Signe Marie Jensen

Signe Marie Jensen is an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Her research bridges statistical methodology with agricultural applications, focusing on precision agriculture and plant phenotyping. She leads innovative work applying computer vision and machine learning to solve critical agricultural challenges.

Education:

• MSc in Mathematical Economics, University of Copenhagen (2007)
• PhD in Applied Statistics, University of Copenhagen (2015)

Dr. Jensen's research interests center on statistical methods for simultaneous inference and analysis of phenotyping data. Her work has increasingly focused on applying advanced machine learning techniques, particularly YOLO algorithms, to agricultural challenges including pest detection, weed management, and plant phenotyping. She develops statistical frameworks that enable more accurate and efficient analysis of complex agricultural data.

Her recent publications reveal a strong trend toward interdisciplinary research combining statistics, computer vision, and plant science. The majority of her 2024-2025 publications focus on applying YOLO-based object detection systems to agricultural problems, particularly in precision farming applications. These works demonstrate her ability to translate statistical theory into practical agricultural technologies that address food security and sustainable farming challenges.

Dr. Jensen's research output shows strong international collaboration, with co-authors from multiple countries working on agricultural technology development. Her work on the PhenoLab platform represents a significant contribution to high-throughput plant phenotyping infrastructure.

She maintains active research in both theoretical statistics (simultaneous inference methods) and applied agricultural technology development, demonstrating remarkable versatility across these domains. Her current work on deep learning applications for crop monitoring represents the cutting edge of precision agriculture research.