Weight measurement system and weight measurement 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] This invention relates to a body weight measurement system and a body weight measurement method.

Background Art

[0002] For the body weight measurement device for pigs described in Patent Document 1, a dust-proof box is attached to the ceiling of the pigsty near the water supply device, and a projector equipped with a grid stripe slide and a video camera are installed in this dust-proof box. When light is projected from the projector, grid 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 shift of the grid stripes in the captured image, the body height (H) of the pig is obtained. Further, 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 formula is close to the dimension of volume (d = 3), 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 sorting criteria of only body length, body length and body weight, and only body weight are provided, and by selecting these criteria, the body length and body weight can be accurately measured efficiently without weakening the fish 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 and flows a certain amount of water, and a passage 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, and 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 in 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 sorting criteria of only the 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 end 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 end 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. 2022-286421 [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 animals 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.

[0010] Conventional weight measurements of target animals have generally been limited to measuring the weight of a single individual, and have not provided measurements that allow for statistical analysis of animal husbandry, such as determining what kind of feed is appropriate or what kind of rearing method is efficient. This embodiment provides a weight measurement system that enables the appropriate measurement of target animal weight and allows for various considerations regarding animal husbandry. [Means for solving the problem]

[0011] The weight measurement system of the embodiment of the present invention includes an image acquisition means that, for target animals managed in multiple groups, images the target animals using cameras provided in each area capable of imaging only the target animals belonging to each group, and obtains a planar image from above and a lateral image from the side of the target animal; a planar dimension / width information acquisition means that obtains planar dimension / width information of the target animal based on the planar image; a lateral dimension / width information acquisition means that obtains lateral dimension / width information of the target animal based on the lateral image; a machine learning model obtained by machine learning with the planar dimension / width information and the lateral dimension / width 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 dimension / width information and the lateral dimension / width information as explanatory variables to the machine learning model to obtain the weight information, which is the objective variable. The system includes: an output format processing means that creates and outputs weight information obtained by the weight information acquisition means as information in a form that can be divided and compared according to the group; an individual information management means that uses the image obtained by the image acquisition means to perform individual identification of the target animals, attach identification information to the image of each individual target animal, and provides 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; an appropriate image selection means that, when providing 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, determines the posture of the target animal by detecting the curvature of the spine, and performs a process of selecting only the images that can be processed appropriately based on this determination result; and the appropriate image selection means Planar image A weight measurement system comprising: 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 that makes the image horizontal becomes horizontal; The system further includes a deviation data acquisition means that performs elliptical approximation along the boundary surface of the target animal's body 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 is characterized by adding 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 providing this to the machine learning model to obtain the target variable, weight information.

[0012] The weight measurement method according to an embodiment of the present invention includes: an image acquisition step in which, for target animals managed in multiple groups, the target animals are imaged by cameras provided in areas where only the target animals belonging to each group can be imaged, and a planar image from above and a lateral image from the side of the target animal are obtained; a planar dimension and width information acquisition step in which planar dimension and width information of the target animal is obtained based on the planar image; a lateral dimension and width information acquisition step in which lateral dimension and width information of the target animal is obtained based on the lateral image; a weight information acquisition step in which the obtained planar dimension and width information and lateral dimension and width information are used as explanatory variables and the weight information of the target animal is used as an objective variable, and the obtained planar dimension and width information and lateral dimension and width information are given to the machine learning model as explanatory variables to obtain the weight information which is the objective variable; an output format processing step in which the weight information obtained in the weight information acquisition step is divided according to the group and output as information in a format that can be compared; and an individual A weight measurement method comprising: an individual information management step that performs identification, attaches identification information to an image of each individual target animal, and provides the image with the attached identification information to the planar dimension / area information acquisition step, the lateral dimension / area information acquisition step, and the weight information acquisition step; an appropriate image selection step that, when providing the image with the attached identification information to the planar dimension / area information acquisition step, the lateral dimension / area information acquisition step, and the weight information acquisition step, determines the posture of the target animal by detecting the curvature of the spine, and selects only images that can be processed appropriately based on this determination result; and a correction processing step that performs elliptical approximation along the body interface of the target animal to the planar image selected in the appropriate image selection step, 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, wherein the planar image selected in the appropriate image selection step is approximated by an ellipse along the body interface of the target animal, and the length of the center line of the obtained ellipse,The machine learning model further includes a step of acquiring deviation data, which involves dividing the planar image into multiple vertical sections to obtain deviation data from the length of the line segment obtained by connecting the highest points in each divided area, and the machine learning model uses the explanatory variables, namely the planar dimensions / area information and the side dimensions / area information, The aforementioned step of acquiring deviation data The 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]

[0013] [Figure 1] A diagram showing the configuration of a weight measurement system according to an embodiment of the present invention, when configured with a computer. [Figure 2] A functional block diagram of a weight measurement system according to the first embodiment of the present invention. [Figure 3] An explanatory diagram of planar dimensions and area information obtained by a weight measurement system according to an embodiment of the present invention. [Figure 4] An explanatory diagram of the side dimensions and width information obtained by the weight measurement system according to an embodiment of the present invention. [Figure 5] A flowchart illustrating the operation of a weight measurement system according to the first embodiment of the present invention. [Figure 5A] Average weight information and weight distribution diagram for group A target animals, generated and output by a weight measurement system according to an embodiment of the present invention. [Figure 5B] Average weight information and weight distribution diagram for group B target animals, generated and output by the weight measurement system according to an embodiment of the present invention. [Figure 5C] This figure illustrates an example of leveling and maximizing profits within a pig pen by swapping fast-growing pigs with slow-growing pigs, using the results obtained from a weight measurement system according to an embodiment of the present invention. [Figure 5D] A diagram showing an example of realizing shortening of the fattening period by selecting an optimal feed using the result of a weight measurement system according to an embodiment of the present invention. [Figure 6] Functional block diagram of a weight measurement system according to a second embodiment of the present invention. [Figure 7] Explanatory diagram of proper image selection by a weight measurement system according to an embodiment of the present invention. [Figure 8] Explanatory diagram of correction processing by a weight measurement system according to an embodiment of the present invention. [Figure 9] Flowchart showing the operation of a weight measurement system according to a second embodiment of the present invention. System [Figure 10] A diagram showing the result of examining the stay time in the feeding place as identification information (ID) with the target animal being a pig.

Embodiments for Carrying Out 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 figure, the same reference numerals are given to the same components and redundant explanations are omitted. FIG. 1 shows a configuration diagram of a computer when the weight measurement system according to the embodiment of the present invention is configured by a computer. That is, the CPU 10 constitutes a weight measurement system 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.

[0015] An external storage device 23 is connected to the external storage interface 13. The external storage device 23 stores programs and data necessary for the operation of this weight measurement system, which the CPU 10 can read and use from the main memory 11 as needed. For this reason, the external storage device 23 stores programs that implement the machine learning models and various means described later. 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 image the target animal. The number of cameras 26-1 to 26-m is arbitrary. Image data obtained by cameras 26-1 to 26-m is taken in by the data input interface 16 to the CPU 10, display interface 15, and other necessary parts. In this embodiment, the target animals are managed in multiple groups, and the feed and rearing methods (such as exercise levels) may differ within each group.

[0016] Figure 2 shows a functional block diagram of a weight measurement system according to the first embodiment of the present invention. In this embodiment, 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 are provided. These means can be implemented by the CPU 10 appropriately reading a program from an external storage device 23 into the main memory 11, or by using a program that is initially stored in the main memory 11. In Figure 2, one camera 26 is depicted, but multiple cameras are provided for each group.

[0017] Camera 26 can be a 3D camera and can be installed in areas where only the target animals belonging to each group can be imaged, for target animals managed in multiple groups. This location can be the feeding area (near the feed box). When the target animals were pigs and their time spent in the feeding area was investigated, the results were as shown in Figure 10. Specifically, the time spent in the feeding area was mostly less than 1 minute, and almost always within 3 minutes. Furthermore, it was found that during the daytime, the pigs visited the feeding area at intervals of "2 to 3 times per hour" at any time of day. Therefore, by using the feeding area as the imaging location for the weight measurement camera 26, it is expected that data with less bias due to individual differences can be obtained.

[0018] The image acquisition means 31 uses the camera 26 to image the target animal and obtains a plan view image from above and a side view image of the target animal. The individual information management means 32 uses the images obtained by the image acquisition means 31 to perform individual identification of the target animal, attaches identification information to the image of each individual target animal, and then provides the images with this identification information to the plan 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 obtains planar dimension / area information of the target animal based on the planar image obtained by the image acquisition means 31. In this embodiment, planar dimension / area information refers to planar width information and planar length information, but of course, dimension information in diagonal directions on the plane or area information of a predetermined part of the plane may also be used. Let's explain the planar width information of the target animal. Assuming that the image of the target animal from above (here, an image facing left) is shown in the planar image U of Figure 3, the width of the animal image (length in the vertical direction in the figure) is measured from the center to the left in the area obtained by dividing this image vertically in the figure, and the measurement value of the largest divided area is taken as the shoulder width (Figure 3(B)). Similarly, the measurement is taken from the center to the right, and the measurement value of the largest divided area is taken as the hip width (Figure 3(B)). These can be used as planar width information. In addition, hip width information, etc., defined by a predetermined definition may also be used as planar width information. The planar width information can be at least one of the following: the shoulder width information of the target animal, the waist width information of the target animal, or the hip width information of the target animal.

[0020] Next, we will explain the planar length information. As described above, the positions of the shoulders and hips are determined in the process of obtaining the planar width information, so the distance between the shoulders and hips is taken as the body length. This body length can be used as the planar length information. The distance between the shoulders and waist can also be taken as the body length.

[0021] The lateral dimension / width information acquisition means 34 obtains lateral dimension / width information of the target animal based on the lateral image obtained by the image acquisition means 31. In this embodiment, the lateral dimension / width information is defined as body height information, but of course, dimensional information in diagonal directions on the side or width information of a predetermined part of the side may also be used. The lateral dimension / width information of the target animal can be body height information at at least one position of the target animal, such as the shoulder, waist, or rump. 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. In addition, waist height information defined by a predetermined definition may also be used as lateral dimension / width information.

[0022] 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.

[0023] The output format processing means 50 creates and outputs the weight information obtained by the weight information acquisition means 40 as information that can be divided and compared according to the group. Specifically, the output format processing means 50 can create and output average weight information and / or weight distribution diagrams for each group of the target animals.

[0024] Figure 5A shows a bar graph of average weight information and weight distribution chart created by the output format processing means 50, where in Group A there were 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 an average weight of 50 kg per animal. Figure 5B shows a bar graph of average weight information and weight distribution chart created by the output format processing means 50, where in Group B there were 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 an average weight of 59.5 kg per animal. From this, it is immediately clear that the feed and rearing methods in Group B are likely to be superior.

[0025] As a result, as shown in Figure 5C, it becomes possible to equalize the pig pens by swapping fast-growing and slow-growing pigs, thereby maximizing profits. By utilizing this embodiment, it is possible to accurately predict the timing of swaps and target individuals, and by equalizing them, it is possible to significantly increase the proportion of "superior" quality meat, which is expected to increase the profit generated, specifically the price difference per gram. Furthermore, as shown in Figure 5D, it is possible to shorten the fattening period by selecting the optimal feed. Using the system of this embodiment, the number of days it takes to go from 30 kg to 120 kg with feed A is reduced by 5 days in the example in Figure 5D. If the feed cost is approximately 200 yen / day, a reduction of 5 days can result in an effect of 1,000 yen / pig. In addition, since various parameters such as breed, temperature, humidity, and season affect pig growth, it is possible to analyze what kind of rearing is optimal by grouping pigs based on a combination of weight changes and these parameters.

[0026] The weight measurement system with the above configuration is processed by a program corresponding to the flowchart shown in Figure 5. The operation will be described below based on the flowchart. The target animal is imaged by camera 26 (S11). The process of imaged by camera 26 continues in the subsequent steps.

[0027] When the camera 26 captures an image of the target animal, the CPU 10 receives the captured image data (S12) and calculates planar dimensions and width information, and lateral dimensions and width information (S13). The CPU 10 then provides the calculated planar dimensions and width information, and lateral dimensions and 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. Furthermore, the obtained weight information is divided according to the above groups and output as information in a format that can be compared (S15).

[0028] Next, a weight measurement system according to a second embodiment will be described. Figure 6 shows a functional block diagram of the weight measurement system 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.

[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, 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.

[0030] 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. Using this 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.

[0031] 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.

[0032] 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)), and 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 that indicates this spinal curvature (e.g., variance) can also be added as an explanatory variable.

[0033] 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.

[0034] <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)).

[0035] The weight measurement system with the above configuration processes data using 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).

[0036] 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 dimensions and width information, and lateral dimensions and width information (S13). The CPU 10 provides the obtained planar dimensions and width information, and lateral dimensions 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 output as comparative morphological information (S15). The processing in step S15 is carried out by the output format processing means 50, just as in the first embodiment, and as explained in Figures 5A, 5B, 5C, and 5D, it is possible to lead to suitable rearing of the target animal.

[0037] 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.

[0038] Furthermore, the weight measurement system 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 dimension / area information and the above side dimension / area information. In this case, the individual information management means 35 performs the operation of providing the planar dimension / area information acquisition means 33, the side dimension / 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 when obtaining weight information.

[0039] 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 maximizing the profits of pig farms by optimizing the timing of shipment. [Explanation of Symbols]

[0040] 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, 26-1~26-m Camera 31 Image acquisition method 32 Individual information management means 33. Means for acquiring planar dimensions and area information 34. Means for acquiring side dimensions and width information 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 50 Output format processing means

Claims

1. For target animals managed in multiple groups, an image acquisition means is provided to capture images of the target animals using cameras installed in areas where only the target animals belonging to each group can be imaged, and to obtain a planar image from above and a lateral image from the side of the target animals. A means for obtaining planar dimensions and area 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, Output format processing means for creating and outputting information in a format that allows comparison by separating the weight information obtained by the weight information acquisition means into the groups, 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. Correction processing means that performs an elliptical approximation along the interface of the target animal's body on the planar image selected by the appropriate image selection means, 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 that makes the image horizontal becomes horizontal, A weight measurement system comprising, 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 system 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 weight measurement system according to claim 1, characterized in that the output format processing means creates and outputs average weight information and / or weight distribution diagrams for each group of target animals.

3. The aforementioned camera captures images of the location where multiple target animals are present. The weight measurement system according to claim 1, further comprising individual target animal image acquisition means for separating images of individual target animals from images obtained by imaging with this camera and acquiring individual target animal images.

4. The weight measurement system according to claim 1, characterized in that the planar dimensions and 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.

5. The weight measurement system according to claim 1, characterized in that the planar dimensions and area information of the target animal is the body length, which is the length from the shoulder to the rump of the target animal.

6. The weight measurement system 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.

7. The weight measurement system 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.

8. 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 planar dimension / area information acquisition means, the side dimension / area information acquisition means, and the weight information acquisition means with an image that has identification information attached along with the type information determined by the type determination means. The weight measurement system according to claim 1, characterized in that the weight information acquisition means adjusts using parameters corresponding to the type information when obtaining weight information.

9. For target animals managed in multiple groups, the image acquisition step involves capturing images of the target animals using cameras installed in areas where only the target animals belonging to each group can be imaged, and obtaining a planar image from above and a lateral image from the side of the target animals. A step of obtaining planar dimensions and area information to obtain planar dimensions and area 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 output format processing step which creates and outputs information in a format that allows comparison by separating the weight information obtained in the weight information acquisition step into the group, 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 correction processing step is performed to apply an ellipse approximation to the planar image selected in the appropriate image selection step, calculate the angle of the ellipse, 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. A method for measuring body weight, comprising: 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.

10. The weight measurement method according to claim 9, characterized in that the output format processing step creates and outputs average weight information and / or weight distribution diagrams for each group of the target animals.

11. The aforementioned camera captures images of the location where multiple target animals are present. The weight measurement method according to claim 9, 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.

12. The weight measurement method according to claim 9, characterized in that the planar dimensions and 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.

13. The method for measuring body weight according to claim 9, characterized in that the planar dimensions and area information of the target animal is the length from the shoulder to the rump of the target animal.

14. The weight measurement method according to claim 9, characterized in that the lateral dimensions and width information of the target animal is height information, which is height information of at least one position of the target animal, such as the shoulder position, waist position, or hip position.

15. The weight measurement method according to claim 9, 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.

16. 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 image, which has identification information attached along 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. The weight measurement method according to claim 9, 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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