Project description
AI-powered robots learn from farmers for resilient agriculture
Climate shocks, labour shortages and rising costs pose major challenges for farms. Innovative solutions that turn local field management and crop care knowledge into effective, safe automation are urgently needed. Supported by the Marie Skłodowska-Curie Actions programme, the FARMAR project will improve farming by using artificial intelligence to teach small robots to learn from and replicate farmers’ techniques. Farmers will actively participate by demonstrating their activities, which will be translated into clear instructions for the robots. A user-friendly app will allow farmers to adjust the robot tasks. Lightweight vehicles and drones will monitor fields and perform low-impact tasks under supervision. The results will provide guides to support sustainable farming, technology use and rural resilience in line with EU goals.
Objective
FARMAR helps farms cope with climate shocks, labour shortages and rising costs by turning everyday know-how and the local heritage of how fields are walked, weeds are managed and crops are cared for into practical and safe automation. Our goal is to capture how work is done on different farms, teach small robots to repeat those skills gently around crops and show that the approach is affordable, lawful, socially acceptable and useful in day-to-day work. We also build people’s skills through international staff exchanges so the benefits last beyond the project.
FARMAR works with farmers and not just for them. On each site, farmers show and explain their routines. Short demonstrations, observations and spoken narrative notes are turned into clear step-by-step instructions that robots can follow. A simple app lets farmers review and adjust suggestions in plain language, with clear explanations. A small field computer coordinates teams of lightweight ground vehicles and drones that scout fields, flag issues and carry out gentle tasks under strict safety limits and human oversight. Trials compare FARMAR-assisted work with current practice to track measures that fully evaluate the outcomes of the project.
Results feed into open guides, training materials and starter legal packs that explain ownership, consent and fair ways to share value from farm-trained models. Business cases by crop and farm size show costs, savings and payback options including shared services for smallholders. FARMAR fits the MSCA Staff Exchanges work programme by linking universities, SMEs and farmer networks across countries and disciplines. Staff co-develop shared tools, learn in real field settings and take that experience back to their home teams. The outcome supports EU goals on sustainable food, trusted digital tech and rural resilience, including safer tasks, reduced inputs, preserved heritage knowledge and a practical path from promising prototypes to everyday use on farms.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering mechatronics automation
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics autonomous robots drones
- agricultural sciences agriculture, forestry, and fisheries agriculture
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-SE-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
DH1 3LE DURHAM
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.