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

Author

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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
    Research Institute of Green Eco Engineering, Institute of Green Bio Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea)

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.

Suggested Citation

  • Jun-gyu Kim & Sang-yeon Lee & In-bok Lee, 2023. "The Development of an LSTM Model to Predict Time Series Missing Data of Air Temperature inside Fattening Pig Houses," Agriculture, MDPI, vol. 13(4), pages 1-18, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:795-:d:1111851
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    References listed on IDEAS

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    1. Benno Staub & Andreas Hasler & Jeannette Noetzli & Reynald Delaloye, 2017. "Gap‐Filling Algorithm for Ground Surface Temperature Data Measured in Permafrost and Periglacial Environments," Permafrost and Periglacial Processes, John Wiley & Sons, vol. 28(1), pages 275-285, January.
    2. Sang-yeon Lee & In-bok Lee & Uk-hyeon Yeo & Jun-gyu Kim & Rack-woo Kim, 2022. "Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network," Agriculture, MDPI, vol. 12(3), pages 1-19, February.
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    Cited by:

    1. Guanying Cui & Lulu Qiao & Yuhua Li & Zhilong Chen & Zhenyu Liang & Chengrui Xin & Maohua Xiao & Xiuguo Zou, 2023. "Division of Cow Production Groups Based on SOLOv2 and Improved CNN-LSTM," Agriculture, MDPI, vol. 13(8), pages 1-21, August.

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