Periodic Reporting for period 1 - DIVINE (DemonstratIng Value of agri data sharIng for boostiNg data Economy in agriculture)
Período documentado: 2022-10-01 hasta 2024-03-31
Agriculture is being managed more tightly than ever before and is generating more data than ever before, but the potential of a data economy in agriculture remains unexplored. However, the sharing of this data with others along the chain and its collective analysis needs more development and demonstration if more efficiencies are to be introduced and further value added to the agri-data economy. While some sharing is taking place on an ad-hoc basis, each new set of potential data sharers must start from scratch and work through the same issues common to all such arrangements. Several dimensions must be considered in policymaking if a fully functioning data economy in the agriculture domain is to emerge; DIVINE aims to build an agri-data ecosystem that incorporates existing common agri-data spaces while deploying industry-led pilots built on data sharing arrangements, to demonstrate the cost-benefit and added value in sharing agricultural data.
During the first reporting period, the following main activities have been carries out and the following main achievements have been accomplished in DIVINE:
- Related State of the Art review focusing on agri-data space elements, including the related policies and regulatory frameworks in place
- Identification of stakeholder requirements
- Extraction of technical requirements and specifications regarding agricultural data space functionality
- Definition of the DIVINE Reference Architecture (RA) for an agricultural data space ecosystem (ADSE)
- Design and implementation of the first release of the main RA software modules (including Agricultural Information Model+ (AIM+); interoperability support; Stakeholder Open Collaboration Space (SOCS); Agri-Data Management and Integration; Data Transparency, Protection, Trust, Sovereignty, Traceability & Usage Monitoring; Targeted agri data analytics, fusion and knowledge extraction; Agricultural domain benchmarking & KPI monitoring support; Transparent Decision Making Support for agri stakeholders; User Interfaces and Adaptive Dashboard Visualisations; etc.)
- Design, implementation and execution of the four pilots of DIVINE
- Design and implementation of multi-actor approach in DIVINE, involving farmers and other stakeholders of the agri-food chain (e.g. advisory services, ICT and other technology providers, agri data (space) providers, policy makers / regulators / standardisation bodies, etc.
- Related State of the Art review focusing on agri-data space elements, including the related policies and regulatory frameworks in place
- Identification of stakeholder requirements
- Extraction of technical requirements and specifications regarding agricultural data space functionality
- Definition of the DIVINE Reference Architecture (RA) for an agricultural data space ecosystem (ADSE)
- Design and implementation of the first release of the main RA software modules (including Agricultural Information Model+ (AIM+); interoperability support; Stakeholder Open Collaboration Space (SOCS); Agri-Data Management and Integration; Data Transparency, Protection, Trust, Sovereignty, Traceability & Usage Monitoring; Targeted agri data analytics, fusion and knowledge extraction; Agricultural domain benchmarking & KPI monitoring support; Transparent Decision Making Support for agri stakeholders; User Interfaces and Adaptive Dashboard Visualisations; etc.)
- Design, implementation and execution of the four pilots of DIVINE
- Design and implementation of multi-actor approach in DIVINE, involving farmers and other stakeholders of the agri-food chain (e.g. advisory services, ICT and other technology providers, agri data (space) providers, policy makers / regulators / standardisation bodies, etc.
During the first reporting period, DIVINE aimed to go beyond the current state of the art work in the following areas:
- Delivery of the DIVINE Reference Architecture that provides a reliable architectural framework for agricultural data spaces, addressing matters of data-space federation, interoperability, related policies, etc.
- Specification of the DIVINE agricultural data space ecosystem (ADSE) with facilities ensuring increased transparency in data sharing, data trust & sovereignty, data traceability & usage monitoring
- The novelty of AIM+ comes to the implementation of use cases that integrate policies and regulations that concern data sharing and governance. The model and its components are involved and contribute to ongoing standardization efforts.
- Achievement of enhanced semantic interoperability and greater transparency on data sharing & usage
- Framework supporting the performance evaluation of the DIVINE facilities and the data sharing costs, benefits, risks, and added values using KPIs in four areas: (i) agronomic performance; (ii) economic performance, (iii) environmental sustainability and (iv) societal impact.
- A self-service dashboard framework (catering web and mobile devices) has been developed, capable of integrating a number of service front-ends, transformation operators and visualisation widgets, implementing proven AI-based decision making approaches providing actionable advices while users will be able to provide their feedback on the DIVINE services’ performance.
- Delivery of innovative (AI-based) models involved in various agricultural operations such as the following: NDVI Super-resolution Model (Significantly improves the resolution of NDVI images, enabling more precise identification of terrain features and vegetation cover); Soil Organic Carbon Estimation Model (Estimates soil organic carbon levels using satellite imagery, a crucial indicator of soil fertility and water retention capacity); Vegetation Classification Model (Differentiates trees from other vegetation and assesses their health status, critical information for forest management and environmental conservation); NDVI Forecasting Model (Predicts NDVI values for grassland five days in advance, allowing for proactive irrigation scheduling, water management and grass clearing activities);
- Support for advanced Decision Making (via a system that integrates all data sources and model outputs to provide real-time decision support for various agricultural practices, namely grass weeding, irrigation, and soil health management), etc. In this respect, improvement in efficient farming practices towards sustainability is delivered; reduction of raw materials such as fertilisers and greenhouse gas emissions, especially carbon; delivery of near real time calculation of CAP relevant indicators based on agricultural practices recorded in the farm's calendar on a farm level and on a group of farms level; optimisation of resource usage and farming practices; etc.
- Delivery of the DIVINE Reference Architecture that provides a reliable architectural framework for agricultural data spaces, addressing matters of data-space federation, interoperability, related policies, etc.
- Specification of the DIVINE agricultural data space ecosystem (ADSE) with facilities ensuring increased transparency in data sharing, data trust & sovereignty, data traceability & usage monitoring
- The novelty of AIM+ comes to the implementation of use cases that integrate policies and regulations that concern data sharing and governance. The model and its components are involved and contribute to ongoing standardization efforts.
- Achievement of enhanced semantic interoperability and greater transparency on data sharing & usage
- Framework supporting the performance evaluation of the DIVINE facilities and the data sharing costs, benefits, risks, and added values using KPIs in four areas: (i) agronomic performance; (ii) economic performance, (iii) environmental sustainability and (iv) societal impact.
- A self-service dashboard framework (catering web and mobile devices) has been developed, capable of integrating a number of service front-ends, transformation operators and visualisation widgets, implementing proven AI-based decision making approaches providing actionable advices while users will be able to provide their feedback on the DIVINE services’ performance.
- Delivery of innovative (AI-based) models involved in various agricultural operations such as the following: NDVI Super-resolution Model (Significantly improves the resolution of NDVI images, enabling more precise identification of terrain features and vegetation cover); Soil Organic Carbon Estimation Model (Estimates soil organic carbon levels using satellite imagery, a crucial indicator of soil fertility and water retention capacity); Vegetation Classification Model (Differentiates trees from other vegetation and assesses their health status, critical information for forest management and environmental conservation); NDVI Forecasting Model (Predicts NDVI values for grassland five days in advance, allowing for proactive irrigation scheduling, water management and grass clearing activities);
- Support for advanced Decision Making (via a system that integrates all data sources and model outputs to provide real-time decision support for various agricultural practices, namely grass weeding, irrigation, and soil health management), etc. In this respect, improvement in efficient farming practices towards sustainability is delivered; reduction of raw materials such as fertilisers and greenhouse gas emissions, especially carbon; delivery of near real time calculation of CAP relevant indicators based on agricultural practices recorded in the farm's calendar on a farm level and on a group of farms level; optimisation of resource usage and farming practices; etc.