Descrizione del progetto
Monitoraggio avanzato per modernizzare la PAC
Nell’ambito del processo in atto per la semplificazione e la modernizzazione della politica agricola comune (PAC) dell’UE, la Commissione europea ha adottato nuove regole per consentire l’utilizzo di una serie di tecnologie moderne durante lo svolgimento di controlli per i pagamenti territoriali della PAC. Ad esempio, la nuova tecnologia satellitare ridurrà il numero di ispezioni sul campo e i costi per l’amministrazione di controlli e verifiche. Sulla base di questi progressi, il progetto DIONE, finanziato dall’UE, sta sviluppando uno strumento di controllo dei pagamenti diretti per orientare una norma sull’attesa modernizzazione della CAP in merito all’impiego della tecnologia avanzata. Un sistema basato sull’apprendimento automatico sviluppato su scala regionale o nazionale valuterà gli attuali livelli di qualità del suolo per formare conclusioni basate su prove relative a eventuali impatti ambientali su un’intera regione.
Obiettivo
DIONE proposes a close-to-market (TRL7) area-based direct payments monitoring toolbox that will address the forthcoming Modernised CAP regulation of using automated technologies to ensure more frequent, accurate and inexpensive compliance checks. In particular, DIONE will:
(i) Capitalise on recent results of ESA’s SEN4CAP project that showcased the capability of Sentinel data to monitor the crop diversification rules. DIONE shall further integrate generated crop-type maps in a way directly exploitable by the paying agencies;
(ii) Include in the analysis the so far neglected EFA types (fallow land of all sizes, buffer strips, hedges, trees), by making use of super-resolution technology that improves the 10-20m Sentinel resolution to an improved resolution range (5-10m). This is enabled through Machine-Learning (ML) based post-processing and data fusion of Copernicus DIAS-sourced data with targeted drone-obtained data. This aims to motivate the use of such EFAs over the –of ambiguous environmental impact- use of productive areas (nitrogen-fixing crops and catch crops).
(iii) Complement the use of EO data with a system of reliable, ground-based geo-tagged photos, captured by the farmers that exploits (a) advances that allow for improved positional accuracy, (ii) low-footprint encryption techniques for improved data security and reliability and (iii) image detecting manipulation techniques (image forensics). The system will allow for an improved LC/LU annotation and ensure the process is untampered.
(iv) Implement a Green Compliance toolbox, integrated with the paying agencies’ aforementioned tools. This will benefit from (a) low-cost spectral sensors measuring soil quality and assessing the status of land-degradation in the land parcels and (b) an ML-based inferencing system deployed on a larger scale (regional, national) to quantify the levels of some of the monitored parameters and consequently extract tangible environmental performance metrics for an entire region
Campo scientifico
- natural sciencescomputer and information sciencescomputer securitydata protection
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- agricultural sciencesagriculture, forestry, and fisheriesagriculture
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
IA - Innovation actionCoordinatore
106 82 ATHINA
Grecia