R&D Project / SPADA
SPAtialized Dss tools for precision Agriculture
R&D PROJECT: AGRITECH
SPAtialized Dss tools for precision Agriculture
Status: Active
Period of reference: 01 July 2021 – 31 August 2025
Funder: SMACT Competence Center – Bando IRIS (PNRR – M4C2)
Grant agreement ID: H89J24001620004
Abinsula Role: Project coordinator
Reference Personnel: Antonio Solinas, Francesco Martini
Contact: antonio.solinas@abinsula.com
The SPADA project (SPAtialized DSS tools for precision Agriculture) is implemented within the Italian National Recovery and Resilience Plan (PNRR), Mission 4 – Component 2 – Investment 2.3, through the IRIS call promoted by the SMACT Competence Center.
Its main objective is the development of a spatialized Decision Support System (DSS) for precision monitoring and advanced management of arable crops, with particular focus on cereals, supporting the ecological and digital transition of agriculture through the integration and analysis of remote sensing and in-field sensor data to optimize environmental and economic sustainability.
The project is led by Abinsula S.r.l., specialized in IoT, embedded, and digital solutions, in collaboration with the SMACT Competence Center and with the technical support of Primo Principio for agronomic modelling. Local stakeholders were also involved, including the Sardinian durum wheat supply-chain network “Sardo Sole”.
From a technical perspective, SPADA consists of a SaaS platform integrating IoT monitoring networks, Sentinel-2 satellite imagery, and predictive mathematical and bioinformatic models. The system includes modules for soil water balance and irrigation needs, mycotoxin risk assessment, and soil workability and trafficability analysis. The use of vegetation and moisture indices (e.g., NDVI, NDMI) enables spatialized recommendations within each field, reducing uncertainty compared to non-spatial DSS approaches.
The project demonstrated potential water savings of 30–40% and improved soil-conservative management practices.
Within SPADA, Abinsula acts as project leader, overseeing research and experimental development, designing data-analytics algorithms, cloud architecture, and web interfaces, and ensuring interoperability between models and data sources through REST-based services, including dynamic calibration to adapt simulations to site-specific agricultural conditions.



