Papers
2
Total Citations
93
H-Index
2
About
Yuhao Ge is a researcher at the forefront of agricultural robotics and precision farming, specializing in the application of deep learning and computer vision for intelligent greenhouse automation. His work focuses on developing robust visual perception systems that enable robots to interact with crops at critical growth stages. Ge’s major contributions include the creation of YOLO-Deepsort, an innovative tracking and counting network that integrates ShuffleNetv2 and CBAM attention mechanisms into the YOLOv5s architecture. This system, detailed in his highly cited 2022 paper (76 citations), allows inspection robots to accurately monitor and count tomatoes across different growth periods, directly supporting yield prediction. He has also advanced pollination robotics by designing a neural network with an attention mechanism and additional feature fusion layer to reliably detect tomato flowering phases under complex lighting conditions (17 citations). By solving the challenges of occluded, variable, and dense agricultural scenes, Ge’s work bridges the gap between state-of-the-art computer vision and practical, real-world farming needs, paving the way for fully autonomous crop management and robotic pollination systems.
Research Focus
Key Achievements
Top Papers
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