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• W4387072955 abstract "Timely monitoring and precise estimation of body composition parameters, such as fat mass (FM) and fat-free mass (FFM), are crucial for pig production. Therefore, this study aimed to utilize three machine learning models, namely multiple linear regression (MLR), random forest regression (RFR), and support vector regression (SVR), to predict FM and FFM in growing-finishing pigs using four input combinations of three variables, i.e., mass of pigs, feed intake, and surface temperature of pigs. An ultrasound-based back-fat depth measurement approach was used to determine FM and FFM, and these measurements were compared with reference measurements obtained from slaughtered pigs. Data from two experimental periods in 2021 and 2022 were used for training and testing these models. Performance metrics, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the models' performance and stability. The results showed that the SVR model had the highest accuracy in predicting FM and FFM, with the ability to explain the relationship between input and target variables up to 94.4% in FM and 94.6% in FFM prediction. Additionally, the SVR model consistently outperformed the RFR and MLR models in predicting FM, with an increase in R2 of up to 6.72% and 27.96%, respectively, and a reduction in RMSE of up to 24.06% and 36.82%, respectively, across different input combinations. Similar results were obtained in FFM prediction, where the SVR model showed an increase in R2 of up to 6.47% and 22.45%, and a reduction in RMSE of up to 23.96% and 36.57% compared to RFR and MLR models, respectively. Moreover, the SVR model demonstrated the highest stability, with only 2.9% to 3.3% decrease in R2 during the testing phase compared to the training phase, while the RFR model exhibited the worst stability. Findings of the present study suggested that the SVR model was the most stable and reliable, along with the ultrasound-based back-fat depth approach for measuring FM and FFM in growing-finishing pigs. This approach could aid in monitoring meat quality and providing a rapid overview of body composition for pig farmers." @default.
• W4387072955 created "2023-09-27" @default.
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• W4387072955 date "2023-10-01" @default.
• W4387072955 modified "2023-10-07" @default.
• W4387072955 title "Prediction of body composition in growing-finishing pigs using ultrasound based back-fat depth approach and machine learning algorithms" @default.
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• W4387072955 doi "https://doi.org/10.1016/j.compag.2023.108269" @default.
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