Hongchao Cheng
Papers
7
Total Citations
265
H-Index
5
About
Hongchao Cheng is a leading researcher in agricultural robotics and computer vision, specializing in deep learning-based detection and pose estimation for fruit harvesting. His work focuses on overcoming occlusion challenges in unstructured farm environments, particularly for strawberries and tomatoes. Cheng’s most influential contribution is the **DSW-YOLO** model (107 citations), which introduced a robust detection method for ground-planted strawberry fruits under varying occlusion levels, significantly improving real-time harvesting accuracy. He further advanced the field with **YOLOv10-pose** and **YOLOv9-pose** (43 citations), enabling real-time strawberry stalk pose detection—a critical step for robotic grasping. His **STRAW-YOLO** (38 citations) and **Tomato 3D pose detection** (48 citations) algorithms integrate keypoint detection with point cloud processing, providing comprehensive visual information for picking robots. Cheng’s multitask convolutional neural network approach (22 citations) unifies fruit detection, pose estimation, and occlusion handling, setting a new standard for precision agriculture. With over 260 total citations across his core papers, his work directly addresses practical bottlenecks in automated harvesting, making him a key figure in bridging computer vision and agricultural robotics.
Research Focus
Key Achievements
Top Papers
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- 4STRAW-YOLO: A detection method for strawberry fruits targets and key points38 citations · 2025
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