CN115777560A - Intelligent sow feeding system based on machine vision analysis technology - Google Patents
Intelligent sow feeding system based on machine vision analysis technology Download PDFInfo
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Abstract
The invention discloses a sow intelligent feeding system based on a machine vision analysis technology, relates to the technical field of intelligent breeding, and solves the technical problems that the feeding process cannot be prejudged in the prior art, so that intervention measures cannot be made in time, and the production and operation of live pigs are influenced; the intelligent feeding system comprises a central control module, and a data acquisition module, a feeding control module and an intelligent terminal which are connected with the central control module; according to the method, physical sign data are acquired through image data, and the food release content is estimated by combining a food release estimation model; before food release, verifying food release contents by combining breeding experience data and basic feeding data, and performing food release only after the verification of the food release contents is passed; based on historical experience, the probability of sow abnormality caused by combination of the feeding content and the feeding environment can be rapidly analyzed, and farmers can make intervention measures in time; in addition, the method collects the actual feed intake of the sow, compares the feeding content and the actual feed intake with the breeding experience data, and can timely position the abnormality of the sow.
Description
Technical Field
The invention belongs to the field of intelligent breeding, relates to a sow feeding technology based on a machine vision technology, and particularly relates to a sow intelligent feeding system based on a machine vision analysis technology.
Background
The breeding sows in the farm are mainly used for breeding piglets and have the advantages of good adaptability, strong disease resistance and the like. The ingestion condition of the sow can directly reflect the health condition of the sow, and the abnormal ingestion condition is generally a sign of illness; but obvious abnormality is easy to be found, and slight abnormality is difficult to be found, which brings hidden trouble for pig production and management.
The prior art is through the real-time sign parameter of regularly gathering the sow of intelligent feeding system, according to the accurate food consumption of controlling the sow of sign parameter to this guarantees the size and the fat condition of sow, reduces the risk of suffering from diseases of sow simultaneously. In the prior art, the feed intake is controlled only according to the physical sign parameters in the feeding process of the sows, the influence of the feeding environment on the feed intake is not comprehensively considered, meanwhile, the whole feeding process cannot be pre-judged, and intervention measures cannot be made in time; therefore, a sow intelligent feeding system based on a machine vision analysis technology is needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides a sow intelligent feeding system based on a machine vision analysis technology, which is used for solving the technical problems that the feed intake control in the prior art is single in consideration, and the feeding flow cannot be pre-judged, so that intervention measures cannot be made in time, and the production and management of live pigs are influenced.
In order to achieve the purpose, the invention provides a sow intelligent feeding system based on a machine vision analysis technology, which comprises a central control module, and a data acquisition module, a feeding control module and an intelligent terminal which are connected with the central control module; the intelligent terminal is used for monitoring the feeding process and receiving early warning information;
basic feeding data are collected through a data sensor connected with a data collection module and forwarded to a central control module; wherein the basic feeding data comprises image data and environmental data;
the central control module analyzes the image data to extract the physical sign data of the sow and estimates the feeding content based on the physical sign data; extracting breeding experience data, and verifying the food release content according to the breeding experience data and the basic feeding data;
the feeding control module controls the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, verifying the actual feed intake according to the breeding experience data, and displaying the flow through an intelligent terminal.
Preferably, the central control module is respectively in communication and/or electrical connection with the data acquisition module, the feeding control module and the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone or a computer;
the feeding control module is used for accurately controlling the feeding content based on the control signal of the central control module.
Preferably, the central control module analyzes the image data to extract sign data of the sow and estimates the feeding content based on the sign data, and the central control module comprises:
extracting physical sign data of the sow by combining the image data after image preprocessing; wherein the physical sign data comprises backfat, age, body type and nursing stage;
integrating and splicing the sign data to generate estimation input data, and inputting the estimation input data into a food release estimation model to obtain estimated food release contents; wherein, the food release estimation model is constructed based on an artificial intelligence model.
Preferably, the food release estimation model is constructed based on an artificial intelligence model, and the method comprises the following steps:
reasonably planning sow feeding data according to historical experience, integrating sow physical sign data in the sow feeding data into model input data, and integrating feeding contents corresponding to the model input data into model output data;
training a constructed artificial intelligence model through model input data and model output data, and marking the trained artificial intelligence model as a food release estimation model; the artificial intelligence model comprises a BP neural network model or an RBF neural network model.
Preferably, the central control module verifies feeding contents according to breeding experience data and basic feeding data, and the central control module comprises:
collecting and organizing culture experience data; wherein the breeding experience data are basic feeding data, food release contents and actual food intake which are summarized in the breeding process and correspond to abnormal sows;
positioning the food release contents in the culture experience data according to the collected basic feeding data, and comparing the food release contents with the estimated food release contents to obtain the food release similarity; when the food release similarity is not greater than the set threshold, determining that the food release content verification is passed; otherwise, the food release similarity is used as the food release alarm index.
Preferably, the central control module verifies the actual feed intake according to the breeding experience data, and the method comprises the following steps:
positioning the actual feed intake in the breeding experience data according to the collected basic feeding data and the food release content, and comparing the actual feed intake with the collected actual feed intake to obtain the feed intake similarity;
respectively marking the ingestion similarity and the corresponding food release similarity as CXD and FXD; calculating a feeding early warning index CYZ by a formula CYZ = alpha x CXD x FXD; wherein alpha is a proportionality coefficient set according to experience;
when the ingestion early warning index is not larger than the ingestion early warning threshold, judging that the actual ingestion amount is normal; otherwise, judging that the actual feed intake is abnormal, and extracting the corresponding abnormal sow for early warning.
The invention provides a sow intelligent feeding method based on a machine vision analysis technology, which comprises the following steps:
basic feeding data are collected through a data sensor connected with a data collection module and forwarded to a central control module; wherein the basic feeding data comprises image data and environment data;
the central control module analyzes the image data to extract sign data of the sow and estimates the feeding content based on the sign data; extracting culture experience data, and verifying the food release content according to the culture experience data and the basic feeding data;
the feeding control module controls the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, verifying the actual feed intake according to the breeding experience data, and displaying the flow through an intelligent terminal.
The invention provides a sow intelligent feeding device based on a machine vision analysis technology, which comprises a storage medium and a processor; the storage medium is used for storing operation instructions, and the processor executes the operation instructions to realize the work flow of the sow intelligent feeding system based on the machine vision analysis technology.
Compared with the prior art, the invention has the beneficial effects that: according to the method, physical sign data are acquired through image data, and the food release content is estimated by combining a food release estimation model; before food release, verifying food release contents by combining breeding experience data and basic feeding data, and performing food release only after the verification of the food release contents is passed; based on historical experience, the probability of sow abnormality caused by combination of the feeding content and the feeding environment can be rapidly analyzed, and farmers can make intervention measures in time; in addition, the method collects the actual feed intake of the sow, compares the feeding content and the actual feed intake with the breeding experience data, and can timely position the abnormality of the sow.
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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, 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 the drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, in a first aspect of the present invention, an intelligent feeding system for sows based on machine vision analysis technology is provided, including a central control module, and a data acquisition module, a feeding control module and an intelligent terminal connected thereto; the intelligent terminal is used for monitoring the feeding process and receiving early warning information; basic feeding data are collected through a data sensor connected with a data collection module and forwarded to a central control module; wherein the basic feeding data comprises image data and environmental data; the central control module analyzes the image data to extract the physical sign data of the sow and estimates the feeding content based on the physical sign data; extracting culture experience data, and verifying the food release content according to the culture experience data and the basic feeding data; the feeding control module controls the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, verifying the actual feed intake according to the breeding experience data, and displaying the flow through an intelligent terminal.
The current field of breeding has begun to realize intelligent pig raising, and different feeds are fed in different stages according to historical breeding experience to ensure that physical sign parameters of pigs are within a reasonable range. However, in the prior art, only the physical sign parameters are used as references, that is, different feeding contents are set according to different physical sign parameters, the influence of the feeding environment on the feeding contents is not considered, and whether the obtained feeding contents and the feeding environment are combined to cause the abnormality of the sow or not cannot be judged, so that a farmer cannot timely make intervention measures.
According to the method, physical sign data are acquired through image data, and the food release content is estimated by combining a food release estimation model; before food release, verifying food release contents by combining breeding experience data and basic feeding data, and performing food release only after the verification of the food release contents is passed; based on historical experience, the probability of sow abnormality caused by combination of the feeding content and the feeding environment can be rapidly analyzed, and farmers can make intervention measures in time; in addition, the method collects the actual feed intake of the sow, compares the feeding content and the actual feed intake with the breeding experience data, and can timely position the abnormality of the sow.
The central control module is respectively in communication and/or electrical connection with the data acquisition module, the feeding control module and the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone or a computer; the data acquisition module is in communication and/or electrical connection with the data sensors of a plurality of types, and the feeding control module accurately controls the feeding content based on the control signal of the central control module.
The central control module is mainly used for processing and judging data and controlling the feeding control module; the intelligent terminal performs data interaction with the central control module, is mainly used for monitoring the whole feeding process and receiving related early warning information; the data acquisition module is mainly used for acquiring image data through a camera and acquiring environmental data through a temperature sensor, a humidity sensor and the like; the feeding control module controls feeding equipment, such as feed feeding, drinking water feeding and the like, according to the central control module. It should be noted that the central control module can be controlled in real time or in a timing manner.
The central control module analyzes the image data to extract the physical sign data of the sow, estimates the feeding content based on the physical sign data, and comprises the following steps: extracting physical sign data of the sow by combining the image data after image preprocessing; and integrating and splicing the sign data to generate estimation input data, and inputting the estimation input data into the food release estimation model to obtain estimated food release contents.
The physical sign data comprise data which affect the feeding content, such as backfat, age (which can be input through an intelligent terminal), body type and feeding stage (which can be input through the intelligent terminal), the physical sign data need to be digitalized, so that the non-digital quantity similar to the feeding stage needs to be reasonably converted, and if different feeding stages are represented by different numbers, the model input data in the process of training the feeding estimation model are processed in the same way. And (3) splicing the feeding stage, the age, the body type (size, weight), the backfat and the like in sequence to generate estimation input data, and combining the feeding estimation model to obtain the corresponding feeding content. Where the contents of the feed include the type of feed and the amount of feed, the same non-numeric amount will require a reasonable conversion.
In an alternative embodiment, the food release estimation model is constructed based on an artificial intelligence model, and comprises the following steps: reasonably planning sow feeding data according to historical experience, integrating sow physical sign data in the sow feeding data into model input data, and integrating feeding contents corresponding to the model input data into model output data; and training the constructed artificial intelligence model through model input data and model output data, and marking the trained artificial intelligence model as a food release estimation model.
Professional and reasonable historical experiences are screened out from the industry, sow feeding data are extracted, the sow feeding data also comprise physical sign data and corresponding feeding contents, and the collected contents correspond to the extracted contents. The artificial intelligence model is trained through a large amount of sow feeding data, the precision of the artificial intelligence model after continuous learning meets the requirement, and the trained artificial intelligence model is marked as a feeding estimation model. At the moment, the physical sign data acquired by combining the image data is input into the feeding estimation model, and the correspondingly estimated feeding content can be acquired.
The central control module verifies the food release content according to the breeding experience data and the basic feeding data, and the method comprises the following steps: collecting and organizing culture experience data; positioning the food release contents in the culture experience data according to the collected basic feeding data, and comparing the food release contents with the estimated food release contents to obtain the food release similarity; when the food release similarity is not greater than the set threshold, determining that the food release content verification is passed; otherwise, the food release similarity is used as the food release alarm index.
The breeding experience data mentioned in the invention are basic feeding data, food release contents and actual food consumption corresponding to abnormal sows summarized in the breeding process, namely the basic feeding data, food release contents and actual food consumption counted when the abnormal sows appear, and each piece of breeding experience data has great reference significance.
According to basic feeding data acquired by the data acquisition module, searching in the breeding experience data in sequence according to the physical sign data and the environment data, and if matching is successful in the breeding experience data, extracting corresponding food releasing contents; and comparing the extracted feeding contents with the estimated feeding contents, calculating the feeding similarity, and when the similarity is greater than a set threshold, indicating that the estimated feeding contents under the basic feeding data may cause abnormality of the sow. The feeding similarity is mainly calculated according to whether the feed types are consistent or not and the difference of the feed amount, namely the feeding similarity is definitely lower when the feed types are inconsistent (the setting can be 0).
In an alternative embodiment, the calculation of the feeding similarity comprises:
marking the feed label as SB, and marking the feed amount difference as SC; acquiring the appetizing similarity through a formula FSD = beta multiplied by SB multiplied by SC; where β is a proportionality coefficient, and SB =1 indicates that the feed types are consistent.
The central control module verifies the actual feed intake according to the culture experience data, and the method comprises the following steps: positioning the actual food intake in the breeding experience data according to the collected basic feeding data and the food release content, and comparing the actual food intake with the collected actual food intake to obtain the food intake similarity; respectively marking the ingestion similarity and the corresponding food release similarity as CXD and FXD; calculating a feeding early warning index CYZ by a formula CYZ = alpha x CXD x FXD; when the ingestion early warning index is not larger than the ingestion early warning threshold, judging that the actual ingestion amount is normal; otherwise, judging that the actual feed intake is abnormal, and extracting the corresponding abnormal sow for early warning.
When the feeding similarity is not greater than the set threshold, it is indicated that no problem exists in the feeding content, and at the moment, whether the feeding state of the sow is problematic needs to be analyzed, so that the feeding early warning index is constructed. When the ingestion early warning index is larger than the ingestion early warning threshold value, the ingestion state is problematic under the corresponding ingestion content, and if the data is sufficient, the specific problem of the sow can be completely positioned according to the ingestion content and the ingestion early warning index. It should be noted that the set threshold, the ingestion early warning threshold, and the coefficients are set empirically.
Referring to fig. 2, a second embodiment of the present invention provides an intelligent sow feeding method based on machine vision analysis technology, including: collecting basic feeding data; wherein the basic feeding data comprises image data and environment data; analyzing the image data, extracting physical sign data of the sow, and estimating the feeding content based on the physical sign data; extracting breeding experience data, and verifying the food release content according to the breeding experience data and the basic feeding data; controlling the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, and verifying the actual feed intake according to the breeding experience data.
The embodiment of the third aspect of the invention provides a sow intelligent feeding device based on a machine vision analysis technology, which comprises a storage medium and a processor; the storage medium is used for storing operation instructions, and the processor executes the operation instructions to realize the work flow of the sow intelligent feeding system based on the machine vision analysis technology.
Part of data in the formula is obtained by removing dimension and taking the value to calculate, and the formula is obtained by simulating a large amount of collected data through software and is closest to a real situation; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or obtained through simulation of a large amount of data.
The working principle of the invention is as follows:
collecting basic feeding data; analyzing the image data, extracting physical sign data of the sow, and estimating the feeding content based on the physical sign data; and extracting culture experience data, and verifying the food release content according to the culture experience data and the basic feeding data.
Controlling the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, and verifying the actual feed intake according to the breeding experience data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A sow intelligent feeding system based on a machine vision analysis technology comprises a central control module, and a data acquisition module, a feeding control module and an intelligent terminal which are connected with the central control module; the intelligent terminal is used for monitoring the feeding process and receiving early warning information; the method is characterized in that:
basic feeding data are collected through a data sensor connected with a data collection module and forwarded to a central control module; wherein the basic feeding data comprises image data and environmental data;
the central control module analyzes the image data to extract the physical sign data of the sow and estimates the feeding content based on the physical sign data; extracting culture experience data, and verifying the food release content according to the culture experience data and the basic feeding data;
the feeding control module controls the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, verifying the actual feed intake according to the breeding experience data, and displaying the flow through an intelligent terminal.
2. The sow intelligent feeding system based on machine vision analysis technology as claimed in claim 1, wherein the central control module is in communication and/or electrical connection with the data acquisition module, the feeding control module and the intelligent terminal respectively; the intelligent terminal comprises an intelligent mobile phone or a computer;
the feeding control module is used for accurately controlling the feeding content based on the control signal of the central control module.
3. The intelligent sow feeding system based on machine vision analysis technology as claimed in claim 1, wherein the central control module analyzes image data to extract sign data of sows, and estimates feeding contents based on the sign data, and comprises:
extracting physical sign data of the sow by combining the image data after image preprocessing; wherein the physical sign data comprises backfat, age, body type and nursing stage;
integrating and splicing the sign data to generate estimation input data, and inputting the estimation input data into a food release estimation model to obtain estimated food release contents; wherein, the food release estimation model is constructed based on an artificial intelligence model.
4. The intelligent feeding system for sows based on machine vision analysis technology as claimed in claim 3, wherein constructing said feeding estimation model based on an artificial intelligence model comprises:
reasonably planning sow feeding data according to historical experience, integrating sow physical sign data in the sow feeding data into model input data, and integrating feeding contents corresponding to the model input data into model output data;
training a constructed artificial intelligence model through model input data and model output data, and marking the trained artificial intelligence model as a food release estimation model; the artificial intelligence model comprises a BP neural network model or an RBF neural network model.
5. The intelligent sow feeding system based on machine vision analysis technology as claimed in claim 1, wherein said central control module verifies feeding contents according to breeding experience data and basic feeding data, and comprises:
collecting and sorting culture experience data; wherein the breeding experience data are basic feeding data, food release contents and actual food intake which are summarized in the breeding process and correspond to abnormal sows;
positioning the food release contents in the culture experience data according to the collected basic feeding data, and comparing the food release contents with the estimated food release contents to obtain the food release similarity; when the food release similarity is not greater than the set threshold, determining that the verification of the food release content is passed; otherwise, the food release similarity is used as the food release alarm index.
6. The intelligent feeding system of sows based on machine vision analysis technique as claimed in claim 5, wherein said central control module verifies actual food intake based on breeding experience data, comprising:
positioning the actual feed intake in the breeding experience data according to the collected basic feeding data and the food release content, and comparing the actual feed intake with the collected actual feed intake to obtain the feed intake similarity;
respectively marking the ingestion similarity and the corresponding food release similarity as CXD and FXD; calculating a feeding early warning index CYZ by a formula CYZ = alpha x CXD x FXD; wherein alpha is a proportionality coefficient set according to experience;
when the ingestion early warning index is not larger than the ingestion early warning threshold, judging that the actual ingestion amount is normal; otherwise, judging that the actual feed intake is abnormal, and extracting the corresponding abnormal sow for early warning.
7. A sow intelligent feeding method based on a machine vision analysis technology, which is operated based on the sow intelligent feeding system based on the machine vision analysis technology of any one of claims 1 to 6, and is characterized by comprising the following steps:
basic feeding data are collected through a data sensor connected with a data collection module and forwarded to a central control module; wherein the basic feeding data comprises image data and environmental data;
the central control module analyzes the image data to extract the physical sign data of the sow and estimates the feeding content based on the physical sign data; extracting culture experience data, and verifying the food release content according to the culture experience data and the basic feeding data;
the feeding control module controls the intelligent feeding device to feed according to the verified feeding content; and then, collecting the actual feed intake of the sow, verifying the actual feed intake according to the breeding experience data, and displaying the flow through an intelligent terminal.
8. An intelligent sow feeding device based on a machine vision analysis technology is characterized by comprising a storage medium and a processor; the storage medium is used for storing operation instructions, and the processor executes the operation instructions to realize the work flow of the sow intelligent feeding system based on the machine vision analysis technology in any one of claims 1 to 6.
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| CN116649248A (en) * | 2023-06-21 | 2023-08-29 | 海阳市鼎立种鸡有限责任公司 | Intelligent feeding system and method for accurately controlling materials |
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| CN116649248A (en) * | 2023-06-21 | 2023-08-29 | 海阳市鼎立种鸡有限责任公司 | Intelligent feeding system and method for accurately controlling materials |
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