Abstract
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.
Highlights
Growth performance of swine is one of the most important element in pig farming because it is directly related to the revenue of the farm
The average daily growth in weight (DG) and the required growth period for growing/finishing phase (GP), which are the one of the most important growth performance metric, are related to the period to grow pigs, such that the low DG and the high GP result in the longer period of growing pig which adversely affects the revenue of the farm
The average daily feed intake (FI) and marketed-pigs per sow per year (MSY), which are important growth performance metrics in pig farming, determine the overall feed cost of the farm and the total number of pigs sold to market, such that they are directly related to the revenue of the farm
Summary
Growth performance of swine is one of the most important element in pig farming because it is directly related to the revenue of the farm. The main contributions of our work are as follows: 1) We develop a prediction strategy for four growth performance metrics of swine, DG, FI, GP, and MSY, using four machine learning techniques, namely logistic regression, linear SVM, decision tree, and random forest. For the prediction, both climate environmental factors such as ambient temperature, comfort temperature, humidity, effective temperature, and farm-specific factors, i.e., IBW, IA, NU, SD, are taken into account which are collected from actual pig farms located across South Korea.
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