Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, \({\mathrm{CO}}_2\) level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

近年来，物联网 (IoT) 引起了工业界和学术界的巨大研究关注。物联网通过将传统技术转变为智能系统，在提高生活水平方面非常有帮助。温室生产被认为是解决随着人口增长而不断增长的全球粮食需求的最终解决方案。温室提供全年生产新鲜蔬菜的设施，与露天种植相比，生产速度提高了约 50%。然而，温室的能源消耗和人工成本占温室生产成本的50%以上。在本文中，我们提出了一种新颖的优化方案，旨在实现能源消耗和温室所需气候设置之间的权衡，即温度\({\mathrm{CO}}_2\)水平和湿度。为了对提议的系统进行性能评估，我们开发了一个温室环境的临时模拟器。对于提议的模型验证和实验分析，我们使用了在韩国济州岛收集的 15 天外部环境数据。将提议的优化方案结果与基线方案进行比较。实验结果的比较分析表明，我们提出的模型保持了所需的室内环境，以最大限度地提高作物产量，能耗比基线方案降低 26.56%。此外，与基线方案相比，所提出的模型实现了 27.76% 的成本降低。所提出方案的更好优化结果使我们有信心进一步研究其在实际环境中的有效性，以实现更高的能源效率。