|Title:||Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques||Authors:||Ting-Hsuan Chen
|Keywords:||smart microclimate-control system (SMCS);machine learning;system dynamics;water-energy-food nexus;agricultural resilience||Issue Date:||Dec-2022||Publisher:||MDPI||Journal Volume:||14||Journal Issue:||23||Start page/Pages:||3941||Source:||Water||Abstract:||
Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses built to protect against typhoons. Short-term microclimate prediction is challenging because meteorological variables are strongly interconnected and change rapidly. Therefore, this study proposes a water-centric smart microclimate-control system (SMCS) that fuses system dynamics and machine-learning techniques in consideration of the internal hydro-meteorological process to regulate the greenhouse micro-environment within the canopy for environmental cooling with improved resource-use efficiency. SMCS was assessed by in situ data collected from a tomato greenhouse in Taiwan. The results demonstrate that the proposed SMCS could save 66.8% of water and energy (electricity) used for early spraying during the entire cultivation period compared to the traditional greenhouse-spraying system based mainly on operators' experiences. The proposed SMCS suggests a practicability niche in machine-learning-enabled greenhouse automation with improved crop productivity and resource-use efficiency. This will increase agricultural resilience to hydro-climate uncertainty and promote resource preservation, which offers a pathway towards carbon-emission mitigation and a sustainable water-energy-food nexus.
|Appears in Collections:||SCI期刊|
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