Integrated data sharing boosts farm advice across agriculture platforms
DS2-Based Cross-Data-Space Interoperability for Precision Agriculture
Distributed, Parallel, and Cluster Computing
Summary
Many farms use different devices and data systems that don’t easily talk to each other, making it hard to combine information. The authors worked on a system to let different agricultural data spaces share and use data securely and transparently. This allows farmers and experts to access combined information like sensor readings, satellite images, and weather forecasts to get better advice on things like when to water crops. The system helps farms make better decisions that improve crops while caring for the environment.
What this means in practice
- •For farm technology providers: Enable integration of diverse farm data sources to deliver unified crop management services that respect farmers’ data control.$Commercial implications: Makes possible commercial platforms combining farm IoT, satellite, and weather data to offer precise advisory tools for farmers.
- •For environmental monitoring teams: Use interoperable agricultural data spaces to collect comprehensive data that supports environmental impact assessments of farming practices.
Authors
Katerina Kyriakou, Ilias Syrigos, Ioannis Moutsinas, Panagiotis Tzimotoudis, Thanasis Korakis
Abstract
Despite the strategies of modern precision agriculture to leverage the integration of legacy agricultural systems, the challenges of IoT data fragmentation, farmers' sovereignty preservation, and limited interoperability still persist. This paper presents our work, conducted within the Horizon Europe DS2 project (DataSpace, DataShare 2.0), that applies an interoperability-oriented framework supporting participants of different agricultural data spaces to share data products and services under secure, sovereign, and transparent methods. The suggested methodology follows a layered reference architecture, where each layer consists of independent operational modules that facilitate the inter-sector data exchange between DigiAgro and AgroScience Data Spaces. The result of this work is an automated ecosystem for sharing diverse farm IoT measurements, satellite images and metrics, weather forecasts, and analytics services across distinct data spaces, aiming to generate accurate recommendations on crop practices, such as irrigation schedules, that farmers and agronomists will rely on to increase crop production while maintaining sustainability.