IEEE International Workshop on Trustworthy Agri-Data Management (TADM 2026)

Goal of the workshop

The workshop aims to bring together researchers, practitioners and policymakers working on multidisciplinary research relevant to data and AI-enabled decision making in the agricultural domain to explore the foundations of trustworthy agri-data management. It seeks to foster dialogue on technical, ethical and socio-economic aspects of agri-data governance, focusing on how to build secure, interoperable and value-driven data ecosystems that empower agricultural stakeholders. The ultimate goal is to promote the responsible and explainable adoption of smart farming technologies, ensuring that data-driven innovation in agriculture contributes to transparency, inclusiveness and sustainability.

Workshop theme

The workshop focuses on agri-data governance in agriculture and farming. It aims to create a multidisciplinary dialogue around trustworthy, explainable, and inclusive agri-data ecosystems, encouraging contributions that explore the intersection of data and AI-enabled technologies. The workshop will accept contributions related to:

(i) Adoption factors of data-driven technologies that enable farmers and agri-stakeholders to unlock the potential of data for sustainable and efficient farming;

(ii) enhanced data governance practices that ensure fair, secure, and transparent data sharing within trustworthy agri-data spaces;

(iii) artificial intelligence and machine learning approaches to predict smart agriculture; and

(iv) innovative business models and value-sharing frameworks that support equitable and community-based agri-data economies.

Scope of the workshop

Researchers and practitioners from academia, industry, and policy domains are invited to share novel approaches, methodologies, tools, and case studies that advance trustworthy data and AI technologies adoption in agriculture. Topics include (but are not limited to):
• Data sharing frameworks for agricultural systems
• Analysis of barriers and drivers for data sharing and reuse
• Privacy-preserving and anonymisation techniques for trustworthy agri-data ecosystems
• Cyber-risk assessment and resilience of agri-data systems
• Data quality assurance and trust frameworks in agri-data value chains
• Data ownership, rights management and governance models
• Responsible and trustworthy AI/ML models and tools for agri-stakeholder decision support
• Socio-ecological and economic value assessment of agricultural data
• Fair data exploitation principles and value-sharing mechanisms
• Community-based and data-driven business models for agri-data ecosystems
• Cross-sector collaboration and stakeholder engagement for responsible use of agricultural data