Stefano Speranza

Papers

Total Citations

111

H-Index

About

Stefano Speranza is a leading researcher at the intersection of artificial intelligence and precision agriculture, with a primary focus on developing data-driven solutions for crop protection. His most impactful work centers on the early detection of pests using deep learning, particularly within the context of the H2020 European project PANTHEON for hazelnut orchard management. Speranza’s major contribution is a YOLO-based pest detection system, which has garnered 97 citations for its innovative application of computer vision to identify harmful insects in real time. This system enables farmers to implement targeted, timely interventions, significantly reducing crop damage and pesticide use. He further refined this approach with a comprehensive data-driven monitoring system (14 citations) specifically designed to detect gall-mites, a devastating pest for hazelnuts. By bridging the gap between cutting-edge AI and practical agricultural needs, Speranza’s work exemplifies how technology can enhance sustainability and food security. His research not only advances the field of precision agriculture but also provides a scalable framework for pest management in other crops, marking him as a key innovator in smart farming.