In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning

Chen, Jin-Tian; He, Pengguang; Jiang, Jinsong; Yang, Yefeng; Wang, Shou-Yi; Pan, Chenghao; Zeng, Li; He, Ye-Fan; Chen, Zhonghao; Lin, Hongjian; Pan, Jinming

doi:10.1016/j.psj.2022.102239

Cited by 4 publications

(13 citation statements)

References 38 publications

(50 reference statements)

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“…The results of correlation analysis on each broiler trait ( Figure 1 ) showed that the breast muscle weight was strongly correlated with BMT, chest width, FBL, and LW traits. The strongest correlation was 0.819, which supported the rationality of using these independent variables to predict the breast muscle weight and was consistent with existing reports ( Swatland, 1989 ; Chen et al, 2023 ). Among the independent variables, the strong correlation between the FBL and LW may be detrimental to the MLR analysis.…”

Section: Resultssupporting

confidence: 90%

“…This result suggests that all 4 features we chose in the study are essential for predicting breast weight, which is consistent with the above correlation analysis and in line with previous studies ( Kleczek et al, 2006 ; Jin-lan et al, 2021 ). Sixteen in vivo measured features were used in Chen's study to predict the weight of breast muscle, and they achieved an even better effect than that in this study ( Chen et al, 2023 ). However, excessive measurements can significantly increase the labor and time costs of workers and lead to a less feasible solution for practical production.…”

Section: Discussionmentioning

confidence: 73%

“…Live weight ( LW ), one of the best manually measurable live traits, has been found to strongly correlate with the weight of the carcass, legs, wings, and breast muscle in poultry ( Laudadio et al, 2009 ). In one study, other live traits, such as shank length, body slope length, shank girth, and neck length, all showed correlations with breast weight and were used to establish a prediction model ( Chen et al, 2023 ). Nevertheless, selecting too many independent variables would result in an increasing expenditure of time and personnel during measurement, defeating the original purpose.…”

Section: Introductionmentioning

confidence: 99%

“…SVM is one of the most popular machine learning algorithms and has shown satisfactory results in numerous studies ( Fan et al, 2022 ). In a recent study on the prediction of breast muscle weight, SVM achieved better results than an artificial neural network ( ANN ) ( Chen et al, 2023 ). Random forest ( RF ), where the output of the model is represented by the voting result of many decision trees, is another typical machine learning approach.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Several models combined with ultrasound techniques to predict breast muscle weight in broilers

Zheng

et al. 2023

Poultry Science

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Section: Resultssupporting

confidence: 90%

Section: Discussionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Several models combined with ultrasound techniques to predict breast muscle weight in broilers

Zheng

et al. 2023

Poultry Science

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“…Therefore, breeding broiler chickens with a high yield of breast muscle holds significant economic value, which requires accurate measurement of the breast muscle weight [4][5][6]. The conventional approach to broiler breeding infers the carcass performance of an individual to be cultivated by using the slaughter value from individuals within the same family [7,8]. However, such methods may consume a large amount of time, compromise animal welfare, and incur high economic costs [9].…”

Section: Introductionmentioning

confidence: 99%

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

Zhu,

Li,

Yang

et al. 2024

Animals

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Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient method for predicting the breast muscle weight of broilers. First, because existing deep learning models struggle to strike a balance between accuracy and memory consumption, this study designed a multistage attention enhancement fusion segmentation network (MAEFNet) to automatically acquire pectoral muscle mask images from X-ray images. MAEFNet employs the pruned MobileNetV3 as the encoder to efficiently capture features and adopts a novel decoder to enhance and fuse the effective features at various stages. Next, the selected shape features were automatically extracted from the mask images. Finally, these features, including live weight, were input to the SVR (Support Vector Regression) model to predict breast muscle weight. MAEFNet achieved the highest intersection over union (96.35%) with the lowest parameter count (1.51 M) compared to the other segmentation models. The SVR model performed best (R2 = 0.8810) compared to the other prediction models in the five-fold cross-validation. The research findings can be applied to broiler production and breeding, reducing measurement costs, and enhancing breeding efficiency.

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An initial investigation into the use of machine learning methods for prediction of carcass component yields in F2 broiler chickens

Bani Saadat,

Vaez Torshizi,

Manafiazar

et al. 2024

Anim. Prod. Sci.

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Context As evaluation of carcass components is costly and time consuming, models for prediction of broiler carcass components are useful. Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period. Methods Three machine learning methods, including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction. In the first scenario, carcass yields were predicted by live bodyweight, shank length and shank diameter features, recorded at 2, 3 and 4 weeks of age. In the second scenario, predictor features recorded at 5, 6 and 7 weeks of age were used. The two scenarios were reanalysed by including effective single-nucleotide polymorphisms associated with bodyweight, shank length and shank diameter as new predictor features. Key results The correlation coefficient between predicted and observed values for predicting weight of carcass traits ranged from 0.50 for wing to 0.59 for thigh in the first scenario, and from 0.63 for wing to 0.74 for carcass in the second scenario. These predictions for the percentage of carcass components ranged from 0.30 for wing to 0.39 for carcass and breast in the first scenario, and from 0.34 for thigh to 0.43 for carcass in the second scenario when random forest was used. Conclusions Predictive accuracy in the first scenario was lower than in the second scenario for all prediction methods. Including single-nucleotide polymorphisms as predictor features in either scenario did not increase the accuracy of the prediction. Implications In general, random forest had the best performance among machine learning methods, and classical linear regression in two scenarios, suggesting that it may be considered as an alternative to conventional linear models for prediction of carcass traits in broiler chickens.

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In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning

Cited by 4 publications

References 38 publications

Several models combined with ultrasound techniques to predict breast muscle weight in broilers

Several models combined with ultrasound techniques to predict breast muscle weight in broilers

In Vivo Prediction of Breast Muscle Weight in Broiler Chickens Using X-ray Images Based on Deep Learning and Machine Learning

An initial investigation into the use of machine learning methods for prediction of carcass component yields in F2 broiler chickens

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