Chenghai Yin
Papers
5
Total Citations
86
H-Index
4
About
Chenghai Yin is a prominent researcher specializing in agricultural robotics, computer vision, and intelligent harvesting systems, with a particular focus on automating fruit detection and picking in complex natural environments. His work sits at the intersection of deep learning and precision agriculture, where he has made significant contributions to advancing the capabilities of robotic harvesting technology. Yin is best known for developing innovative adaptations of lightweight neural network architectures for real-world orchard conditions. His highly cited 2022 study on dragon fruit detection, which has garnered 34 citations, introduced an improved YOLOv5s model incorporating ghost modules and coordinate attention mechanisms, enabling reliable all-weather fruit detection. Building on this foundation, his YOLOMS framework—a multi-task CNN model for mango recognition and picking-point localization—has attracted 26 citations and directly informed the development of a sophisticated dual robotic arm mango harvesting system, demonstrating a rare integration of perception and physical automation. His motion planning work using an improved RRT-Connect algorithm further addresses the practical challenges of robotic harvesting in cluttered environments. With nearly 90 citations across his most recognized publications, Yin's research is shaping the future of autonomous agricultural machinery, offering practical solutions to labor shortages and inefficiencies in tropical fruit harvesting.
Research Focus
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