Papers
3
Total Citations
65
H-Index
2
About
Yonglin Han is a researcher at the forefront of agricultural robotics and human–machine interaction, with key contributions spanning computer vision, deep learning, and biomedical signal processing. His most impactful work addresses the challenge of green citrus detection in natural environments—a critical bottleneck for intelligent harvesting—where he developed a deep convolutional neural network method that significantly improves detection accuracy and robustness despite the color similarity between fruit and foliage (52 citations). Han also pioneered a novel cognition framework for citrus picking robots, inspired by human visual attention mechanisms, to optimize harvesting sequence planning using RGB-D data and YOLO-based detection. Extending his expertise to human augmentation, he has advanced lower limb rehabilitation and exoskeleton control by developing multijoint continuous motion estimation techniques from surface electromyography signals, enabling more natural and responsive robotic assistance. With a growing citation record and work published in 2021 and 2025, Han’s research bridges the gap between intelligent agricultural systems and assistive robotics, demonstrating a clear trajectory toward more autonomous, perceptive machines that operate effectively in complex, unstructured environments.
Research Focus
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