A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs

Ruchay, Alexey; Гриценко, С. А.; Ermolova, Evgenia; Bochkarev, A.; Ermolov, S.; Guo, Hao; Pezzuolo, Andrea

doi:10.3390/ani12091152

Cited by 7 publications

(4 citation statements)

References 29 publications

(35 reference statements)

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“…In the present study, the CC trait showed the highest correlation with BW in Sujiang pigs. Another study predicted live body weight with R 2 = 0.352 in Duroc X Landrace X Yorkshire crossbred pigs using the stacking regressor algorithm (Ruchay et al, 2022). They reported that chest girth was a useful predictor of BW in pigs.…”

Section: Resultsmentioning

confidence: 99%

“…Ensemble learning strategies have been widely used in machine learning by combining multiple learning algorithms to predict or classify target values. Supervised ensemble algorithms, including Voting, Bagging, and Stacking Regressors, were applied to morphological data on Duroc, Landrace, and Yorkshire pig breeds (Ruchay et al, 2022). Moreover, the LASSO regression model was also used in pigs for comparison with traditional multiple linear regression models (Gauthier et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Data normalization is carried out to convert data into a standard form, making it easier to process and analyze cattle data. Data normalization aims to eliminate scale differences to ensure that each attribute has a balanced contribution to obtaining more accurate research results [16].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The experimental results show that the random forest regressor (RFR) method produces better error values with a mean absolute error (MAE) of 3,099 kg compared to other machines. Learning regression algorithm method [8], [16]. By using the training dataset (70%), test dataset (30%), and validation dataset (20% of the training dataset), this research uses the Stacking Regressor algorithm to produce the best performance in predicting pig weight with an MAE of 4,331 and MAPE 4,296 on the dataset testing.…”

Section: Introductionmentioning

confidence: 99%

Cattle Weight Estimation Using Linear Regression and Random Forest Regressor

Anjar Setiawan,

Ema Utami,

Dhani Ariatmanto

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The data set used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length and chest circumference. Find the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning.

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On-barn cattle facial recognition using deep transfer learning and data augmentation

Ruchay,

Kolpakov,

Guo

et al. 2024

Computers and Electronics in Agriculture

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A Comparative Study of Machine Learning Methods for Predicting Live Weight of Duroc, Landrace, and Yorkshire Pigs

Cited by 7 publications

References 29 publications

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

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

Cattle Weight Estimation Using Linear Regression and Random Forest Regressor

On-barn cattle facial recognition using deep transfer learning and data augmentation

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