(2022) Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network
Journal : Agriculture 12(3):318
Author : Sang-yeon Lee, In-bok Lee*, Uk-hyeon Yeo, Jun-gyu Kim, Rack-woo Kim
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
Download Link : https://doi.org/10.3390/agriculture12030318
Journal : Agriculture 12(3):318
Author : Sang-yeon Lee, In-bok Lee*, Uk-hyeon Yeo, Jun-gyu Kim, Rack-woo Kim
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
Download Link : https://doi.org/10.3390/agriculture12030318