CN116307118A - Chicken pectoral muscle weight prediction method, system and storage medium - Google Patents
Chicken pectoral muscle weight prediction method, system and storage medium Download PDFInfo
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Abstract
The invention discloses a chicken pectoral muscle weight prediction method, a chicken pectoral muscle weight prediction system and a storage medium, wherein the method comprises the steps of obtaining a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel; training an elastic net model by utilizing a sample data set to determine a trained elastic net model; obtaining a character combination of chickens to be predicted; and determining the weight of chicken pectoral muscles according to the character combination of the chicken to be predicted and the trained elastic net model. The invention can improve the prediction accuracy of the chicken breast muscle weight.
Description
Technical Field
The invention relates to the field of chicken pectoral muscle weight prediction, in particular to a chicken pectoral muscle weight prediction method, a chicken pectoral muscle weight prediction system and a storage medium.
Background
In recent years, with the gradual rise of healthy light diet fashion, chicken breast muscle is more and more favored by consumers due to the characteristics of rich protein content and less fat. However, in the breeding of broilers, pectoral muscles are heavy to carcass traits, and can only be obtained after the carcass of the broiler is split, and individuals with accurate values can not be reserved for breeding even though the performance is excellent.
In the current white feather broiler pectoral muscle heavy character breeding work, target characters are indirectly improved through selection of apparent characters such as chest width, keel length and the like, or pectoral muscle heavy breeding values of individuals of candidate groups are ordered through an ABLUP or GBLUP method through slaughtering results of reference groups. Both the above methods can generate a large amount of errors in the process, and the slaughtering work needs to consume a large amount of manpower and material resources, thus seriously impeding the promotion of the pectoral muscle genetic progress. Therefore, an accurate and reliable method for measuring the pectoral muscle in vivo of white feather broilers, which is convenient to apply in actual breeding work, is urgently needed to be developed.
Disclosure of Invention
The invention aims to provide a chicken breast muscle weight prediction method, a chicken breast muscle weight prediction system and a storage medium, which can improve the accuracy of chicken breast muscle weight prediction.
In order to achieve the above object, the present invention provides the following solutions:
a method for predicting the weight of chicken pectoral muscles, comprising:
acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
training an elastic net model by utilizing a sample data set to determine a trained elastic net model;
obtaining a character combination of chickens to be predicted;
and determining the weight of chicken pectoral muscles according to the character combination of the chicken to be predicted and the trained elastic net model.
Optionally, the training the elastic net model by using the sample data set to perform training, and determining the trained elastic net model further includes:
and establishing an elastic network model by using the R language glmnet package.
Optionally, training the elastic net model by using the sample data set, and determining the trained elastic net model specifically includes:
the elastic net model was parameter adjusted using 10-fold cross validation.
A chicken pectoral muscle weight prediction system comprising:
the sample data set acquisition module is used for acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
the elastic net model determining module is used for training the elastic net model by utilizing the sample data set to perform training and determining a trained elastic net model;
the chicken trait combination obtaining module is used for obtaining the chicken trait combination to be predicted;
and the chicken pectoral muscle weight determining module is used for determining the chicken pectoral muscle weight according to the character combination of the chicken to be predicted and the trained elastic net model.
Optionally, the method further comprises:
and the elastic network model building module is used for building an elastic network model by utilizing the R language glmcet package.
A chicken pectoral muscle weight prediction system comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method.
A storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the chicken pectoral muscle weight prediction method, the chicken pectoral muscle weight prediction system and the storage medium provided by the invention have the advantages that a large amount of manpower and material resources required by slaughtering are saved, the cost is saved, and the character combination is selected. Through researches, the four characteristics of the breast muscle weight, the breast width, the breast muscle thickness and the keel length are the most suitable, the breast muscle weight is strongly related to the breast muscle weight, the measuring difficulty is low, the on-site implementation is convenient, the training of technicians is also convenient, the production practice is met, and the method is suitable for collecting phenotypes during large-scale broiler breeding. And selecting a prediction model for establishing the characteristics to the target characteristics by using an elastic network algorithm. The elastic net algorithm can capture the ability of linear relationships between independent variables, and thus exhibits superior performance over machine learning algorithms. The invention has the advantages of cost saving, convenient operation and high accuracy.
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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 that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for predicting chicken pectoral muscle weight.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a chicken breast muscle weight prediction method, a chicken breast muscle weight prediction system and a storage medium, which can improve the accuracy of chicken breast muscle weight prediction.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for predicting the weight of chicken pectoral muscle provided by the invention comprises the following steps:
s101, acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
the acquisition process of the character combination comprises the following steps:
the live weight is that the broiler chickens of the age of the market are taken off for 12 hours and then weighed by an electronic scale;
chest width is measured by using a tape measure to measure the distance between shoulder joints at two sides in order to keep the individual flat and fixed against the desktop;
the length of the keel is the distance from the middle point of the collarbone to the tail end of the keel measured by using a tape measure;
the pectoral muscle thickness is measured as the distance from the pectoral muscle skin to the bottom surface of the keel using a portable veterinary B-ultrasonic instrument (10 Mhz, ranging 6 cm).
And (5) after the corresponding weight of the pectoral muscle of the chicken is acquired as the property, slaughtering, stripping the pectoral muscle, weighing and recording.
S102, training an elastic net model by using a sample data set to perform training, and determining a trained elastic net model; and establishing an elastic network model by using the R language glmnet package.
The elastic net model was parameter adjusted using 10-fold cross validation.
S103, obtaining a character combination of the chicken to be predicted;
s104, determining the weight of chicken pectoral muscles according to the character combination of the chicken to be predicted and the trained elastic net model.
The following describes the advantageous effects of the present invention by means of specific examples:
example 1
In 9 generations of white feather broilers of a certain strain, 350 individuals are extracted, and the characteristics and pectoral muscle weight data are collected according to the following ratio of 4:1 into a training set and a test set, fitting an elastic net model by using living body and pectoral muscle weight data of the training set, selecting optimal parameters (alpha=0.9 and lambda= 0.1399) under ten times cross validation, and inputting living body data of the test set into the model to obtain predicted pectoral muscle weight, wherein the fitting goodness is 78.46%.
Example 2
In the 10 th generation of the population, 482 individuals are extracted for test, and the characteristics and pectoral muscle weight data are collected according to the following ratio of 4:1 into a training set and a test set, fitting an elastic net model by using living body and pectoral muscle weight data of the training set, selecting optimal parameters (alpha=0.1 and lambda= 0.7204) under ten times cross validation, and inputting living body data of the test set into the model to obtain predicted pectoral muscle weight, wherein the fitting goodness is 70.88%.
Example 3
The 11 th generation of the population, 186 individuals are extracted for test, and the characteristics and pectoral muscle weight data are collected according to the following steps of 4:1 into a training set and a test set, fitting an elastic net model by using living body and pectoral muscle weight data of the training set, selecting optimal parameters (alpha=0.2 and lambda= 0.4326) under ten times cross validation, and inputting living body data of the test set into the model to obtain predicted pectoral muscle weight, wherein the fitting goodness is 77.79%.
As another specific embodiment, the present invention also provides a weight prediction system for chicken pectoral muscle, comprising:
the sample data set acquisition module is used for acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
the elastic net model determining module is used for training the elastic net model by utilizing the sample data set to perform training and determining a trained elastic net model;
the chicken trait combination obtaining module is used for obtaining the chicken trait combination to be predicted;
and the chicken pectoral muscle weight determining module is used for determining the chicken pectoral muscle weight according to the character combination of the chicken to be predicted and the trained elastic net model.
The invention also provides a chicken pectoral muscle weight prediction system, which further comprises:
and the elastic network model building module is used for building an elastic network model by utilizing the R language glmcet package.
In order to execute the corresponding method of the above embodiment to achieve the corresponding functions and technical effects, the present invention further provides a chicken pectoral muscle weight prediction system, which includes: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method.
Based on the above description, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned computer storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (7)
1. A method for predicting chicken pectoral muscle weight, comprising:
acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
training an elastic net model by utilizing a sample data set to determine a trained elastic net model;
obtaining a character combination of chickens to be predicted;
and determining the weight of chicken pectoral muscles according to the character combination of the chicken to be predicted and the trained elastic net model.
2. The method of claim 1, wherein training the elastic mesh model with the sample data set, determining the trained elastic mesh model, further comprises:
and establishing an elastic network model by using the R language glmnet package.
3. The method for predicting chicken pectoral muscle weight according to claim 1, wherein training the elastic net model by using the sample data set to perform training, and determining the trained elastic net model specifically comprises:
the elastic net model was parameter adjusted using 10-fold cross validation.
4. A chicken breast muscle weight prediction system, comprising:
the sample data set acquisition module is used for acquiring a sample data set; the samples in the sample dataset include: the character combination of the chicken and the weight of the corresponding chicken breast muscle; the trait combination comprises: the chicken has the advantages of loose weight, chest width, thick pectoral muscle and long keel;
the elastic net model determining module is used for training the elastic net model by utilizing the sample data set to perform training and determining a trained elastic net model;
the chicken trait combination obtaining module is used for obtaining the chicken trait combination to be predicted;
and the chicken pectoral muscle weight determining module is used for determining the chicken pectoral muscle weight according to the character combination of the chicken to be predicted and the trained elastic net model.
5. The chicken breast muscle weight prediction system of claim 1, further comprising:
and the elastic network model building module is used for building an elastic network model by utilizing the R language glmcet package.
6. A chicken breast muscle weight prediction system, comprising: at least one processor, at least one memory and computer program instructions stored in the memory, which when executed by the processor, implement the method of any one of claims 1-3.
7. A storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1-3.
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| CN114627963A (en) * | 2022-05-16 | 2022-06-14 | 北京肿瘤医院(北京大学肿瘤医院) | Protein data filling method, system, computer device and readable storage medium |
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