Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques

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.

Analyzing the efficiency of Arduino UNO microcontroller in monitoring and controlling the microclimatic parameters of greenhouse

“…Previous research revealed that, when compared to the traditional greenhouse spraying system, the Smart Microclimate Control System for Greenhouses could save 66.8% of the water and energy (electricity) used for early spraying throughout the cultivation period. This finding demonstrates how the SMCS will strengthen agricultural resilience to hydro-climate uncertainty and encourage the conservation of resources (Chen et al, 2022). Using a Supervisory Control and Data Acquisition (SCADA) system and a Programmable Logic Controller (PLC), a monitoring system was created to precisely control and predict the conditions of a greenhouse (Ding et al, 2018).…”

Artificial Intelligence Techniques in Hydrology and Water Resources Management

“…The prediction of a short-term microclimate is a challenging task due to the rapid changes and strong interconnections among meteorological variables. To address this issue, Chen et al introduced a water-centric smart microclimate control system (SMCS) that incorporates system dynamics and machine learning techniques, which can regulate the micro-environment within a greenhouse canopy to induce environmental cooling while improving resource-use efficiency [25]. The proposed SMCS demonstrates the practicality of machine-learning-enabled greenhouse automation that enhances crop productivity and resource-use efficiency, thereby contributing to the mitigation of carbon emissions and a sustainable water-energy-food nexus.…”

Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction

“…ANNs are utilized in hydrological studies, including urban flood forecasting [19,20], groundwater level prediction [21], rainfall-runoff prediction [22], water quality modeling [23,24], and microclimate prediction [25]. ANNs are becoming increasingly popular in predicting greenhouse microclimates as they can quickly and accurately analyze large amounts of data [26,27].…”