Enhancing Energy Efficiency of Greenhouses using AI-based Climate Control
February 28, 2023 | Advances in Applied Energy |
Introduction: Researchers from Cornell University in USA proposed the use of novel artificial intelligence (AI)-based control framework to enhance the energy efficiency of greenhouses while accurately regulating their indoor climate. The AI-based strategy learns from historical greenhouse climate data and adapts to current weather conditions and crop growth.
Key findings: In comparison to traditional control methods, the AI-based framework demonstrated remarkable performance. In a case study focused on controlling the greenhouse climate for tomato crops, the proposed strategy led to a substantial 57% reduction in energy consumption compared to conventional methods.
Read more: Energy-efficient AI-based Control of Semi-closed Greenhouses Leveraging Robust Optimization in Deep Reinforcement Learning
Fig. | Greenhouse simulation depicting the external disturbances, control actuators, and greenhouse states along with its components.
Viewed Articles
February 28, 2023 | Advances in Applied Energy |Â Â Introduction: Researchers from Cornell University in USA proposed the use of novel artificial intelligence (AI)-based control framework to enhance the
Read More
May 24, 2025 | Computers and Electronics in Agriculture |  Introduction: Timely and accurate monitoring of rice pests and diseases is essential for limiting production losses estimated at 10–30% of an
February 1, 2022 | Agricultural Water Management |Â Introduction: This review, conducted by researchers from the University for Development Studies and Kwame Nkrumah University of Science and Technolog
January 17, 2025 | Scientific Reports |Â Introduction: Greenhouses require stable energy supplies for maintaining optimal environmental conditions, yet their heavy reliance on grid electricity increase
May 1, 2024 | Trends in Food Science & Technology | Source |Â Introduction: Agriculture significantly contributes to greenhouse gas emissions, affecting climate change and global food security. Researc
April 15, 2026 | IJOCTA |Â Introduction: Real-time irrigation management faces a fundamental challenge: existing frameworks typically use either static optimization models or reactive threshold-based c
