Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case...

Area of Science:

• Agricultural Science
• Environmental Science
• Computer Science

Background:

• The agri-food sector requires significant transformation for net-zero transitions, necessitating innovations in production, delivery, technology, and data infrastructure.
• Achieving net-zero emissions demands fundamental changes in food systems, including advanced technologies and data-driven approaches.

Purpose of the Study:

• To explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector.
• To investigate the benefits of Internet of Things (IoT) sensors and AI for resource efficiency, carbon footprint reduction, and decision-making in soft-fruit production and brewery operations.

Main Methods:

• Case studies focusing on soft-fruit production and brewery operations.
• Analysis of AI and IoT sensor integration for sustainability improvements.
• Identification of barriers to technology adoption and data quality issues.

Main Results:

• AI and IoT sensors show potential for improving resource use, reducing carbon footprints, and enhancing decision-making.
• Key challenges include user engagement, data quality affected by environmental volatility, model generalizability, and socio-technical barriers.
• User engagement, granular data availability, and transparent carbon calculations are crucial.

Conclusions:

• AI is a valuable tool, not a silver bullet, for achieving net-zero goals in the agri-food industry.
• AI solutions must be appropriately designed and deployed in synergy with other approaches.
• Future directions include semantic data integration, synthetic data generation, and multi-objective optimization systems.