Precision Agriculture

Challenge

Precision agriculture depends on data that is high-quality, real-time, and context. This use case

demonstrates how such data, originating from fields, satellites, weather models, and AI services, is

shared, processed, and used to support smarter, more sustainable farming in Northern Greece.

Requirements Co-creation and Architecture Validation

Precision agriculture delivers IoT-intensive requirements with sensor and satellite data, creating the most datarich validation environment.

Non-functional Properties

Data sovereignty: Farmers retain ownership and control over their data at all times.
Privacy preservation: Sensitive and proprietary data is not shared unless explicitly permitted; Federated Learning reduces exposure.
Compliance: All operations align with GDPR and European data governance frameworks (IDSA, GAIA-X).
Scalability: The system architecture supports increased data volume, additional farms, and new service providers.
Interoperability: Based on open standards and modular integration, supporting future cross-sector extensions.

Complex Data Lifecycle and Regulatory Compliance Testing

Precision Agriculture creates federated learning scenarios requiring distributed data control across agricultural cooperatives.

Description

At its core is AgroNIT, a digital platform with dual functionality. First, it provides farmers with direct

access to monitoring tools that they can use to observe conditions in their fields, track crop

development, and view localized weather predictions. Second, and more structurally significant, AgroNIT functions as a federated marketplace. Within it, data and services are published, discovered, and consumed. Farmers receive tailored recommendations. Agronomists offer predictive tools.

Analysts contribute models. Services are exchanged securely and transparently.

Dataspaces

Two distinct dataspaces support this system: DigiAgro DS and AgroScience DS. AgroScience DS, which is operated by CROWNEST SA, delivers analytics, decision support tools, and consultancy services by integrating external datasets, such as satellite imagery, short-term forecasts, agronomic baselines, and making them actionable. The DigiAgro DS, operated by the University of Thessaly, serves as a dedicated infrastructure for the acquisition, management, and controlled sharing of agricultural field data.

Purpose

This use case addresses the integration of real-time agricultural data, AI services, and cross-sector collaboration within a federated digital environment. It focuses on precision farming in Northern Greece, where diverse actors, such as farmers, agronomists, researchers, service providers, and the agri-food industry, interact through a shared infrastructure. The scope is intentionally broad in technological reach yet specific in its application context: improving crop productivity, resource efficiency, and decision-making, while maintaining full control over data assets.

Modules Used

DS2 modules (e.g., ORC, PAE, RET, DRM) ensure data control, policy enforcement, and secure cross-dataspace interaction.

Offered Service

The service supports precision farming by offering AI-driven recommendations on irrigation, crop monitoring, and sourcing logistics. It helps farmers, agronomists, and cooperatives make real-time, data-informed decisions to boost productivity and sustainability. Delivered through the AgroNIT web platform1, the service combines dashboards, AI models, and a federated data marketplace. Core data products include IoT sensor data, high-res crop images, weather forecasts, and satellite indices. These are processed across two dataspaces: DigiAgro data space (run by UTH) and AgroScience data space (run by CROWNEST).

Functional Properties

Real-time monitoring of environmental and crop conditions via IoT sensors and cameras.
Visualization of weather forecasts, field metrics, and crop imagery.
Federated training of AI models at the edge (e.g., yield estimation).
Discovery, publication, and consumption of datasets and digital services within a federated marketplace.
Secure data exchange between independently operated dataspaces (DigiAgro DS and AgroScience DS).
Role-based access and dynamic policy enforcement for data usage and service invocation.

Modules Used in the Use Case