Papers
2
Total Citations
30
H-Index
2
About
Yongkai Ye is a researcher whose work bridges machine learning, financial technology, and agricultural robotics. His key research areas include extreme learning machines, computer vision, and precision agriculture. Ye made a notable contribution with his development of an incremental multiple kernel extreme learning machine, applied to Robo-advisors—a study that has accumulated 22 citations and demonstrates how adaptive learning algorithms can enhance automated financial decision-making. More recently, his research has focused on agricultural automation, specifically addressing the challenge of broccoli maturity recognition and localisation under occlusion. Using an RGB-D instance segmentation network, his 2025 paper (8 citations) tackles the complex problem of identifying and locating crops in cluttered field environments, a critical step toward fully autonomous harvesting systems. This work highlights Ye’s ability to apply advanced deep learning techniques to real-world, occlusion-heavy scenarios. His contributions are particularly valuable for students and researchers interested in the intersection of machine learning, robotics, and sustainable agriculture, showcasing how algorithmic innovation can drive practical solutions in both finance and farming.
Research Focus
Key Achievements
Top Papers
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- 2