Analysis of Growth Performance in Swine Based on Machine Learning

Woongsup Lee, Younghwa Ham, Tae Won Ban, Ohyun Jo

Research output: Contribution to journalArticlepeer-review


Estimating the growth performance in pigs is important in order to achieve a high productivity of pig farming. We herein analyze and verify the machine learning based estimations for the growth performance in swine which includes the daily gain of body weight (DG), feed intake (FI), required growth period for growing/finishing phase (GP), and marketed-pigs per sow per year (MSY), based on the farm specific data and climate, i.e., temperature, humidity, initial age (IA), initial body weight (IBW), number of pigs (NU) and stocking density (SD). The growth data used in our work is collected from 55 pig farms which are located across South Korea for the period between October 2017 and September 2018. In the estimation of growth performance, four machine learning schemes are applied, which are the logistic regression, linear support vector machine (SVM), decision tree, and random forest. Through the evaluation, we confirm that the accuracy of estimation for growth performance can be improved by 28% using machine learning techniques compared to the base line performance which is obtained by the ZeroR classifier. We also find that the accuracy of estimation is heavily dependent on the pre-process of growth data.

Original languageEnglish
Article number8890821
Pages (from-to)161716-161724
Number of pages9
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2018R1D1A1B07040796, and in part by the Cooperative Research Program for Agriculture Science and Technology Development (Project title: Development of Swine Management Model With Animal-Metric for Livestock Welfare, under Project PJ0105412015) Rural Development Administration, South Korea.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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