The Development of an LSTM Model to Predict Time Series Missing Data of Air Temperature inside Fattening Pig Houses

Jun-gyu Kim, Sang-yeon Lee and In-bok Lee ()
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Jun-gyu Kim: Agriculture, Animal & Aquaculture Intelligence Research Center, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
Sang-yeon Lee: Agriculture, Animal & Aquaculture Intelligence Research Center, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
In-bok Lee: Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

Agriculture, 2023, vol. 13, issue 4, 1-18

Abstract: Because of the poor environment inside fattening pig houses due to high humidity, ammonia gas, and fine dust, it is hard to accumulate reliable long-term data using sensors. Therefore, it is necessary to conduct research for filling in the missing environmental data inside fattening pig houses. Thus, this research aimed to develop a model for predicting the missing data of the air temperature inside fattening pig houses using a long short-term memory (LSTM) model, which is one of the artificial neural networks (ANNs). Firstly, the internal and external environmental data of the fattening pig house were monitored to develop the LSTM models for data filling of the missing data and to validate the developed LSTM model. The LSTM model for data filling of the missing data was developed by learning the measured temperature inside the pig house. The LSTM model developed in this study was validated by comparing the air temperature data predicted by the LSTM model with the air temperature data measured in the fattening pig house. The LSTM model was accurate within a 3.5% error rate for the internal air temperature. Finally, the accuracy and applicability of the developed LSTM model were evaluated according to the order of learning data and the length of the missing data. In the future, for information and communication technologies (ICTs) and the convergence and application of smart farms, the LSTM models developed in this study may contribute to the accumulation of reliable long-term data at the fattening pig house.

Keywords: environmental monitoring; imputation; machine learning; pig house; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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