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About

Marianne Bakken is a leading researcher in agricultural robotics and precision agriculture, with a focus on developing autonomous navigation systems for crop management. Her work centers on integrating computer vision and deep learning to enable low-cost, flexible robotic platforms that can operate across diverse crop types and field conditions. Bakken’s major contributions include pioneering end-to-end learning approaches for crop row-following, which eliminate the need for extensive re-engineering when deploying robots in new environments. She also introduced robot-supervised learning techniques that automate label generation for semantic segmentation, significantly reducing the manual effort required for training neural networks in field applications. Her most cited paper, “Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields” (61 citations), demonstrates robust guidance systems for agri-robots. Bakken’s work on bin picking of reflective steel parts, using a dual-resolution CNN trained in simulation, showcases her versatility in applying deep learning to industrial robotics. With over 100 total citations, her research is driving the adoption of intelligent robotic systems in agriculture and manufacturing.