CN110672189A - Weight estimation method, device, system and storage medium - Google Patents
Weight estimation method, device, system and storage medium Download PDFInfo
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- CN110672189A CN110672189A CN201910922985.6A CN201910922985A CN110672189A CN 110672189 A CN110672189 A CN 110672189A CN 201910922985 A CN201910922985 A CN 201910922985A CN 110672189 A CN110672189 A CN 110672189A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G17/00—Apparatus for or methods of weighing material of special form or property
- G01G17/08—Apparatus for or methods of weighing material of special form or property for weighing livestock
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G23/00—Auxiliary devices for weighing apparatus
- G01G23/18—Indicating devices, e.g. for remote indication; Recording devices; Scales, e.g. graduated
- G01G23/35—Indicating the weight by photographic recording
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Abstract
The invention discloses a weight estimation method, a weight estimation device, a weight estimation system and a storage medium, and relates to the field of animal monitoring. The weight estimation method comprises the following steps: detecting the outline of a detected object in the acquired image, wherein the detected object is an animal; determining image information of the measured object according to the outline of the measured object; and inputting the weight estimation reference information comprising the image information into a weight estimation model trained in advance to obtain the estimated weight of the tested object, wherein the weight estimation model is trained by adopting the actual weight of the sample tested object and the image comprising the sample tested object. Therefore, the weight of the animal can be estimated on the premise of not using a weight scale, the deviation caused by a manual estimation mode is avoided, and the convenience and accuracy of animal weight estimation are improved.
Description
Technical Field
The invention relates to the field of animal monitoring, in particular to a weight estimation method, a weight estimation device, a weight estimation system and a storage medium.
Background
The traditional pig raising industry adopts a large fence to raise fattening pigs. During the feeding process or the selling process of the fattening pigs, the weight of the pigs needs to be known by a farm so as to evaluate the growth condition of the pigs. In addition, when the meat-feed ratio of the pigs needs to be evaluated, the weight of the pigs also needs to be acquired at any time.
At present, the following method is generally adopted to obtain the weight of a fattening pig. The first method is to weigh the weight of a group of pigs by a weighbridge when the fattening pigs are sold in a market. The second method is to drive the pig onto a weight scale for individual weighing. The third approach is to observe the pigs by an experienced breeder and empirically estimate the weight of the pigs.
Disclosure of Invention
The inventor finds that the pig only moves ceaselessly during weighing, so that the measuring result is not accurate, and the pig is difficult to catch up. The deviation of the manual estimation method is large, and the experience of the breeder can seriously affect the accuracy of the weight estimation. Therefore, the implementation difficulty of the mode provided by the related art is large and the accuracy is low.
The embodiment of the invention aims to solve the technical problem that: how to improve the convenience and the accuracy of animal weighing.
According to a first aspect of some embodiments of the present invention, there is provided a weight estimation method, comprising: detecting the outline of a detected object in the acquired image, wherein the detected object is an animal; determining image information of the measured object according to the outline of the measured object; and inputting the weight estimation reference information comprising the image information into a weight estimation model trained in advance to obtain the estimated weight of the tested object, wherein the weight estimation model is trained by adopting the actual weight of the sample tested object and the image comprising the sample tested object.
In some embodiments, the weight estimation model is a logistic regression model.
In some embodiments, the image includes a plurality of measurands; the weight estimation method further comprises the following steps: and determining the total body weight or the average body weight of the plurality of the tested objects according to the estimated body weight of each tested object in the image.
In some embodiments, the plurality of measured objects are located in the same column and have the same growth information, and the growth information is the age of day or age.
In some embodiments, the weight estimation reference information further includes at least one of growth information, breed, sex of the measured object.
In some embodiments, the image information of the measured object includes information of pixel points in a contour of the measured object, and the information of the pixel points in the contour includes at least one of a pixel value, coordinates of the pixel points, and a number of the pixel points.
In some embodiments, the image information of the measured object includes coordinates of pixel points on the outline of the measured object.
In some embodiments, the weight estimation method further comprises: weighing a plurality of sample measured objects to obtain the actual weight of each sample measured object; acquiring a sample image comprising one or more sample measurands; marking the outline of a sample measured object in a sample image; determining sample image information of the sample measured object according to the profile of the sample measured object; inputting estimated weight reference information including sample image information into a weight estimation model to obtain the estimated weight of the sample measured object; and training the weight estimation model according to the estimated weight of the sample measured object and the actual weight of the sample measured object.
In some embodiments, the subject has a marker drawn on the body; the weight estimation method further comprises the following steps: identifying the mark on the body of the sample measured object in the sample image; and according to the pre-established correspondence between the identification and the actual weight, corresponding the actual weight and the estimated weight of the tested object in the same sample.
According to a second aspect of some embodiments of the present invention, there is provided a weight estimation device, including: the contour detection module is configured to detect the contour of a measured object in the acquired image, wherein the measured object is an animal; an image information determination module configured to determine image information of the object under test according to a profile of the object under test; and the estimation module is configured to input the weight estimation reference information comprising the image information into a weight estimation model trained in advance to obtain the estimated weight of the tested object, wherein the weight estimation model is trained by adopting the image which is marked with the weight of the sample tested object in advance and comprises the sample tested object.
In some embodiments, the weight estimation device is located in the computing gateway.
According to a third aspect of some embodiments of the present invention, there is provided a weight estimation system, comprising: any one of the aforementioned weight estimation devices; and the camera is used for acquiring an image comprising the measured object.
In some embodiments, the camera is deployed above a field for housing the measurand.
In some embodiments, the weight estimation system further comprises: and a management platform configured to acquire the weight of the measured object estimated by the weight estimation device.
According to a fourth aspect of some embodiments of the present invention, there is provided a weight estimation device, including: a memory; and a processor coupled to the memory, the processor configured to perform any of the foregoing weight estimation methods based on instructions stored in the memory.
According to a fifth aspect of some embodiments of the present invention, there is provided a computer readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements any of the weight estimation methods described above.
Some embodiments of the above invention have the following advantages or benefits: by the embodiment of the invention, the image information of the measured object can be determined by detecting the outline of the measured object in the image, and the weight estimation reference information comprising the image information is input into the weight estimation model trained in advance. Therefore, the weight of the animal can be estimated on the premise of not using a weight scale, the deviation caused by a manual estimation mode is avoided, and the convenience and accuracy of animal weight estimation are improved.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow chart illustrating a method of weight estimation according to some embodiments of the invention.
Fig. 2 is a flow diagram of an estimated weight model training method according to some embodiments of the invention.
FIG. 3 is a flow diagram illustrating a population duplication estimation method according to some embodiments of the present invention.
Fig. 4 is a schematic structural diagram of a weight estimation device according to some embodiments of the invention.
Fig. 5 is a schematic diagram of a weight estimation system according to some embodiments of the invention.
Fig. 6 is a schematic structural diagram of a weight estimation device according to other embodiments of the present invention.
Fig. 7 is a schematic structural diagram of a weight estimation device according to further embodiments of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 is a flow chart illustrating a method of weight estimation according to some embodiments of the invention. As shown in fig. 1, the weight estimation method of this embodiment includes steps S102 to S106.
In step S102, a contour of a measured object in the acquired image is detected, wherein the measured object is an animal.
The object to be measured may be, for example, a pig. In some embodiments, the camera arranged above the colony house and the column can be used for acquiring the image of the measured object.
In some embodiments, the measurand in the image may be determined by a target recognition algorithm. For example, the image may be edge-clipped using a MaskR-CNN model segmentation algorithm to obtain the contour of the measured object.
In some embodiments, the contour may be identified by coordinates of pixel points on the contour of the measurand.
In step S104, image information of the object under test is determined based on the contour of the object under test.
The image information is information related to the object in the acquired image, and may be represented by an image or in another form. The image information may include information of pixel points related to the measured object determined by the contour or the contour itself, such as pixel values, coordinates of the pixel points in the image, the number of the pixel points, and the like.
The image information of the measured object can represent the information such as the position, the pixel value and the like of the measured object in the image. The image information can reflect the characteristics of the shape, size, color, texture and the like of the measured object.
In step S106, estimated weight reference information including image information is input into a weight estimation model trained in advance, which is trained using an actual weight of a sample measured object and an image including the sample measured object, to obtain an estimated weight of the measured object.
The estimation reference information may include attribute information of the object to be measured in addition to the image information. Such as age, or the like, or breed, gender, and the like.
In some embodiments, the weight estimation model may be, for example, a logistic regression model. Each type of information in the re-estimation reference may be used as a value of one or more independent variables of the logistic regression model. And obtaining the predicted value of the logistic regression model according to the result of multiplying the value of each independent variable by the corresponding independent variable coefficient.
With the method of the above embodiment, the image information of the measured object can be determined by detecting the contour of the measured object in the image, and the weight estimation reference information including the image information is input into the weight estimation model trained in advance. Therefore, the weight of the animal can be estimated on the premise of not using a weight scale, the deviation caused by a manual estimation mode is avoided, and the convenience and accuracy of animal weight estimation are improved.
Two methods of determining image information of a measured object are exemplarily described below.
In some embodiments, the image information of the measured object includes information of pixel points in a contour of the measured object, and the information of the pixel points in the contour includes at least one of a pixel value, coordinates of the pixel points, and a number of the pixel points.
For example, the pixels in the outline of the image may be extracted and synthesized with a transparent background image of a predetermined size to generate a sub-image as image information of the object. Therefore, the sub-image comprises the pixel value of the measured object and the coordinates of the pixel point. And, for different measured objects, sub-image characterization with the same size can be adopted.
For another example, the pixel values of the respective pixel points in the contour in the image may be used as the respective elements in the vector, and a vector corresponding to the measured object may be generated as the image information.
In some embodiments, the image information of the measured object includes coordinates of pixel points on the outline of the measured object.
For example, the coordinates of the pixel points on the contour may be used as each element in the vector to generate the vector corresponding to the contour as the image information of the measured object. Then, vectors corresponding to the generated contours and other information may be input into the weight estimation model as weight estimation reference information.
For another example, a new channel may be added to the acquired image to identify the contour of the measured object in the image. Then, the image after the new channel is added is used as the image information of the measured object.
In some embodiments, the position of the measured object in the acquired image may also be included in the image information, so that the distance between the measured object and the camera may be considered in the weight estimation, so as to improve the accuracy of the weight estimation.
An embodiment of the weight estimation model training method of the present invention is described below with reference to fig. 2.
Fig. 2 is a flow diagram of an estimated weight model training method according to some embodiments of the invention. As shown in fig. 2, the estimated weight model training method of this embodiment includes steps S202 to S212.
In step S202, a plurality of sample objects to be measured are weighed, and an actual weight of each sample object to be measured is obtained. For example, a sample of a weight scale may be weighed by the subject.
The sample measurand may be marked in advance, for example, a number is drawn on the back of the sample object, so as to correlate the actual weight of the measurand with the outline in the image.
In some embodiments, growth information of the measurand may also be obtained. The weight estimation model can thus be trained in conjunction with the growth information.
In step S204, a sample image including one or more sample measurands is acquired.
In step S206, the outline of the sample measured object in the sample image is marked.
In step S208, sample image information of the sample measured object is determined based on the profile of the sample measured object. The mode of determining the sample image information is consistent with the mode of determining the image information of the tested object in the actual estimation stage.
In step S210, the estimated weight of the sample measured object is obtained by inputting the estimated weight reference information including the sample image information into the weight estimation model.
In step S212, a weight estimation model is trained based on the estimated weight of the sample test object and the actual weight of the sample test object.
In some embodiments, the subject has a marker drawn on the body. Therefore, in the training stage, the identification on the body of the sample tested object in the sample image can be identified, and the actual weight and the estimated weight of the same sample tested object are corresponded according to the corresponding relation between the identification and the actual weight established in advance, so that the weight estimation model is trained according to the estimated weight and the actual weight of the same sample object.
By the method of the embodiment, the weight estimation model can be trained according to the pre-acquired data, and the accuracy of weight estimation through the weight estimation model is improved.
Some embodiments of the invention may also be used for population estimation of a subject. An embodiment of the population estimation of the present invention is described below with reference to fig. 3.
FIG. 3 is a flow diagram illustrating a population duplication estimation method according to some embodiments of the present invention. As shown in fig. 3, the population weight estimation method of this embodiment includes steps S302 to S308.
In step S302, a profile of a measured object in an acquired image is detected, wherein the image includes a plurality of measured objects.
In step S304, image information of each measured object is determined based on the profile of the measured object.
In step S306, the weight estimation reference information of each measured object is respectively input into a weight estimation model trained in advance, and the estimated weight of each measured object is obtained, wherein the weight estimation reference information of each measured object includes image information of the corresponding measured object.
In step S308, the total body weight or the average body weight of the plurality of subjects is determined based on the estimated body weight of each subject in the image.
Thus, the identity of each measurand need not be resolved. The weight of each measured object in the image is obtained in turn, so that the average weight of the measured objects in the image can be obtained.
In some embodiments, the multiple measured objects are located in the same column and have the same growth information. For example, in a large-column feeding scenario, during the pig-out stage, the breeder needs to provide relevant data of the pig. By adopting the method of the embodiment, the average weight of each pig can be known by the breeder, so that the breeder can conveniently find the pig at the corresponding position when selling the pigs.
An embodiment of the weight estimation device of the present invention is described below with reference to fig. 4.
Fig. 4 is a schematic structural diagram of a weight estimation device according to some embodiments of the invention. As shown in fig. 4, the weight estimation device 400 of this embodiment includes: a contour detection module 4100 configured to detect a contour of a measured object in the acquired image, wherein the measured object is an animal; an image information determination module 4200 configured to determine image information of the object to be measured according to a contour of the object to be measured; and an estimation module 4300 configured to input the estimated weight reference information including the image information into a weight estimation model trained in advance to obtain the estimated weight of the measured object, wherein the weight estimation model is trained by using an image including the sample measured object, in which the weight of the sample measured object is marked in advance.
In some embodiments, the weight estimation model is a logistic regression model.
In some embodiments, the image includes a plurality of measurands; the weight estimation apparatus 400 further includes: a population estimation module 4400 configured to determine a total body weight or an average body weight of the plurality of the measured objects according to the estimated body weight of each measured object in the image.
In some embodiments, the plurality of measured objects are located in the same column and have the same growth information, and the growth information is the age of day or age.
In some embodiments, the weight estimation reference information further includes at least one of growth information, breed, sex of the measured object.
In some embodiments, the image information of the measured object includes information of pixel points in a contour of the measured object, and the information of the pixel points in the contour includes at least one of a pixel value, coordinates of the pixel points, and a number of the pixel points.
In some embodiments, the image information of the measured object includes coordinates of pixel points on the outline of the measured object.
In some embodiments, the weight estimation apparatus 400 further comprises: a training module 4500 configured to weigh a plurality of sample measured objects, obtaining an actual weight of each sample measured object; acquiring a sample image comprising one or more sample measurands; marking the outline of a sample measured object in a sample image; determining sample image information of the sample measured object according to the profile of the sample measured object; inputting estimated weight reference information including sample image information into a weight estimation model to obtain the estimated weight of the sample measured object; and training the weight estimation model according to the estimated weight of the sample measured object and the actual weight of the sample measured object.
In some embodiments, the subject has a marker drawn on the body; the training module 4500 is further configured to identify an identity on the body of the sample measurand in the sample image; and according to the pre-established correspondence between the identification and the actual weight, corresponding the actual weight and the estimated weight of the tested object in the same sample.
In some embodiments, the weight estimation device 400 is located in a computing gateway.
An embodiment of the weight estimation system of the present invention is described below with reference to fig. 5.
Fig. 5 is a schematic diagram of a weight estimation system according to some embodiments of the invention. As shown in fig. 5, the weight estimation system 50 of this embodiment includes: a weight estimation device 510; and a camera 520 for acquiring an image including the object to be measured. For the specific implementation of the weight estimation device 510, reference may be made to the foregoing embodiments, which are not described herein again.
In some embodiments, the camera 520 is disposed above a field for housing the measurand.
In some embodiments, the weight estimation system 50 further comprises a management platform 530 configured to obtain the weight of the measured object estimated by the weight estimation device.
Fig. 6 is a schematic structural diagram of a weight estimation device according to other embodiments of the present invention. As shown in fig. 6, the weight estimation device 60 of this embodiment includes: a memory 610 and a processor 620 coupled to the memory 610, the processor 620 being configured to perform the weight estimation method of any of the previous embodiments based on instructions stored in the memory 610.
Fig. 7 is a schematic structural diagram of a weight estimation device according to further embodiments of the invention. As shown in fig. 7, the weight estimation device 70 of this embodiment includes: the memory 710 and the processor 720 may further include an input/output interface 730, a network interface 740, a storage interface 750, and the like. These interfaces 730, 740, 750, as well as the memory 710 and the processor 720, may be connected, for example, by a bus 760. The input/output interface 730 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 740 provides a connection interface for various networking devices. The storage interface 750 provides a connection interface for external storage devices such as an SD card and a usb disk.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, wherein the program is configured to implement any one of the weight estimation methods described above when executed by a processor.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (16)
1. A method of weight estimation, comprising:
detecting the outline of a detected object in the acquired image, wherein the detected object is an animal;
determining image information of the measured object according to the outline of the measured object;
and inputting the weight estimation reference information comprising the image information into a weight estimation model trained in advance to obtain the estimated weight of the tested object, wherein the weight estimation model is trained by adopting the actual weight of the sample tested object and the image comprising the sample tested object.
2. The weight estimation method according to claim 1, wherein the weight estimation model is a logistic regression model.
3. The method of estimating body weight according to claim 1, wherein the image includes a plurality of measured objects;
the weight estimation method further comprises:
and determining the total body weight or the average body weight of the plurality of the tested objects according to the estimated body weight of each tested object in the image.
4. The method of claim 3, wherein the plurality of subjects are located in the same column and have the same growth information, and the growth information is a day age or an age.
5. The method for weight estimation according to any one of claims 1 to 4, wherein the weight estimation reference information further includes at least one of growth information, breed, and sex of the subject.
6. The weight estimation method according to claim 1, wherein the image information of the object to be measured includes information of pixel points in a contour of the object to be measured, and the information of the pixel points in the contour includes at least one of a pixel value, coordinates of the pixel points, and the number of the pixel points.
7. The weight estimation method according to claim 1, wherein the image information of the object to be measured includes coordinates of pixel points on a contour of the object to be measured.
8. The method of weight estimation according to claim 1, further comprising:
weighing a plurality of sample measured objects to obtain the actual weight of each sample measured object;
acquiring a sample image comprising one or more sample measurands;
marking the outline of a tested object of the sample in the sample image;
determining sample image information of the sample measured object according to the profile of the sample measured object;
inputting the weight estimation reference information comprising the sample image information into a weight estimation model to obtain the estimated weight of the sample measured object;
and training the weight estimation model according to the estimated weight of the sample measured object and the actual weight of the sample measured object.
9. The method of estimating body weight according to claim 8, wherein a marker is drawn on the body of the subject;
the weight estimation method further comprises:
identifying the mark on the body of the sample measured object in the sample image;
and according to the pre-established correspondence between the identification and the actual weight, corresponding the actual weight and the estimated weight of the tested object in the same sample.
10. A weight estimation device comprising:
the system comprises a contour detection module, a contour detection module and a contour detection module, wherein the contour detection module is configured to detect the contour of a measured object in an acquired image, and the measured object is an animal;
an image information determination module configured to determine image information of the measured object according to a profile of the measured object;
and the estimation module is configured to input the weight estimation reference information comprising the image information into a weight estimation model trained in advance to obtain the estimated weight of the tested object, wherein the weight estimation model is trained by adopting an image which is marked with the weight of the sample tested object in advance and comprises the sample tested object.
11. The weight estimation device of claim 10, wherein the weight estimation device is located in a computing gateway.
12. A weight estimation system, comprising:
the weight estimation device of claim 10 or 11; and
and the camera is used for acquiring an image comprising the measured object.
13. The weight estimation system of claim 12, wherein the camera is disposed above a field for housing a subject.
14. The weight estimation system of claim 12, further comprising:
a management platform configured to acquire the weight of the object estimated by the weight estimation device.
15. A weight estimation device comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of weight estimation according to any of claims 1-9 based on instructions stored in the memory.
16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of weight estimation according to any one of claims 1 to 9.
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| CN111369378A (en) * | 2020-02-25 | 2020-07-03 | 成都睿畜电子科技有限公司 | Live pig supervision method and system based on computer vision recognition |
| CN111507432A (en) * | 2020-07-01 | 2020-08-07 | 四川智迅车联科技有限公司 | Intelligent weighing method and system for agricultural insurance claims, electronic equipment and storage medium |
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| CN113344001A (en) * | 2021-07-02 | 2021-09-03 | 河南牧原智能科技有限公司 | Organism weight estimation method, device, equipment and storage medium |
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| CN114973332A (en) * | 2022-06-21 | 2022-08-30 | 河北农业大学 | Weight measuring method and device, electronic equipment and living livestock measuring system |
| CN115100683A (en) * | 2022-06-22 | 2022-09-23 | 马上消费金融股份有限公司 | Duplication estimating method, duplication estimating device, duplication estimating equipment and storage medium |
| CN115135973A (en) * | 2020-02-18 | 2022-09-30 | 国立大学法人宫崎大学 | Weight estimation device and program |
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