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2019 Vol.28, Issue 2 Preview Page
Prediction of Air Temperature and Relative Humidity in Greenhouse via a Multilayer Perceptron Using Environmental Factors

환경요인을 이용한 다층 퍼셉트론 기반 온실 내 기온 및 상대습도 예측

Hayoung Choi1
,
Taewon Moon1
,
Dae Ho Jung1
,
Jung Eek Son1*
최 하영1
,
문 태원1
,
정 대호1
,
손 정익1*
1Department of Plant Science and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
1서울대학교 식물생산과학부
*Corresponding author: sjeenv@snu.ac.kr
30 April 2019. pp. 95-103
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Abstract

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse (1,032 m2) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature (R2 = 0.988) and with four hidden layers and 64 nodes for relative humidity (R2 = 0.990). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

Keywords
artificial neural network
deep learning
machine learning
mango
model-free prediction

온도와 상대습도는 작물 재배에 있어서 중요한 요소로 써, 수량과 품질의 증대를 위해서는 적절히 제어 되어야 한다. 그리고 정확한 환경 제어를 위해서는 환경이 어떻 게 변화할지 예측할 필요가 있다. 본 연구의 목적은 현 시점의 환경 데이터를 이용한 다층 퍼셉트론(multilayer perceptrons, MLP)을 기반으로 미래 시점의 기온 및 상 대습도를 예측하는 것이다. MLP 학습에 필요한 데이터 는 어윈 망고(Mangifera indica cv. Irwin)을 재배하는 8 연동 온실(1,032m2)에서 2016년 10월 1일부터 2018년 2 월 28일까지 10분 간격으로 수집되었다. MLP는 온실 내부 환경 데이터, 온실 외 기상 데이터, 온실 내 장치 의 설정 및 작동 값을 사용하여 10~120분 후 기온 및 상대습도를 예측하기 위한 학습을 진행하였다. 사계절이 뚜렷한 우리나라의 계절에 따른 예측 정확도를 분석하기 위해서 테스트 데이터로 계절별로 3일간의 데이터를 사 용했다. MLP는 기온의 경우 은닉층이 4개, 노드 수가 128개일 때(R2 = 0.988), 상대습도는 은닉층 4개, 노드 수 64개에서 가장 높은 정확도를 보였다(R2 = 0.990). MLP 특성상 예측 시점이 멀어질수록 정확도는 감소하 였지만, 계절에 따른 환경 변화에 무관하게 기온과 상대 습도를 적절히 예측하였다. 그러나 온실 내 환경 제어 요 소 중 분무 관수처럼 특이적인 데이터의 경우, 학습 데이 터 수가 적기 때문에 예측 정확도가 낮았다. 본 연구에서 는 MLP의 최적화를 통해서 기온 및 상대습도를 적절히 예측하였지만 실험에 사용된 온실에만 국한되었다. 따라 서 보다 일반화를 위해서 다양한 장소의 온실 데이터 이 용과 이에 따른 신경망 구조의 변형이 필요하다.

키워드
기계학습
딥러닝
비모델 예측
인공신경망
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Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Protected Horticulture and Plant Factory
  • Journal Title(Ko) :시설원예ㆍ식물공장
  • Volume : 28
  • No :2
  • Pages :95-103
  • Received Date : 2019-01-11
  • Revised Date : 2019-02-22
  • Accepted Date : 2019-02-25
  • DOI :https://doi.org/10.12791/KSBEC.2019.28.2.95
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