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http://dx.doi.org/10.6109/jkiice.2017.21.8.1560

Prediction of Water Usage in Pig Farm based on Machine Learning  

Lee, Woongsup (Department of Information and Communication Engineering, Gyeongsang National University)
Ryu, Jongyeol (Department of Information and Communication Engineering, Gyeongsang National University)
Ban, Tae-Won (Department of Information and Communication Engineering, Gyeongsang National University)
Kim, Seong Hwan (Department of Information and Communication Engineering, Gyeongsang National University)
Choi, Heechul (Livestock Environment Division, National Institute of Animal Science)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.8, 2017 , pp. 1560-1566 More about this Journal
Abstract
Recently, accumulation of data on pig farm is enabled through the wide spread of smart pig farm equipped with Internet-of-Things based sensors, and various machine learning algorithms are applied on the data in order to improve the productivity of pig farm. Herein, multiple machine learning schemes are used to predict the water usage in pig farm which is known to be one of the most important element in pig farm management. Especially, regression algorithms, which are linear regression, regression tree and AdaBoost regression, and classification algorithms which are logistic classification, decision tree and support vector machine, are applied to derive a prediction scheme which forecast the water usage based on the temperature and humidity of pig farm. Through performance evaluation, we find that the water usage can be predicted with high accuracy. The proposed scheme can be used to detect the malfunction of water system which prevents the death of pigs and reduces the loss of pig farm.
Keywords
Machine learning; Water usage; Pig farm; Prediction;
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Times Cited By KSCI : 3  (Citation Analysis)
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