Digital twin of an urban-integrated hydroponic farm

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2020-12-29 DOI:10.1017/dce.2020.21
M. Jans-Singh, K. Leeming, R. Choudhary, M. Girolami
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引用次数: 23

Abstract

Abstract This paper presents the development process of a digital twin of a unique hydroponic underground farm in London, Growing Underground (GU). Growing 12x more per unit area than traditional greenhouse farming in the UK, the farm also consumes 4x more energy per unit area. Key to the ongoing operational success of this farm and similar enterprises is finding ways to minimize the energy use while maximizing crop growth by maintaining optimal growing conditions. As such, it belongs to the class of Controlled Environment Agriculture, where indoor environments are carefully controlled to maximize crop growth by using artificial lighting and smart heating, ventilation, and air conditioning systems. We tracked changing environmental conditions and crop growth across 89 different variables, through a wireless sensor network and unstructured manual records, and combined all the data into a database. We show how the digital twin can provide enhanced outputs for a bespoke site like GU, by creating inferred data fields, and show the limitations of data collection in a commercial environment. For example, we find that lighting is the dominant environmental factor for temperature and thus crop growth in this farm, and that the effects of external temperature and ventilation are confounded. We combine information learned from historical data interpretation to create a bespoke temperature forecasting model (root mean squared error < 1.3°C), using a dynamic linear model with a data-centric lighting component. Finally, we present how the forecasting model can be integrated into the digital twin to provide feedback to the farmers for decision-making assistance.
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城市综合水培农场的数字孪生
摘要:本文介绍了伦敦一个独特的地下水培农场——地下种植(Growing underground, GU)的数字孪生体的开发过程。与英国传统温室农业相比,该农场每单位面积的产量高出12倍,同时每单位面积的能耗也高出4倍。该农场和类似企业持续运营成功的关键是找到最小化能源使用的方法,同时通过保持最佳生长条件最大化作物生长。因此,它属于受控环境农业,其中室内环境被仔细控制,通过使用人工照明和智能供暖,通风和空调系统来最大化作物生长。我们通过无线传感器网络和非结构化的手工记录,跟踪了89个不同变量的环境条件和作物生长变化,并将所有数据合并到一个数据库中。我们展示了数字孪生如何通过创建推断数据字段为像GU这样的定制站点提供增强的输出,并展示了商业环境中数据收集的局限性。例如,我们发现光照是影响农场温度和作物生长的主要环境因素,而外部温度和通风的影响是相互混淆的。我们将从历史数据解释中获得的信息结合起来,使用以数据为中心的照明组件的动态线性模型创建定制的温度预测模型(均方根误差< 1.3°C)。最后,我们介绍了如何将预测模型集成到数字孪生模型中,为农民提供决策帮助的反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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