Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2020

Идентификатор DOI: 10.7717/peerj.8764

Ключевые слова: oil and oil products, evaporation, losses, loading and unloading operations, condensation of vapors

Аннотация: 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.

Журнал: PEERJ

Выпуск журнала: Vol. 8

Номера страниц: 8764

ISSN журнала: 21678359

Место издания: LONDON

Издатель: PEERJ INC

• Bakoev Siro (LK Ernst Fed Sci Ctr Anim Husb, Moscow, Russia)
• Getmantseva Lyubov (LK Ernst Fed Sci Ctr Anim Husb, Moscow, Russia)
• Kolosova Maria (Don State Agr Univ, Persianovsky, Rostov Region, Russia)
• Kostyunina Olga (LK Ernst Fed Sci Ctr Anim Husb, Moscow, Russia)
• Chartier Duane R. (ICAI, Culver City, CA USA)
• Tatarinova Tatiana V. (Univ La Verne, Dept Biol, La Verne, CA USA; Russian Acad Sci, Inst Informat Transmiss Problems, Moscow, Russia; Vavilov Inst Gen Genet, Moscow, Russia; Siberian Fed Univ, Krasnoyarsk, Russia)

• Web of Science Core Collection
• Scopus
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