1Institute of Smart Farm, Gyeongsang National University, Jinju, 52828, Korea
2Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali-3814, Bangladesh
3Department of Bio-systems Engineering, Gyeongsang National University (Institute of Smart Farm), Jinju, 52828, Korea
*Corresponding Author: Hyeon Tae Kim, Department of Biosystems Engineering, Gyeongsang National University (Institute of Smart Farm), Jinju, 52828, Korea, Email: bioani@gnu.ac.kr
Online published on 17 June, 2025.
Experiments were conducted to analyze environmental parameters and growth-related factors to identify the most influential factors in estimating pig's body weight (PBW) using artificial neural networks (ANNs) and multiple linear regression (MLR)-based models.
Four different ANNs, including Cascade Forward Back-propagation (CFBP), Layer Recurrent (LR), Feed Forward Back-propagation (FFBP) and Elman (EL) and MLR models were developed to estimate the body weight of pigs. The current research was conducted for 92 days during the two experimental periods (2021-2022) in a pig barn.
The Levenberg-Marquardt training function, gradient descent weight and bias learning function, tan-sigmoid transfer function and two hidden layers with 16 neurons in each layer were shown to be the most effective architecture of the FFBP model in predicting PBW. According to the sensitivity analysis, length of pig (LP) was the most influential factor in estimating the PBW for MLR/ANN models. However, the environmental parameters along with growth-related factors could not always be the same association with PBW. Therefore, further research on viable alternative breeds with different management conditions may be considered to evaluate MLR and ANN model's performance.
Artificial neural networks, Body weight, Model, Multiple linear regression, Pig
