Lifeng Wu

Minzu University of China, Capital Normal University

Papers

Total Citations

H-Index

About

Lifeng Wu is a researcher whose work bridges agricultural engineering and energy systems, with a focus on precision agriculture and sustainable power management. In a key contribution to agricultural AI, Wu proposed a modified UNet3+ algorithm for green walnut image segmentation, addressing the critical problem of missed or false detections in natural environments. This work, published in 2024, integrates channel and spatial attention mechanisms to improve segmentation accuracy, offering a robust solution for automated walnut recognition—a task essential for smart harvesting systems. With 8 citations, this paper demonstrates Wu’s impact in applying deep learning to real-world agricultural challenges. Earlier, Wu made notable strides in mobile robotics by developing a hybrid power management strategy for Li-Fe battery and supercapacitor systems. The 2014 study introduced a two-phase power-optimization approach that balances high energy density with peak power demands, enabling mobile robots to handle fluctuating workloads efficiently. This work, also garnering 8 citations, showcases Wu’s versatility in addressing both computational and hardware challenges. Together, these contributions highlight Wu’s role in advancing intelligent systems for agriculture and robotics, with a clear focus on practical, deployable solutions.