Multi-paradigm digital twin framework for biophysical systems: Application to vertical farming

Inappropriate food autonomy poses serious threats to the sustainable development of Canada and the global society in general. This issue is further exacerbated by the four-season climate of Quebec that prevents year-round open-air farming. To cope with this challenge, Controlled Environment Agriculture (CEA) uses managed artificial environmental conditions to produce vegetables and fruit in a closed environment. Experimenting with environmental configuration on real plants is not feasible, nor is the manual configuration of complex CEA environments. To tackle these issues, this project delivers a digital twin framework that can aid human CEA experts. The framework achieves this by supporting various simulators and machine learning components and orchestrating them appropriately.
The project is carried out in collaboration with Ferme d’Hiver, a leading Quebec-based vertical farming company targeting the production of off-season fruits and vegetables in environmentally controlled growth environment.

Faculty Supervisor:

Houari Sahraoui;Eugene Syriani

Student:

Partner:

Ferme d’Hiver

Discipline:

Computer science

Sector:

Agriculture and Food; Information and Communications Technology; Clean Technology

University:

Université de Montréal

Program: