Background & Motivation
AgriProAct contributes to the transition towards a more resilient, efficient, and sustainable agricultural system, where decisions are supported by reliable data and forward-looking insights rather than uncertainty.
The Challenge
Agriculture is increasingly exposed to climate variability, extreme weather events, and growing pressure on natural resources. Farmers are required to make critical decisions on irrigation, fertilisation, and crop management under conditions of high uncertainty. At the same time, public authorities need reliable data to design effective policies, monitor environmental impact, and support sustainable agricultural practices. However, existing agricultural systems often rely on fragmented data, delayed information, and reactive decision-making approaches.
The key challenge is not the lack of data, but the lack of integration and usability.
Farmers and policymakers require:
- timely and reliable information
- easy-to-understand insights
- tools that support proactive decision-making
Without this, valuable data remains underutilised and decision-making remains reactive rather than predictive.
The Opportunity
Recent advances in technology offer a unique opportunity to transform agriculture:
EARTH OBSERVATION
Provides continuous, large-scale environmental monitoring.
IoT
SENSORS
Deliver real-time field-level data
WEATHER FORECASTING MODELS
Improve short-term predictability.
ARTIFICIAL INTELLIGENCE (AI)
Enables predictive insights from complex datasets.
Despite these advancements, these data sources are rarely integrated into a unified system that delivers clear, actionable insights for end users.
The AgriProAct Approach
AgriProAct addresses this gap by bringing together diverse data sources into a single, intelligent platform.
By combining satellite data, in-situ sensors, and environmental information, the project creates digital representations of agricultural parcels and applies AI models to generate predictive insights.
This enables:
- earlier identification of risks
- optimisation of resource use
- more informed and timely decisions
Improving decision-making in agriculture is critical for:
- reducing water and fertiliser use
- minimising environmental impact
- increasing resilience to climate change
- supporting sustainable food production
At the same time, providing better data and tools to public authorities enables more effective and evidence-based policymaking.
Concept & Methodology
Overall Concept
AgriProAct is built on the concept of transforming diverse agricultural and environmental data into actionable intelligence at the level of individual fields. By combining satellite imagery, in-situ sensors, and weather information, the platform creates dynamic digital representations of agricultural parcels (“digital twins”) that reflect both current conditions and expected developments. This enables farmers and public authorities to better understand their environment and make more informed, forward-looking decisions.
Methodology
To achieve the objectives of AgriProAct and deliver its expected outcomes, an integrated data-driven architecture is defined and progressively developed throughout the project. This approach provides a unified platform that brings together multiple data sources, including satellite imagery, in-situ sensors, and meteorological information, abstracting the complexity of data collection and processing through a set of harmonisation, modelling, and analytics components.
The platform enables the generation of parcel-level digital twins, supports the execution of predictive models, and facilitates the delivery of actionable insights through user-oriented tools. These include interfaces for farmers and dashboards for public authorities, allowing users to access forecasts, monitor conditions, and support decision-making. The methodology also incorporates continuous validation through pilot deployments, ensuring that the models and tools are tested, refined, and adapted to real-world agricultural environments.
Objectives
Improve Farm Resilience
Enable farmers to make timely, data-driven decisions and reduce crop losses from climate events
Optimise Resource Use
Reduce irrigation and fertiliser use through precise recommendations and improve water and energy efficiency
Support Policy & Planning
Provide authorities with forward-looking risk insights and enable better subsidy planning and CAP reporting
Advance AI in Agriculture
Deploy parcel-level AI forecasting models and continuously improve accuracy through real-time learning
Environmental Impact
Reduce greenhouse gas emissions (N₂O & CH₄) and support sustainable agricultural practices
