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Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network. | LitMetric

AI Article Synopsis

  • - The study aimed to create a model for estimating the carcass weight of Hanwoo cattle based on body measurements using three different methods: multiple regression, partial least square regression, and neural networks.
  • - Researchers analyzed data from 134 Hanwoo cattle, extracting relevant variables to predict carcass weight, with 100 data points for model development and 34 for testing.
  • - The models achieved high accuracy in carcass weight estimation—R² values around 0.93 with all 19 variables—while the neural network was the most accurate, demonstrating that proper model selection is crucial for reliable estimations.

Article Abstract

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.

Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.

Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.

Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463082PMC
http://dx.doi.org/10.5713/ajas.19.0748DOI Listing

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