No 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.

<p>Extreme weather often causes crop losses with sharp fluctuations in agricultural prices, which imposes negative impacts on sustainable agricultural development. Greenhouse farming is regarded as an effective measure against extreme weather. Thus, it requires a better understanding of the growing complexity of agri-food systems involving greenhouse environmental and societal tradeoffs under climate variations. Considering high energy consumption of greenhouses, this study aims at adopting machine learning with IoT-big data mining to innovatively develop a smart greenhouse environmental control service model under the nexus between meteorology, water, energy, food, and greenhouse environmental control while exploring pathways to low-carbon greenhouse cultivation. The proposed model will be applied to greenhouses in Taiwan for evaluating cross-sectoral synergies and environmental benefits. The results are expected to support greenhouse owners and authorities to make the best use of resources of water, energy, and food through the optimal environmental operation on greenhouse cultivation under extreme climatic events for achieving sustainable development goals (SDGs) and move towards green economy.</p>