Between 5-7 September 2022, in Salamanca was held the ON-SITE Conference - International Joint Conferences HAIS-SOCO-CISIS-ICEUTE & STARTUP OLÉ and our colleague Oliviu Matei has presented an article about the MUSHNOMICS Platform Architecture.

1. MUSHNOMICS
Unlocking data-driven innovation for
improving productivity and data sharing
in mushroom value chain
Security Centric
Scalable Architecture
for Distributed
Learning and
Knowledge
Preservation
Rudolf ERDEI, Daniela DELINSCHI, Oliviu MATEI
Oliviu Matei
oliviu.matei@holisun.com
17th International Conference on Soft Computing Models in
Industrial and Environmental Applications SOCO2022

2. Outline
 About MUSHNOMICS project
 Meta Requirements
 Architecture of the System
 Validation and Results
 Conclusions
17th International Conference on Soft Computing Models in Industrial and
Environmental Applications SOCO September 07 2022
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3. About MUSHNOMICS
17th International Conference on Soft Computing Models in Industrial and
Environmental Applications SOCO September 07 2022
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• AGRI-FOOD project for
optimization and green production
of mushrooms
• 3 stages:
• Substrate production
• Intelligent mushroom growing
• Spent substrate valorization
• Digital platform for optimization
of the whole system.

4. Meta-Requirements
Software architecture
Security
Centric
Scalable
Distributed
Learning
Knowledge
Preservation
17th International Conference on Soft Computing Models in Industrial and
Environmental Applications SOCO September 07 2022
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5. Functional Requirements
17th International Conference on Soft Computing Models in Industrial and
Environmental Applications SOCO September 07 2022
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Security Centric
FR3 - Data-private
functionalities, with
support for data
sharing within
consortia of producers
FR4 - Privacy Centric
utilization of platform
services
Scalable
FR2 - Production
ready automated
pipeline, as well as
research support
FR6 - 3rd party
integrations of
applications within
the system
Distributed Learning
•FR1 - Ability to use a
classical ML pipeline,
as well as a Federated
Learning pipeline
Knowledge
Preservation
FR5 - Ability to retain
discovered
knowledge per
project

6. Nonfunctional Requirements
17th International Conference on Soft Computing Models in Industrial and
Environmental Applications SOCO September 07 2022
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Security Centric
NFR2 - Platform Resilience, as
the ability to manage and
recover several failure classes
within the system;
Scalable
•NFR1 - Security, Scalability and
Stability of the resulting
system;
NFR3 - Low-spec hardware
requirements for the Cloud
Component (leveraging
Serverless Components);
NFR4 -Portability/Accessibility,
as the possibility of the
platform to be accessed from
various operating systems,
including mobile;
Distributed Learning
NFR5 - Flexibility, as the
platform should be used both
for scientific as well as
production purposes.

7. Architecture of the System
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Environmental Applications SOCO September 07 2022
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• 3 components: Edge, Cloud and UI;
• Edge gathers and prepares data, prepares partial models;
• Cloud sets up the client projects, their methodologies and
performs the execution of pipelines;
• Cloud sends settings to the Edge;
• Cloud integrates an Ontology based Knowledge Base;
• UI enables interaction of owners and scientists to the platform;

8. Edge components
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Environmental Applications SOCO September 07 2022
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9. Cloud component
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Environmental Applications SOCO September 07 2022
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10. Multi-modal UI
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Environmental Applications SOCO September 07 2022
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11. Validation
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Environmental Applications SOCO September 07 2022
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 Done with Software Architecture Analysis Method (SAAM) and Active Reviews
for Intermediate Design (ARID);
 Validation was done together with stakeholders and scientists that will use
the final platform.

12. Results
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Environmental Applications SOCO September 07 2022
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Classical Federated
Data transfer frequency Daily Daily
Daily data quantity 60 MB 0.1 MB
Data transfer importance High Medium
Data storage Cloud Edge

13. Conclusions
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Environmental Applications SOCO September 07 2022
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 Successfully integrates all functional and non-functional requirements, addressing
safety, security and trust issues regarding the edge nodes, over the medium of
communication.


 TRL 4 – currently being validated for TRL5
 Further research for the Quality of the Model, as a subset of Model Trust, that has a
huge importance as it defines the weight the model will have in the overall average
of the project.
 A Model Quality Index is being researched, that will allow better general models to
be generated from this methodology.

14. FUNDEDBY
Unlocking data-driven innovation for
improving productivity and data sharing in
mushroom value chain
Thank
You !
oliviu.matei@holisun.com
https://holisun.com/
+40 262 226 778
Oliviu Matei

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Environmental Applications SOCO September 07 2022 15

16. Acknowledgment
This research was made possible by funding from the ICT-AGRI-FOOD
2019 Joint Call. This work was supported by a grant of the Romanian
National Authority for Scientific Research and Innovation, CCCDI-
UEFISCDI, project number COFUND-ICT-AGRI-FOOD-MUSHNOMICS
205.2020, within PNCDI III.
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Environmental Applications SOCO September 07 2022
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