Yifan Wei

About

Yifan Wei’s research lies at the intersection of agricultural robotics and intelligent vision systems, with a strong focus on enabling precise, real-time weed management through deep learning. His most impactful work centers on developing efficient semantic segmentation models that can run on resource-constrained embedded devices—critical for deploying smart weeding robots in the field. His 2024 paper, “A hybrid CNN-transformer network: Accurate and efficient semantic segmentation of crops and weeds,” has already garnered 6 citations, reflecting its timely contribution to sustainable agriculture. Wei also explores robotic manipulation and locomotion, as seen in his studies on an under-actuated dexterous hand and biped robot gait planning. His 2023 work on attention-aided lightweight networks further underscores his commitment to balancing accuracy with hardware efficiency, directly addressing the global challenge of weed control while reducing reliance on herbicides. By advancing vision algorithms that are both powerful and deployable, Wei is helping to build a future of environmentally friendly, labor-saving precision agriculture.

Research Focus

Key Achievements

Top Papers

1. 1
   A hybrid CNN-transformer network: Accurate and efficient semantic segmentation of crops and weeds on resource-constrained embedded devices

   6 citations · 2024
2. 2
   Analyses of a Novel Under-Actuated Double Fingered Dexterous Hand

   3 citations · 2017
3. 3
   The Research on the Method of Gait Planning for Biped Robot

   3 citations · 2017
4. 4
   Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation

   2 citations · 2023

Key Collaborators

Contact & Links