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

Sang-yeon Lee, In-bok Lee, Uk-hyeon Yeo, Jun-gyu Kim and Rack-woo Kim
Additional contact information
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
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

Agriculture, 2022, vol. 12, issue 3, 1-19

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.

Keywords: duck house; environmental monitoring; prediction of internal environments; machine learning; 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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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