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Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

Author

Listed:
  • Sang-yeon Lee

    (Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanakno, Gwanakgu, Seoul 08826, Korea)

  • In-bok Lee

    (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanakno, Gwanakgu, Seoul 08826, Korea
    Research Institute of Green Eco Engineering, Institute of Green Bio Science and Technology, Seoul National University, 1477 Pyeongchang-daero, Daehwa-myeon, Pyeongchang-gun 25354, Korea)

  • Uk-hyeon Yeo

    (Agriculture, Animal & Aquaculture Intelligence Research Center, Electronics and Telecommunications Research Institute, 218 Gajeongno, Yuseong-gu, Daejeon 305-700, Korea)

  • Jun-gyu Kim

    (Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanakno, Gwanakgu, Seoul 08826, Korea)

  • Rack-woo Kim

    (Department of Smart Farm Engineering, College of Industrial Sciences, Kongju National University, 54 Daehak-ro, Yesan-eup, Yesan-gun 32439, Korea)

Abstract

The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models were used to predict the internal air temperature and relative humidity of mechanically and naturally ventilated duck houses. The models were developed according to the type of duck houses, seasons, and environmental variables by learning the monitoring data of the internal and external environments. The optimal sequence length of learning data for the development of the RNN model was selected as 120 min. As a result of the validation, both air temperature and relative humidity could be accurately predicted within 1% error. In addition, simplified RNN models were additionally developed by learning only from the data of external air temperature, relative humidity, and duck weight, which are relatively easy to acquire at the farms. The accuracy of the simplified RNN models was similar to the basic model for predicting the internal air temperature and relative humidity of duck houses in real time. In the future, for the convergence of information and communications technologies (ICTs) and application of smart farms in duck houses, the RNN models of duck houses developed in this study can be applied to predict and control the internal environments of duck houses using the model predictive control (MPC) technique.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:318-:d:755684
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Hang Yin & Zeyu Wu & Junchao Wu & Junjie Jiang & Yalin Chen & Mingxuan Chen & Shixuan Luo & Lijun Gao, 2023. "A Hybrid Medium and Long-Term Relative Humidity Point and Interval Prediction Method for Intensive Poultry Farming," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    3. Qiongyi Cheng & Hui Wang & Xin Xu & Tengfei He & Zhaohui Chen, 2024. "Indoor Thermal Comfort Sector: A Review of Detection and Control Methods for Thermal Environment in Livestock Buildings," Sustainability, MDPI, vol. 16(4), pages 1-19, February.

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