means;
An insect candidate region acquisition step of acquiring an insect candidate region by acquiring an edge from the read image;
A background region deletion step of deleting a background region other than the acquired insect candidate region;
An insect region extracting step for extracting an insect region from the insect candidate region;
Extract at least a body region, a leg region and a heel region from the extracted insect region, calculate a body length, a body width and a ratio of the body length and the body width from the extracted body region, and calculate a leg length and a leg length from the leg region. A feature extraction step of calculating a ratio of body length, and calculating a ratio of a heel area and a heel area to a body area from the heel area to obtain Merckmar data of morphological features;
A method for identifying a captured insect, comprising: an identification step for identifying the insect by comparing the acquired Merckmar data with standard Merckmar data set and stored in advance for each insect.   Further, as the Merckmar, including at least one of body color, tail length, ratio of tail length to body length, slaughtered part, heel angle, head direction, rear leg state, roundness of body part, eye characteristics, The method for identifying a capture insect according to claim 1.   The said bug candidate area | region acquisition step consists of acquiring an edge from the said read said image, dividing the said image into blocks, acquiring the block containing an edge, and labeling. Identification method of capture insects.   4. The method for identifying a captured insect according to claim 3, wherein the background region removing step comprises deleting blocks other than the block including the edge using HSV color information.   2. The insect region extracting step includes edge detection for the insect candidate region, setting a target pixel in a gradient direction, and sequentially integrating similar luminance pixels adjacent to the target pixel. 5. The method for identifying a capture insect as described in any one of 4 above.   The identification step includes a step of setting a representative value for a plurality of types of Merckmar, a step of calculating a Euclidean distance from the insect to be identified for the representative value, and determining a plurality of types of identification candidates in order of increasing distance. A method for identifying a capture insect as claimed in any one of claims 1 to 5, comprising a step. The identification step includes
A classification process for classifying the identification target insects into three types: arrogant, arrogant, and giant, and setting a Merckmar threshold width for each of the reptiles, and the Merckmar value of the identification target insect is one of the reptiles Primary identification step of identifying whether or not included within the threshold width, and identifying the identification target insect as the insect when it is included,
The threshold width is expanded, and it is identified whether or not the identification target insects not identified in the primary identification step are included in the expanded threshold width, and if included, the identification target insects Secondary identification step for identifying
A representative value is set for multiple types of Merckmar, and the Euclidean distance between the representative value and the reptile to be identified is calculated, and multiple types of identification candidates are determined in order from the shortest distance. The identification method of the capture insects in any one of Claims 1 thru | or 5 which consists of the tertiary identification process which identifies.   The method for identifying a captured insect according to claim 7, wherein the expansion of the threshold width in the secondary identification step is performed from an insect having a high capture probability.   In final identification from among a plurality of types of identification candidates in the tertiary identification step, the candidate ranking is raised on condition that the feature is satisfied for a reptile having a unique feature. The method for identifying a capture insect as described in 7 or 8. An insect trapping device that catches insects with an adhesive sheet, an image reading means for reading an image of an adhesive sheet to which insects recovered from the insect trapping apparatus are adhered, and an insect captured by analyzing the image read by the image reading means And an output device for outputting the result identified in the analysis device,
The analysis device includes preprocessing means for performing image processing for distinguishing an insect area and a background area on the image read by the image reading means, and a morphological feature of the insect from the preprocessed image. Feature extraction means for obtaining Merckmar data, data storage means for storing standard Merckmar data obtained by standardizing Merckmar of morphological features of reptiles, Merckmar data of morphological features of worms extracted by the feature extraction means, An identification system for captured reptiles, comprising: identification means for identifying the reptile by comparing the standard Merckmar data stored in the data storage means.   At least the body length, the body width, the ratio of the body length to the body width, the leg length, the ratio of the leg length to the body length, the heel area, the heel area, and the body area, The system of claim 10, wherein a ratio of

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