Bakoev, S.; Getmantseva, L.; Kolosova, M.; Kostyunina, O.; Chartier, D.; Tatarinova, T.V. PigLeg: Prediction of Swine Phenotype Using Machine Learning. Preprints2019, 2019110047
APA Style
Bakoev, S., Getmantseva, L., Kolosova, M., Kostyunina, O., Chartier, D., & Tatarinova, T.V. (2019). PigLeg: Prediction of Swine Phenotype Using Machine Learning. Preprints. https://doi.org/
Chicago/Turabian Style
Bakoev, S., Duane Chartier and Tatiana V. Tatarinova. 2019 "PigLeg: Prediction of Swine Phenotype Using Machine Learning" Preprints. https://doi.org/
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 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 several 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. Muscle Thickness, Back Fat amount, and Average Daily Gain serve as significant predictors of 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.
Biology and Life Sciences, Animal Science, Veterinary Science and Zoology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
