jats:titleAbstract</jats:title>jats:pThis 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.</jats:p>