Max Robotics — Global Service Robot Directory + US Certification

About

Dae-Hyun Lee is a leading researcher in agricultural robotics and precision farming, specializing in computer vision and deep learning for autonomous agricultural systems. His work focuses on enabling robots to perceive and interact with crops and orchards through stereo vision, 3D imaging, and convolutional neural networks. Lee’s most impactful contribution is his stereo-vision-based crop height estimation method for agricultural robots, which has garnered 103 citations and provides a foundational tool for automated plant monitoring. He also developed a patch-based CNN approach for path detection in orchards (68 citations), advancing autonomous navigation in unstructured environments. His notable work on 2D pose estimation of multiple tomato fruit-bearing systems for robotic harvesting (36 citations) directly supports the development of selective harvesting robots. Lee has also contributed to tillage boundary detection for autonomous tractors and real-time pose estimation of melon fruit-pedicel pairs, demonstrating the breadth of his impact across diverse agricultural tasks. His research consistently bridges the gap between machine vision and practical field robotics, making him a key figure in the push toward fully autonomous farming.