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About

Yun Ge is a leading researcher in agricultural robotics, with a focused expertise in the automation of safflower harvesting. Their work addresses the critical challenge of developing intelligent picking robots, integrating advanced path planning, computer vision, and 3D point cloud analysis. Ge’s most significant contribution is a series of novel algorithms for precise picking point localization, a fundamental bottleneck in robotic harvesting. Their highly cited 2022 paper on an improved ant colony algorithm for path planning (19 citations) established a foundation for efficient robot navigation in complex field environments. This was followed by the SBP-YOLOv8s-seg network (2024, 14 citations), which enabled real-time identification of picking points throughout the full harvest period. Most recently, Ge introduced PointSafNet (2025, 5 citations), a pioneering three-stage 3D point cloud framework that addresses the challenge of morphologically diverse plants. Cumulatively, their work has garnered significant attention, demonstrating a clear trajectory from theoretical path optimization to practical, deep-learning-based perception systems. Ge’s research is pivotal for advancing precision agriculture, directly impacting the viability of automated safflower harvesting.