AIoT-Based Energy-Efficient Temperature Regulation for Closed-System Greenhouses Using Classification-Based Control

Information from the abstract

Closed-system greenhouses have gained increasing popularity due to their ability to precisely control environmental conditions, support vertical farming, and enhance crop yield in limited spaces. However, maintaining optimal temperature control under continuously changing outdoor weather conditions while improving energy efficiency remains a significant challenge. This study presents the development of an Artificial Intelligence (AI) and Internet of Things (IoT)based architecture incorporating energy management for temperature regulation in a closed-system green-house using a machine learning-based classification control decision approach. IoT sensors continuously collect environmental and electrical parameters, including indoor and outdoor temperature, humidity, voltage, current, and power consumption, and transmit the data to a cloud platform for monitoring and analysis. Machine learning classification models were trained offline in MATLAB/Simulink and employed to determine air-conditioning activation for energy-efficient greenhouse operation. Three modelsQuadratic Support Vector Machine (QSVM), Cubic Support Vector Machine (CSVM), and Ensemble Bagged Trees (BT)were trained and evaluated. Experimental results show that the BT model is suitable for real greenhouse deployment, achieving 98.46% accuracy comparable to QSVM with lower computational complexity, while its lightweight nature suits cloud platforms with limited communication resources. Furthermore, real-world implementation testing conducted over a two-day period demonstrates that the BT-based control system reduces energy consumption by approximately 20.54% compared with a conventional system without AI-based control. This approach has the potential to reduce energy consumption in closed-system greenhouses; however, it would require adjustments in data collection and model configuration to account for differing plant physiological characteristics.

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Related topics: Greenhouse Technology and Climate Control · Smart Agriculture and AI · Innovations in Aquaponics and Hydroponics Systems