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Energy-Efficient Deployment of Laser Illumination for Rotating Vertical Farms

文献类型： 外文期刊

作者： Liu, Tian 1 ; Ye, Yunxiang 1 ; Tan, Shiyi 1 ; Xue, Xianglei 1 ; Zheng, Hang 1 ; Ren, Ning 1 ; Shen, Shuai 1 ; Yu, Guohong 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310021, Peoples R China

2.Minist Agr & Rural Affairs, Co Construct Minist & Prov, Key Lab Agr Equipment Hilly & Mt Areas Southeaster, Hangzhou 310021, Peoples R China

关键词： vertical farming; artificial lighting; sensor deployment; laser diodes; controlled environment agriculture; differential evolution

期刊名称：ELECTRONICS （ 影响因子：2.6； 五年影响因子：2.6 ）

ISSN： 2079-9292

年卷期： 2025 年 14 卷 3 期

页码：

收录情况： SCI

摘要： As the global population grows, vertical farming offers a promising solution by using vertically stacked shelves in controlled environments to grow crops efficiently within urban areas. However, the shading effects of farm structures make artificial lighting a significant cost, accounting for approximately 67% of total operational expenses. This study presents a novel approach to optimizing the deployment of laser illumination in rotating vertical farms by incorporating structural design, light modeling, and photosynthesis. By theoretically analyzing the beam pattern of laser diodes and the dynamics in the coverage area of rotating farm layers, we accurately characterize the light conditions on each vertical layer. Based on these insights, we introduce a new criterion, cumulative coverage, which accounts for both light intensity and coverage area. Then, an optimization framework is formulated, and a swarm intelligence algorithm, Differential Evolution (DE) is used to solve the optimization while considering the structural and operational constraints. It is found that tilting lights and placing them slightly off-center are more effective than traditional vertically aligned and center-aligned deployment. Our results show that the proposed strategy improves light coverage by 4% compared to the intensity-only optimization approach, and by 10% compared to empirical methods. This study establishes the first theoretical framework for designing energy-efficient artificial lighting deployment strategies, providing insights into enhancing the efficiency of vertical farming systems.

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