Summary
- Closed for EoI
- Profile Type
- Research & Development Request
- POD Reference
- RDRFR20250702011
- Term of Validity
- 2 July 2025 - 2 July 2026
- Company's Country
- France
- Type of partnership
- Research and development cooperation agreement
- Targeted Countries
- All countries
Contact the EEN partner nearest to you for more information.
Find my local partner
General information
- Short Summary
- This project responds to HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61, aiming to develop foundation AI models processing large agricultural datasets (crowdsourced, remote sensing, IoT, weather) to improve crop mapping, area estimation, and yield prediction. The consortium includes partners from France, Cyprus, Switzerland, Albania, and Germany. Partners sought include data scientists, agronomic scientists, and dissemination specialists.
- Full Description
-
This project is being developed in response to the HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61 call: “Foundation models for science: Artificial Intelligence (AI) for complex systems and scientific challenges”. Specifically, the proposal aims to develop and test foundation models capable of processing large-scale agricultural datasets, including:
• Crowdsourced data from farmers
• Remote sensing data (e.g., satellite, drone imagery)
• IoT sensor data
• Meteorological and climate information
The purpose is to improve agricultural base layers, such as, but not only:
• Field boundary detection
• Crop type identification
• Crop area estimation
• Yield prediction
The foundation models will be co-developed with and validated by universities and research center working in the field of machine learning, earth observation data and agricultural sciences and universities closely working with farmers’ association and actively operating for digital impact and transformation of the agricultural sector in their area. This user-driven approach ensures practical relevance, uptake, and future scalability.
Foundation models are large-scale AI architectures trained on massive, often multimodal datasets. Once developed, they can be fine-tuned for specific use cases and offer a common technological backbone adaptable across a wide range of applications. In agriculture, they are already being explored for crop monitoring, disease detection, yield estimation, climate impact forecasting (e.g., drought prediction), and data integration to deliver personalised agronomic recommendations.
To address this challenge, a multinational and interdisciplinary consortium is being assembled. To date, the consortium includes:
• Three partners currently being onboarded:
1/ a research institute on artificial intelligence in France,
2/ another research institute on Artificial intelligence in Germany
3/ a geospatial and observation public center in Cyprus.
• One confirmed partner is an university in Albania
• The coordinator will be a research institute based in Germany
• A French SME will have a role into the foundational model design, creation of the inputs and training dataset and market preparedness and readiness analysis.
Expertise sought are :
• Universities and institutes to benefit of scientific guidelines and fundamental research artificial intelligence, in agricultural and agronomic sciences.
• Companies, farmers association, labs and technical institute specialized in crop, livestock, soil and water sustainability and resilience.
• Dissemination companies to value the technical progress, and set the necessary requirements for a consistent and interdisciplinary service towards farmers, producers, and agricultural systems as a whole - Advantages and Innovations
-
The foundation models will be co-developed with and validated by universities and research center working in the field of machine learning, earth observation data and agricultural sciences and universities closely working with farmers’ association and actively operating for digital impact and transformation of the agricultural sector in their area. This user-driven approach ensures practical relevance, uptake, and future scalability.
Foundation models are large-scale AI architectures trained on massive, often multimodal datasets. Once developed, they can be fine-tuned for specific use cases and offer a common technological backbone adaptable across a wide range of applications. In agriculture, they are already being explored for crop monitoring, disease detection, yield estimation, climate impact forecasting (e.g., drought prediction), and data integration to deliver personalised agronomic recommendations. - Technical Specification or Expertise Sought
-
Expertise sought are :
• Universities and institutes to benefit of scientific guidelines and fundamental research
• Companies, and farmers association, labs and technical institute specialized in crop, livestock, soil and water sustainability and resilience.
• Dissemination companies to value the technical progress, and set the necessary requirements for a consistent and interdisciplinary service towards farmers, producers, and agricultural systems as a whole - Stage of Development
- Concept stage
- Sustainable Development Goals
- Goal 17: Partnerships to achieve the Goal
- Goal 2: Zero Hunger
- Goal 15: Life on Land
- Goal 5: Gender Equality
- Goal 9: Industry, Innovation and Infrastructure
- IPR status
- Secret know-how
Partner Sought
- Expected Role of a Partner
-
Expertise sought are :
• Universities and institutes to benefit of scientific guidelines and fundamental research artificial intelligence, in agricultural and agronomic sciences.
• Companies, farmers association, labs and technical institute specialized in crop, livestock, soil and water sustainability and resilience.
• Dissemination companies to value the technical progress, and set the necessary requirements for a consistent and interdisciplinary service towards farmers, producers, and agricultural systems as a whole - Type and Size of Partner
- SME <=10
- R&D Institution
- University
- SME 50 - 249
- SME 11-49
- Type of partnership
- Research and development cooperation agreement
Call details
- Framework program
- Horizon Europe
- Call title and identifier
-
Horizon Europe call HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61: AI Foundation models in science (GenAI4EU) (RIA)
- Submission and evaluation scheme
-
single stage
- Anticipated project budget
-
30 000 000
- Coordinator required
-
No
- Deadline for EoI
- Deadline of the call
- Web link to the call
- https://urlr.me/rUsPyV
- Horizon Europe
Dissemination
- Technology keywords
- 01003003 - Artificial Intelligence (AI)
- Market keywords
- 09002006 - Other finance, insurance and real estate
- 02006004 - Data processing, analysis and input services
- 02005006 - Data I/O devices
- 09005 - Agriculture, Forestry, Fishing, Animal Husbandry & Related Products
- 02006005 - Big data management
- Targeted countries
- Afghanistan
- Aland Islands
- Albania
- Algeria
- American Samoa
- Andorra
- Angola
- Anguilla
- Antarctica
- Antigua and Barbuda
- Argentina
- Armenia
- Aruba
- Australia
- Austria
- Azerbaijan
- Bahamas
- Bahrain
- Bangladesh
- Barbados
- Belarus
- Belgium
- Belize
- Benin
- Bermuda
- Bhutan
- Bolivia
- Bonaire, Saint Eustatius and Saba
- Bosnia and Herzegovina
- Botswana
- Bouvet Island
- Brazil
- British Indian Ocean Territory
- British Virgin Islands
- Brunei
- Bulgaria
- Burkina Faso
- Burundi
- Cabo Verde
- Cambodia
- Cameroon
- Canada
- Cayman Islands
- Central African Republic
- Chad
- Chile
- China
- Christmas Island
- Cocos Islands
- Colombia
- Comoros
- Cook Islands
- Costa Rica
- Croatia
- Cuba
- Curacao
- Cyprus
- Czechia
- Democratic Republic of the Congo
- Denmark
- Djibouti
- Dominica
- Dominican Republic
- Ecuador
- Egypt
- El Salvador
- Equatorial Guinea
- Eritrea
- Estonia
- Eswatini
- Ethiopia
- Falkland Islands
- Faroe Islands
- Fiji
- Finland
- France
- French Guiana
- French Polynesia
- French Southern Territories
- Gabon
- Gambia
- Georgia
- Germany
- Ghana
- Gibraltar
- Greece
- Greenland
- Grenada
- Guadeloupe
- Guam
- Guatemala
- Guernsey
- Guinea
- Guinea-Bissau
- Guyana
- Haiti
- Heard Island and McDonald Islands
- Honduras
- Hong Kong
- Hungary
- Iceland
- India
- Indonesia
- Iran
- Iraq
- Ireland
- Isle of Man
- Israel
- Italy
- Ivory Coast
- Jamaica
- Japan
- Jersey
- Jordan
- Kazakhstan
- Kenya
- Kiribati
- Kosovo
- Kuwait
- Kyrgyzstan
- Laos
- Latvia
- Lebanon
- Lesotho
- Liberia
- Libya
- Liechtenstein
- Lithuania
- Luxembourg
- Macao
- Madagascar
- Malawi
- Malaysia
- Maldives
- Mali
- Malta
- Marshall Islands
- Martinique
- Mauritania
- Mauritius
- Mayotte
- Mexico
- Micronesia
- Moldova
- Monaco
- Mongolia
- Montenegro
- Montserrat
- Morocco
- Mozambique
- Myanmar
- Namibia
- Nauru
- Nepal
- Netherlands
- Netherlands Antilles
- New Caledonia
- New Zealand
- Nicaragua
- Niger
- Nigeria
- Niue
- Norfolk Island
- North Korea
- North Macedonia
- Northern Mariana Islands
- Norway
- Oman
- Pakistan
- Palau
- Palestinian Territory
- Panama
- Papua New Guinea
- Paraguay
- Peru
- Philippines
- Pitcairn
- Poland
- Portugal
- Puerto Rico
- Qatar
- Republic of the Congo
- Reunion
- Romania
- Russia
- Rwanda
- Saint Barthelemy
- Saint Helena
- Saint Kitts and Nevis
- Saint Lucia
- Saint Martin
- Saint Pierre and Miquelon
- Saint Vincent and the Grenadines
- Samoa
- San Marino
- Sao Tome and Principe
- Saudi Arabia
- Senegal
- Serbia
- Seychelles
- Sierra Leone
- Singapore
- Sint Maarten
- Slovakia
- Slovenia
- Solomon Islands
- Somalia
- South Africa
- South Georgia and the South Sandwich Islands
- South Korea
- South Sudan
- Spain
- Sri Lanka
- Sudan
- Suriname
- Svalbard and Jan Mayen
- Sweden
- Switzerland
- Syria
- Taiwan
- Tajikistan
- Tanzania
- Thailand
- Timor Leste
- Togo
- Tokelau
- Tonga
- Trinidad and Tobago
- Tunisia
- Turkey
- Turkmenistan
- Turks and Caicos Islands
- Tuvalu
- U.S. Virgin Islands
- Uganda
- Ukraine
- United Arab Emirates
- United Kingdom
- United States
- United States Minor Outlying Islands
- Uruguay
- Uzbekistan
- Vanuatu
- Vatican
- Venezuela
- Vietnam
- Wallis and Futuna
- Western Sahara
- Yemen
- Zambia
- Zimbabwe