Papers
1
Total Citations
5
H-Index
1
About
Pin-Xin Lee has made notable contributions at the intersection of robotics, computer vision, and agricultural automation. His key research areas include robotic arm guidance, image data processing, and precision agriculture. Lee’s most cited work, "Quad-Partitioning-Based Robotic Arm Guidance Based on Image Data Processing with Single Inexpensive Camera For Precisely Picking Bean Defects in Coffee Industry" (2019), introduces a cost-effective approach to defect detection and removal in coffee processing. By employing a single low-cost camera and a quad-partitioning algorithm, Lee’s method enables precise robotic manipulation to identify and pick defective beans, significantly reducing waste and improving quality control in the coffee industry. This work has garnered 5 citations, reflecting its practical relevance and potential for scalable, affordable automation in resource-constrained settings. Lee’s research demonstrates a commitment to bridging advanced robotics with real-world agricultural challenges, offering accessible solutions that enhance productivity and sustainability. His achievements highlight the value of integrating simple hardware with intelligent software to address complex industrial tasks, making him a promising voice in the field of agricultural robotics and computer vision.
Research Focus
Key Achievements
Top Papers
- 1