Szukaj

This report details the findings and methodologies related to the simulation and remotisation tools utilized within the AgrifoodTEF project. The Simulation Software section provides an overview of various tools, including their functionalities and applications, supported by case studies that illustrate their effectiveness in agricultural contexts. The Remotisation Software section similarly explores tools that facilitate remote control and monitoring of agricultural processes, detailing their integration and practical use cases. The Discussion and Future work section synthesizes the key findings from the previous sections, highlighting their implications for the agricultural sector and identifying areas for improvement. This section also outlines the plans for ongoing evaluation and development of these tools, emphasizing the project's commitment to continuous enhancement and adaptation to meet evolving agricultural challenges.

While other work packages (WP) of the project focus on physical and digital facilities for technical assessment of AI and Robotics solutions, mainly aiming at performance, quality and safety, WP 3 emphasizes aspects such as ethical, legal, societal, lifecycle, and cybersecurity assessments. These aspects are essential within the existing regulatory and standardization framework, particularly in the AI Act, the Machinery Regulation, the Data Act, and the Cybersecurity Act. The objective of WP 3 is to conduct a detailed analysis of the associated requirements and enable the project to offer a range of services that effectively address these aspects. Particularly as the agrifood sector faces significant challenges in achieving regulatory compliance for AI and robotics solutions due to the inherent complexity of agricultural environments and the evolving nature of AI technologies. This deliverable explores these challenges and provides a comprehensive analysis of regulatory frameworks and best practices to support TEF partners in navigating fields of application of AgrifoodTEF’s relevant standards and conformity assessment processes. The deliverable also delves into key regulatory frameworks such as the AI Act, Machine Regulation, and Cybersecurity Act and others from the New Legislative Framework, emphasizing their relevance to the agrifood sector. CE Marking is highlighted as a critical step for market readiness, offering competitive advantages and fostering trust among stakeholders. The analysis identifies gaps in existing standards and comments on strategies for harmonization and future development. AgrifoodTEF’s role is to provide comprehensive support to clients, offering tools, infrastructures, and expertise to streamline compliance efforts. This deliverable serves as a roadmap for achieving regulatory alignment and optimizing market adoption of AI and robotics solutions in the agrifood sector, with actionable recommendations for stakeholders to address identified challenges and gaps.

This deliverable D3.7 presents the finalised catalogue of ELSA (Ethical, Legal, and Social Aspects) services developed within the AgrifoodTEF project and evaluates their operational performance during the validation phase. The scope of the document encompasses a detailed description of ten specific services provided by European partners, ranging from hazard analysis to ethical scanning and legal guidance, all designed to support the agri-food sector in responsible artificial intelligence (AI) development and navigating complex regulatory environments. Beyond merely listing these services, the report synthesises empirical data collected through customer feedback, national stakeholder workshops, internal cross-service reflections, and developer self-assessments to determine their actual utility and performance. By merging these external market signals with internal operational insights, the report aims to validate the transition of the ELSA infrastructure from a theoretical pilot phase into a robust, industrial-grade ecosystem capable of de-risking agricultural AI. The primary objective of this deliverable is to assess the performance of the TEF ELSA services in transitioning the ELSA infrastructure from a theoretical framework into a validated operational reality that supports the market introduction of trustworthy AI and robotics in Europe. By analysing the performance data, this report aims to demonstrate how these ELSA services effectively bridge the gap between laboratory prototypes and market deployment for customers, ensuring that technological innovations are ethically sound, legally compliant, and socially acceptable. Furthermore, the document identifies structural challenges and strategic pathways for the future of these ELSA services, aiming to establish a sustainable, long-term service ecosystem that assists stakeholders in the farm-to-fork chain. The following findings, representing preliminary evidence, are more related to relatively more robust and mature ELSA services, and therefore, they may not be interpreted as statistically generalisable performance metrics for the entire portfolio. Still, new ELSA services can benefit from the results to improve their potential performance.

The task "Technology Outlook and Use Cases" within the agrifoodTEF project aims to continuously monitor the latest technologies and use cases in the field of testing and validation of AI solutions and advanced technologies for the agrifood sector. A core focus of the task is collaboration with a broad network of ecosystem partners, with particular attention given to the needs of small farms and businesses. This task is an ongoing effort throughout the project’s duration. The deliverable "D1.2 Online Technology and Use Cases Outlook Y2," produced at the end of the second year of the project (M24), is a key outcome of this task. It provides an overview of the activities conducted so far, the technological trends identified, and the business-focused use cases analysed, considering the needs and requirements of technology firms. The findings serve as a basis for guiding the development and enhancement of the agrifoodTEF infrastructure, including both physical services offered at dedicated facilities and updates to the digital infrastructure and online services. This report documents the steps taken and the data collected, offering recommendations for further refinement of services within the project. The report “Online Technology & Use-Cases Outlook Y2” was conceived as an operational guide rather than a deskbound study. Its purpose is to help agrifoodTEF partners decide which technologies, skills and infrastructure merit investment over the coming years – and how to translate those choices into services that reach the market. Accordingly, it speaks simultaneously to the TEF strategic team, to service-line managers, and to SMEs and start-ups seeking a venue in which to validate their ideas.

This deliverable provides the third-year progress report on the development, implementation, and refinement of the agrifoodTEF Training Catalogue. Its purpose is to document the training activities carried out in 2025, present the updated catalogue and its methodological foundations, report on needsdriven adaptations, and evaluate the effectiveness of delivered training formats. The deliverable also addresses feedback from the Y2 review and the interim EC evaluation, which requested a more structured catalogue, clearer learning objectives, alignment with competency frameworks, and concise reporting focused on actions and outcomes. These recommendations are fully reflected in the updated methodology and in the catalogue entries produced in 2025. Finally, the report outlines the role of the training catalogue within agrifoodTEF’s broader mission: strengthening EDIH capacity to guide SMEs through testing, validation, AI adoption, robotics integration, and data-driven innovation in the agrifood sector, within a complementary European support model in which agrifoodTEF focuses on pre-market testing and experimentation, while EDIHs support market uptake and adoption by SMEs and farmers.

This deliverable represents the fourth iteration of the plan for dissemination and communication activities. It refines the overall strategy and planned initiatives considering the progress achieved and insights gained from previous versions submitted in M06, M12 and M24. In Y3, the agrifoodTEF team has continued to adopt targeted multichannel actions and campaigns to ensure effective engagement with its key stakeholder groups. In comparison with D5.1 V3, this deliverable details stakeholder-engagement activities at node level and across the consortium too. Following this version, further updates of the report are planned at M48 and M60 to align communication and dissemination activities with the project’s evolving priorities and full-scale operation.

This deliverable presents a strategic and operational framework for the development and implementation of regulatory sandboxes within the AgrifoodTEF project. Regulatory sandboxes are controlled testing environments where innovative technologies—such as AI, robotics, drones, and data-driven systems—can be tested in real-world conditions, including in cases where current regulations may not yet accommodate such innovations. In the agri-food sector, which faces urgent challenges related to climate change, food safety, and sustainability, regulatory sandboxes serve as a critical mechanism to bridge the gap between innovation and regulation. They allow for supervised experimentation without requiring full compliance with market regulations from the outset, thereby accelerating technological development while preserving public safety and legal oversight. Special attention is given to the legal requirements of the AI Act and the supporting role that AgrifoodTEF can play as a facilitator and infrastructure provider, not as a legal authority, but as a partner offering testing facilities, legal and technical expertise, and deep domain knowledge. The deliverable includes practical examples of sandbox cases across Europe (Belgium, Spain, Sweden, Austria, Denmark, France, and Poland), highlighting use cases such as autonomous drones, virtual fencing for livestock, and AI for food quality control. These cases illustrate how technology providers and regulators collaborate to test innovative solutions under real-world constraints. Conclusion: Regulatory sandboxes within AgrifoodTEF offer a powerful tool to enable safe, responsible, and forward-looking innovation in the agri-food sector. By providing a structured and collaborative environment for testing emerging technologies, they support the development of better regulation, reduce time-to-market for critical innovations, and strengthen public trust in AI and robotics applications in agriculture.