Method and device for acquiring daily average milk yield of dairy cow
By obtaining cow face images and using machine learning models to identify healthy status and calculating the average daily milk production, it solves the problem that ranchers find it difficult to accurately obtain cow milk production, and achieves scientific management and accurate milk production of cows Decision support.
Patent Information
- Application Number
- CN202411272450.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-11
- Publication Date
- 2024-12-10
AI Technical Summary
Ranchers who raise dairy cows find it difficult to accurately obtain the average daily milk production of different breeds of dairy cows, resulting in mis-raising. Consultations that lack real data support often mislead ranchers.
By obtaining cow face images within the preset milk production statistics cycle, using trained machine learning models to identify the health status of cows, screen out healthy cows, and calculate their average daily milk production, using smart neck data and Digital cow model for data analysis and output.
It has achieved accurate acquisition of the average daily milk production of different breeds of dairy cows, helped ranchers to make more wise feeding decisions, reduced the impact of misleading information, and improved the scientific nature of milk production management.
Smart Images
Figure CN119107673A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and device for obtaining the average daily milk production of a dairy cow. Background Art
[0002] Ranch owners who raise dairy cows usually want to raise dairy cow breeds with high milk production. The above-mentioned ranch owners learn about dairy cow breeds with high milk production through consultation and other means, but the consultation opinions often lack real data as support, which can easily mislead the above-mentioned ranch owners and cause them to raise the wrong dairy cow breeds. Therefore, how to accurately obtain the average daily milk production of different breeds of dairy cows has become a difficult problem. Summary of the invention
[0003] In view of the problems in the prior art, the embodiments of the present invention provide a method and device for obtaining the average daily milk production of a dairy cow, which can at least partially solve the problems in the prior art.
[0004] In one aspect, the present invention provides a method for obtaining the average daily milk production of a dairy cow, comprising:
[0005] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0006] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0007] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0008] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0009] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0010] The method of calculating the average daily milk production of dairy cows corresponding to each breed based on the average daily milk production of multiple healthy dairy cows corresponding to each breed includes:
[0011] The average daily milk production of multiple healthy cows of the same breed is taken as the average daily milk production of the cows of the same breed.
[0012] Wherein, the method for obtaining the average daily milk production of dairy cows also includes:
[0013] Obtaining the health status of multiple dairy cows corresponding to each breed within a preset time period after the preset milk production statistical period;
[0014] If there is a target cow with abnormal health status, the target cow is removed from a plurality of healthy cows corresponding to each breed;
[0015] According to the updated average daily milk production of multiple healthy cows corresponding to each breed, the updated average daily milk production of the cows corresponding to each breed is calculated.
[0016] The preset cow health status recognition model is obtained by training a machine learning model according to the cow health status recognition sample data, including:
[0017] Acquire a dairy cow data set; the dairy cow data set includes a plurality of photographed image samples corresponding to the cow faces of a plurality of dairy cows corresponding to each breed, and physiological parameter detection data of a plurality of dairy cows corresponding to each breed;
[0018] Perform health status analysis on physiological parameter detection data of multiple dairy cows corresponding to each breed, use multiple image samples with normal health status as positive samples, and use multiple image samples with abnormal health status as negative samples;
[0019] The preset cow health status recognition model is obtained by training a machine learning model according to the positive samples and the negative samples.
[0020] Wherein, obtaining a plurality of photographed image samples corresponding to the cow faces of a plurality of cows corresponding to each breed includes:
[0021] The original captured images are initially recognized, the noise images are eliminated, and the retained images are used as multiple captured image samples corresponding to the cow faces of multiple dairy cows corresponding to each breed.
[0022] Among them, the physiological parameter detection data of multiple dairy cows corresponding to each breed are obtained, including:
[0023] By reading the data from the smart neck ring pre-installed on the neck of each cow, the physiological parameter detection data of multiple cows corresponding to each breed are obtained.
[0024] Wherein, after the step of calculating the average daily milk production of the cows corresponding to each breed, or calculating the updated average daily milk production of the cows corresponding to each breed, the method for obtaining the average daily milk production of the cows further includes:
[0025] According to the pre-established digital cow model corresponding to each breed, the average daily milk production of the cows corresponding to each breed, or the updated average daily milk production of the cows corresponding to each breed, is output respectively.
[0026] In one aspect, the present invention provides a device for obtaining the average daily milk production of a dairy cow, comprising:
[0027] An acquisition unit is used to acquire a plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0028] An identification unit, used to identify the photographed images corresponding to each cow face based on a preset cow health status identification model, and obtain a health status identification result of each cow;
[0029] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0030] A screening unit, used for screening a plurality of dairy cows corresponding to each breed according to the health identification status result, to obtain a plurality of healthy dairy cows corresponding to each breed;
[0031] The calculation unit is used to calculate the average daily milk production of the dairy cows corresponding to each breed according to the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0032] In another aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, and a bus, wherein:
[0033] The processor and the memory communicate with each other via the bus;
[0034] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the following method:
[0035] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0036] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0037] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0038] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0039] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0040] An embodiment of the present invention provides a non-transitory computer-readable storage medium, including:
[0041] The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the following method:
[0042] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0043] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0044] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0045] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0046] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0047] The method and device for obtaining the average daily milk production of dairy cows provided by the embodiments of the present invention obtain multiple photographic images corresponding to the cow faces of multiple dairy cows corresponding to each breed within a preset milk production statistical period; each dairy cow is a healthy cow within a preset cow age and when the first photographic image is taken; the photographic images corresponding to each cow face are identified based on a preset dairy cow health status recognition model to obtain a health recognition status result of each dairy cow; wherein the preset dairy cow health status recognition model is obtained by training a machine learning model based on dairy cow health status recognition sample data; according to the health recognition status result, multiple dairy cows corresponding to each breed are screened to obtain multiple healthy dairy cows corresponding to each breed; according to the average daily milk production of the multiple healthy dairy cows corresponding to each breed, the average daily milk production of the dairy cows corresponding to each breed is calculated, so that the average daily milk production of dairy cows of different breeds can be accurately obtained, and the dairy ranch owner can be effectively assisted in deciding the dairy cow breed to be raised. BRIEF DESCRIPTION OF THE DRAWINGS
[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:
[0049] Figure 1 It is a flow chart of a method for obtaining the average daily milk production of a dairy cow provided in one embodiment of the present invention.
[0050] Figure 2 It is a schematic diagram illustrating the average daily milk production of a digital cow provided by an embodiment of the present invention.
[0051] Figure 3 It is a schematic diagram of the structure of a device for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention.
[0052] Figure 4 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0053] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily.
[0054] Figure 1 FIG. 1 is a flow chart of a method for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention. Figure 1 As shown, the method for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention includes:
[0055] Step S1: obtaining a plurality of images corresponding to the faces of a plurality of cows of each breed within a preset milk production statistical period; each cow is within a preset age and is healthy when the first image is taken.
[0056] Step S2: recognizing the photographed images corresponding to each cow face based on a preset cow health status recognition model to obtain a health status recognition result of each cow;
[0057] The preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data.
[0058] Step S3: screening multiple cows corresponding to each breed according to the health identification status result to obtain multiple healthy cows corresponding to each breed.
[0059] Step S4: Calculate the average daily milk production of the cows corresponding to each breed according to the average daily milk production of multiple healthy cows corresponding to each breed.
[0060] In the above step S1, the device obtains multiple photographic images corresponding to the cow faces of multiple cows of each breed within a preset milk production statistical period; each cow is a healthy cow within a preset age and when the first photographic image is taken. The device can be a computer device that executes the method, for example, it can include a server, specifically a ranch management server. It should be noted that the acquisition and analysis of data involved in the embodiments of the present invention are authorized by the user.
[0061] The preset milk production statistical period can be set according to actual conditions, for example, one month.
[0062] The preset cow age time can be determined according to the milk production time of the corresponding cows of each breed. For example, if the corresponding cows of each breed produce milk within 2-4 years after birth, the multiple cows corresponding to each breed selected are cows 2-4 years old after birth.
[0063] Multiple dairy cows corresponding to each breed can be raised in different cow sheds. In order to accurately calculate the average daily milk production of dairy cows corresponding to each breed, the environmental layout and environmental parameters of these cow sheds should be as consistent as possible. The milk production of dairy cows is greatly affected by the health status of dairy cows, that is, under the same conditions, the milk production of unhealthy dairy cows is lower than that of healthy dairy cows.
[0064] In addition, the type and amount of feed fed to multiple dairy cows of each breed should be kept consistent as much as possible.
[0065] The multiple cows corresponding to each selected breed are cows that are 2-4 years old after birth, and are currently in a healthy state, that is, they are in a healthy state when the first image is taken.
[0066] The cows corresponding to breed U can be expressed as {u1,u2…un}, which means that there are n cows of breed U.
[0067] For the i-th cow ui of breed U, the captured images are recorded as {ui1, ui2…uix}, indicating that the i-th cow of breed U has x captured images.
[0068] The number of cows of different breeds may be different. For example, the number of cows corresponding to breed V may not be equal to n.
[0069] The number of images captured for different cows of the same breed may be different. For example, the number of images captured for the j-th cow uj of breed U may not be equal to x.
[0070] The corresponding number of cameras can be set according to the actual situation, so that the unobstructed cow face image can be clearly captured. The shooting time between two adjacent images can be set independently according to the actual situation. For example, at feeding time, t1 can be used as the shooting time between two adjacent images, and at sleeping time, t2 can be used as the shooting time between two adjacent images, and t2>t1.
[0071] An identification label can be attached to the face of each cow to distinguish different cows of each breed, or the cow's face can be identified through other models to distinguish different cows of each breed.
[0072] In the above step S2, the device identifies the captured images corresponding to each cow face based on the preset cow health status recognition model to obtain the health recognition status result of each cow; multiple captured images (T1~TS) corresponding to each cow face can be input into the preset cow health status recognition model at one time, and the output result of the preset cow health status recognition model is used as the health recognition status result of the cow corresponding to this cow face at this time. The time period of the input model can be set independently according to the actual situation. Next time, multiple captured images (TS+1~TS+N) can be input into the preset cow health status recognition model at one time, and so on, to ensure that no captured image is missed.
[0073] The preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data. The specific model of the machine learning model is not limited, and the training method can be a conventional training method in the field.
[0074] In the above step S3, the device screens multiple cows corresponding to each breed according to the health identification status result to obtain multiple healthy cows corresponding to each breed. Cows with abnormal health identification status results are removed from the cowshed, and the remaining cows are used as multiple healthy cows corresponding to each breed.
[0075] In the above step S4, the device calculates the average daily milk production of cows corresponding to each breed according to the average daily milk production of multiple healthy cows corresponding to each breed. The method of calculating the average daily milk production of cows corresponding to each breed according to the average daily milk production of multiple healthy cows corresponding to each breed includes:
[0076] The average daily milk production of multiple healthy cows of the same breed is taken as the daily average milk production of the cows of the same breed. Referring to the above description, taking breed U as an example, the kth healthy cow uk in breed U, uk belongs to {ui1, ui2…uiz}, z<=x, and the daily average milk production AU of the cows corresponding to breed U can be calculated according to the following formula:
[0077] AU=(AU1+…+AUz) / z
[0078] Among them, AU1~AUz is the average daily milk production of each healthy dairy cow in breed U, and z is the number of healthy dairy cows in breed U.
[0079] Similarly, the average daily milk production of each breed of dairy cows, namely BU and CU, can be calculated.
[0080] The method for obtaining the average daily milk production of dairy cows also includes:
[0081] The health status of multiple cows corresponding to each breed is obtained within a preset time period after the preset milk production statistical period; the preset time period can be set independently according to actual conditions, and can be selected as 3 days. It is understandable that if a cow is unwell during the preset milk production statistical period, but has not yet reached an abnormal health state, the milk production will be reduced at this time, and the milk production counted at this time will be less than that of a cow in a completely healthy state.
[0082] If there is a target cow with abnormal health status, the target cow is removed from the multiple healthy cows corresponding to each breed; that is, the target cow that may cause inaccurate statistical milk production is removed from the statistics.
[0083] According to the updated average daily milk production of multiple healthy cows corresponding to each breed, the updated average daily milk production of cows corresponding to each breed is calculated. The calculation method can refer to the above description and will not be repeated here.
[0084] After the step of calculating the average daily milk production of the cows corresponding to each breed, or calculating the updated average daily milk production of the cows corresponding to each breed, the method for obtaining the average daily milk production of the cows further includes:
[0085] According to the pre-established digital cow model corresponding to each breed, the average daily milk production of the cow corresponding to each breed, or the updated average daily milk production of the cow corresponding to each breed is output respectively. The digital cow model can be a virtual image model simulating the cow corresponding to each breed, such as Figure 2As shown, they are respectively recorded as digital cow A and digital cow B. The average daily milk production can be pushed to the ranch owner's terminal, and the cow information for calculating the average daily milk production can also be pushed to the ranch owner's terminal. The cow information can include the number of healthy cows participating in the calculation, the name of the ranch where these healthy cows are located, the location, breeding environment, feeding feed and other related information, so as to further increase data support. According to the displayed data, the ranch owner can be effectively assisted in reasonably choosing the dairy cow breed to raise.
[0086] The preset cow health status recognition model is obtained by training a machine learning model according to the cow health status recognition sample data, including:
[0087] Acquire a cow data set; the cow data set includes a plurality of captured image samples corresponding to the cow faces of a plurality of cows corresponding to each breed, and physiological parameter detection data of a plurality of cows corresponding to each breed; acquire a plurality of captured image samples corresponding to the cow faces of a plurality of cows corresponding to each breed, including:
[0088] Perform initial image recognition on the original captured images, remove noise images, and use the retained images as multiple captured image samples corresponding to the cow faces of multiple dairy cows corresponding to each breed. Initial image recognition may include identifying whether the initial outline of the cow face is clear and visible. If it is not clear, it means that it is a noise image and is removed. Initial image recognition can be achieved using existing technologies.
[0089] Obtain physiological parameter test data for multiple cows corresponding to each breed, including:
[0090] By reading the data in the smart neck ring pre-installed on the neck of each cow, the physiological parameter detection data of multiple cows corresponding to each breed are obtained. The data in the smart neck ring may include the cow's movement information, heart rate, blood pressure and other data. The physiological parameter detection data may include the data in the above-mentioned smart neck ring, and may also include data calculated by using the data in the smart neck ring as intermediate data.
[0091] The health status analysis is performed on the physiological parameter detection data of multiple dairy cows corresponding to each breed, and multiple image samples with normal health status are used as positive samples, and multiple image samples with abnormal health status are used as negative samples. The ratio of the number of positive samples and negative samples can be set independently according to actual conditions to prevent overfitting in data training.
[0092] The preset cow health status recognition model is obtained by training the machine learning model according to the positive samples and the negative samples. After the preset cow health status recognition model is trained, the cow health status can be directly identified by photographing the cow's face, without the need to use a smart neck ring, which each cow needs to wear and is expensive, while the camera is cheap, and multiple cows can share one camera, which reduces the overall feeding and management costs.
[0093] In addition, not using smart neck collars can also make cows more comfortable and help increase milk production.
[0094] The method for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention comprises the following steps: obtaining a plurality of photographed images corresponding to the faces of a plurality of dairy cows corresponding to each breed within a preset milk production statistical period; each dairy cow is a healthy cow within a preset age period and when the first photographed image is taken; identifying the photographed images corresponding to each dairy cow face based on a preset dairy cow health status recognition model to obtain a health recognition status result of each dairy cow; wherein the preset dairy cow health status recognition model is obtained by training a machine learning model based on dairy cow health status recognition sample data; screening a plurality of dairy cows corresponding to each breed according to the health recognition status result to obtain a plurality of healthy dairy cows corresponding to each breed; calculating the average daily milk production of the dairy cows corresponding to each breed according to the average daily milk production of the plurality of healthy dairy cows corresponding to each breed, thereby being able to accurately obtain the average daily milk production of dairy cows of different breeds, and effectively assisting ranch owners in deciding on dairy cow breeds to be raised.
[0095] Furthermore, the average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed, including:
[0096] The average daily milk production of multiple healthy cows of the same breed is taken as the daily milk production of the cows of the same breed.
[0097] Furthermore, the method for obtaining the average daily milk production of dairy cows also includes:
[0098] The health status of multiple cows corresponding to each breed is obtained within a preset time period after the preset milk production statistical period; please refer to the above embodiment for description and no further details will be given.
[0099] If there is a target cow with abnormal health status, the target cow is removed from a plurality of healthy cows corresponding to each breed; the above-mentioned embodiment can be referred to for description and will not be described in detail.
[0100] According to the updated average daily milk production of multiple healthy cows corresponding to each breed, the updated average daily milk production of cows corresponding to each breed is calculated. The above-mentioned embodiments can be referred to for explanation, which will not be described in detail.
[0101] Furthermore, the preset cow health status recognition model is obtained by training a machine learning model according to the cow health status recognition sample data, including:
[0102] Acquire a cow data set; the cow data set includes a plurality of captured image samples corresponding to the faces of a plurality of cows corresponding to each breed, and physiological parameter detection data of a plurality of cows corresponding to each breed; the above-mentioned embodiment can be referred to for description and will not be repeated here.
[0103] The health status analysis is performed on the physiological parameter detection data of multiple cows corresponding to each breed, and multiple image samples with normal health status are used as positive samples, and multiple image samples with abnormal health status are used as negative samples; the above embodiment can be referred to for description and will not be repeated here.
[0104] The preset cow health status recognition model is obtained by training the machine learning model according to the positive samples and the negative samples. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.
[0105] Furthermore, a plurality of photographed image samples corresponding to the faces of a plurality of dairy cows corresponding to each breed are obtained, including:
[0106] The original captured images are initially recognized, the noise images are removed, and the retained images are used as multiple captured image samples corresponding to the cow faces of multiple cows corresponding to each breed. The above embodiments can be referred to for description and will not be repeated here.
[0107] Furthermore, physiological parameter detection data of multiple dairy cows corresponding to each breed are obtained, including:
[0108] By reading the data in the smart collar pre-installed on the neck of each cow, the physiological parameter detection data of multiple cows corresponding to each breed are obtained. The above-mentioned embodiments can be referred to for explanation and will not be described in detail.
[0109] Furthermore, after the step of calculating the average daily milk production of the cows corresponding to each breed, or calculating the updated average daily milk production of the cows corresponding to each breed, the method for obtaining the average daily milk production of the cows further includes:
[0110] According to the pre-established digital cow model corresponding to each breed, the average daily milk production of the cow corresponding to each breed, or the updated average daily milk production of the cow corresponding to each breed is output respectively. The above embodiments can be referred to for explanation and will not be repeated here.
[0111] Figure 3 FIG. 1 is a schematic diagram of a device for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention. Figure 3 As shown, the device for obtaining the average daily milk production of a dairy cow provided in an embodiment of the present invention includes an obtaining unit 301, an identifying unit 302, a screening unit 303 and a calculating unit 304, wherein:
[0112] The acquisition unit 301 is used to obtain multiple photographed images corresponding to the faces of multiple cows corresponding to each breed within a preset milk production statistical period; each cow is a healthy cow within a preset age period and when the first photographed image is taken; the recognition unit 302 is used to recognize the photographed images corresponding to each cow face based on a preset cow health status recognition model, and obtain the health recognition status result of each cow; wherein the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data; the screening unit 303 is used to screen multiple cows corresponding to each breed according to the health recognition status result, and obtain multiple healthy cows corresponding to each breed; the calculation unit 304 is used to calculate the average daily milk production of cows corresponding to each breed according to the average daily milk production of multiple healthy cows corresponding to each breed.
[0113] Specifically, the acquisition unit 301 in the device is used to obtain multiple photographed images corresponding to the faces of multiple cows corresponding to each breed within a preset milk production statistical period; each cow is a healthy cow within a preset age period and when the first photographed image is taken; the recognition unit 302 is used to recognize the photographed images corresponding to each cow face based on a preset cow health status recognition model, and obtain the health recognition status result of each cow; wherein the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data; the screening unit 303 is used to screen multiple cows corresponding to each breed according to the health recognition status result, and obtain multiple healthy cows corresponding to each breed; the calculation unit 304 is used to calculate the average daily milk production of cows corresponding to each breed according to the average daily milk production of multiple healthy cows corresponding to each breed.
[0114] The device for obtaining the average daily milk production of a dairy cow provided by an embodiment of the present invention obtains a plurality of photographed images corresponding to the faces of a plurality of dairy cows corresponding to each breed within a preset milk production statistical period; each dairy cow is a healthy cow within a preset age period and when the first photographed image is taken; the photographed images corresponding to each dairy cow's face are identified based on a preset dairy cow health status recognition model to obtain a health recognition status result of each dairy cow; wherein the preset dairy cow health status recognition model is obtained by training a machine learning model based on dairy cow health status recognition sample data; according to the health recognition status result, a plurality of dairy cows corresponding to each breed are screened to obtain a plurality of healthy dairy cows corresponding to each breed; according to the average daily milk production of the plurality of healthy dairy cows corresponding to each breed, the average daily milk production of the dairy cows corresponding to each breed is calculated, so that the average daily milk production of dairy cows of different breeds can be accurately obtained, and the dairy ranch owner can be effectively assisted in deciding the dairy cow breed to be raised.
[0115] The embodiment of the present invention provides an embodiment of a device for obtaining the average daily milk production of a cow, which can be specifically used to execute the processing flow of the above-mentioned method embodiments. Its functions are not repeated here, and reference can be made to the detailed description of the above-mentioned method embodiments.
[0116] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 4 As shown, the electronic device includes: a processor (processor) 401, a memory (memory) 402 and a bus 403;
[0117] The processor 401 and the memory 402 communicate with each other via a bus 403;
[0118] The processor 401 is used to call the program instructions in the memory 402 to execute the methods provided by the above method embodiments, for example, including:
[0119] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0120] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0121] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0122] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0123] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0124] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the methods provided by the above method embodiments, for example, including:
[0125] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0126] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0127] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0128] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0129] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0130] This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above method embodiments, for example, including:
[0131] A plurality of photographed images corresponding to the faces of a plurality of dairy cows of each breed are obtained within a preset milk production statistical period; each dairy cow is within a preset age and is a healthy dairy cow when the first photographed image is taken;
[0132] Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow;
[0133] Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data;
[0134] Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed;
[0135] The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
[0136] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0137] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0140] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
[0141] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for obtaining the average daily milk production of dairy cows, characterized in that: include: Acquire multiple photographed images corresponding to the faces of multiple dairy cows of each breed within a preset milk production statistical period; Each cow was within the preset age range and was healthy when the first image was taken; Based on the preset cow health status recognition model, the photographed images corresponding to each cow face are recognized to obtain the health status recognition result of each cow; Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data; Screening a plurality of dairy cows corresponding to each breed according to the health identification status result to obtain a plurality of healthy dairy cows corresponding to each breed; The average daily milk production of dairy cows corresponding to each breed is calculated based on the average daily milk production of multiple healthy dairy cows corresponding to each breed.
2. The method for obtaining the average daily milk production of dairy cows according to claim 1, characterized in that: The method of calculating the average daily milk production of dairy cows corresponding to each breed based on the average daily milk production of multiple healthy dairy cows corresponding to each breed includes: The average daily milk production of multiple healthy cows of the same breed is taken as the average daily milk production of the cows of the same breed.
3. The method for obtaining the average daily milk production of dairy cows according to claim 1, characterized in that: The method for obtaining the average daily milk production of dairy cows also includes: Obtaining the health status of multiple dairy cows corresponding to each breed within a preset time period after the preset milk production statistical period; If there is a target cow with abnormal health status, the target cow is removed from a plurality of healthy cows corresponding to each breed; According to the updated average daily milk production of multiple healthy cows corresponding to each breed, the updated average daily milk production of the cows corresponding to each breed is calculated.
4. The method for obtaining the average daily milk production of dairy cows according to claim 1, characterized in that: The preset cow health status recognition model is obtained by training a machine learning model according to the cow health status recognition sample data, including: Acquire a dairy cow data set; the dairy cow data set includes a plurality of photographed image samples corresponding to the cow faces of a plurality of dairy cows corresponding to each breed, and physiological parameter detection data of a plurality of dairy cows corresponding to each breed; Perform health status analysis on physiological parameter detection data of multiple dairy cows corresponding to each breed, use multiple image samples with normal health status as positive samples, and use multiple image samples with abnormal health status as negative samples; The preset cow health status recognition model is obtained by training a machine learning model according to the positive samples and the negative samples.
5. The method for obtaining the average daily milk production of dairy cows according to claim 4, characterized in that: A plurality of captured image samples corresponding to the faces of a plurality of dairy cows corresponding to each breed are obtained, including: The original captured images are initially recognized, the noise images are eliminated, and the retained images are used as multiple captured image samples corresponding to the cow faces of multiple dairy cows corresponding to each breed.
6. The method for obtaining the average daily milk production of dairy cows according to claim 4, characterized in that: Obtain physiological parameter test data for multiple cows corresponding to each breed, including: By reading the data from the smart neck ring pre-installed on the neck of each cow, the physiological parameter detection data of multiple cows corresponding to each breed are obtained.
7. The method for obtaining the average daily milk production of dairy cows according to claim 1 or 3, characterized in that: After the step of calculating the average daily milk production of the cows corresponding to each breed, or calculating the updated average daily milk production of the cows corresponding to each breed, the method for obtaining the average daily milk production of the cows further includes: According to the pre-established digital cow model corresponding to each breed, the average daily milk production of the cows corresponding to each breed, or the updated average daily milk production of the cows corresponding to each breed, is output respectively.
8. A device for obtaining the average daily milk production of dairy cows, characterized in that: include: An acquisition unit, used for acquiring a plurality of photographed images corresponding to the faces of a plurality of dairy cows corresponding to each breed within a preset milk production statistical period; Each cow was within the preset age range and was healthy when the first image was taken; An identification unit, used to identify the photographed images corresponding to each cow face based on a preset cow health status identification model, and obtain a health status identification result of each cow; Wherein, the preset cow health status recognition model is obtained by training a machine learning model based on cow health status recognition sample data; A screening unit, used for screening a plurality of dairy cows corresponding to each breed according to the health identification status result, to obtain a plurality of healthy dairy cows corresponding to each breed; The calculation unit is used to calculate the average daily milk production of the dairy cows corresponding to each breed according to the average daily milk production of multiple healthy dairy cows corresponding to each breed.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.