The Green ERA-Hub (GEH) represents 15 currently active EU ERA-Net Cofunds and self-sustained initiatives in the field of Agri-food and biotechnology. Together with already ended predecessor initiatives, they represent 29 co-ordination actions in which more than 1100 Mio Euro funding has been mobilised via 89 transnational calls. The ERA-Net scheme is a success model for transnational co-operation of national research programmes. The partners consider GEH an ideal measure to continue and extend this fruitful co-operation. The aims of GEH are to: - continue to build on previous achievements and further enhance cross-sector collaborations between Agri-food and biotechnology ERA-Nets, through joint transnational funding of collaborative research projects - valorise and implement joint activities supporting the market, regulatory or societal uptake of results after the end of individual ERA-Nets - identify common research and innovation priorities, agreed upon by the participating national programmes, and address them via new joint calls - preserve best practice and managerial competences - contribute to the planning and complement the implementation of the new HEU Partnerships and Missions - broaden the actions and impact of initiatives towards stakeholders and in terms of geographical coverage - contribute to achieve the strategic goals of the SDGs, in particular zero hunger, industry innovation and infrastructure, responsible consumption and production, life on land, partnership for the goals, the farm2fork strategy and EU's Green Deal. GEH will provide clear perspectives for ERA-Net (Cofunds), national research funders and researchers in the Agri-food and biotechnology sector to find dedicated support at the level of information, communication, co-operation, networking and joint transnational funding that best fits their needs. In turn, GEH will contribute to maintaining and strengthening EU's highly competitive position in Agri-food and biotechnology research.

EmergeNOW uses digital, molecular tools for rapid CBS, HLB detection, enabling early warning at EU import points. Consortium introduces novel tools and autonavigated robots for inspectors: a) on-site Phyllosticta citricarpa (P.c) and Candidatus Liberibacter’ (Ca. L) species rapid detection a molecular tool based on Recombinase Polymerase Amplification kits, b)Fluorescent Array-based Sensing Technology for P.c and Ca.L rapid detection, c) Artificial Intelligence- powered mobile apps for rapid CBS and HLB image analysis detection integrated with low-cost hyperspectral imagers, d) UAVs for AI-based RGB and hyperspectral imaging of abiotic and biotic symptom response of CBS and HLB and similar to these; e) Autonomous Mobile Robots for HLB and CBS AI imaging symptoms detection discriminate from abiotic stress. Also, deployment plans are in place for detecting the vector Diaphorina citri (ACP), Trioza erytreae (T.e ), and Cacopsylla citrisuga (C.c) by: f) e-Nose sensors, g) AI - based robotic traps for real time detection and monitoring, and h) AI-powered mobile apps for their early detection. Τhese systemic innovations could be incorporated in the EFSA Survey Cards. Digital tools link in systemic alert system with blockchain, ML analytics and will enable informed decisions for import control. Focusing on showcasing real problem scenarios, the developed tools and methods will be initially tested and optimised in biosecure enviroments at Plant Health Centers (PHC) and afterwards validated in citrus orchards in USA (CBS, HLB, ACP), Uganda (CBS, HLB, T.e), Vietnam (HLB, ACP, C.c), and in Cyprus (ACP). The optimised tools will be demonstrated in authorized Border Control Posts in Greece, Italy, Spain and Cyprus.

Forests have an important role in the achievement of the objectives of the European Green Deal. Forest trees are increasingly threatened by invasive pests, with many of them being regulated in the Union territory. FORSAID has as an overall goal to develop a comprehensive combination of innovative digital technologies aimed at detecting regulated forest pests at an early stage, surveying their occurrence in the territory, and providing essential information for the adoption of phytosanitary measures to limit their spread and impacts. The project adopts a multi-actor and multidisciplinary approach tailored to develop and favour the adoption of digital technologies at different spatial and temporal scales associated with a selected list of important regulated forest pests. The Internet of Things (IoT) will be used to create networks of insect traps for major guilds of insects, thanks to innovative deep learning analysis of images sent remotely from the traps. Robotized devices will be developed and tested for the automatic barcoding of the captured pests. Drones equipped with sensors will be used for the scanning of plant health status through the measure of physiological variables. Remote sensing techniques will be used to validate existing ground-truth data on the occurrence of tree alterations associated with abiotic and biotic factors, and models based on Artificial Intelligence (AI) and machine learning (ML) will be developed to discriminate different types of stresses as soon as they appear. An economic analysis will address the costs and benefits of using digital technologies for the detection and surveillance measures, considering the economic, environmental, and social impacts of regulated pests in EU forests. Stakeholders from the forest sector will be involved in a multi-actor approach to drive the research to applicable results and co-construct guidelines for the best use of new digital technologies for forest pest detection and monitoring.