PigLeg: prediction of swine phenotype using machine learning

Abstract

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

“…However, other methods such as neural networks, support vector machines, linear and non-linear density based classifiers, decision trees, naive Bayes, wavelet analysis, k-nearest neighbor, and k-means have also being reported in the literature in terms of classification analysis ( Butterworth, 2018 ; Koltes et al, 2019 ; Nayeri et al, 2019 ). For instance, Bakoev et al (2020) , evaluated the prediction accuracy of nine machine learning classification algorithms and reported that Random Forest and K-Nearest Neighbors better predicted pig leg weakness based on measurements taken at an early stage of the animal development.…”

Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

“…A summary of the concepts, advantages and disadvantages of each algorithm is given in Table A1 in Appendix A. Further, the criteria for selecting these methods included (i) successful application in other animal science studies [16,19,20] and (ii) ability to handle multiclass categorization [24]. Three traditional (ordinal logistic, multinomial regression [31,32] and linear discriminant analysis (LDA) [33]) statistical models (white box or low-level machine learning models), two low-level black models (random forest (RF) [34] and classification and regression trees (CART) [35]) and four high-level black box models (support vector machines (SVM) [36] and k-nearest neighbors (K-NN) [37,38], neural networks (ANN), and gradient boosting decision trees (XGB) [39]) were compared.…”

“…Machine learning utilizes algorithms whose logic can be learned directly from unique patterns in the data or inexplicitly through pre-programmed classical statistical methods [17]. The successful use of ML algorithms in various fields of science warrants their application in animal production problem solving [18,19]. Ideally, it should be possible to install this computer-acquired intelligence into modern weighing systems to automatically explore patterns in lifetime liveweights and predict BCS.…”

Genomic Regions and Candidate Genes Linked to Capped Hock in Pig

“…Limb diseases and specifically limb weakness (osteochondrosis) in pigs can lead to large economic losses due to a decrease in productivity [1][2][3][4][5]. Moreover, one of the serious problems facing pig farmers is the spread of bursitis of the hock and capped hock [6][7][8].…”