Weight measurement device and weight measurement method

Abstract

To provide a body weight measuring device capable of measuring a body weight of an object animal appropriately.SOLUTION: A body weight measuring device includes: image acquisition means 31 for imaging an object animal with a camera 26 and acquiring a plane image from above and a side image from a side of the object animal; plane dimension and width information acquisition means 33 for acquiring plane dimension and width information on the object animal on the basis of the plane image; side surface dimension and width information acquisition means 34 for acquiring side surface dimension and width information on the object animal on the basis of the side image; and body weight information acquisition means 40 including a machine learning model 41 acquired by machine learning with the plane dimension and width information and the side surface dimension and width information as explanatory variables, and with weight information on the object animal as objective variables for acquiring body weight information, which is the objective variables, by giving the acquired plane dimension and width information, and side surface dimension and width information to the machine learning model as the explanatory variables.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This invention relates to a body weight measuring device and a body weight measuring method.

Background Art

[0002] The body weight measuring device for pigs described in Patent Document 1 is equipped with a dust-proof box on the ceiling of the pigsty near the water feeder, and in this dust-proof box, a projector equipped with a lattice stripe slide and a video camera are installed. When light is projected from the projector, lattice stripes are projected onto the floor and the body surface of the pig. This image is captured by the video camera, and based on the displacement of the lattice stripes in the captured image, the body height (H) of the pig is determined. Also, the captured image is subjected to binary black-and-white processing to obtain the projected area (A) of the pig. There is a close relationship of W = aHbAc among the body height (H), projected area (A), and body weight (W) of the pig. Furthermore, since the dimension (d = b + 2c) of its multiple regression equation is close to the dimension (d = 3) of volume, the body weight (W) is calculated using the body height (H) and the projected area (A).

[0003] Patent Document 2 discloses that during the fish farming process or at the time of shipment, three types of sorting criteria are provided: only body length, body length and body weight, and only body weight. By selecting these criteria, it is possible to accurately measure the body length and body weight of fish efficiently without weakening them while alive, and a measuring device capable of multi-stage sorting is provided.

[0004] This measuring device is provided with a weighing chute that slopes downward to flow a certain amount of water, and a passing detector for detecting fish passing through this weighing chute is attached in the middle. A light source and a camera capable of taking a still image of the fish are provided above and below the weighing chute according to the detection signal of this detector. On the other hand, the movable part of a load cell that transmits an output proportional to the body weight of the fish is attached to the weighing chute, and the fixed part is attached to the weighing instrument frame respectively. Image processing is performed by an image processing device and an image memory built into the control device, and this image and the load cell output are respectively calculated into body length and body weight data by the CPU in the control device. The sorting criterion setting device of the control device is configured to compare with the three types of sorting criteria of only input body length, body length and body weight, and only body weight, and to perform multi-stage sorting with a sorting device provided in the subsequent stage.

[0005] Patent Document 3 describes the provision of an animal weight measuring device, a measuring facility, and an automatic measuring device.

[0006] This weight measuring device consists of a load sensor, a measuring platform mounted horizontally above it and having a predetermined size according to the target animal, side fences positioned approximately vertically at the left and right ends of the measuring platform and having a predetermined height and depth according to the target animal, and a front fence installed on the front end of the measuring platform and lower in height than the side fences. The rear end between the side fences is left open. The measurement facility consists of one or more weight measuring devices arranged in a ring or horizontal row, a feeder 11 positioned near the front of the front fence, and a barrier member to prevent entry to the feeder side positioned outside the side fences. The automatic measurement device consists of a weight measuring device, a cable for transmitting its sensor signal, and a data acquisition and processing device. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2002-286421 [Patent Document 2] Japanese Patent Application Publication No. 8-050052 [Patent Document 3] Japanese Patent Publication No. 2003-114145 [Overview of the project] [Problems that the invention aims to solve]

[0008] In the method described in Patent Document 1, it is difficult to project a grid pattern onto the floor and the surface of the pig's body, and accurate measurements are not always possible. In Patent Document 2, the method is based on measuring fish and has the problem of being difficult to apply to measuring the weight of other animals.

[0009] Patent Document 3 requires guiding the animal to the weighing platform, which is difficult. The biggest challenge for pig farms is the aging workforce and the declining working population. Weighing pigs requires a great deal of effort, which means that they cannot weigh the pigs and are not able to ship them at the optimal time. Embodiments of the present invention provide a weight measuring device that can appropriately measure the weight of target animals. [Means for solving the problem]

[0010] The weight measuring device according to an embodiment of the present invention includes: an image acquisition means that captures images of a target animal using a camera and obtains a planar image from above and a lateral image of the target animal; a planar dimension and width information acquisition means that obtains planar dimension and width information as planar width information and planar length information of the target animal based on the planar image; a lateral dimension and width information acquisition means that obtains lateral dimension and width information of the target animal based on the lateral image; and a machine learning model obtained by machine learning with the planar dimension and width information and the lateral dimension and width information as explanatory variables and the weight information of the target animal as the target variable, and the obtained planar dimension and width information and the lateral dimension and width information are given to the machine learning model as explanatory variables to obtain the weight information, which is the target variable. Individual information management means that performs individual identification of the target animal using weight information acquisition means and images obtained by the image acquisition means, attaches identification information to each image of the target animal, and provides the images with the attached identification information to the planar dimension / area information acquisition means, the side dimension / area information acquisition means and the weight information acquisition means; appropriate image selection means that, when providing the images with the attached identification information to the planar dimension / area information acquisition means, the side dimension / area information acquisition means and the weight information acquisition means, determines the posture of the target animal by detecting the curvature of the spine, and performs processing to select only the images that can be processed appropriately based on this determination result; and the appropriate image selection means Planar image The system comprises a correction processing means that performs an elliptical approximation along the body interface of the target animal, calculates the angle of the ellipse, and performs a correction process that rotates the image using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse becomes horizontal. A weight measuring device, The system further includes a deviation data acquisition means that performs elliptical approximation along the body boundary of the target animal on the planar image selected by the appropriate image selection means, and obtains deviation data between the length of the center line of the obtained ellipse and the length of the line segment obtained by connecting the highest points in each divided area obtained by dividing the planar image into multiple vertical sections. The machine learning model is obtained by machine learning with the planar dimensions / area information and the side dimensions / area information as explanatory variables, the deviation data obtained by the deviation data acquisition means as explanatory variables, and the target animal's weight information as the target variable. The weight information acquisition means adds the deviation data obtained by the deviation data acquisition means as explanatory variables to the planar dimensions / area information and the side dimensions / area information as explanatory variables and provides it to the machine learning model to obtain the weight information, which is the target variable. It is characterized by the following:

[0011] The weight measurement method according to an embodiment of the present invention includes: an image acquisition step of capturing an image of a target animal with a camera and obtaining a planar image from above and a lateral image of the target animal; a planar dimension and width information acquisition step of obtaining planar dimension and width information as planar width information and planar length information of the target animal based on the planar image; a lateral dimension and width information acquisition step of obtaining lateral dimension and width information of the target animal based on the lateral image; a weight information acquisition step of using a machine learning model obtained by machine learning with the planar dimension and width information and the lateral dimension and width information as explanatory variables and the weight information of the target animal as the objective variable, and providing the obtained planar dimension and width information and the lateral dimension and width information as explanatory variables to the machine learning model to obtain weight information, which is the objective variable; an individual information management step of performing individual identification of the target animal using the image obtained in the image acquisition step, attaching identification information to the image of each individual target animal, and providing the image with the attached identification information to the planar dimension and width information acquisition step, the lateral dimension and width information acquisition step and the weight information acquisition step; and the attached identification information A weight measurement method comprising: an appropriate image selection step in which, when an image is provided to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step, the posture of the target animal is determined by detecting the curvature of the spine, and only images that can be processed appropriately based on this determination result are selected; and a correction processing step in which the planar image selected in the appropriate image selection step is approximated by an ellipse along the boundary surface of the target animal's body, the angle of the ellipse is calculated, and the image is rotated to a predetermined orientation using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse that makes the image horizontal is horizontal, the method further comprising a deviation data acquisition step in which the planar image selected in the appropriate image selection step is approximated by an ellipse along the boundary surface of the target animal's body, and deviation data is obtained between the length of the center line of the obtained ellipse and the length of the line segment obtained by connecting the highest position in each divided area obtained by dividing the planar image into multiple vertical sections, and the machine learning model uses the planar dimension / area information and the side dimension / area information, which are explanatory variables, The aforementioned step of acquiring deviation dataThe results are obtained by machine learning using the deviation data obtained as explanatory variables and the weight information of the target animal as the target variable. The weight information acquisition step is characterized by adding the deviation data obtained in the deviation data acquisition step as explanatory variables to the planar dimensions / area information and the side dimensions / area information, which are the explanatory variables, and providing these to the machine learning model to obtain the weight information, which is the target variable. [Brief explanation of the drawing]

[0012] [Figure 1] A diagram showing the configuration of a weight measuring device according to an embodiment of the present invention, when it is configured with a computer. [Figure 2] A functional block diagram of a weight measuring device according to the first embodiment of the present invention. [Figure 3] An explanatory diagram of planar dimensions and area information obtained by a weight measuring device according to an embodiment of the present invention. [Figure 4] An explanatory diagram of the side dimensions and width information obtained by a weight measuring device according to an embodiment of the present invention. [Figure 5] A flowchart illustrating the operation of a weight measuring device according to the first embodiment of the present invention. [Figure 6] A functional block diagram of a weight measuring device according to a second embodiment of the present invention. [Figure 7] An explanatory diagram of appropriate image selection by a weight measuring device according to an embodiment of the present invention. [Figure 8] An explanatory diagram of the correction process by a weight measuring device according to an embodiment of the present invention. [Figure 9] A flowchart illustrating the operation of a weight measuring device according to a second embodiment of the present invention. [Modes for carrying out the invention]

[0013] Hereinafter, a weight measurement device and a weight measurement method according to an embodiment of the present invention will be described with reference to the accompanying drawings. In each figure, the same reference numerals are assigned to the same components, and redundant descriptions are omitted. FIG. 1 shows a configuration diagram of a computer when the weight measurement device according to the embodiment of the present invention is configured by a computer. That is, the CPU 10 constitutes the weight measurement device using programs and data in the main memory 11. An external storage interface 13, an input interface 14, a display interface 15, and a data input interface 16 are connected to the CPU 10 via a bus 12.

[0014] An external storage device 23 is connected to the external storage interface 13. Programs and data for the operation of this weight measurement device are stored in the external storage device 23, and these can be appropriately read out by the CPU 10 to the main memory 11 and used. Therefore, the external storage device 23 stores a machine learning model and programs for realizing each means, which will be described later. An input device 24 such as a keyboard or a touch panel and a pointing device 22 such as a mouse are connected to the input interface 14. A display device 25 having a screen such as an LCD is connected to the display interface 15. Cameras 26-1 to 26-m are connected to the data input interface 16, and these cameras 26-1 to 26-m function to image the target animal. The number of cameras 26-1 to 26-m is arbitrary. The image data obtained by the cameras 26-1 to 26-m is taken in by the data input interface 16 to required parts such as the CPU 10 and the display interface 15.

[0015] FIG. 2 shows a functional block diagram of the weight measurement device according to the first embodiment of the present invention. In this embodiment, a camera 26, an image acquisition means 31, an individual information management means 32, a planar dimension / area information acquisition means 33, a side dimension / area information acquisition means 34, and a weight information acquisition means 40 are provided. These means can be realized by the CPU 10 appropriately reading out the program of the external storage device 23 to the main memory 11, and can also be realized using a program stored in the main memory 11 from the beginning.

[0016] The camera 26 can be a 3D camera. The image acquisition means 31 captures the target animal with the camera 26 and obtains a planar image from above the target animal and a side image from the side. The individual information management means 32 performs individual identification of the target animal using the images obtained by the image acquisition means 31, attaches identification information to the images of each individual of the target animal, and performs an operation of providing the images with the attached identification information to the planar dimension / width information acquisition means 33, the side dimension / width information acquisition means 34, and the body weight information acquisition means 40.

[0017] The planar dimension / width information acquisition means 33 obtains the planar dimension / width information of the target animal based on the planar image obtained by the image acquisition means. Here, in the present embodiment, the planar dimension / width information is the planar width information and the planar length information, but it is needless to say that dimension information in an oblique direction or the like on the plane or width information of a predetermined portion of the plane may be used. The planar width information of the target animal will be described. Assuming that the image from above the target animal (here, the left-facing image) is as shown in U in FIG. 3, the width of the animal image (the length in the vertical direction in the figure) is measured from the center to the left in the area obtained by dividing this in the vertical direction of the figure, and the measured value of the divided area where it becomes the maximum is taken as the shoulder width (FIG. 3(B)). Also, measuring from the same center to the right as above, the measured value of the divided area where it becomes the maximum is taken as the hip width (FIG. 3(B)). These can be used as the planar width information. Alternatively, hip width information defined by a predetermined definition or the like may be used as the planar width information. The planar width information can be at least one of the shoulder width information of the target animal, the hip width information of the target animal, and the hip width information of the target animal.

[0018] Next, the planar length information will be described. Since the positions of the shoulders and the hips are obtained in the process of obtaining the planar width information as described above, the distance between the positions of the shoulders and the hips is taken as the body length. This body length can be used as the planar length information. The distance between the position of the shoulders and the position of the waist can also be taken as the body length.

[0019] The side dimension / width information acquisition means 34 obtains the side dimension / width information of the target animal based on the side image obtained by the image acquisition means 31. In this embodiment, the side dimension / width information is body height information, but of course, dimension information such as diagonal directions on the side or width information of a predetermined part of the side may also be used. Side dimensions / width information This can be body height information, which is body height information for at least one of the shoulder, waist, and rump positions of the target animal. For example, as shown in Figure 4, using a planar image U and a lateral image S of the target animal, first, the highest point in the same divided area of ​​the lateral image S is determined in the length direction. Measurements are taken from the center of the image to the left, and the height measurement of the divided area with the maximum is taken as the shoulder height. Measurements are taken from the center of the image to the right, and the height measurement of the divided area with the maximum is taken as the rump height. Alternatively, the lateral dimensions and width information of the waist, as defined by a predetermined definition, may be used as planar width information. Side dimensions / width information This can be height information, which is height information for at least one of the shoulder, hip, and rump positions of the target animal.

[0020] The weight information acquisition means 40 includes a machine learning model 41 obtained by machine learning, with the above-mentioned planar dimensions / area information and the above-mentioned side dimensions / area information as explanatory variables and the weight information of the target animal as the objective variable. This machine learning model 41 can be created by performing machine learning with the planar dimensions / area information and the above-mentioned side dimensions / area information obtained in this embodiment as explanatory variables and the weight information of the target animal measured as the objective variable. The weight information acquisition means 40 provides the obtained planar dimensions / area information and the above-mentioned side dimensions / area information as explanatory variables to the machine learning model 41 to obtain the weight information, which is the objective variable.

[0021] The weight measurement device with the above configuration processes data according to the program corresponding to the flowchart shown in Figure 5. The operation will be described below based on the flowchart. The camera 26 captures an image of the target animal (S11). The process of capturing an image of the target animal with the camera 26 continues in the subsequent steps.

[0022] When the camera 26 captures an image of the target animal, the CPU 10 receives the captured image data (S12) and calculates planar width information, planar length information, and side dimensions / width information (S13). The CPU 10 then provides the calculated planar width information, planar length information, and side dimensions / width information to a machine learning model to obtain the weight of the target animal (S14). In this way, it becomes possible to accurately measure the weight of the target animal.

[0023] Next, a weight measuring device according to a second embodiment will be described. Figure 6 shows a functional block diagram of the weight measuring device according to the second embodiment of the present invention. In this embodiment, the camera 26 and image acquisition means 31 are the same as in the first embodiment. In this embodiment, an individual information management means 35, an individual target animal image acquisition means 36, an appropriate image selection means 37, and a correction processing means 38 are provided. In this embodiment, the target animal is described as a pig, but the target animal of the present invention is not limited to this.

[0024] The individual information management means 35 uses the images obtained by the image acquisition means 31 to perform individual identification (image recognition) of the target animals, attaches identification information to the image of each individual target animal, and then provides the images with this identification information to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40. In other words, when multiple target animals are being raised, identification information is attached to each target animal to manage their images. More specifically, tracking is performed using RGB images, and a banding box is generated for each pig. If there are multiple pigs, a separate banding box and its coordinates are calculated for each pig, and these are passed to the individual target animal image acquisition means 36, which is the target selection unit.

[0025] This embodiment addresses cases where multiple target animals are kept together, and the camera 26 captures images of the area where multiple target animals are present. As a result, multiple target animals appear to overlap in the image. The individual target animal image acquisition means 36 separates the images of individual target animals from the images obtained by the camera 26 and acquires individual target animal images. In this embodiment, a segmentation method can be employed. By using this segmentation method, even overlapping pigs can be successfully separated, and mask images of each individual pig are generated and used for processing by the weight information acquisition means 40.

[0026] The appropriate image selection means 37 performs the process of selecting only images that can be properly processed when images with the above-mentioned identification information are provided to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40. If the pig's posture is bent, the values ​​for each part cannot be measured correctly. Therefore, we considered what to do with images in a bent state. In this embodiment, since a fixed-point camera is used, the same pig can be photographed multiple times, so we decided to discard data in a bent state and use only those photographed in a good state.

[0027] Therefore, we decided to exclude images where the back is curved. Next, the determination of whether the body is curved can be made as follows. When obtaining planar dimensions and area information, multiple divided areas are created as shown in Figure 3, and the line connecting the highest points in the height direction of each divided area in Figure 7 corresponds to the spine. The posture of the pig can be determined by detecting the degree of curvature of this spine. Since an ellipse has been fitted in the prior image processing, the center line of this ellipse is the ideal position of the spine. Images with a large deviation from this line are judged to have high curvature and can be excluded (Figure 7(B)), while those with a small deviation from this line can be kept (7(A)). For example, images where the distance from the line connecting the highest points in the height direction of each divided area is greater than a predetermined value can be excluded. Also, since even pigs judged to be normal have a slight curve, deviation data from the ideal position indicating this spinal curvature (e.g., variance) can also be added as an explanatory variable.

[0028] The correction processing means 38 performs a correction process on the image selected by the appropriate image selection means 37 to set a predetermined orientation. In this embodiment, the 3D image of one animal extracted by the individual target animal image acquisition means 36 using a segmentation method is used to measure each part using the planar dimension / area information acquisition means 33 and the side dimension / area information acquisition means 34. The parts to be measured can be shoulder width information, waist width information, hip width information, length from shoulder to hip, and the area of ​​the entire body. To measure each part, it is easier to calculate by slicing the image of the pig into cross-sections at regular intervals and processing them sequentially from the head, as described above. However, in this embodiment, since the pigs are photographed as they come to eat, images with various orientations are obtained. The posture is also not consistent, so ingenuity is required. The processing performed by the correction processing means 38, including solutions to these problems, will be described below.

[0029] <Correction process> If the image of the pig is cleanly cut out, a 3D image can be binarized and an ellipse approximation can be performed along the body's boundaries. The center of this ellipse will correspond to the center of the body, and the angle of the ellipse will directly match the angle of the pig. Therefore, we calculate the angle of the ellipse in the state shown in Figure 8(A), and rotate the image using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse that makes the image horizontal becomes horizontal (Figure 8(B)).

[0030] The weight measurement device with the above configuration processes data according to a program corresponding to the flowchart shown in Figure 9. The operation will be explained below based on the flowchart. The CPU 10 captures an image of the target animal using the camera 26 (S11). Once the camera 26 has captured an image of the target animal, the CPU 10 receives the captured image data (S12) and separates the overlapping images of the target animals into a single image (S21).

[0031] Next, the CPU 10 performs individual identification (image recognition) and assigns identification information to each image of the target animal (S22). Then, it discards images that are distorted and selects only images that are captured in good condition (S23). The CPU 10 also performs elliptic approximation along the body interface of the target animal, calculates the angle of the ellipse, and corrects the image by rotating it using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse that makes the image horizontal becomes horizontal (S24). Furthermore, it obtains planar width information, planar length information, and side dimensions / width information (S13). The CPU 10 provides the obtained planar width information, planar length information, and side dimensions / width information to a machine learning model to obtain the weight of the target animal (S14). In this way, it becomes possible to appropriately measure the weight of the target animal.

[0032] Furthermore, the weight information acquisition means 40 may calculate the average value of the weight information obtained within a predetermined time for a target animal identified as a single individual, and use the calculated average value as the weight of that target animal. Also, even if the target animal remains within the field of view, there may be cases where weight estimation is not possible due to poor conditions such as posture, which may result in fewer weight measurements. If the acquired weight measurement value is below a set threshold, the acquired data may be discarded. Alternatively, if weight measurements are obtained more than the threshold number of times, the data may be averaged and the process completed as the weight measurement value for one target animal.

[0033] Furthermore, the weight measuring device may be equipped with a program that implements a species determination means (not shown) that determines species information within the target animal based on the above planar dimensions / area information and the above side dimensions / area information. In this case, the individual information management means 35 performs the operation of providing the planar dimensions / area information acquisition means 33, the side dimensions / area information acquisition means 34, and the weight information acquisition means 40 with an image that has identification information attached along with the species information determined by the species determination means, and the weight information acquisition means 40 may adjust the weight information using parameters corresponding to the species information within the target animal when obtaining weight information.

[0034] Each of the above embodiments can reduce the effort required to measure weight, enable daily management and data collection of pig weights, and lead to the maximization of profits for pig farms by optimizing the timing of shipment. [Explanation of symbols]

[0035] 10 CPU 11 Main memory 12 buses 13 External Storage Interface 14 Input Interfaces 15 Display Interface 16. Data Input Interface 22 Pointing devices 23 External storage device 24 Input devices 25 Display device 26 cameras 31 Image acquisition method 32 Individual information management means 33 Information acquisition means 34 Information acquisition means 35 Individual information management means 36. Means for acquiring images of individual target animals 37. Appropriate Image Selection Method 38 Correction processing means 40. Means for acquiring weight information 41 Machine Learning Models

Claims

1. Image acquisition means that captures images of the target animal using a camera and obtains a planar image from above and a lateral image from the side of the target animal, A means for acquiring planar dimensions and area information that obtains planar width information and planar length information of the target animal based on the aforementioned planar image, A means for obtaining lateral dimensions and width information of the target animal based on the aforementioned lateral image, The system includes a machine learning model obtained by machine learning with the aforementioned planar dimensions / area information and the aforementioned side dimensions / area information as explanatory variables and the weight information of the target animal as the objective variable, and a weight information acquisition means that provides the obtained planar dimensions / area information and the aforementioned side dimensions / area information as explanatory variables to the machine learning model to obtain the weight information, which is the objective variable, Individual information management means that uses the image obtained by the image acquisition means to perform individual identification of the target animal, attach identification information to the image of each individual target animal, and provide the image with the attached identification information to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means. When providing the image with the aforementioned identification information to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means, the appropriate image selection means determines the posture of the target animal by detecting the curvature of the spine, and selects only the images that can be processed appropriately based on this determination result. A weight measuring device comprising: a correction processing means that performs an elliptical approximation along the interface of the target animal's body on a planar image selected by the appropriate image selection means, calculates the angle of the ellipse, and performs a correction processing that rotates the image using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse that makes the image horizontal becomes horizontal; The system further includes a deviation data acquisition means that performs elliptical approximation along the body surface of the target animal to the planar image selected by the appropriate image selection means, and obtains deviation data between the length of the center line of the obtained ellipse and the length of the line segment obtained by connecting the highest points in each divided area obtained by dividing the planar image vertically into multiple sections. The aforementioned machine learning model is obtained by machine learning with the aforementioned planar dimensions / area information and side dimensions / area information as explanatory variables, the deviation data obtained by the deviation data acquisition means as explanatory variables, and the weight information of the target animal as the objective variable. The weight measurement device is characterized in that the weight information acquisition means adds the deviation data obtained by the deviation data acquisition means as an explanatory variable to the explanatory variables, namely the planar dimension / width information and the side dimension / width information, and provides this to the machine learning model to obtain the target variable, namely the weight information.

2. The aforementioned camera captures images of the location where multiple target animals are present. The weight measuring device according to claim 1, further comprising means for acquiring individual target animal images by separating images of individual target animals from images obtained by imaging with this camera.

3. The weight measuring device according to claim 1, characterized in that the planar width information of the target animal is at least one of the shoulder width information of the target animal, the waist width information of the target animal, and the hip width information of the target animal.

4. The weight measuring device according to claim 1, characterized in that the planar length information of the target animal is the body length, and is the length from the shoulder to the rump of the animal.

5. The weight measuring device according to claim 1, characterized in that the lateral dimensions and width information of the target animal is height information of at least one position of the target animal, such as the shoulder position, waist position, or hip position.

6. The weight measuring device according to claim 1, characterized in that the weight information acquisition means calculates the average value of weight information obtained within a predetermined time for a target animal identified as a single individual, and uses the calculated average value as the weight of the target animal.

7. The system includes a species determination means for determining species information within the target animal based on the aforementioned planar dimensions and area information and the aforementioned side dimensions and area information. The individual information management means performs the operation of providing the type information determined by the type determination means along with an image with the type information attached to it to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means. The weight measurement device according to claim 1, characterized in that the weight information acquisition means adjusts using parameters corresponding to the type information when obtaining weight information.

8. Image acquisition step: The camera is used to image the target animal, and an image acquisition step is taken to obtain a planar image from above and a lateral image from the side of the target animal. A step to obtain planar dimensions and area information, in which planar dimensions and area information are obtained as planar width information and planar length information of the target animal based on the aforementioned planar image, A step of obtaining side dimensions and width information to obtain side dimensions and width information of the target animal based on the aforementioned side image, A weight information acquisition step involves using a machine learning model obtained by machine learning, with the aforementioned planar dimensions / area information and the aforementioned side dimensions / area information as explanatory variables and the weight information of the target animal as the objective variable, and providing the obtained planar dimensions / area information and the aforementioned side dimensions / area information as explanatory variables to the machine learning model to obtain the weight information, which is the objective variable. An individual information management step is performed which involves using the images obtained in the image acquisition step to perform individual identification of the target animals, attaching identification information to the image of each individual target animal, and providing the image with the attached identification information to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step. When providing the image with the aforementioned identification information to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step, the appropriate image selection step determines the posture of the target animal by detecting the curvature of the spine, and selects only the images that can be processed appropriately based on this determination result. A weight measurement method comprising: a correction processing step which involves applying an ellipse approximation along the interface of the target animal's body to the planar image selected in the appropriate image selection step, calculating the angle of the ellipse, and performing a correction processing to rotate the image to a predetermined orientation using an affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse that makes the image horizontal becomes horizontal; The system further includes a step to acquire deviation data, which involves performing an elliptical approximation along the body surface of the target animal with respect to the planar image selected in the appropriate image selection step, and obtaining deviation data between the length of the center line of the resulting ellipse and the length of the line segment obtained by connecting the highest points in each divided area obtained by dividing the planar image vertically into multiple sections. The aforementioned machine learning model is obtained by machine learning with the planar dimensions / area information and the side dimensions / area information, which are the explanatory variables, and the deviation data obtained in the deviation data acquisition step as explanatory variables, and the weight information of the target animal as the objective variable. The weight measurement method is characterized in that the weight information acquisition step adds the deviation data obtained in the deviation data acquisition step as an explanatory variable to the explanatory variables, namely the planar dimension / width information and the side dimension / width information, and provides these to the machine learning model to obtain the weight information, which is the target variable.

9. The aforementioned camera captures images of the location where multiple target animals are present. The weight measurement method according to claim 8, further comprising a step of acquiring individual target animal images by separating images of individual target animals from images obtained by imaging with this camera.

10. The weight measurement method according to claim 8, characterized in that the planar width information of the target animal is at least one of the shoulder width information of the target animal, the waist width information of the target animal, and the hip width information of the target animal.

11. The weight measurement method according to claim 8, characterized in that the planar length information of the target animal is the length from the shoulder to the rump of the target animal.

12. The weight measurement method according to claim 8, characterized in that the lateral dimensions and width information of the target animal is height information of at least one position of the target animal, such as the shoulder position, waist position, or hip position.

13. The weight measurement method according to claim 8, characterized in that the weight information acquisition step involves determining the average value of weight information obtained within a predetermined time for a target animal identified as a single individual, and using the determined average value as the weight of the target animal.

14. The system includes a species determination step in which species information within the target animal is determined based on the aforementioned planar dimensions and area information and the aforementioned side dimensions and area information. The individual information management step performs the operation of providing the type information determined in the type determination step along with an image with the type information attached to it to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step. The weight measurement method according to claim 8, characterized in that the weight information acquisition step is performed by adjusting the weight information using parameters corresponding to the type information when obtaining weight information.

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