Papers
3
Total Citations
17
H-Index
2
About
Jinglin Liu is a researcher whose work spans electrical engineering and precision agriculture, with a focus on motor design and intelligent animal monitoring. Liu’s early contributions include advancing the performance of outer rotor Interior Permanent Magnet Synchronous Motors (IPMSM), where they developed methods to reduce cogging torque—a key factor limiting precision in servo applications such as robotics and automotive control. This foundational work has garnered 10 citations and established Liu’s expertise in electromechanical systems. More recently, Liu has pioneered the integration of deep learning and digital twin technologies in poultry farming. Their 2025 study on detecting beak deformities in caged layer chickens, with 5 citations, addresses the critical need for real-time, automated health monitoring in large-scale farms, replacing inefficient manual inspection. Liu further advanced this field with DualHet-YOLO, a dual-backbone heterogeneous YOLO network designed for inspection robots to recognize behaviors of yellow-feathered chickens in floor-raised houses. This work, with 2 citations, enables precise behavioral analysis linked to health and environmental comfort, directly improving breeding practices. Liu’s research demonstrates a unique ability to apply cutting-edge AI and digital twin solutions to practical agricultural challenges, bridging engineering innovation with sustainable farming. Their work is increasingly relevant as the industry seeks efficient, data-driven approaches to animal welfare and production optimization.
Research Focus
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