Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

Banik, S.; Naskar, S.; Barman, Keshab; Das, Pranab Jyoti; Kumar, Sunil; Rajkhowa, S.

doi:10.18805/ijar.b-4174

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…From Fig 3, there was a positive correlation between growthrelated factors and body weights, specifically the height of pig (HP), age (AG), length (LP) and girth length (GL). Other researchers have reported similar findings, in where FI (Pierozan et al, 2016), DW (Arulmozhi et al, 2020), HP (Yang et al, 2019), AG (Birteeb et al, 2015), LP (Banik et al, 2021), GL (Banik et al, 2021) and RCO 2 (Basak et al, 2022) are highly associated with pig's body weight. In addition, a negative correlation existed between PBW and the pig's body temperature (PBT).…”

Section: Input Variables Selectionsupporting

confidence: 60%

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Basak¹,

Paudel²,

Deb³

et al. 2023

IJAR

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Background: 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. Methods: 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. Result: 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.

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Section: Input Variables Selectionsupporting

confidence: 60%

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Basak¹,

Paudel²,

Deb³

et al. 2023

IJAR

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show abstract

“…This result may be obtained due to Gompertz (1825), yt = Ae -bexp (-kt) t Brody (1945), yt = A (1 -be kt ) t Von Bertalanffy (1957), yt = A (1 -be kt ) 3 t the used of younger animals live weight data at the time of study. Similar factors were explained for fluctuation of asymptotic weight of Nelore cows for both Gompertz and von-Bertalanffy models (Forni et al, 2009;Garnero et al, 2005;Banik et al, 2020). Using the estimates of the parameters of the non-linear models, crossbreds can be selected from the maturity rate, because, crossbred with higher maturity rates was earlier than RCC and ND, those showed lower maturity rates in Brody and von Bertalanffy models.…”

Section: Model Parametersmentioning

confidence: 83%

Nonlinear Models for the Prediction of Yearly Live Weight of Cattle

Sultana

Khan

Momin

2022

AJDFR

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Background: Growth of animals is important for milk and meat production. In developing countries record keeping is difficult and usually complete recording cannot be obtained from the cattle farming. Mathematical models are used to predict values from incomplete or partially recorded data and reduces the confusion for calculating the yield. Therefore, a study was conducted to know the growth of cattle by comparing three non-linear models and predicts the mature live weight. Mehods: The live weight of cattle at 15 day intervals from 15 to 365 days was recorded and calculated the yearly live weight and weight gains of three genotypes (Red Chittagong cattle, Non-descriptive deshi and their cross) of cattle. Three nonlinear model (Brody, Gompertz and von Bertalanffy model) was fitted to weight-age data of 120 female cattle from 15 to 365 days in three locations. Result: The average live weight and weight gain of three genotype cattle ranging from 198.5 to 207.89 kg and 306 to 361 g/day, respectively. The brody model provides better goodness of fit than other models in all genotypes and Gompertz and the von Bertalanffy model showed better matched between the observed and estimated weights

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“…Several studies have reported the relationships between morphological traits and body weight in pigs. Path coefficients indicate that foreleg height, body length, and heart and belly circumference lengths are important indicators for determining preweaning live body weight in pigs (Banik et al, 2021). In addition, for pigs that exceeded 40 kg, body length, heart girth, and body height parameters were identified as the best representative traits for predicting body weight with an R 2 value of 0.92 in the developed regression model (Valugembe et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the prediction performances of artificial neural network, nearest neighbor, and CART algorithms for body weight in Sujiang pigs using morphological measurements

Ergin,

Koskan

2024

Preprint

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The objective of this study was to evaluate machine learning algorithms for predicting body weight in Sujiang pigs. Sujiang pigs originated from the Duroc and Jiangquhai blood lines to improve both the growth rate and lean percentage of native breeds. K nearest neighbor, decision tree (CART), and artificial neural network algorithms were used to predict body weight (BW) using morphological traits such as body length (BL), body height (BH), chest circumference (CC), hip width (HW), and backfat thickness (BFT). The age of the pigs (180±5) was also included as a nominal predictor. For this purpose, all morphological measurements taken from 365 Sujiang pigs in a previous study were used. In total, 219 individual measurements were obtained after data preprocessing. In the Sujiang pig dataset, BW had a significantly positive and high linear relationship with BH (0.66), BL (0.72), CW (0.81), HW (0.84), and CC (0.88) (p < 0.01). Overall, the ANN algorithm outperformed the KNN and DT algorithms in this pig dataset according to the goodness of fit criteria of R2 = 0.91 and RMSE = 3.1. Nevertheless, the KNN algorithm also demonstrated good predictions on the test dataset (R2 = 0.86 and RMSE = 3.57). In the ANN algorithm, several training algorithms were compared, such as the Levenberg‒Marquardt algorithm, Bayesian regularization, and scaled conjugate gradient. In addition, the number of neurons in the hidden layer was manipulated to 2, 3, or 4. All training algorithms yielded similar results. However, when the predictor variables were CC, HW, BL, and BH, the Levenberg–Marquardt network had a superior ability to predict body weight in Sujiang pigs, with R2 = 0.89 and RMSE = 3.05. Furthermore, when BL measurements were not included in the model, the model’s predictive ability decreased by approximately 6%. According to the results, the use of Levenberg‒Marquardt and Bayesian Regularization in the ANN algorithm could help to improve breeding strategies. The traits determined to be the best predictors of BW in Sujiang pigs via the ANN algorithm can be used as indirect selection criteria in the future. However, this study suggested that different age stages, breeds, and traits should be considered in the model to accurately predict BW.

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Nonlinear Prediction Models for Estimation of Pre-weaning Body Weight of Pigs using Morphometric Traits

Cited by 4 publications

References 6 publications

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Nonlinear Models for the Prediction of Yearly Live Weight of Cattle

Evaluating the prediction performances of artificial neural network, nearest neighbor, and CART algorithms for body weight in Sujiang pigs using morphological measurements

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