AI for Sustainable Food Production from Farm to Fork

The project contributes to reducing greenhouse gas emissions (GHG), and develops solutions that accelerate the transition to sustainable cities.






Main financing

Research area

Project manager at MDU

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Description of the project

Indoor vertical farms (IVF) can create symbiosis with their host-buildings, improving energy and resource efficiency of the whole system. This may become a key factor in global greenhouse gas (GHG) emissions reduction, and also demonstrate how integration of other infrastructures may lead to sustainable urban areas. Cities occupy 2% of the planet’s landmass. With 75% of the population living in cities in Europe, thus moving food production to the consumers creates additional advantages, e.g. shorter transportations. Through its presence IVF may also facilitate the public’s participation and engagement in climate actions. Our societies are under rapid transition towards electrification and digitalization, thus there is an urgent need to reevaluate what a city is, and how they will need to evolve in order to meet future needs.

The project will develop, firstly, a multi-agent based resource and climate model that explains the flows of information and resources between IVF, its host-building, and other actors in the whole value chain ending with the citizens; secondly, a proof of concept for autonomous orchestration of the IVF. Two annual demonstrations, and scientific assessments of the model and the demonstration results for generalized knowledge will be produced.

The major expected impacts are: 1) resource efficiency and optimal control of an IVF, and enabling exchange of energy, heat, and water between infrastructures to reduce total GHG emissions; 2) autonomous and connected farming for interaction with the after-production actors for capacity and demand matching, reducing food waste, and encouraging behaviour change towards sustainable consumption.

Purpose and objective

The purpose of the project is to demonstrate energy & water usage as well as CO2 reduction through (1) data-driven system development for orchestration of infrastructures for energy and resource efficiency, (2) a holistic approach that incorporates multi-stakeholder perspectives, and circular/shared economy aspects (3) integration of the Indoor Vertical Farm (IVF) with its host building for resource efficient operation, (4) development of support tools for the IVF in creation of mid-term tactical and strategic long-term plans for decision making.

The project objectives are, firstly, reduction of energy and water usage compared to both traditional farming and other IVF solutions for the same amount of plant-based food. Secondly, inform the citizens about food produced in the cities; facilitate networking and sharing of information. Thirdly, demonstrate complete value chain of food production and supply, i.e. ‘from farm to fork’. Lastly, increased collaboration between AI and climate researchers.


This is an end-user focused, demonstrator driven project and the technology development process is incremental prototyping, i.e. functionalities will be added when they are tested separately.

  • M1-6: Defining the requirements from different multi-stakeholder, which also feeds into system requirements is central. In parallel edge-AI development.
  • M7-12: Initial system development leading towards Year 1 demo.
  • M13-18: Demo evaluation leading to further development. Dissemination incl. WSs. Focus on publications.
  • M19-25: Final demo development and impact measurement.