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About BRIDGE-AI

Building ResIlient Development with
GEnerative AI in Education & Agriculture

A Horizon Europe Research and Innovation Action improving African rural societies by integrating GenAI-based solutions into agricultural optimisation and digital skills acquisition.

Building a Climate-Resilient Future

BRIDGE-AI is a European project that aims to help farmers in Africa make better decisions by using advanced digital tools based on artificial intelligence (AI).

The project focuses on rural areas in Nigeria, Kenya and Tunisia, where agriculture is essential for people's livelihoods, but is increasingly affected by climate change, water scarcity and soil degradation.

At the same time, access to digital tools, data and technical support is often limited. This makes it difficult for farmers to plan their activities and respond to challenges.

Our Mission

BRIDGE-AI develops tools that combine satellite data, weather information, and field measurements with artificial intelligence. These tools provide farmers with practical advice to help them manage their crops more effectively and sustainably presented in a simple and understandable way.

Why it matters: Farmers get practical guidance on when to plant or harvest, how to detect problems early, how to adapt to changing weather, and how to use water more efficiently.

African agriculture and farming

Our Impact

BRIDGE-AI is transforming agriculture across Africa through six key pillars

Europe-Africa Partnership

A strong collaboration between European and African partners to deliver real impact for farmers across the continent.

Better Decision Making

Digital tools that transform complex environmental data into practical recommendations for everyday farming activities.

Climate-Smart Agriculture

Helping farmers respond to drought, changing weather patterns and water scarcity with sustainable and resilient practices.

Accessible AI

Using Generative Artificial Intelligence to make advanced technologies easier to understand and use in rural communities.

Working Together

Farmers, researchers, cooperatives, SMEs and public authorities co-design solutions through Living Labs in three countries.

Building Skills for the Future

Training, workshops and educational resources to strengthen digital skills and support adoption of innovative technologies.

Our Objectives

BRIDGE-AI works toward six primary objectives across research, capacity building, and replication

01

Technology Demonstration

Deploy and validate GenAI-enhanced solutions across three African countries with documented performance data.

02

Farmer Empowerment

Equip farmers with smart farming capabilities through accessible digital tools and practical recommendations.

03

Youth & Women Inclusion

Actively engage youth and women through targeted training, mentoring, and leadership pathways in agritech.

04

Knowledge Transfer

Deliver modular training, bootcamps, SME mentoring, and replication resources across East and West Africa.

05

Research & Evidence

Document pilot outcomes, lessons learned, and best practices for reporting to Horizon Europe and the public.

06

Scalability & Replication

Develop replication playbooks and toolkits for adoption across Africa beyond the pilot regions.

Countries & Use Cases

BRIDGE-AI works in three African countries with four agricultural use cases

Maize farming in Nigeria
Use Case 1

Maize Production

Nigeria

Maize is a key crop for food security and livelihoods in Nigeria. BRIDGE-AI helps farmers better anticipate climate conditions and make informed decisions about planting, harvesting and crop management.

Digital Shadows
Mushroom cultivation in Kenya
Use Case 2

Smart Mushroom Cultivation

Kenya

Mushroom cultivation requires carefully controlled growing conditions. BRIDGE-AI develops tools that help farmers monitor their production environment and optimise growing conditions. The pilot also promotes the involvement of young people and women in digital agriculture.

IoT + GenAI
Pasture in Tunisia
Use Case 3

Pasture Management

Tunisia

Pasturelands are essential for many farming communities. This pilot combines environmental information and digital tools to help farmers better understand pasture growth and make informed grazing decisions.

Earth Observation
Pasture in Tunisia
Use Case 4

Pomegranate Cultivation

Tunisia

Pomegranate growers face increasing challenges related to water availability and changing climate conditions. BRIDGE-AI provides tailored recommendations to improve irrigation, crop management and harvest planning.

Earth Observation

How It Works

BRIDGE-AI combines multiple cutting-edge technologies to deliver practical solutions

Generative AI (GenAI)

AI models predict yields, detect anomalies, and provide decision support for farmers and agronomists. GenAI makes advanced technologies easier to understand and use.

Internet of Things (IoT)

Real-time monitoring of temperature, humidity, CO₂, light, and substrate moisture using low-power sensors. Data is transmitted wirelessly for continuous analysis.

Digital Shadows

Virtual replicas of physical systems for "what-if" scenario testing, yield forecasting, and training simulation — without risking real crops.

Earth Observation (EO)

Satellite data integration for monitoring crop health, weather patterns, and environmental conditions. Helps farmers understand their environment better.

Semantic Interoperability (FIWARE/NGSI-LD)

Using open standards for seamless data exchange between systems and platforms, ensuring different technologies work together smoothly.

Low-Bandwidth Advisory

Voice alerts and SMS-based advisory in local languages (Swahili, French, Arabic) for farmers with limited connectivity. Simple, accessible, and practical.

Project Timeline

BRIDGE-AI follows a structured six-phase approach from research to impact

Phase 01 Listening & Learning

Understanding Local Needs

The project starts by working directly with farmers, cooperatives and local communities in Nigeria, Kenya and Tunisia to better understand their needs, challenges and priorities.

Phase 02 Turning Data into Tools

Developing Digital Solutions

Combining satellite imagery, weather information, field sensors and artificial intelligence to create digital tools that help farmers make informed decisions.

Phase 03 Testing on Real Farms

Real-World Validation

The tools are tested in real agricultural environments through four pilot cases focused on maize, mushrooms, pastures and pomegranates across three countries.

Phase 04 Working Hand in Hand

Living Labs

Farmers and local stakeholders are actively involved through Living Labs, allowing users to test tools, share feedback and shape solutions that respond to local needs.

Phase 05 Sharing Knowledge

Training & Capacity Building

Providing training, workshops and learning opportunities to help farmers, entrepreneurs and local organisations understand and use new digital technologies.

Phase 06 Expanding Impact

Scalability & Replication

Sharing knowledge and solutions so they can be adopted in other regions and agricultural contexts, contributing to a more sustainable and resilient future for agriculture across Africa.

Our Methodology

BRIDGE-AI combines technology, local knowledge and collaboration to develop practical solutions for agriculture in Africa.

Combines information from satellites, weather forecasts and field sensors
Uses Artificial Intelligence to help farmers better understand their environment
Developed together with farmers, cooperatives, researchers and local organisations
Transforms complex information into clear and accessible recommendations
Validated through pilot projects in different agricultural contexts
Creates solutions that continue to support farmers beyond the project

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