Project description
Advanced monitoring for modernising CAP
As part of its ongoing move to simplify and modernise the EU’s common agricultural policy (CAP), the European Commission has adopted new rules to allow a range of modern technologies to be used when carrying out checks for area-based CAP payments. For instance, new satellite technology will reduce the number of field inspections and the costs for administrating controls and checks. Building on these advances, the EU-funded DIONE project is developing a direct payment controlling instrument to direct the expected modernised CAP rule on the use of advanced technology. A machine learning-based system developed on a regional or national scale will evaluate current soil quality levels to form evidence-based conclusions regarding eventual environmental impacts on an entire region.
Objective
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
Fields of science
- 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
Keywords
Programme(s)
Funding Scheme
IA - Innovation actionCoordinator
106 82 ATHINA
Greece