In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning.
Poult Sci
; 102(1): 102239, 2023 Jan.
Article
in En
| MEDLINE
| ID: mdl-36335741
ABSTRACT
The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 73 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male R2 = 0.950; female R2 = 0.955) and abdominal fat (male R2 = 0.802; female R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Chickens
/
Neural Networks, Computer
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Animals
Language:
En
Journal:
Poult Sci
Year:
2023
Document type:
Article
Affiliation country:
China