Papers
2
Total Citations
44
H-Index
2
About
Heng Fu is a leading researcher in agricultural robotics and intelligent perception systems, with a primary focus on enhancing automation for fruit harvesting. His work addresses critical challenges in orchard environments, particularly the accurate detection of occluded apples under variable lighting conditions—a persistent bottleneck in harvest robotics. Fu’s landmark paper, "MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics" (2024), has already garnered 28 citations for its novel approach to reducing missed and false detections, significantly improving recognition accuracy for occluded fruit. Building on this, his subsequent study, "Dynamic Task Planning for Multi-Arm Apple-Harvesting Robots Using LSTM-PPO Reinforcement Learning Algorithm" (2025), with 16 citations, pioneers a deep reinforcement learning framework that integrates Long Short-Term Memory networks with Proximal Policy Optimization. This work enables multi-arm robots to dynamically plan picking tasks, addressing labor shortages and rising costs in agriculture. Fu’s contributions are pivotal for advancing autonomous harvesting systems, blending computer vision, machine learning, and robotic coordination. His research not only pushes the boundaries of precision agriculture but also offers scalable solutions for real-world deployment, making him a notable figure in the field of agricultural robotics.
Research Focus
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Top Papers
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