=Paper=
{{Paper
|id=Vol-3327/paper09
|storemode=property
|title=Digital Twin Modelling for Eco-Cyber-Physical Systems: In the Case of A Smart Agriculture Living Lab
|pdfUrl=https://ceur-ws.org/Vol-3327/paper09.pdf
|volume=Vol-3327
|authors=Ginta Majore,Ivars Majors
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/MajoreM22
}}
==Digital Twin Modelling for Eco-Cyber-Physical Systems: In the Case of A Smart Agriculture Living Lab==
https://ceur-ws.org/Vol-3327/paper09.pdf
Digital Twin Modelling for Eco-Cyber-Physical
Systems: In the Case of A Smart Agriculture Living
Lab
Ginta Majore1,2,∗,† , Ivars Majors1,2,†
1
Sociotechnical Systems Engineering Institute, Vidzeme University of Applied Sciences, 10 Tērbatas street, Valmiera,
LV-4201, Latvia
2
EcoIGM SIA, 79 Jāņa Daļiņa street, Valmiera, Latvia
Abstract
Digital Twin (DT) models are becoming popular in various industries as simulation environments or
as digital representations within the virtual environment. Most of them are cyber-physical, socio-
cyber-physical or socio-technical systems. Eco-Cyber-Physical Systems (ECPS) are coming into the
arena of cyber-space with the advancement of technological solutions for environmental modelling by
the application of remote sensing technologies and intelligent solutions for interfering technologies.
Modelling DT is a complex task because it requires using information from real word phenomena that is
incorporated into digital representations. Adding verification and validation processes to this procedure
makes it more complex. This paper suggests using Enterprise Modelling (EM) as the baseline for DT
design as a representation and functional analysis of (ECPS). Living Lab (LL) methodology is applied
as a continuous improvement test bed for DT of ECPS. This also provides resilience to technological
solutions applied in the DT design, development and verification and validation processes of ECPS. This
paper presents a practical application of a proposed solution in the case of organic potato production.
The proposal is based on an analysis of the related literature and real life field experiments conducted in
Latvia.
Keywords
Enterprise Modelling, Eco-Cyber-Physical Systems, Digital Twin, Smart Agriculture, Internet of Thing
1. Introduction
Enterprise Modelling (EM) is currently spreading among a variety of industries and not only
covers manufacturing, logistics, healthcare [1], cybersecurity [2],but also agriculture. Complex
solutions for data acquisition and process automation in smart agriculture are possible using
the Internet of Things (IoT) and Artificial Intelligence (AI). A big challenge remains in the
requirements process because of the continuous changing environmental conditions and the
behaviour of real-life objects and their surrounding environments. EM as a methodological
driving force for complex analysis and digital transformation is being applied in various business,
PoEM Forum 22: Practice of Enterprise Modelling, November 22, 2022, London, UK
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open ginta.majore@va.lv (G. Majore); ivars.majors@gmail.com (I. Majors)
Orcid 0000-0002-9514-7229 (G. Majore); 0000-0002-5108-1870 (I. Majors)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
industrial and social aspects and has become more popular especially for the analysis of complex
systems with an emphasis on security and resilience aspects. But, so far, it has not often been
applied in the agricultural sector. But, the rapid changes in the climate and the environment
requires smart solutions and the deployment of intelligent automated systems in agriculture and
environmental management, and that is leading to a new kind of methodological solution for
requirements development [3]. The focus of this paper is on the expansion of EM methodologies
for the application of the development of DT in agriculture through a case study in a living lab.
Digital Twin (DT) is the next step in the evaluation process of a simulation model and virtual
and augmented representation. It is designed and evaluated through the integration of various
technologies such as the Internet of Things, artificial intelligence, machine learning, and data
science, which enable living digital simulation models to be created that reflect the changes
of the physical counterparts [4]. Due to the multiple existing concepts and solutions based
on DT across industries, a diverse and only incomplete understanding of this field exists [5].
Digital Twin, in general terms, is a real-world representation within the digital world. Many
definitions exist for this term and its contexts. Examining DT in any context, one might identify
a common understanding of Digital Twin as producing digital counterparts of physical objects
[5]. For context of this paper, we use Kritzinge’s definition of DT , which defines DT as the
digital representation of an existing or planned physical object with the data flows between an
existing physical object and a digital object being fully integrated in both directions ([5]. In
such a combination, the digital object might also act as a controlling factor for the physical
object. There might also be other objects, physical or digital, which induce changes of state in
the digital object. A change in state of the physical object directly leads to a change in state of
the digital object and vice versa [5]. For a more complex and deeper view, we would define it as
not only representation but also as an active influence or action from cyberspace back to the real
environment. This simultaneously realised link is the representation and main characteristic
that distinguishes DT from the complex simulation model.
Eco-Cyber-Physical System (ECPS) is a complex phenomenon that has arisen in the last
decade with the accelerated development of the Internet of Things (IoT) and remote sensing
(RS) technologies which are incorporated in practically all industries and processes. Previously
in the modelling field, scientists and practitioners dealt with such terms as sociotechnical [6],
cyber-physical [7], and socio-cyber-physical systems [8] modelling. All these terms are also
used within the modelling field nowadays. But,the explanation varies according to the context
and complexity of the real-life phenomena and available technologies for process management.
A socio-technical system refers both to the interrelatedness of social and technical aspects of an
organisation or the society as a whole, whereas technology, not including material things, does
include organisational structures and processes [8].
The case of a smart agriculture living lab (LL) is presented in order to prove the concept
in a real-life situation. A living lab by definition, is a physical or virtual space used to solve
societal challenges, especially for urban areas, by bringing together various stakeholders for
collaboration and collective ideation [9]. Through a review of the pertinent literature the
characteristics of living labs have been taken into account in the development of a smart
agriculture living lab: context (e.g. context research, familiar context, real-world context),
users (involving users as co-creators), activity (e.g. co-creation, technical testing, evaluation),
challenges (discovery), and innovative outcomes (e.g. large-scale solutions) [9]. LLs are an open
innovation ecosystem for society, communities, or stakeholder members used to: (a) identify
problems in their lives and; (b) propose solutions [10]. The LL integrates the research and
innovation processes within a public–private–people partnership and is a tool and platform that
can be used effectively in allowing suitable representatives to participate in the decision-making
process in various contexts in order to find solutions to practical problems in real life and to
directly participate in solving those problems [10]. In order to improve innovative solutions,
the co-creation paradigm comes in to the arena [11] which is not typical for the EM situation.
This is interesting concept and approach, where the EM model is updated on the basis of
real-life solution evaluations, is incorporated can be accessed by the stakeholders as well as the
modelling facilitators and project leaders. This paper provides validating evidence for using DT
in potato (Solanum tuberosum) production, where digital twin (DT) modelling is designed to
provide technological solutions in the growing stage and to make harvest predictions according
to climatic conditions and the specific properties of a potato variety. As a benefit, DT in crop
production may help in decision-making for precise timing and application of plant protection
products to improve plant health and crop yield. DT, used in agriculture, is a simultaneous
digital representation of real-life objects with real-time data and the factors that influence plant
growth. The development of DT for agriculture is a complex process and requires the live
knowledge-sharing process with adaptation to a particular environment (farm conditions). The
potato crop was chosen because of the fact that it is a crop more sensitive to water stress, and
building a DT model can contribute to more precise decisions regarding irrigation based on
sensor data and climatic conditions.
This research paper is organized as follows: after the introduction section, the second
section refers to eco-cyber-physical systems modelling and its context-specific requirements. It
outlines characteristics of real-world phenomena in the context of modelling tasks, and specifies
architecture requirements for DT development, highlights various viewpoints and incorporates
the stakeholders’ involvement in terms of knowledge-sharing and case design planning and
evolution which is an important part for such complex solutions. The third section includes a
DT modelling roadmap for ECPS. It outlines the main processes for planning and deploying
DT projects in order to reach the goal for simultaneous updating of a modern technological
solution. The fourth section describes the application of the 4EM methodology for ECPS and
DT development and expands the notation necessary in a particular context. The fifth section
describes the smart agriculture Living Lab (LL) dedicated to testing and evaluating the proposed
approach. This step is being conducted in a field trial conducted in 4 places in Latvia ie. the
districts of Vestiena, Priekuļi, Līči and Stende. The section outlines the results and main findings
for future work. The sixth section includes the conclusion and suggestions for future work.
2. Eco-Cyber-Physical Systems Modelling
The term Eco-systems in computer science has been well-known for a quite long time [12], [13]
and has been applied in various contexts, but often, it is a term describing an organizational
system within its environment [14]. The term Eco-Cyber-Physical-System (ECPS) is new and,
in the literature, is defined as “ a combination of the living and non-living components of
the ecosystem in conjunction with the cyber-physical sensors and intelligent agents in the
environment, interacting as a system” [15]. The goal of the ECPS is to use the power of artificial
intelligence combined with the Internet of Things (IoT) in order to provide smart solutions for
rural, agricultural and natural ecosystems. The collected data from satellite and Unmanned
Aerial Vehicles (UAVs), Unmanned Ground Vehicle (UGVs), smart enterprises and smart villages
can be used for modelling the living and farming behaviours of rural communities (Majidi
et al., 2021). The proposed definition perfectly describes the main function of ECPS in the
surroundings outlined above, but does not incorporate digital twin as a representation and
component from cyberspace that can interact and influence real-life objects or phenomena. The
authors define the Eco-Cyber-Physical system in this context as “an interaction of the living and
non-living components of the ecosystem virtually incorporated within the digital environment
considering self-regulation, adaptation, and interoperability principles”.
ECPS positioning (Figure 1) happens through the incorporation of six types of systems
integration: 1) physical systems which are remote sensing and actuating systems for DT
support with on-line data flow, including equipment and vehicles for application within the
agricultural domain; 2) cyber systems are information systems (local or cloud based) which
provide information storing, processing and visualisation as well as cybersecurity; 3) cyber-
physical systems highlight the links between digital and physical entities in systems such
as agricultural systems, and rural areas wherein physical objects and processes are replaced,
or complemented, by digital ones [8]; 4) ecosystem is a biological community of interacting
organisms in their physical environment [16]; 5) the eco-physical systems approach is designed
in order to integrate the energy dimension into the physical design when selecting materialized
views, one of the redundant optimization structures [17]; 6) is an interesting domain identified
by the authors and these are artificial intelligence based systems working in cyberspace with the
aim to simulate, predict or recognize an ecosystem’s phenomena (image recognition systems).
Figure 1: Positioning of Eco-Cyber-Physical Systems (ECPS) adapted from [8] and [18]
ECPS consists of:
1. A biological phenomenon which is a general object for digital representation and simulation.
In our case, it is a potato plant;
2. The environment is characterised by various parameters. In our case, it is soil characteristics
(permanent and changing within a relatively short time, weather conditions, environmental
characteristics like air temperature and humidity, soil temperature and humidity);
3. Physical equipment takes parameters from the environment and biological phenomena
and effects or influences phenomena or their environment;
4. Cyberspace where DT is reflecting and simulating real-life objects.
A Digital Twin model can significantly enhance the required control capabilities of ECPS by
enabling the decoupling of physical and information aspects of farm management [19]. Table 1
includes components of ECPS for digital twin modelling that can be used to monitor the actual
state of objects, prescribe desired states, predict future states, and to remotely correct the state
of real-life objects [19]. Components can vary in response to the application field. Table 1
analyses the components of the agriculture living lab.
The next chapter explains the DT modelling process for ECPS in more detail.
3. DT Modelling Road Map for ECPS
For DT modelling in ECPS in smart agriculture the following issues have to be taken in consid-
eration:
1. DT as phenomena or object – what we need to reflect on, analyse or predict;
2. Quantity, granularity and equality or influence – do we analyse each object or do we
collect them and do they interfere with each other;
3. DT environmental or influence factors – what is influencing real real-life object;
4. DT life cycle – what will be the life length of one DT instance;
5. DT development stages – what are the states of DT, how long are they and what outcomes
do they produce;
6. DT data security – it is important to follow the security triad (confidentiality, integrity,
availability).
In order to develop DT models for ECPS in smart agriculture, it is very important to incorporate
the Living Lab (LL) approach for a better understanding of real-life phenomena and reach more
precise results and beneficial outcomes for stakeholders. Figure 2 represents a process road
map for DT development for ECPS in smart agriculture.
In figure 2 the full process starts with the DT real-life object definition, which in practice
means that we have to define very precisely what the object will be. In the case of smart
agriculture it could be a crop or an animal or some disease. It depends on the type of DT,
and according to that, we choose and define the object. The next step is the definition of
the environment, which means influential factors. In the smart agriculture case, the factors
influencing crops are soil type, fertility and humidity as well as air temperature and humidity.
With DT development for ECPS the complexity lies in various factors influencing DT real-life
objects. When we develop DT and its surroundings, we assume that the probability of other
influential factors is very low. The third step is the author’s proposed building of the 4EM
model with extended notation, which includes the mentioned aspects in Table 1. After the
Table 1
Components of Eco-Cyber-Physical Systems from the Enterprise Modelling Perspective (from the Case
of Smart Agriculture Living Lab)
Component type by Aspect for Modelling Parameters to include Technological solu-
the application do- in the 4EM as notation tion for interaction
main with DT
Eco (living) Life cycle length Full development length Data repository
in days (database for col-
lecting and processing
data)
Behaviour Growing stages Imaging technologies
(cameras, UAV, UGV)
Health/disease Healthy, damaged, in- Imaging technologies,
fected Thermal cameras
Output (harvest or ani- Amount of biomass or Digital measuring
mals ready to be slaugh- livestock
tered)
Eco (non-living) Environmental char- Sensing frequency of Environmental sensors
acteristics for living measurements
creatures, plants and
organisms
Tools for creature protec- Application conditions Automatic spreading via
tion against diseases or sprinkles, UAV or trac-
other living organisms tors
Physical On-site sensing Measure- Sensing frequency Data from environmen-
ments tal sensors
Equipment for surround- Process prerequisites, fre- Precise time planning,
ing environment prepara- quency of application Im- work done, time spent,
tion pact on environment resources spent
Cyber Data repository Data repository struc- Distributed computing
ture
Brokers functions and Brokers for IoT data in- Third party involvement
data teroperability
Visualisation screens Visualisation from stake- Visualisation according
holder’s view to the stakeholder’s need
Security Data security Functions and proce-
dures for data protection
Data transfer on real- Data transmission peri- Algorithm in the case of
time mode od/synchronization no communication chan-
nel available
Granularity or complex- DT level of details and Precise definition of DT
ity surroundings
DT type According to type Type definition within
4EM
Figure 2: Process road map for DT development for ECPS in smart agriculture
model development comes the design and set up of the field experiment and initial design of DT,
which includes building information and communication infrastructure and development of the
information system according to requirements defined in 4EM model. The next steps are the
evaluation of initial results, DT update and development of the final version. The Improvement
of DT could be in various steps, but we need to take into account that agriculture is a field which
is characterised by seasonality, and it means that no more than 2 cycles are possible within one
project. The next section reflects the expansion of 4EM for EPS for smart agriculture.
4. Expansion of 4EM for ECPS
The 4EM Enterprise Modelling method [20] consists of six sub-models for modelling goals,
concepts, business rules, business processes, actors and resources, and information system
technical components and requirements models [21]. They are designed to be sufficiently general
to be able to capture and represent most modelling problems related to the organizational designs
[22]. Expansion of the 4EM methodology for ECPS for smart agriculture is required in order to
cover all aspects mentioned in Table 1 (Section 2) and for the development of a DT component
and deployment of a smart agriculture Living Lab. Expansion is required because DT models
go beyond static product designs, like CAD models, or representation of logistics processes,
design of robotics or other engineering systems. The DT model comprises dynamic behaviour
and is the perfect solution for the elaboration of ECPS for smart agriculture. This dynamic
nature of DT may include the representation of current behaviour of real-life objects, but also
the simulation or prediction of future behaviour and the recollection of historical behaviour
[19]. The challenging task is to reflect dynamic behaviour with static structure in order to
plan, monitor, control and optimize farm processes [19]. The main idea of expansion for the
4EM method came through collaboration with farmers within the STARGATE project, where
discussion about requirements elicitation and constructive discussion arises. The main goal
was to develop the next stage for the simulation model – DT and modern technologies in smart
agriculture offer a large range of possibilities for application. The author’s idea was to expand
the existing 4EM methodology with necessary aspects for the design of DT for ECSP in smart
agriculture. Before designing a smart agriculture living lab (described in the next section), the
authors studied the literature about the requirements for a framework or methodology for DT for
ECSP in smart agriculture [19], [4], [23], [24]. For the expansion of the 4EM method Verdous’s
basic principles for framework development where applied to create a practical application for
a smart agriculture living lab as described in 5 section [19]:
1. Representation of a cyber-physical control cycle of smart farming or a particular DT;
2. Sensing and monitoring, smart analyses and planning and smart control of farm operations
for all relevant farm processes or a particular DT elaboration;
3. Includes imaginary, monitoring, predictive, prescriptive, autonomous and recollection DT
types;
4. Supports the implementation of essential characteristics of DT, ie.timeliness, fidelity,
integration, intelligence, and complexity;
5. Addresses the specific challenges of implementing DT in farm management, ie.farm object
complexity, farm network dynamics and farm process dynamics.
Table 2 represents additional extensions for the 4EM method [22] in order to apply it to the
development of DT for ECSP in smart agriculture.
Table 2
Extension for 4EM sub-models and DT aspects for ECSP in smart agriculture
4EM sub-models Aspects form ECPS perspective Principles applied
Goal model Granularity or complexity, DT type Addressed specific challenges
Business rules model Life cycle length, Health/disease, Rules for real-life imaginary, sensing
Data transfer on real-time mode and control, intervention frequency
Concepts model Output, Environmental characteris- Specification of characteristics
tics for living creatures, plants and
organisms
Business process Behaviour DT types, sensing, monitoring and
model prevention techniques
Actors and recourses Crop type, characteristic or animal DT real-life object characteristic
model type and characteristic
Technical components Tools for creature protection against Supports the implementation of es-
and requirements diseases or other living organisms, sential characteristics of DT
model On-site sensing Measurements,
Equipment for surrounding en-
vironment preparation, Data
repository
This next section describes the implementation of DT for ECPS within a smart agriculture
living lab. It also represents a practical implementation of requirements in the technical solution
and describes the evaluation process and improvement of DT for practical deployment on farms.
5. Case of A Smart Agriculture Living Lab
A Living Lab is the perfect tool for the design of Eco-Cyber-Physical systems as it contributes
to a better understanding of the ecosystem and its representation within and collaboration with
cyberspace and the impact of the physical equipment. Living Lab (LL) is designed as the space
for so that Living Labs are spaces for innovative and participative research, for development
and activities that use multidisciplinary approaches, and promote the co-creation paradigm
and focus on many different arenas of human life [11]. LL is characterised by an innovation
and co-creation process [11]. A common practice for LL applications is the involvement of
users at all stages of developing new solutions for socio-economic or technological challenges.
Living labs are suitable for developing, co-creating, validating, and testing technologies [9].
This practice perfectly fits and resonates with the 4EM paradigm and suits DT development
through the continuity of the co-creation process and the active solution improvement within
real-life settings by evaluating co-creation cycles. The overall method consists of four steps: 1)
Living Lab set up and identification of main challenges; 2) Expansion of the 4EM method for
analysis and representation; 3) building DT in order to help decision making; 4) field trials for
practical evaluation.
The first step – The Living Lab process was organised in various places in Latvia. There
were organised meetings with stakeholders in order to gather knowledge and establish the
requirements for DT design In total four sessions were organised. In practice there are two
parts: 1) Living Lab set up and planning; 2) Development of 4EM and expansion for DT design.
Face-to-face sessions in the practitioner’s community were held to gather knowledge related
to the 4EM model and to set up field experiments. Both steps were conducted simultaneously
during the specially organised sessions with stakeholders. A Living Lab in this step serves as
a platform for requirements elicitation from stakeholders regarding main challenges facing
climate-smart agriculture and ascertaining how climate and environmental sensor data can give
a benefit or make a contribution to meet the challenges. The first session was held in Priekuļi,
Latvia with representatives of the Institute of Agricultural Resources and Economics. This
session was organised in two parts where the first part was dedicated to explaining the research
field and any problems. The second part was dedicated directly to requirements elicitation.
During the first part, moderators explained the possibilities of IoT and remote sensing methods
and their application for building DT which could help in the decision-making process for
climate-smart agriculture. The second session was organised in Stende at a branch of the
Institute of Agricultural Resources and Economics. This session was organised in a similar
way to the session which was organized in Priekuļi. Additional to the meeting on the institute
premises, a field trip to potato fields was organized. This was done to understand the potato
production process and its main challenges and to identify the necessary data flow for potato
production and yield gap calculation. The third session was organised on the farming operation
“Vietalas” near Valmiera. This session took place on the field and included the same topic about
challenges in potato production. Additionally,a demonstration of remote sensing technologies
for crop health evaluation was conducted . A practical demonstration was carried out by the
authors. It helped to engender fresh and innovative thoughts about technological possibilities
concerning smart agriculture. The fourth session was organised on a farm named “Dzeņi” near
Varakļāni, Latvia. This session was similar to the session organised in “Vietalas” but with an
additional demonstration from farmers about the new functionality of modern tractors and how
they are applied in potato organic production. After the sessions all results where summarised
in two main outcomes: 1) aspects for 4EM extension; 2) identification of main components for
DT design.
The second step - 4EM method development and expansion was elaborated based on
principles for model development [21]. Crop development stages were incorporated as an
extension from traditional model, where business processes and information flow is identified.
The outline from the developed model is shown in the 3. The first task from face-to-face
sessions elaborated in step one of the LL was to choose a crop for DT development. There were
two options: 1) wheat; 2) potatoes.
The authors made the decision to choose potatoes for DT development because of three main
issues:
1) the significance of the DT development in terms of yield gap reduction as it provides more
precise predictions. Potato (Solanum tuberosum) is a crop which is sensitive to water stress, as
this has a negative influence on-field production either as a lack of water or too much water.
There are many phenological characteristics which can be reflected as particular parameters
within the digital environment and these can be captured by various types of sensors and remote
sensing equipment;
2) availability of a large set of historical data needed for DT. Latvia has a long history in
potato production and research done by the Institute of Agricultural Resources and Economics
(https://www.arei.lv/en) gives extensive background on potato production processes and a solid
data set related to all DT types.
3) economical benefit for potato processing because there are two large production companies
who require good quality potatoes as production material. These companies are Aloja Starkelsen
SIA (https://alojas.lv) and ORKLA branch “Ādažu čipsi” (https://cipsi.lv/en/about-us/). Repre-
sentatives of these companies and those from the Union of Potato Producers and Processors
expressed interest in this issue.
Figure 3 represents an outline of the 4EM model and its extension. The model represents all
six sub-models:
• Goal model (marked with green colour and labelled with G) – shows the main goals that
need to be reached by the elaboration of smart agriculture LL;
• Actors model (marked with yellow colour and labelled with A) – shows the potato as the
main actor participating in the DT;
• The challenges model (marked with orange and labelled with Ch) – shows challenges that
need to be addressed or taken into account for DT development;
• The business process model (marked with white and labelled with P) – shows potato
development stages and the duration of each stage. Traditionally this model includes processes
and knowledge flow. In the extended version of 4EM, the authors are able to demonstrate crop
development stages as a process and show impact factors as an extension for the rules model
which reduce the actual potato harvest;
• The business rules model (marked with pink and labelled with R) – incorporates specific
rules for crop development and also the reduction factors within the development stages in the
water-limited situation;
• Concepts model (marked with cyan and labelled with C) – reflects concepts explained from
the other models and shows characteristics of the crop environment;
• Technical component and requirement model (marked with grey and labelled with T) –
reflects digital twin (DT) and Geographical Information System (GIS) as the main components
with the data flow to them.
Figure 3: Expansion of 4EM model for DT development for a smart agriculture living lab
The third step – building DT for its application in the living lab. Design includes four
conceptual parts: farmer as stakeholder who identified requirements and gets benefit from DT;
conceptual modelling component where 4EM modelling results are incorporated in the further
processes; a data acquisition system and applied technologies for data gathering; components
that are included in DT. The designed concept is incorporated in the smart agriculture living
lab.
The fourth step – Field trials as living labs were elaborated in four different places:
Vestiena, Priekuļi, Stende an Līči. Figure 4 shows the map with location of the living labs.
Within the living lab,a field trial was elaborated. In all places, there was a plan for the potato
field set up. according to best practices for field trials in potato production. Figure 6 reflects all
DT components from a real life potato crop as the central object. To explain, a Living Lab is
set up as a reflection of a real-life object from one aspect and also as a test bed or simulation
environment from a different aspect. It means that various scenarios can be simulated in the
virtual environment to show potential outcomes. For example, we use soil moisture sensor data
for precise decisions on irrigation to see the outcome or economic benefit of the application
of a particular amount of water. The irrigation functionality was only installed in the Veseta
living lab and is now being tested (during summer of 2022). The potato crop DT model is
constantly updated with new data sets which the authors see as valuable for the simulation
model behind the DT and also for new findings about technologies applied for remote sensing
data incorporation in DT.
Figure 4: Map with locations of the Living Labs
6. Conclusions and Future Work
EM methodologies till now have been applied mostly in the manufacturing, logistics, healthcare,
organizational problem domains. In the literature research conducted by the authors of this
paper no cases for the application of EM methods for the agriculture domain were found. This
creates an interesting and challenging task because of the dynamical changes in the domain as
itself. The challenge relates to a large variety of dependent variables and also to the traditional
approaches used by agricultural experts to conduct research. The 4EM methodology has a lot
of advantages. . The paper’s authors have almost 20 years experience that show that 4EM has
proven its real value when applied to very challenging and also relatively easier issues in domain
analysis and requirements definition for information systems. New challenges are arising in DT
development and incorporation not only in manufacturing, construction building and medicine,
but also in agriculture. The agriculture domain requires an ecosystem approach for analysis
of problem domains. The Eco-cyber-physical systems (ECPS) approach to modelling, system
thinking and the building of DT is a valuable and necessary tool in order to get appropriate
results for decision making and intervention in production.
The application of LL within the DT process gives value not only by developing a more
iterative process, but it is also significant for ECPS development as far as new findings that
give technological solutions, and that initiate new conclusions which may change sensing and
intervention practices. Smart LL for potato organic production is an example of such a result.
An IoT solution provided more detailed and precise data about environmental conditions of
crops and visual sensing gave results about crop development stages. Based on these data
new frequencies for the measurement of soil moisture and humidity was set up in order to
get more precise data about the necessity for and quantity of irrigation. In this Living Lab the
4EM model served as a big picture for the technical team generating a better understanding
of domain and DT development directions. It contributed to decision making about practical
aspects, such as: what kind of sensing equipment is needed; how many sensors are needed
on the field and how to distribute them. The authors recognize that they were fortunate that
they benefitted from close collaboration from other research institutions in terms of knowledge
sharing and innovative strategies. But,there remains the challenge of more fully representing
all the dynamics involved in this type of modelling for ECPS to evolve to its potential.
Future work lies in ensuring there is continuous improvement of DT in field trials and the
next step should happen as a field trial on a real farm. Future technological development should
come in ambient IoT development focussing on 4EM with incorporated extensions.
Acknowledgments
This research was funded by European Commission, Research Executive Agency grant number
818187 ’reSilienT fARminG by Adaptive microclimaTe managEment’ (STARGATE)..
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