Technologies Applied to Artificial Lighting in Indoor Agriculture: A Review

Luisa F. Lozano-Castellanos (), Luis Manuel Navas-Gracia (), Isabel C. Lozano-Castellanos and Adriana Correa-Guimaraes
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Luisa F. Lozano-Castellanos: TADRUS Research Group, Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, 34004 Palencia, Spain
Luis Manuel Navas-Gracia: TADRUS Research Group, Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, 34004 Palencia, Spain
Isabel C. Lozano-Castellanos: Faculty of Science and Engineering, Curtin University, Perth 6102, Australia
Adriana Correa-Guimaraes: TADRUS Research Group, Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, 34004 Palencia, Spain

Sustainability, 2025, vol. 17, issue 7, 1-22

Abstract: Artificial lighting is essential in indoor agriculture, directly influencing plant growth and productivity. Optimizing its use requires advanced technologies that improve light management and adaptation to crop needs. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, examines recent advancements in artificial lighting technologies, focusing on their applications, challenges, and future directions. A systematic search in Web of Science (WOS) and Scopus identified 70 relevant studies published between 2019 and 2024. The analysis highlights five major technology groups: (i) lighting control systems, with Light-Emitting Diodes (LEDs) as the dominant solution; (ii) Internet of Things (IoT) incorporating sensors, deep neural networks, Artificial Intelligence (AI), digital twins, and machine learning (ML) for real-time optimization, as well as communication technologies, enabling remote control and data-driven adjustments; (iii) simulation and modeling tools, refining lighting strategies to enhance plant responses and system performance; and (iv) complementary energy sources, improving lighting sustainability. IoT-driven automation has significantly improved artificial lighting efficiency, optimizing adaptation and plant-specific management. However, challenges such as system complexity, high energy demands, and scalability limitations persist. Future research should focus on refining IoT-driven adaptive lighting, improving sensor calibration for precise real-time adjustments, and developing cost-effective modular systems to enhance widespread adoption and optimize resource use.

Keywords: LED lighting; controlled environment agriculture; internet of things; light efficiency; light efficacy; artificial intelligence; sensors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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