CN116912887B - Broiler chicken breeding management method and system - Google Patents
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Abstract
The application belongs to the technical field of data acquisition and intelligent breeding, and provides a broiler breeding management method and system, which specifically comprises the following steps: acquiring broiler chicken data through an industrial CCD camera to obtain an original broiler chicken image, preprocessing the original broiler chicken image to form a detection image, constructing a texture pre-batch model through the detection image to obtain a pre-batch reference value, and constructing a broiler chicken breeding management database according to the pre-batch reference value. In the image data acquisition process, the recognition accuracy of tendons, films or fat in the broiler images is improved through the pre-batch reference values, pattern quantization data are formed for morphological characteristics quantization of the interested areas in the images, the accuracy of models constructed in broiler population or broiler breeding is greatly improved, better data support is effectively provided for model judgment and quantization results, and therefore the accuracy of applying the pattern data to big data models is improved.
Description
Technical Field
The application belongs to the technical field of data acquisition and intelligent breeding, and particularly relates to a broiler breeding management method and system.
Background
The broiler plays an important role in agriculture and food production, the meat is widely applied to the fields of food processing, catering industry, household consumption and the like, and in modern broiler breeding, broiler breeders utilize gene selection, preferably culture and grow fast broiler chickens with high pectoral muscle content through broiler breeding management. Fat accumulation of chicken breast is one of the key quality factors of the broiler in the broiler culturing process, the traditional culturing direction is to sample the broiler through chicken slicing and quantitatively treat the fat accumulation of the broiler, so that the quality of the broiler is formed, and the breeding management of the broiler is further optimized.
However, the method is based on images of chicken slicing treatment, and the treatment method is complex and complicated, so that resource waste is even easy to be caused by improper treatment of sampled chickens or the operation cost is increased, the prior art can generally capture images of chickens in a production line or a production line through chickens, collect images of the cleaned chickens after the viscera are completely removed, and further quantitatively treat the quality of chickens in different batches by utilizing big data. However, the image data acquisition of the broiler chickens in the production line often has insufficient accuracy of judging tendons, films or fat in the broiler chickens image, so that the evaluation result or the judgment accuracy of the model is affected, and therefore, in order to further improve the accuracy and stability of broiler chickens breeding management, optimization processing is needed to be performed on the obtained broiler chickens image.
Disclosure of Invention
The application aims to provide a broiler breeding management method and system, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the above object, according to an aspect of the present application, there is provided a broiler breeding management method comprising the steps of:
s100, acquiring data of the broiler chickens through an industrial CCD camera to obtain an original image of the broiler chickens;
s200, preprocessing an original image of the broiler chicken to form a detection image;
s300, constructing a texture pre-batch model by detecting an image to obtain a pre-batch reference value;
s400, constructing a database for broiler breeding management according to the pre-batch reference values.
Further, in step S100, the method for acquiring the original image of the broiler chicken by acquiring the data of the broiler chicken by using the industrial CCD camera is as follows: the CCD camera is an area array CCD camera or a linear array CCD camera; in a production line of broiler chicken production, image acquisition is carried out on broiler chicken with all viscera removed and cleaned, and an acquired image of the broiler chicken is used as an original image of the broiler chicken.
Further, in step S200, after preprocessing the original image of the broiler chicken, the method for forming the detection image is as follows: gray processing is carried out on the original graph of the broiler chicken, a region of interest is cut out from the original graph of the broiler chicken through an edge detection algorithm based on a Canny operator, a Sobel operator or a Laplacian operator, image corrosion is carried out on the cut-out image, and the finally obtained image is used as a detection image.
Further, in step S300, a texture pre-batch model is constructed by detecting an image, and the method for obtaining the pre-batch reference value is as follows: performing binarization processing on the detected image, wherein an algorithm adopted in the binarization processing is an OTSU method, and the obtained image is recorded as a detected binary image; the detection binary image is divided into areas, and the areas with the pixel values of 255 in the detection binary image are respectively used as the information identification areas WG.
Further, in step S300, a texture pre-batch model is constructed by detecting an image, and the method for obtaining the pre-batch reference value is as follows: defining the total quantity of pixel points in the information identification area WG as a domain measurement value HPLt, obtaining the domain measurement value HPLt in each information identification area WG to form a sequence as a first domain sequence HPLt_Ls, calculating the arithmetic average value of all elements in the first domain sequence and being called screening definition LmtLs; constructing a new sequence by each element less than or equal to the screening threshold amount in the first domain sequence, and recording the new sequence as a second domain sequence R_HPLt_ls; the arithmetic average value of all elements in the second domain sequence is recorded as a defined average value E_LmtLs; the calculation method of the pre-batch reference value is called FRV:
；
wherein exp () is an exponential function with a natural constant e as a base, ds < > is a polar function, and the result of the polar function is the difference between the maximum value and the minimum value in the call sequence.
The pre-batch reference value is obtained by combining the binary image calculation of the detection image, so that the morphological characteristics of the region of interest in the image are quantized effectively to form graphic quantized data, however, under the condition of insufficient brightness in the image acquisition environment, the phenomenon of insufficient quantization degree of the pre-batch reference value FRV calculated by the method possibly occurs, because the method reduces the sensitivity of the relation between the outline form of each information identification area and the corresponding domain measurement value, the accuracy of the ideal domain measurement value obtained by screening is lost, and no viable technology exists at present to enhance the sensitivity of the relation between the outline form of each information identification area WG and the corresponding domain measurement value, so that the influence of the outline form of the information identification area WG on the domain measurement value is eliminated.
Preferably, in step S300, a texture pre-batch model is constructed by detecting an image, and a method for obtaining a pre-batch reference value is as follows:
if at least one gray value of a pixel point exists in eight adjacent areas of a certain pixel point in the information identification area WG, defining the pixel point as a boundary area pixel of the information identification area WG; in an information identification area WG, taking any one pixel point to a border domain pixel closest to the pixel point as a near border domain pixel of the pixel point, taking the distance between the pixel point and the corresponding near border domain pixel as the border domain distance EDis of the pixel point, and acquiring EDis of all the pixel points in the information identification area WG to form a sequence as a path sequence;
taking the maximum value in the path sequence as the different-direction path EDL of the information identification area WG; the different paths of the information authentication areas WG are obtained to form a sequence which is used as a different path sequence EDL_Ls; calculating the average deviation GRAD of the information discrimination area WG, and taking n as the information discriminationThe number of the identification area is equal to the GRAD n The calculation formula of (2) is as follows:
；
wherein mean<>EDL as a function of arithmetic mean n Is the calculated different-direction diameter EDL of the nth information identification area in the detection binary diagram; calculating a screening bound domain GRMD of the detected image:
；
wherein BecEDL </SUB > is a standard deviation function of the call sequence obtained after Bezier correction, cpf represents a statistical compensation coefficient, the value range is cpf epsilon [1.05,1.15], and the default value is set to be 1.10; if the nth information identification area accords with the condition GRADn less than or equal to GRMD, defining the information identification area as a first information identification area RWG, and defining the different-direction access of the first information identification area as a first different-direction access REDL; each first differential path REDL forms a first differential path sequence redl_ls; the pre-batch reference value is denoted as FRV and is calculated as follows:
；
wherein i1 is an accumulation variable, k is the number of first information identification areas, exp () is an exponential function with a natural constant e as a base.
The beneficial effects are that: the information identification area WG is screened by utilizing the different paths with the most obvious characteristics in the information identification area WG, so that the influence of the profile form of the information identification area WG on screening is effectively eliminated; and meanwhile, the statistical compensation coefficient is introduced to perform secondary screening, so that a first information identification region with higher quantization advantage can be obtained, and the accuracy of the obtained first information identification region quantization model and the sensitivity of the associated attribute are improved. In the image data acquisition process, the recognition accuracy of tendons, films or fat in the broiler images is improved through the pre-batch reference values, pattern quantization data are formed for morphological characteristics quantization of the interested areas in the images, the accuracy of models constructed in broiler population or broiler breeding is greatly improved, better data support is effectively provided for model judgment and quantization results, and therefore the accuracy of applying the pattern data to big data models is improved.
Further, in step S400, the method for constructing the database for broiler breeding management according to the pre-batch reference value is as follows: taking all detection images obtained by broilers in the same production batch as a same batch detection image sequence, respectively obtaining the pre-batch reference values of the detection images when the collection of the same batch detection image sequence is completed or the detection images are obtained by broilers in the same production batch, and constructing a sequence by using all the obtained pre-batch reference values as a pre-batch sequence; and constructing a box-type diagram by using the pre-batch sequence, judging abnormal data through the box-type diagram to obtain a plurality of abnormal values, removing detection images corresponding to the abnormal values from the same batch of detection diagram sequences, and storing the same batch of detection diagram sequences into a database applied to broiler breeding management.
Preferably, all undefined variables in the present application, if not explicitly defined, may be thresholds set manually.
The application also provides a broiler breeding management system, which comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements steps in the broiler breeding management method when executing the computer program, the broiler breeding management system can be operated in a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud data center, and the like, and the executable system can include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to operate in units of the following systems:
the image acquisition unit is used for acquiring data of the broiler chickens through the industrial CCD camera to obtain an original image of the broiler chickens;
the image preprocessing unit is used for preprocessing the original image of the broiler chickens to form a detection image;
the model construction unit is used for constructing a texture pre-batch model through detecting the image to obtain a pre-batch reference value;
the database construction unit is used for constructing a database for broiler breeding management according to the pre-batch reference values;
the beneficial effects of the application are as follows: in the process of acquiring image data, the accuracy of recognition of tendons, films or fat in a broiler image is improved through a pre-batch reference value, pattern quantization data are formed for morphological characteristics quantization of an interested region in the image, accuracy of a model constructed in broiler population or broiler breeding is greatly improved, better data support is effectively provided for model judgment and quantization results, accuracy of the pattern data applied to a big data model is improved, interference components similar to fat pixels in the broiler image are quantized, abnormal investigation is conducted on the quantized values, redundant data or abnormal images in the acquired broiler image are efficiently and accurately screened, learning data or training data used for feature extraction of broiler fat content are further improved, and more accurate and reliable data support is provided for broiler breeding strategy adjustment and management. By means of the technology, the chicken fat content can be accurately estimated in a farm, and potential problems such as unbalanced chicken feed or genetic diseases can be rapidly found after analysis of abnormal conditions of the quantized values. More accurate data reserves can help farms take corrective action. The accurate database reserve provides more accurate information for researchers and farm operators in the field of broiler breeding, and can better optimize the breeding environment and management strategy.
Drawings
The above and other features of the present application will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present application, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a broiler breeding management method;
fig. 2 is a diagram showing a structure of a broiler breeding management system.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Referring to fig. 1, a flowchart of a broiler breeding management method according to an embodiment of the application is illustrated below in conjunction with fig. 1, the method comprising the steps of:
s100, acquiring data of the broiler chickens through an industrial CCD camera to obtain an original image of the broiler chickens;
s200, preprocessing an original image of the broiler chicken to form a detection image;
s300, constructing a texture pre-batch model by detecting an image to obtain a pre-batch reference value;
s400, constructing a database for broiler breeding management according to the pre-batch reference values.
Further, in step S100, the method for acquiring the original image of the broiler chicken by acquiring the data of the broiler chicken by using the industrial CCD camera is as follows: the CCD camera is an area array CCD camera or a linear array CCD camera; in a production line of broiler chicken production, image acquisition is carried out on broiler chicken with all viscera removed and cleaned, and an acquired image of the broiler chicken is used as an original image of the broiler chicken.
Further, in step S200, after preprocessing the original image of the broiler chicken, the method for forming the detection image is as follows: gray processing is carried out on the original graph of the broiler chicken, a region of interest is cut out from the original graph of the broiler chicken through an edge detection algorithm based on a Canny operator, a Sobel operator or a Laplacian operator, image corrosion is carried out on the cut-out image, and the finally obtained image is used as a detection image.
Further, in step S300, a texture pre-batch model is constructed by detecting an image, and the method for obtaining the pre-batch reference value is as follows: performing binarization processing on the detected image, wherein an algorithm adopted in the binarization processing is an OTSU method, and the obtained image is recorded as a detected binary image; the detection binary image is divided into areas, and the areas with the pixel values of 255 in the detection binary image are respectively used as the information identification areas WG.
Further, in step S300, a texture pre-batch model is constructed by detecting an image, and the method for obtaining the pre-batch reference value is as follows: defining the total quantity of pixel points in the information identification area WG as a domain measurement value HPLt, obtaining the domain measurement value HPLt in each information identification area WG to form a sequence as a first domain sequence HPLt_Ls, calculating the arithmetic average value of all elements in the first domain sequence and being called screening definition LmtLs; constructing a new sequence by each element less than or equal to the screening threshold amount in the first domain sequence, and recording the new sequence as a second domain sequence R_HPLt_ls; the arithmetic average value of all elements in the second domain sequence is recorded as a defined average value E_LmtLs; the calculation method of the pre-batch reference value is called FRV:
；
wherein exp () is an exponential function with a natural constant e as a base, ds < > is a polar function, and the result of the polar function is the difference between the maximum value and the minimum value in the call sequence.
Preferably, in step S300, a texture pre-batch model is constructed by detecting an image, and a method for obtaining a pre-batch reference value is as follows:
if at least one gray value of a pixel point exists in eight adjacent areas of a certain pixel point in the information identification area WG, defining the pixel point as a boundary area pixel of the information identification area WG; in an information identification area WG, taking any one pixel point to a border domain pixel closest to the pixel point as a near border domain pixel of the pixel point, taking the distance between the pixel point and the corresponding near border domain pixel as the border domain distance EDis of the pixel point, and acquiring EDis of all the pixel points in the information identification area WG to form a sequence as a path sequence;
taking the maximum value in the path sequence as the different-direction path EDL of the information identification area WG; the different paths of the information authentication areas WG are obtained to form a sequence which is used as a different path sequence EDL_Ls; calculating the average deviation GRAD of the information identification area WG, taking n as the serial number of the information identification area, and then the average deviation GRAD of the nth information identification area n The calculation formula of (2) is as follows:
；
wherein mean<>EDL as a function of arithmetic mean n Is the calculated different-direction diameter EDL of the nth information identification area in the detection binary diagram; calculating a screening bound domain GRMD of the detected image:
；
wherein BecEDL </SUB > is a standard deviation function of the call sequence obtained after Bezier correction, cpf represents a statistical compensation coefficient, the value range is cpf epsilon [1.05,1.15], and the default value is set to be 1.10;
if the nth information identification area accords with the condition GRADn less than or equal to GRMD, defining the information identification area as a first information identification area RWG, and defining the different-direction access of the first information identification area as a first different-direction access REDL; each first differential path REDL forms a first differential path sequence redl_ls; the pre-batch reference value is denoted as FRV and is calculated as follows:
；
wherein i1 is an accumulation variable, k is the number of first information identification areas, exp () is an exponential function with a natural constant e as a base.
Further, in step S400, the method for constructing the database for broiler breeding management according to the pre-batch reference value is as follows: taking all detection images obtained by broilers in the same production batch as a same batch detection image sequence, respectively obtaining the pre-batch reference values of the detection images when the collection of the same batch detection image sequence is completed or the detection images are obtained by broilers in the same production batch, and constructing a sequence by using all the obtained pre-batch reference values as a pre-batch sequence; and constructing a box-type diagram by using the pre-batch sequence, judging abnormal data through the box-type diagram to obtain a plurality of abnormal values, removing detection images corresponding to the abnormal values from the same batch of detection diagram sequences, and storing the same batch of detection diagram sequences into a database applied to broiler breeding management.
The embodiment of the application provides a broiler breeding management system, as shown in fig. 2, which is a structural diagram of the broiler breeding management system of the application, and the broiler breeding management system of the embodiment comprises: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps in one embodiment of a broiler breeding management system as described above when the computer program is executed.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
the image acquisition unit is used for acquiring data of the broiler chickens through the industrial CCD camera to obtain an original image of the broiler chickens;
the image preprocessing unit is used for preprocessing the original image of the broiler chickens to form a detection image;
the model construction unit is used for constructing a texture pre-batch model through detecting the image to obtain a pre-batch reference value;
the database construction unit is used for constructing a database for broiler breeding management according to the pre-batch reference values;
the broiler breeding management system can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The broiler breeding management system can comprise, but is not limited to, a processor and a memory. It will be appreciated by those skilled in the art that the example is merely an example of a broiler breeding management system and is not limiting of a broiler breeding management system, and may include more or fewer components than examples, or may combine certain components, or different components, e.g., the broiler breeding management system may further include input and output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor is a control center of the running system of the broiler breeding management system, and is connected with various parts of the running system of the whole broiler breeding management system by various interfaces and lines.
The memory may be used to store the computer program and/or modules, and the processor may implement various functions of the broiler breeding management system by running or executing the computer program and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (5)
1. A broiler breeding management method, characterized in that the method comprises the following steps:
s100, acquiring data of the broiler chickens through an industrial CCD camera to obtain an original image of the broiler chickens;
s200, preprocessing an original image of the broiler chicken to form a detection image;
s300, constructing a texture pre-batch model by detecting an image to obtain a pre-batch reference value;
s400, constructing a database for broiler breeding management according to the pre-batch reference values;
in step S300, a texture pre-batch model is constructed by detecting an image, and the method for obtaining the pre-batch reference value is as follows: performing binarization processing on the detected image, wherein an algorithm adopted in the binarization processing is an OTSU method, and the obtained image is recorded as a detected binary image; dividing the detection binary image into areas, and taking the areas with the pixel values of 255 in the detection binary image as information identification areas WG;
if at least one gray value of a pixel point exists in eight adjacent areas of a certain pixel point in the information identification area WG, defining the pixel point as a boundary area pixel of the information identification area WG; in an information authentication zone WG; for any pixel point, obtaining the distance between each boundary region pixel and the pixel point, and taking the boundary region pixel with the smallest distance as a near-boundary region pixel; taking the distance between a pixel point and a corresponding near-field pixel as the field distance EDis of the pixel point, and acquiring the field distances of all the pixel points in the information identification area WG to form a sequence as a path sequence;
taking the maximum value in the path sequence as the information discrimination area WGDifferent-direction diameter EDL; the different paths of the information authentication areas WG are obtained to form a sequence which is used as a different path sequence EDL_Ls; calculating the average deviation GRAD of the information identification area WG, taking n as the serial number of the information identification area, and then the average deviation GRAD of the nth information identification area n The calculation formula of (2) is as follows:
；
wherein mean<>EDL as a function of arithmetic mean n Is the calculated different-direction diameter EDL of the nth information identification area in the detection binary diagram; calculating a screening bound domain GRMD of the detected image:
；
wherein BecEDL<>For the standard deviation function of the call sequence obtained after Bessel correction, cpf represents a statistical compensation coefficient with a value range of cpf E [1.05,1.15]]Setting the default value to be 1.10; if the nth information authentication zone meets the GRAD n Defining the information identification area as a first information identification area RWG and defining the different-direction access of the first information identification area as a first different-direction access REDL if GRMD is not more than; each first differential path REDL forms a first differential path sequence redl_ls; the pre-batch reference value is denoted as FRV and is calculated as follows:
；
wherein i1 is an accumulation variable, k is the number of first information identification areas, exp () is an exponential function with a natural constant e as a base.
2. The method for managing broiler breeding according to claim 1, wherein in step S100, the method for acquiring the broiler data by using an industrial CCD camera to obtain the original image of broiler is as follows: the CCD camera is an area array CCD camera or a linear array CCD camera; in a production line of broiler chicken production, image acquisition is carried out on broiler chicken with all viscera removed and cleaned, and an acquired image of the broiler chicken is used as an original image of the broiler chicken.
3. The method for managing broiler breeding according to claim 1, wherein in step S200, after preprocessing the original image of broiler chicken, the method for forming the detection image is as follows: gray processing is carried out on the original graph of the broiler chicken, a region of interest is cut out from the original graph of the broiler chicken through an edge detection algorithm based on a Canny operator, a Sobel operator or a Laplacian operator, image corrosion is carried out on the cut-out image, and the finally obtained image is used as a detection image.
4. The method according to claim 1, wherein in step S400, the method for constructing a database for broiler breeding management from the pre-batch reference values is as follows: taking all detection images obtained by broilers in the same production batch as a same batch detection image sequence, respectively obtaining the pre-batch reference values of the detection images when the collection of the same batch detection image sequence is completed or the detection images are obtained by broilers in the same production batch, and constructing a sequence by using all the obtained pre-batch reference values as a pre-batch sequence; and constructing a box-type diagram by using the pre-batch sequence, judging abnormal data through the box-type diagram to obtain a plurality of abnormal values, removing detection images corresponding to the abnormal values from the same batch of detection diagram sequences, and storing the same batch of detection diagram sequences into a database applied to broiler breeding management.
5. A broiler breeding management system, characterized in that it comprises: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the steps of a broiler breeding management method according to any of claims 1-4 when the computer program is executed, the broiler breeding management system running on a desktop computer, a notebook computer, a palm computer or a cloud server.
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