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About

Davide Quaglia is a leading researcher at the intersection of cyber-physical systems, agricultural robotics, and Industry 4.0 safety. His work focuses on leveraging intelligent sensing and predictive modeling to solve real-world challenges in dynamic environments. A key contribution is his development of a novel approach for greenhouse climatic sensing, where he integrates agricultural robots with Recurrent Neural Networks to overcome the limitations of static sensor networks, enabling precise, cost-effective microclimate monitoring (2023, 6 citations). In the industrial domain, Quaglia addresses critical safety and privacy concerns in smart manufacturing. He has pioneered process-driven collision prediction models for human-robot work environments, demonstrating how long in advance collisions can be anticipated to prevent injuries and downtime (2022, 3 citations). His work on the ICE Laboratory case study further explores enhancing safety and privacy protocols in Industry 4.0 settings (2024, 4 citations). By combining robotics, machine learning, and systems engineering, Quaglia’s research is shaping safer, more efficient, and intelligent automation for both agriculture and industry.