Lee Dae-Hyun, Lee Seung-Hyun, Cho Byoung-Kwan, Wakholi Collins, Seo Young-Wook, Cho Soo-Hyun, Kang Tae-Hwan, Lee Wang-Hee
Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea.
National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54875, Korea.
Asian-Australas J Anim Sci. 2020 Oct;33(10):1633-1641. doi: 10.5713/ajas.19.0748. Epub 2019 Dec 24.
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.
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.
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.
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.
本研究的目的是使用三种不同的建模方法开发一个模型，用于根据体尺测量来估计韩牛的胴体重：i）多元回归分析，ii）偏最小二乘回归分析，以及iii）神经网络。
从韩国国家动物科学研究所获得了总共134头韩牛的数据。在原始数据的372个变量中，提取了20个与胴体重和体尺测量相关的变量，用于多元回归、偏最小二乘回归和人工神经网络，以通过与胴体重显著相关的七个体尺测量中的任何一个或所有19个体尺测量变量来估计韩牛的冷胴体重。为了开发和训练模型，使用了100个数据点，而其余34个数据点用于测试模型估计。
使用七个显著变量通过多元回归、偏最小二乘回归和人工神经网络对开发的模型进行测试时，R2值分别为0.91、0.91和0.92，而使用所有19个体尺测量变量时，所有方法的R2值均约为0.93。此外，相对误差在4%以内，表明开发的模型在估计韩牛胴体重方面是可靠的。神经网络表现出最高的准确性。
开发的模型适用于使用体尺测量来估计韩牛的胴体重。由于程序和所需变量可能因模型类型而异，因此有必要选择适合用于计算模型的系统的最佳模型。
