Body weight measuring system and body weight measuring method

Abstract

To provide a body weight measuring system capable of measuring a body weight of an object animal appropriately and enabling various considerations on breeding.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 eight 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] The present invention relates to a weight measurement system and a weight measurement method. [Background technology]

[0002] The pig weight measuring device described in Patent Document 1 consists of a dustproof box attached to the ceiling of a pigpen near the water supply, a projector with a checkered slide attached, and a video camera installed inside the dustproof box. When light is projected from the projector, a checkered pattern is projected onto the floor and the surface of the pig's body. This image is captured by a video camera, and the pig's withers height (H) is calculated based on the misalignment of the checkered pattern in the captured image. The captured image is also binarized to black and white to determine the pig's projected area (A). There is a close relationship between the pig's withers height (H), projected area (A), and weight (W), expressed as W = aHbAc. Furthermore, the dimension of this multiple regression equation (d = b + 2c) is close to the dimension of volume (d = 3). Therefore, weight (W) is calculated using the withers height (H) and projected area (A).

[0003] Patent Document 2 discloses that a measuring device is provided that sets three selection criteria for fish during the fish farming process or at the time of shipping: body length only, body length and weight, and weight only, and that by selecting one of these criteria, the body length and weight of the fish can be measured efficiently and accurately without weakening them while they are still alive, allowing for multi-stage selection.

[0004] This measuring device is equipped with a measuring chute that slopes downward to allow a constant flow of water, and a passage detector attached midway to detect fish passing through the chute. Light sources and cameras capable of taking still images of fish based on the detector's detection signal are attached above and below the measuring chute, while a load cell that emits an output proportional to the fish's weight has its movable part attached to the measuring chute and its fixed part attached to the weighing frame. Images are processed using an image processing device and image memory built into the control device, and the image and load cell output are calculated into body length and weight data by the control device's CPU. The control device's sorting criteria setting device compares the input with three sorting criteria: body length only, body length and weight, and weight only, and a sorting device attached downstream sorts the fish into multiple stages.

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

[0006] This weighing device is composed of a load sensor, a weighing platform attached horizontally to the top of the sensor and of a predetermined size depending on the target animal, side fences arranged approximately vertically on the left and right ends of the weighing platform and of a predetermined height and depth depending on the target animal, and a front fence installed on the front end of the weighing platform and shorter than the side fences.The rear end between the side fences is open.The weighing facility is composed of one or more weighing devices arranged in a ring or horizontal row, a feeder 11 placed near the front of the front fence, and barrier members placed outside the side fences to prevent access to the feeder.The automatic weighing device is composed of a weighing device, a cable for transmitting the sensor signal, and a data collection and processing device. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Publication No. 2022-286421 [Patent Document 2] Japanese Patent Application Publication No. 8-050052 [Patent Document 3] Japanese Patent Application Laid-Open No. 2003-114145 Summary of the Invention [Problem to be solved by the invention]

[0008] In the case of Patent Document 1, it is difficult to project the grid pattern onto the floor and the surface of the pig's body, and therefore appropriate measurements cannot always be made. The case of Patent Document 2 is based on measuring fish, and has the problem of being difficult to apply to measuring the weight of other animals.

[0009] The system described in Patent Document 3 requires the animals to be guided to a weighing platform, which is difficult. The biggest challenge facing pig farms is the aging population and declining working population. Weighing pigs requires a great deal of effort, which means that pigs cannot be weighed and are not shipped at the optimal time.

[0010] Conventional weight measurements of target animals have basically been limited to measuring the weight of a single individual, and measurements that allow for consideration of rearing based on statistical weight measurements, such as what kind of feed is appropriate or what kind of rearing method is efficient, have not been performed. In this embodiment, a weight measurement system is provided that makes it possible to appropriately measure the weight of a target animal and enables various considerations about rearing. [Means for solving the problem]

[0011] A weight measurement system according to an embodiment of the present invention comprises: an image acquisition means for acquiring planar images of the target animals from above and side images of the target animals using cameras installed in each area that can image only the target animals belonging to each group, the target animals being managed in a plurality of groups; a planar dimension and area information acquisition means for acquiring planar dimension and area information of the target animals based on the planar images; a lateral dimension and area information acquisition means for acquiring lateral dimension and area information of the target animals based on the lateral images; a machine learning model obtained by machine learning with the planar dimension and area information and the lateral dimension and area information as explanatory variables and the weight information of the target animals as a target variable, and a weight information acquisition means for providing the obtained planar dimension and area information and the lateral dimension and area information as explanatory variables to the machine learning model to obtain weight information, which is the target variable; and an output format processing means for dividing the weight information obtained by the weight information acquisition means into groups and creating and outputting information in a comparable format.

[0012] A weight measurement method according to an embodiment of the present invention comprises an image acquisition step of acquiring planar images of the target animals from above and side images of the target animals, the planar images being obtained using cameras provided in areas that can image only the target animals belonging to each group, and a side image of the target animals being acquired using cameras; a planar dimension and area information acquisition step of acquiring planar dimension and area information of the target animals based on the planar images; a side dimension and area information acquisition step of acquiring side dimension and area information of the target animals based on the side images; a weight information acquisition step of using a machine learning model obtained by machine learning with the planar dimension and area information and the side dimension and area information as explanatory variables and the weight information of the target animals as a target variable, and providing the planar dimension and area information and the side dimension and area information as explanatory variables to the machine learning model to obtain weight information, which is the target variable; and an output format processing step of dividing the weight information obtained in the weight information acquisition step into groups and creating and outputting information in a comparable format. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a configuration diagram of a weight measurement system according to an embodiment of the present invention configured using a computer. [Figure 2] FIG. 1 is a functional block diagram of a weight measurement system according to a first embodiment of the present invention. [Figure 3] FIG. 2 is an explanatory diagram of planar dimension / area information acquired by the weight measurement system according to the embodiment of the present invention. [Figure 4] FIG. 3 is an explanatory diagram of side dimension and area information acquired by the weight measurement system according to the embodiment of the present invention. [Figure 5] 3 is a flowchart showing the operation of the weight measurement system according to the first embodiment of the present invention. [Figure 5A] 1 shows average weight information of the target animals in Group A and a weight distribution diagram of the target animals, created and output by a weight measurement system according to an embodiment of the present invention. [Figure 5B]10 shows average weight information of the target animals in Group B and a weight distribution diagram of the target animals, created and output by the weight measurement system according to an embodiment of the present invention. [Figure 5C] FIG. 10 is a diagram showing an example of leveling and maximizing profits within a pig farm achieved by switching between fast-growing pigs and slow-growing pigs using the results of a weight measurement system according to an embodiment of the present invention. [Figure 5D] FIG. 10 is a diagram showing an example of shortening the fattening period by selecting optimal feed using the results of the weight measurement system according to an embodiment of the present invention. [Figure 6] FIG. 10 is a functional block diagram of a weight measurement system according to a second embodiment of the present invention. [Figure 7] FIG. 10 is an explanatory diagram of appropriate image selection by the weight measurement system according to the embodiment of the present invention. [Figure 8] FIG. 4 is an explanatory diagram of a correction process performed by the weight measurement system according to the embodiment of the present invention. [Figure 9] 10 is a flowchart showing the operation of a weight measurement system according to a second embodiment of the present invention. [Figure 10] This figure shows the results of investigating the identification information (ID) of pigs as the target animals and their stay time at the feeding area. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, a weight measurement system and a weight measurement method according to an embodiment of the present invention will be described with reference to the accompanying drawings. In each drawing, the same components are designated by the same reference numerals, and duplicate explanations will be omitted. FIG. 1 shows a configuration diagram of a computer in which the weight measurement system according to an embodiment of the present invention is configured by a computer. That is, a CPU 10 configures the weight measurement system using programs and data in a main memory 11. An external memory 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.

[0015] An external storage device 23 is connected to the external storage interface 13. The external storage device 23 stores programs and data for the operation of this weight measurement system, which can be read out and used by the CPU 10 in the main memory 11 as appropriate. For this reason, the external storage device 23 stores programs for implementing the machine learning models and various means described below. An input device 24 such as a keyboard or 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 capture images of the target animal. The number of cameras 26-1 to 26-m is arbitrary. Image data obtained by the cameras 26-1 to 26-m is input by the data input interface 16 to required parts such as the CPU 10 and the display interface 15. In this embodiment, the target animals are managed by dividing them into a plurality of groups, and the groups may be reared on different feeds or using different rearing methods (such as exercise amounts).

[0016] Fig. 2 shows a functional block diagram of a weight measurement system according to a first embodiment of the present invention. This embodiment is equipped with a camera 26, image acquisition means 31, individual information management means 32, planar dimension / area information acquisition means 33, side dimension / area information acquisition means 34, weight information acquisition means 40, and output format processing means 50. These means can be realized by the CPU 10 appropriately reading a program from the external storage device 23 into the main memory 11, or can be realized using a program that is initially stored in the main memory 11. Although Fig. 2 shows one camera 26, multiple cameras are provided for each group.

[0017] The camera 26 can be a 3D camera, and is installed in each area where it can capture images of only the target animals belonging to each group of target animals managed in multiple groups. This location can be a feeding area (near a feed box). When pigs were used as target animals and their time spent at the feeding area was investigated, the results are shown in Figure 10. It can be seen that most of the time spent at the feeding area was less than one minute, and almost always less than three minutes. It can also be seen that during the day, pigs visit the feeding area approximately two to three times per hour, regardless of the time of day. Therefore, by using the weight measurement camera 26 to capture images at the feeding area, it is expected that data with little bias from individual animals can be obtained.

[0018] The image acquisition means 31 images the target animal using the camera 26, and obtains a planar image from above and a side image from the side of the target animal. The individual information management means 32 uses the images obtained by the image acquisition means 31 to identify the target animal, attaches identification information to the image of each individual target animal, and provides the image with this identification information attached to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40.

[0019] The planar dimension / area information acquisition means 33 acquires planar dimension / area information of the target animal based on the planar image acquired by the image acquisition means 31. In this embodiment, the planar dimension / area information refers to planar width information and planar length information. However, it goes without saying that dimensional information, such as diagonal dimension information on a plane, or area information on a specific portion of the plane may also be used. Planar width information of the target animal will now be described. Assuming that an image of the target animal from above (here, an image facing left) is shown as planar image U in FIG. 3, the image is divided vertically in the figure, and the width of the animal image (the vertical length in the figure) is measured from the center to the left in the resulting areas. The measurement value of the largest divided area is taken as the shoulder width (FIG. 3(B)). Furthermore, measurements are also made from the same center to the right, and the measurement value of the largest divided area is taken as the hip width (FIG. 3(B)). These values ​​can be used as planar width information. Alternatively, hip width information, etc., determined by a predetermined definition, may also be used as planar width information. The planar width information can be at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

[0020] Next, the planar length information will be explained. As described above, the shoulder position and hip position are determined in the process of obtaining the planar width information, and the distance between the shoulder position and hip position is taken as the body length. This body length can be used as the planar length information. The distance between the shoulder position and hip position can also be taken as the body length.

[0021] The lateral dimension / area information acquisition means 34 acquires the lateral dimension / area information of the target animal based on the lateral image acquired by the image acquisition means 31. In this embodiment, the lateral dimension / area information is body height information, but it goes without saying that dimension information, such as diagonal dimension information on the lateral side, or area information on a specific portion of the lateral side, may also be used. The lateral dimension / area information of the target animal may be body height information at at least one of the shoulder, hip, and hip positions of the target animal. For example, as shown in FIG. 4, using a planar image U and a lateral image S of the target animal, first, the highest point in the longitudinal direction of the same divided area on the lateral image S is determined. Measurements are made from the center of the image to the left, and the height measurement value of the highest divided area is taken as the shoulder height. Measurements are made from the center of the image to the right, and the height measurement value of the highest divided area is taken as the hip height. Alternatively, waist height information, etc., determined by a predetermined definition, may be used as the lateral dimension / area information.

[0022] The weight information acquisition means 40 is equipped with a machine learning model 41 obtained by machine learning using the planar dimension / area information and the side dimension / area information as explanatory variables and the weight information of the target animal as a response variable. This machine learning model 41 can be created by performing machine learning using the planar dimension / area information and the side dimension / area information obtained in this embodiment as explanatory variables and actually measuring the weight information of the target animal as a response variable. The weight information acquisition means 40 provides the obtained planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model 41 to obtain weight information, which is the response variable.

[0023] The output format processing means 50 creates and outputs information in a comparable format by dividing the weight information obtained by the weight information acquisition means 40 according to the groups. Specifically, the output format processing means 50 can create and output information on the average weight of the target animals for each group and / or a weight distribution chart.

[0024] FIG. 5A shows a bar graph representing average weight information and a weight distribution diagram created and output by the output form processing means 50 when Group A had 5 animals weighing 20 kg, 10 animals weighing 30 kg, 20 animals weighing 40 kg, 30 animals weighing 50 kg, 20 animals weighing 60 kg, 10 animals weighing 70 kg, and 5 animals weighing 80 kg, with the average weight per animal being 50 kg. FIG. 5B shows a bar graph representing average weight information and a weight distribution diagram created and output by the output form processing means 50 when Group B had 5 animals weighing 30 kg, 10 animals weighing 40 kg, 20 animals weighing 50 kg, 30 animals weighing 60 kg, 20 animals weighing 70 kg, and 15 animals weighing 80 kg, with the average weight per animal being 59.5 kg. This makes it clear at a glance that the feed and rearing methods used in Group B are likely to be superior.

[0025] As shown in Figure 5C, this allows for the smoothing of the pig farm by switching between fast-growing and slow-growing pigs, thereby maximizing profits. By accurately predicting the timing and target individuals for replacement, this embodiment significantly increases the proportion of high-quality meat through smoothing, potentially increasing the profit-generating unit price difference per gram. Furthermore, as shown in Figure 5D, the fattening period can be shortened by selecting the optimal feed. Using the system of this embodiment, the number of days required to grow from 30 kg to 120 kg using Feed A can be reduced by five days in the example shown in Figure 5D. Assuming that feed costs are approximately ¥200 per day, a five-day reduction can result in a ¥1,000 gain per head. Furthermore, because pig growth is affected by various parameters, such as breed, temperature, humidity, and season, optimal rearing methods can be analyzed by grouping pigs based on weight changes and combinations of these parameters.

[0026] The weight measurement system configured as described above performs processing according to a program corresponding to the flowchart shown in Fig. 5. The operation will be explained below based on the flowchart. The target animal is imaged by the camera 26 (S11). The process of imaging the target animal by the camera 26 continues in the processes in the following steps.

[0027] When the target animal is imaged by the camera 26, the CPU 10 receives the image data (S12) and calculates the planar dimension and width information and the lateral dimension and width information (S13). The CPU 10 provides the calculated planar dimension and width information and the lateral dimension and 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. Furthermore, the obtained weight information is divided according to the above groups and created and output as information in a comparable format (S15).

[0028] Next, a weight measurement system according to a second embodiment will be described. Fig. 6 shows a functional block diagram of a weight measurement system according to a second embodiment of the present invention. In this embodiment, the camera 26 and image acquisition means 31 are the same as those in the first embodiment. This embodiment also includes individual information management means 35, individual target animal image acquisition means 36, appropriate image selection means 37, and correction processing means 38. Furthermore, in this embodiment, the target animal will be described as a pig, but the target animal of the present invention is not limited to this.

[0029] 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, assigns identification information to the images of each individual target animal, and provides the images with this identification information to the planar dimension / area information acquisition means 33, the lateral dimension / area information acquisition means 34, and the weight information acquisition means 40. In other words, when multiple target animals are kept together, the individual information management means 35 assigns identification information to each target animal and manages the images. More specifically, it uses RGB images for tracking and generates a bounding box for each pig. When there are multiple pigs, a separate bounding box and its coordinates are calculated for each pig, and the results are passed to the individual target animal image acquisition means 36, which is a target selection unit.

[0030] This embodiment is suitable for cases where multiple target animals are kept together, and the camera 26 captures an image of the location where the multiple target animals are present. As a result, the multiple target animals appear overlapping in the image. The individual target animal image acquisition means 36 separates the images of the individual target animals from the image captured by the camera 26 to acquire the individual target animal images. In this embodiment, a segmentation method can be adopted. Using this method, even overlapping pigs can be clearly separated, and mask images of each pig are generated and used for processing by the weight information acquisition means 40.

[0031] The appropriate image selection means 37 performs processing to select only images that can be processed appropriately when providing images with the above-mentioned 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. If the pig's posture is bent, the values ​​of each part cannot be measured correctly. Therefore, we considered what to do with images in which the body is bent. Since this embodiment uses a fixed camera, it is possible to photograph the same pig multiple times, so we decided to discard data when the body is bent and use only images taken in a good state.

[0032] Therefore, we decided to eliminate images in which the back was bent. Next, we can determine whether the body is bent as follows. When obtaining planar dimension and width information, multiple divided areas are created as shown in Figure 3, and the line connecting the highest points of each divided area in the height direction in Figure 7 corresponds to the spine. Detecting the degree of curvature of this spine can determine the pig's posture. An ellipse is fitted in advance using image processing, and the center line of this ellipse is the ideal position of the spine. Images that deviate significantly from this line are judged to have high curvature and are eliminated (Figure 7(B)), while images that deviate less from this line can be retained (7(A)). For example, images that are more than a certain distance from the line connecting the highest points of each divided area in the height direction can be eliminated. Furthermore, because pigs judged to be normal are also slightly bent, data on the deviation from the ideal position indicating the curvature of the spine (e.g., variance) can also be added as an explanatory variable.

[0033] The correction processing means 38 performs correction processing to orient the image selected by the appropriate image selection means 37 in a predetermined direction. In this embodiment, the planar dimension / area information acquisition means 33 and the lateral dimension / area information acquisition means 34 use 3D images of a single animal extracted by the individual target animal image acquisition means 36 using a segmentation technique to measure each part. The parts to be measured can include shoulder width information, waist width information, hip width information, shoulder-to-hip length, and total body area. To measure each part, it is easy to calculate the measurements by cutting the image of the pig into slices at regular intervals and processing them sequentially from the head, as described above. However, in this embodiment, the pig is photographed as it comes to eat, so images are acquired in various orientations. Since the posture is not constant, some ingenuity is required. The processing performed by the correction processing means 38, including solutions to these issues, is described below.

[0034] <Correction processing> The 3D image is binarized and an ellipse is approximated along the boundary of the body. If the image of the pig is cut out cleanly, an ellipse that fits the body can be drawn, with the center of the ellipse corresponding to the center of the body and the angle of the ellipse matching the angle of the pig. Therefore, the angle of the ellipse in the state shown in Figure 8(A) is calculated, and the image is rotated using an affine transformation with the center of the ellipse as the rotation axis so that the angle of the ellipse that makes the image horizontal is horizontal (Figure 8(B)).

[0035] The weight measurement system configured as described above performs processing according to a program corresponding to the flowchart shown in Fig. 9. The operation will be explained below based on the flowchart. CPU 10 captures an image of the target animal with camera 26 (S11). Once the target animal is captured by camera 26, CPU 10 receives the captured image data (S12) and separates overlapping images of the target animals into a single image (S21).

[0036] Next, CPU 10 performs individual identification (image recognition) and assigns identification information to the image of each individual target animal (S22). Next, distorted images are discarded and only images captured in good condition are used (S23). CPU 10 also performs ellipse approximation along the boundary surface of the target animal's body, calculates the angle of the ellipse, and performs correction by rotating the image using affine transformation with the center of the ellipse as the axis of rotation so that the angle of the ellipse is horizontal (S24). Furthermore, planar dimension and width information, and lateral dimension and width information are calculated (S13). CPU 10 provides the calculated planar dimension and width information and lateral dimension and width information to a machine learning model to obtain the weight of the target animal (S14). This makes it possible to appropriately measure the weight of the target animal. Furthermore, the obtained weight information is divided into the above groups and created and output as comparable information (S15). The processing of step S15 is performed by the output form processing means 50, as in the first embodiment, and can lead to appropriate care of the target animal, as explained with reference to Figures 5A, 5B, 5C, and 5D.

[0037] Furthermore, the weight information acquisition means 40 may calculate the average value of weight information obtained within a predetermined time period for a target animal identified as an individual, and use the calculated average value as the weight of the target animal. Even if the target animal remains within the field of view, there may be cases where weight estimation is not possible due to poor posture or other conditions, resulting in a reduced number of weight measurements. If the acquired weight measurement value is below a set threshold, the acquired data may be discarded. Only when weight measurements are obtained the threshold number of times or more may the data be averaged, and the weight measurement value for one target animal may be completed.

[0038] Furthermore, the weight measurement system may be provided with a program realizing a type determination means (not shown) that determines type information within the target animal based on the planar dimension / area information and the side dimension / area information. In this case, the individual information management means 35 performs an operation of providing an image with identification information attached together with the type information determined by the type determination means to the planar dimension / area information acquisition means 33, the side dimension / area information acquisition means 34, and the weight information acquisition means 40, and the weight information acquisition means 40 may adjust the weight information using parameters corresponding to the type information.

[0039] The above-described embodiments can reduce the labor required for measuring pig weight, enable daily management and data collection of pig weight, and can also lead to maximization of profits for pig farms by optimizing shipping times. [Explanation of symbols]

[0040] 10 CPU 11 Main Memory 12 Bus 13 External memory interface 14 Input Interface 15 Display Interface 16 Data Input Interface 22 Pointing Device 23 External storage device 24 Input Devices 25 Display device 26, 26-1~26-m camera 31 Image acquisition means 32 Individual information management means 33 Method of obtaining floor plan dimensions and area information 34 Means for obtaining side dimensions and width information 35 Individual information management means 36 Individual target animal image acquisition means 37 Appropriate image selection method 38 Correction processing means 40 Weight information acquisition means 41 Machine Learning Models 50 Output form processing means

Claims

1. an image acquisition means for capturing images of target animals managed in a plurality of groups using cameras provided in each area capable of capturing images of only target animals belonging to each group, and for obtaining planar images from above and side images of the target animals; a planar size / area information acquisition means for acquiring planar size / area information of the target animal based on the planar image; side dimension / area information acquisition means for acquiring side dimension / area information of the target animal based on the side image; a weight information acquisition means for providing the planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model, the weight information being the objective variable, and the planar dimension / area information and the side dimension / area information as explanatory variables to the machine learning model; and an output format processing means for dividing the weight information obtained by the weight information obtaining means into groups and creating and outputting the information in a format that can be compared; A weight measurement system comprising:

2. 2. The weight measurement system according to claim 1, wherein the output format processing means creates and outputs information on average weights of the target animals for each group and / or a weight distribution chart.

3. The weight measurement system according to claim 1, further comprising an individual information management means for performing individual identification of the target animal using the image obtained by the image acquisition means, attaching identification information to the image of each individual target animal, and providing the image with this identification information attached to the planar dimension / area information acquisition means, the lateral dimension / area information acquisition means, and the weight information acquisition means.

4. the camera captures an image of a location where a plurality of the target animals are present; 2. The weight measurement system according to claim 1, further comprising an individual target animal image acquisition means for acquiring individual target animal images by separating images of the individual target animals from the images obtained by the camera.

5. The weight measurement system according to claim 3, further comprising an appropriate image selection means for selecting only images that can be processed appropriately when the image with the identification information attached is provided to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means.

6. 6. The weight measurement system according to claim 5, further comprising a correction processing unit that performs a correction process on the image selected by the appropriate image selection unit so that the image is oriented in a predetermined direction.

7. 2. The weight measurement system according to claim 1, wherein the planar dimension / area information of the target animal is at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

8. The weight measurement system according to claim 1, wherein the planar dimension / area information of the target animal is a body length, which is the length from the shoulder to the rump of the target animal.

9. 2. The weight measurement system according to claim 1, wherein the side dimension / area information of the target animal is withers height information of at least one of the shoulder position, waist position, and hip position of the target animal.

10. The weight measurement system according to claim 3, characterized in that the weight information acquisition means calculates the average value of weight information obtained within a predetermined time period for a target animal identified as an individual, and sets the calculated average value as the weight of the target animal.

11. a species determination means for determining species information within the target animal based on the planar dimension / area information and the side dimension / area information; the individual information management means performs an operation of providing an image to which identification information is attached together with the type information determined by the type determination means to the planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means; 4. The weight measurement system according to claim 3, wherein the weight information acquisition means adjusts the weight information by using a parameter corresponding to the type information when acquiring the weight information.

12. an image acquisition step of capturing images of target animals managed in a plurality of groups using cameras provided in areas capable of capturing images of only target animals belonging to each group, and obtaining planar images from above and side images of the target animals; a planar size / area information acquisition step of acquiring planar size / area information of the target animal based on the planar image; a side dimension / area information acquisition step of acquiring side dimension / area information of the target animal based on the side image; a weight information acquisition step of using a machine learning model obtained by machine learning with the planar dimension / area information and the side dimension / area information as explanatory variables and the weight information of the target animal as a target variable, and providing the planar dimension / area information and the side dimension / area information obtained as explanatory variables to the machine learning model to obtain weight information as a target variable; an output format processing step of dividing the weight information obtained in the weight information obtaining step into groups and creating and outputting information in a format that can be compared; A weight measurement method comprising:

13. 13. The weight measurement method according to claim 12, wherein the output format processing step creates and outputs information on average weights of the target animals for each group and / or a weight distribution chart.

14. The weight measurement method according to claim 12, further comprising an individual information management step of performing individual identification of the target animal using the image obtained by the image acquisition step, attaching identification information to the image of each individual target animal, and providing the image with this identification information attached to the planar dimension / area information acquisition step, the lateral dimension / area information acquisition step, and the weight information acquisition step.

15. the camera captures an image of a location where a plurality of the target animals are present; The weight measurement method according to claim 12, further comprising an individual target animal image acquisition step of acquiring individual target animal images by separating images of the individual target animals from the image obtained by imaging with the camera.

16. The weight measurement method according to claim 14, further comprising an appropriate image selection step for selecting only images that can be processed appropriately when the image with the identification information attached is provided to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step.

17. 17. The weight measurement method according to claim 16, further comprising a correction processing step of performing a correction process to orient the image selected in the appropriate image selection step so that the image is oriented in a predetermined direction.

18. The weight measurement method according to claim 12, wherein the planar dimension / area information of the target animal is at least one of shoulder width information of the target animal, waist width information of the target animal, and hip width information of the target animal.

19. The weight measurement method according to claim 12, wherein the planar dimension / area information of the target animal is the length from the shoulder to the buttocks of the target animal.

20. The weight measurement method according to claim 12, characterized in that the side dimension / area information of the target animal is withers height information which is withers height information of at least one of the shoulder position, waist position, and buttock position of the target animal.

21. The weight measurement method described in claim 14, characterized in that the weight information acquisition step calculates the average value of weight information obtained within a predetermined time for a target animal identified as a single individual, and sets the calculated average value as the weight of the target animal.

22. a type determination step of determining type information of the target animal based on the planar dimension / area information and the side dimension / area information, the individual information management step performs an operation of providing an image to which identification information is attached together with the type information determined in the type determination step to the planar dimension / area information acquisition step, the side dimension / area information acquisition step, and the weight information acquisition step; 15. The weight measurement method according to claim 14, wherein the weight information acquisition step adjusts the weight information by using a parameter corresponding to the type information when obtaining the weight information.

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