Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Predicting the Greenhouse Air Humidity Using Artificial Neural Network Model Based on Principal Components Analysis

PCA에 기반을 둔 인공신경회로망을 이용한 온실의 습도 예측

  • Owolabi, Abdulhameed B. (Department of Agricultural Engineering, Institute of Agricultural Science & Technology, Kyungpook National University) ;
  • Lee, Jong W (Institute of Agricultural Science & Technology, Kyungpook National University) ;
  • Jayasekara, Shanika N. (Department of Agricultural Engineering, Institute of Agricultural Science & Technology, Kyungpook National University) ;
  • Lee, Hyun W. (Department of Agricultural Engineering, Institute of Agricultural Science & Technology, Kyungpook National University)
  • Received : 2017.07.26
  • Accepted : 2017.09.12
  • Published : 2017.09.30
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Abstract

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

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References

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