Papers
2
Total Citations
51
H-Index
2
About
Mao-Yuan Pai is a researcher at the forefront of applying deep learning and computer vision to industrial automation, with a particular focus on the coffee production chain. His primary research areas include automated visual inspection, robotic guidance systems, and data augmentation techniques for manufacturing quality control. Pai’s most impactful work, "Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry" (2019, 46 citations), addresses a critical bottleneck in coffee processing: the labor-intensive removal of defective beans. By integrating Generative Adversarial Networks (GANs) for automated data augmentation, he significantly improved the robustness and accuracy of deep-learning-based defect detection, reducing reliance on manual labeling. In a complementary study, "Quad-Partitioning-Based Robotic Arm Guidance Based on Image Data Processing with Single Inexpensive Camera For Precisely Picking Bean Defects in Coffee Industry" (2019, 5 citations), Pai demonstrated a cost-effective method for guiding robotic arms to precisely remove defects using only a single camera. His contributions are notable for bridging the gap between advanced AI techniques and practical, low-cost automation solutions, directly impacting efficiency in the coffee industry and offering a scalable model for other agricultural sorting tasks.
Research Focus
Key Achievements
Top Papers
- 1
- 2