Dandan Huang

Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China
Author    Correspondence author
International Journal of Horticulture, 2024, Vol. 14, No. 3   doi: 10.5376/ijh.2024.14.0022
Received: 10 Apr., 2024    Accepted: 25 Jun., 2024    Published: 03 Jul., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Huang D.D., 2024, Optimizing engineered SynComs for controlled environment agriculture (CEA): from theory to commercialization, International Journal of Horticulture, 14(3): 195-206 (doi: 10.5376/ijh.2024.14.0022)

This study synthesizes research findings on the use of Digital Twin architectures, machine learning models, genetic engineering, and automated control systems to optimize SynComs for CEA. Key findings include the effective use of Digital Twin and reinforcement learning models to improve crop management, the importance of breeding and genetic engineering in developing crops suited for controlled environments, and the deployment of advanced automation systems to enhance precision in environmental control. This study also highlights the significant improvements in energy efficiency through technological advancements in lighting and climate control. The implications of these findings for researchers, policymakers, and industry stakeholders are discussed, emphasizing the need for interdisciplinary collaboration and continued research to fully realize the potential of SynComs in CEA. This study calls for supportive policies, investment in state-of-the-art technologies, and collaborative efforts to drive innovation and sustainability in controlled environment agriculture.

Synthetic microbial communities (SynComs); Controlled environment agriculture (CEA); Genetic engineering; Automated control systems; Energy efficiency