Papers
2
Total Citations
20
H-Index
2
About
Shilin Li is a researcher at the forefront of two transformative fields: precision agricultural robotics and next-generation energy storage. Their work bridges the gap between intelligent perception systems and sustainable power solutions for autonomous machines. In agricultural technology, Li pioneered a lightweight, attention-based neural network built on the YOLOv5 architecture for real-time detection of field flat jujubes, achieving high accuracy with low computational complexity—a critical breakthrough for automated fruit picking systems. This work, cited 18 times, directly addresses the core challenge of efficient target identification in unstructured field environments. Simultaneously, Li is advancing energy storage for mobile robotics through defect-engineered MnO₂@Ni foam electrodes for aqueous zinc-ion batteries. This innovative approach tackles the practical limitations of AZIBs, offering a safer, more environmentally friendly, and cost-effective power source for autonomous robots. By integrating cutting-edge computer vision with materials science, Li’s research demonstrates a rare ability to solve interconnected challenges—enabling robots to both see their environment and sustain their operation. Their dual focus on algorithmic efficiency and electrochemical performance positions them as a key contributor to the next generation of intelligent, self-powered agricultural machines.
Research Focus
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Top Papers
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