A digital twin reference architecture for pharmaceutical cannabis production

Figures & data

Table 1. DT characteristics.

Characteristic	Definition
Timeliness	DTs communicate with its physical twins in real-time. The state of the physical object are (instantly) identified and visually orchestrated (Durão et al. 2018; Park et al. 2019; Tao et al. 2018; Verdouw et al. 2016).
Fidelity	The reliability and the high levels of security must be indubitable providing the end user enormous trust towards decision-making processes by the system (Durão et al. 2018; Tao et al. 2018; Verdouw et al. 2015).
Integration	DTs should integrate data from different convergence input sources and aspects of the physical entity in a concise format (Kritzinger et al. 2018; Park et al. 2019; Schleich et al. 2017; Tao et al. 2018).
Intelligence	DTs additionally may include models that illustrate, predict and analyse the behavior of their physical twins, creating them more intelligent and independent towards decision making (Durão et al. 2018; Glaessgen and Stargel 2012; Kritzinger et al. 2018; Park et al. 2019; Schleich et al. 2017).
Complexity	DTs can represent a wide range of physical objects, resources process and activities in different level of detail scale (El and Fieee 2002; Glaessgen and Stargel 2012; Grieves 2014; Kahlen, Flumerfelt, and Alves 2016; Verdouw et al. 2016).

Table 2. DT categories and typologies.

DT	Typology
Imaginary Digital Twin	May be considered as a proof-of-concept (predefined requirements, 3D production models, disposal specs, etc.), with the purposes of visualizing an object not yet existent. Yet, all the needed information that will later be used as an input in the materialization process of its physical twin (Kahlen, Flumerfelt, and Alves 2016; Verdouw et al. 2015) is provided, with the ability to simulate the behaviour between predefined thresholds (Flumerfelt 2017; Kahlen, Flumerfelt, and Alves 2016).
Monitoring Digital Twin	Is a digital illustration of the actual form of a real-life physical entity monitoring in real-time the aspects that are needed to be tracked. It may obtain forecasting characteristics, providing information about in present or past states of the connected real-life object (growth predictive model tuned with weather forecast model).
Predictive Digital Twin	A digital representation of the future behaviour and states of the corresponding physical system of entities using predictive algorithms, simulation, artificial intelligence, machine learning methods, mathematical models for statistical forecasting etc.
Prescriptive Digital Twin	Is considered as a sophisticated digital object that recommends preventive or corrective activities on the physical object based on optimization models. The output of monitoring and predictive twins is used as an input to consult the end user which actions need to be taken to reach the predefined requirements for the ultimate outcome. The decisions on the recommendations from the prescriptive DT are still taken by the end users or experts, remotely or on-site.
Autonomous Digital Twin	Can operate independently while controlling fully the behaviour of real-life objects without human intervention. (Porter and Heppelmann 2015; Verdouw et al. 2016). Autonomous twins also can become self-adaptive systems that are self-configurable based on explicitly modeled domain expertise (Bolender et al. 2021).
Recollective Digital Twin	Stores all the historical data of the physical object of no existence in real-life while forming a digital copy of the object. This twin is of high importance for environmental impact reduction of disposals as well as for optimization of the next smart digital representation of object production (Kahlen, Flumerfelt, and Alves 2016; Tao et al. 2019).

Figure 1. Main steps of the life cycle of pharmaceutical cannabis plants from the perspective of a producer (adapted from Chandra, Lata, and ElSohly 2017).

Figure 2. Anatomy of a cannabis plant.

Figure 3. Adopted research methodology.

Figure 4. Guideline for the application of reference architecture.

Table 3. Design requirements.

Design Requirement	Description
R1	The reference architecture must include the interaction of all the counterparts between the Physical Twin and the DT;
R2	The reference architecture must support the business design and technical implementation of DT-based production management systems applied in pharmaceutical cannabis production;
R3	The reference architecture must support the processes for the production pharmaceutical cannabis production in controllable facilities across its life cycle (design, implement, use, retire);
R4	The reference architecture must provide information about the systems used to sense, predict and control the cultivation of pharmaceutical cannabis;
R5	The reference architecture must represent all the greenhouse management activities plan, control and monitor of production enabled by the designed DT;
R6	The reference architecture must provide all the corresponding information about of the data integration and data management processes from all sources and aspects of the Physical Twin in concise format ensuring convergence;
R7	The reference architecture should provide information about systems and algorithms that describe, analyze, predict, prescribe and control the Physical Twin’s state and behavior.

Table 4. Views point & Definition applied in the case study.

View	Definition	Viewpoint	Reference Architecture
Context Diagram	Presents all the entities and their interactions involved in the System (Kossiakoff et al. 2011)	IDEF0	TOGAF
Information Model	Defines the structure (e.g. relations, attributes, services) of all the information for Virtual Entities on a conceptual level) (Kramp, van Kranenburg, and Lange 2013)	UML	IoT-A
Functional Decomposition View	Analyses the complexity of an IoT system by categorizing it into smaller and more manageable functional parts, while understanding and demonstrating their relationship. (Kramp, van Kranenburg, and Lange 2013)	OSI	IoT-A
Deployment View	Defines a detailed technical architecture in UML notation of system concerns and elements (Köksal and Tekinerdogan 2019; Kramp, van Kranenburg, and Lange 2013; Tekinerdogan and Sözer 2012; Verdouw et al. 2019)	UML	IoT-A

Figure 5. Context diagram.

Figure 6. Information model.

Figure 7. Functional Decomposition view.

Figure 8. Deployment view.

Figure 9. Functional Decomposition view of the Proof of Concept.

Figure 10. Internal view from bee avatar of the cannabis facility with plants in the flower formation stage.

Figure 11A. Plant canopy & data visualization of connected sensors.

Figure 11B. 3D version of cannabis plants in flower formation.

Table 5. Results of the expert validation.

Evaluation Criteria	Question	Average score (1=poor, 5= excellent; n = 5)
User-friendly	Could you validate the DT in terms of user experience in scale from 1 to 5?	3,8
Complete	To what extent addresses the DT the production phases for pharmaceutical cannabis?	3,6
Practical	How recognizable is the DT?	3,6
Technical performance	Could you validate the DT’s system response time?	4,4
Inspiring	To what extent provides the DT inspiration for future innovation?	4,8

Table A1. Sensor readings visualised in Figure 11a.

Sensor Name	Reading
CO2 Sensor Live Reading	547.2
PAR Sensor Live Reading	127.5
Humidity deficit	8.14
Dewpoint	16.63
Temperature (indoors)	24.15
Absolute Humidity	13.79
Vent Position	99.0
PH Sensor	6.1
Air Pressure	1020
Global Radiation	503.54
Rain	0.0
Rain Probability	1.0
Relative Humidity	72.3
Temperature (outdoors)	19.4
Wind Direction	239.7
Wind Speed	5.4

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