Thomas J. Peters
Papers
1
Total Citations
26
H-Index
1
About
Thomas J. Peters is a leading researcher at the intersection of precision agriculture and computer vision, whose work is pioneering the use of ground robots for real-time, field-based weed and crop detection. His most cited paper, "Field-based multispecies weed and crop detection using ground robots and advanced YOLO models" (2024, 26 citations), exemplifies his dual focus on data-centric and model-centric approaches. In this landmark study, Peters compared YOLOv8 and YOLOv9 models across four distinct field environments, ultimately developing a customized, lightweight model from YOLOv9base that enables efficient, real-time weed identification. His contributions are critical for reducing herbicide use and advancing sustainable farming through automated, site-specific weed management. By demonstrating that robust detection can be achieved under challenging field conditions, Peters has set a new standard for deployable agricultural robotics. His work is already influencing the next generation of smart farming technologies, making him a key figure in the push toward fully autonomous, data-driven crop management systems.
Research Focus
Key Achievements
Top Papers
- 1