Chenghan Yang
Papers
2
Total Citations
18
H-Index
2
About
Chenghan Yang is a rising researcher in precision agriculture robotics, with key contributions in autonomous navigation and deep learning for crop monitoring. His work focuses on developing efficient, real-time solutions for agricultural robots operating in complex field environments. Yang’s most cited paper, "AgriPath," introduces a robust multi-objective path planning framework that enables robots to navigate dynamic fields with static and moving obstacles, dense vegetation, and uneven terrain—a critical advancement for safe, autonomous farming operations. His second major work, "AgriLiteNet," presents a lightweight neural network for multi-scale tomato pest and disease detection, achieving high accuracy and speed while being energy-efficient for edge-computing robots. Together, these papers have garnered 18 citations since 2025, reflecting their immediate relevance to the precision agriculture community. Yang’s research bridges the gap between theoretical robotics and practical farm deployment, addressing real-world challenges like obstacle avoidance and real-time pest identification. His work is particularly notable for its focus on resource-constrained platforms, making advanced AI accessible for small-scale and sustainable farming.
Research Focus
Key Achievements
Top Papers
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