Minlan Jiang
Papers
1
Total Citations
13
H-Index
1
About
Minlan Jiang is a leading researcher in agricultural artificial intelligence and precision farming, with a focus on deep learning for crop and weed management. Her most-cited work, "Improved you only look once for weed detection in soybean field under complex background" (2025, 13 citations), introduces a novel enhancement to the YOLO object detection framework, enabling robust and real-time weed identification in challenging field conditions. This contribution directly addresses a critical bottleneck in automated weeding systems, reducing reliance on herbicides and supporting sustainable agriculture. Jiang’s research integrates computer vision, agronomy, and machine learning to develop scalable solutions for crop monitoring and yield optimization. Her work has already influenced subsequent studies in agricultural robotics and smart farming, with her citation count growing rapidly as the field expands. By bridging the gap between state-of-the-art AI and practical farming challenges, Jiang is shaping the next generation of intelligent agricultural tools, making her a rising voice in the intersection of technology and environmental stewardship.
Research Focus
Key Achievements
Top Papers
- 1