Prediction of greenhouse temperature and humidity across growing seasons:Hybridization of process-based model and deep neural networksOA
Accurate prediction and precise management of greenhouse environments contribute to improving crop yield and quality.However,conventional process-based models exhibit inherent recalibration constraints due to their fixed parameterization schemes,particularly when confronting nonlinear dynamics in cross-seasonal greenhouse environments.This study presents a novel hybrid mechanistic model capable of dynamic parameter calibration for multi-season applications.These dynamic parameters in the mechanistic model were adaptively predicted by deep neural networks.The greenhouse model calibration results showed that the errors for indoor air temperature and relative humidity were RMSE=1.6104°C,6.9379%,MAE=1.0463°C,4.3797%,R^(2)=0.9090,0.8290,and PBIAS=−1.3807%,−0.0005%,respectively.Finally,in the absence of indoor environmental reference data,the hybrid greenhouse model with adaptive parameters was validated from September 5,2024,to November 25,2024.The validation involves two approaches:one using measured weather data as inputs,and the other utilizing a public weather forecast API.The validation results indicate robust model performance regardless of the input data source,including both measured weather data and weather forecast data.This study develops a robust tool for forecasting greenhouse microclimates throughout growing seasons,thereby enabling optimized environmental management.
Kaige Liu;Tao Ji;Ming Li;Xinting Yang;Junli Sun;Huiying Liu;Ran Liu
Agricultural College/Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps,Shihezi University,Shihezi 832003,Xinjiang,China Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability,Beijing 100097,ChinaAgricultural College/Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps,Shihezi University,Shihezi 832003,Xinjiang,ChinaInformation Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability,Beijing 100097,ChinaAgricultural College/Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps,Shihezi University,Shihezi 832003,Xinjiang,China Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability,Beijing 100097,ChinaAgricultural College/Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps,Shihezi University,Shihezi 832003,Xinjiang,ChinaAgricultural College/Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization of Xinjiang Production and Construction Corps,Shihezi University,Shihezi 832003,Xinjiang,ChinaCollege of Horticulture Science/State Key Laboratory for Development and Utilization of Forest Food Resources,Zhejiang A&F University,Hangzhou 311300,Zhejiang,China Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability,Beijing 100097,China
农业科技
Greenhouse microclimateAdaptive learningSeasonal forecastingOptimization algorithmDeep learningHybrid model
《Information Processing in Agriculture》 2026 (2)
P.238-252,15
supported by the Youth Project of the National Natural Science Foundation of China(32302453)the Key Research and Development Program of the Xinjiang Uygur Autonomous Region(2022B02032-3)the National Talent Plan Project(KZ617201).
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