Jiangtao Ji
Papers
9
Total Citations
206
H-Index
5
About
Jiangtao Ji is an agricultural robotics researcher whose work sits at the intersection of machine vision, intelligent control systems, and precision automation for modern farming. His most influential contribution, a 2020 study on fuzzy PID controller-based hydraulic transplanting robots (146 citations), established him as a leading voice in automating seedling transplantation — a labor-intensive bottleneck in facility agriculture. Building on this foundation, Ji has developed sophisticated visual perception systems using depth cameras such as the Intel RealSense D415 and Kinect to enable seedling edge recognition, obstacle avoidance, and selective picking, significantly reducing transplantation losses and crop damage. More recently, Ji has expanded his research portfolio into mushroom harvesting automation, developing the lightweight FES-YOLOv5s detection model to accurately identify *Agaricus bisporus* in complex, occluded environments and integrating it into intelligent harvesting devices. He has also contributed to autonomous agricultural navigation through deep learning-based ridge path extraction using Res2Net50. Collectively, his publications reflect a coherent mission: replacing costly, inefficient manual agricultural labor with vision-guided robotic systems. With nearly 200 cumulative citations, Ji's work is making meaningful strides toward scalable, intelligent precision agriculture.
Research Focus
Key Achievements
Top Papers
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- 4FES-YOLOv5s: A Lightweight Model for Agaricus Bisporus Detection7 citations · 2024
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- 7Obstacle avoidance transplanting method on Kinect visual processing4 citations · 2021
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