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.
