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Transpiration Prediction of Sweet Peppers Hydroponically-grown in Soilless Culture via Artificial Neural Network Using Environmental Factors in Greenhouse

온실의 환경요인을 이용한 인공신경망 기반 수경 재배 파프리카의 증산량 추정

Du Sung Nam1
,
Joon Woo Lee1
,
Tae Won Moon1
,
Jung Eek Son1*
남 두성1
,
이 준우1
,
문 태원1
,
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 November 2017. pp. 411-417
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Abstract

Environmental and growth factors such as light intensity, vapor pressure deficit, and leaf area index are important variables that can change the transpiration rate of plants. The objective of this study was to compare the transpiration rates estimated by modified Penman-Monteith model and artificial neural network. The transpiration rate of paprika (Capsicum annuum L. cv. Fiesta) was obtained by using the change in substrate weight measured by load cells. Radiation, temperature, relative humidity, and substrate weight were collected every min for 2 months. Since the transpiration rate cannot be accurately estimated with linear equations, a modified Penman-Monteith equation using compensated radiation (Shin et al., 2014) was used. On the other hand, ANN was applied to estimating the transpiration rate. For this purpose, an ANN composed of an input layer using radiation, temperature, relative humidity, leaf area index, and time as input factors and five hidden layers was constructed. The number of perceptons in each hidden layer was 512, which showed the highest accuracy. As a result of validation, R2 values of the modified model and ANN were 0.82 and 0.94, respectively. Therefore, it is concluded that the ANN can estimate the transpiration rate more accurately than the modified model and can be applied to the efficient irrigation strategy in soilless cultures.

Keywords
black box modelling
deep learning
paprika
Penman-Monteith equation

광도, 포차와 같은 환경요인과 엽면적 지수와 같은 생 육요인은 증산 속도를 변화시키는 중요한 변수이다. 본 연구에서는 Penman-Monteith의 증산 모델과 인공신경망 (ANN)에 학습에 의한 증산속도 추정값을 비교하는 것을 목표로 하였다. 파프리카(Capsicum annuum L. cv. Fiesta)의 증산속도 추정은 로드셀을 이용한 배지의 중량 변화를 통해 계산하였다. 온도, 상대습도, 배지 중량 데 이터는 1분 단위로 2개월간 수집하였다. 증산량은 일차 식으로는 정확한 추정이 어렵기 때문에, 기존의 Penman-Monteith식에 보정 광도를 사용한 수정식 Shin 등(2014)을 사용하였다. 이와는 별개로 ANN을 사용하여 증산량을 추정 비교하였다. 이를 위하여 광도, 온도, 습 도, 엽면적지수, 시간을 사용한 입력층과 5개의 은닉층 으로 구성된 ANN을 구축하였다. 각 은닉층의 퍼셉트론 개수는 가장 정확성이 높은 512개로 하였다. 검증 결과, 보정된 Penman-Monteith 모델식의 R2 = 0.82이었고, ANN의 R2 = 0.94로 나타났다. 따라서 ANN은 일반적인 모델식에 비해 정확한 증산량 추정이 가능한 것으로 나 타났고, 추후 수경재배의 효율적인 관수전략 수립에 있 어 적용 가능할 것으로 판단되었다.

키워드
블랙박스 모델링
딥러닝
파프리카
Penman-Monteith 방정식
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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 : 26
  • No :4
  • Pages :411-417
  • Received Date : 2017-09-08
  • Revised Date : 2017-10-21
  • Accepted Date : 2017-10-23
  • DOI :https://doi.org/10.12791/KSBEC.2017.26.4.411
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