Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

As Artificial Intelligence (AI) continues to transform the public sector, particularly in the domain of public deliberation and democratic governance, it also presents distinct challenges. This chapter explores the insights from the ORBIS project, which is situated at the intersection of AI technology and democratic processes. The chapter will critically present and discuss how the project is contributing to advance AI-enhanced democratic and inclusive deliberations by developing AI tools that are ethically grounded and trustworthy by-design. These tools are designed to enhance the accessibility, understandability, and overall quality of deliberative processes, enabling broader participation and facilitating a deeper comprehension of diverse public opinions across various policy issues. By employing advanced data analytics, NLP models, and algorithms for distilling a large volume of public inputs into actionable insights, ORBIS serves as a privilege testbed for discussing how to engage stakeholders into the design of AI-based tools, ensuring their social relevance. This chapter focuses on how the processes of continuous and iterative stakeholder engagement and co-creation have been adopted to ensure alignment of AI functionalities with user needs and democratic values, thus exploring implications towards enhancing the legitimacy of the policymaking process. The discussion highlights how ORBIS serves as a model for integrating AI into scalable democratic practices for public decision-making, reflecting on both the technological advancements and ethical considerations essential for responsible AI deployment in public decision-making.

The chapter presents the process behind the development of the ORBIS toolkit as a set of software components and services de-signed to facilitate large-scale online deliberation through the combination of human (collective intelligence) and artificial intelligence technologies. The platform is built on a modular and flexible architecture that combines different AI heterogeneous components. The main layers of the toolkit are: (i) Enabling layer: Provides core services such as authentication, authorisation, and resources management. (ii) Data Layer: Manages deliberation content, participant profiles, AI models, and connects to external knowledge bases, (iii) Service Layer: Contains the key AI-powered deliberation functionalities such as Argument Mining, Explanations Generator, Feedback Aggregation, Conflict Detection and Policy Recommendation, and (iv) User Interface Layer: Composed by existing well-adopted deliberation services designed to consume in a seamless to the end-user and orchestrated way, the external deliberation augmentation services. The chapter points out the key innovations of ORBIS toolkit, which include: (i) argument visualisation and clustering to organise lengthy discussions, (ii) AI-assisted explanation and interpretation of complex issues, (iii) summarisation of key ideas and points in long deliberations, and (iv) reporting mechanisms to assist policy-makers in their decision making procedures. By combining collective and artificial intelligence, the chapter ultimately describes how ORBIS aims to achieve scaling of online deliberations while maintaining discussion quality. Ultimately, it is discussed how the further promising research that this open architecture invites, and how it can inform future development and advances in the emerging field of AI-augmented collective intelligence.