Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network
| Autor: | Byoung-Kwan Cho, Collins Wakholi, Wang-Hee Lee, Tae-Hwan Kang, Youngwook Seo, Soo-Hyun Cho, Dae-Hyun Lee, Seung Hyun Lee |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
lcsh:Animal biochemistry Neural Network Carcass Weight 01 natural sciences Article Korean Native Animal Products Linear regression Partial least squares regression Statistics Partial Least Square Regression lcsh:QP501-801 lcsh:SF1-1100 Mathematics Artificial neural network 0402 animal and dairy science Regression analysis Statistical model 04 agricultural and veterinary sciences 040201 dairy & animal science Body Measurement Data point Hanwoo Animal Science and Zoology Multiple Regression lcsh:Animal culture 010606 plant biology & botany Food Science |
| Zdroj: | Asian-Australasian Journal of Animal Sciences Asian-Australasian Journal of Animal Sciences, Vol 33, Iss 10, Pp 1633-1641 (2020) |
| ISSN: | 1976-5517 1011-2367 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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