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2023 Vol.32, Issue 4 Preview Page

Original Articles

Estimation of Greenhouse Tomato Transpiration through Mathematical and Deep Neural Network Models Learned from Lysimeter Data

라이시미터 데이터로 학습한 수학적 및 심층 신경망 모델을 통한 온실 토마토 증산량 추정

Meanne P. Andes1
,
Mi-young Roh2
,
Mi Young Lim3
,
Gyeong-Lee Choi3
,
Jung Su Jung4
,
Dongpil Kim3*
메안 P 안데스1
,
노 미영2
,
임 미영3
,
최 경이3
,
정 정수4
,
김 동필3*
1Engineer II, Bureau of Plant Industry-Los Baños National Crop Research Development and Production Support Center 4030, Philippines
2Senior Researcher, Protected Horticulture Research Institute, NIHHS, RDA, Haman 52054, Korea
3Researcher, Protected Horticulture Research Institute, NIHHS, RDA, Haman 52054, Korea
4Rural Extension Instructor, Gyeongsangbuk-do Provincial Agricultural Research and Extension Services, Daegu 41404, Korea
1필리핀 식물산업국 국립 작물연구개발 및 생산지원센터(BPI-LBNCRDPSC) 연구원
2국립원예특작과학원 시설원예연구소 농업연구관
3국립원예특작과학원 시설원예연구소 농업연구사
4경상북도 농업기술원 농촌지도사
*Corresponding Author
31 October 2023. pp. 384-395
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Abstract

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R2 values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R2 and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R2 values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.

Keywords
tomato transpiration model
regression model
Artificial Neural Network
LSTM
GRU

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀(MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

키워드
토마토 증산 모델
회귀분석 모델
인공신경망
LSTM
GRU
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Information
  • Publisher :The Korean Society for Bio-Environment Control
  • Publisher(Ko) :(사)한국생물환경조절학회
  • Journal Title :Journal of Bio-Environment Control
  • Journal Title(Ko) :생물환경조절학회지
  • Volume : 32
  • No :4
  • Pages :384-395
  • Received Date : 2023-10-06
  • Revised Date : 2023-10-24
  • Accepted Date : 2023-10-24
  • DOI :https://doi.org/10.12791/KSBEC.2023.32.4.384
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