Paolo Rommel Sanchez
Papers
2
Total Citations
11
H-Index
2
About
Paolo Rommel Sanchez is a rising researcher at the intersection of precision agriculture and deep learning, whose work is pioneering the use of computer vision for autonomous weed control. His research focuses on developing and optimizing vision-based systems for agricultural robots, enabling them to accurately detect and manage weeds in real-time field conditions. Sanchez’s major contributions include a landmark 2021 study comparing one-stage object detection models—Scaled-YOLOv4-CSP, YOLOv5s, and SSD Mobilenet V2—for weed detection in mulched onions, which demonstrated that YOLOv5s offers the best balance of speed and accuracy for practical deployment. This work has garnered 8 citations, establishing a foundation for efficient, low-latency detection in crop management. In a subsequent 2022 study, Sanchez advanced the field by using simulation to systematically evaluate how travel velocity, inferencing speed, and camera configurations affect CNN-based plant detection performance, addressing critical optimization gaps that limit real-world robotic efficiency. His simulation-aided approach provides a scalable framework for developing robust vision modules without extensive field trials. Sanchez’s research is directly shaping the next generation of intelligent farming equipment, making him a key voice in sustainable, data-driven agriculture.
Research Focus
Key Achievements
Top Papers
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