Papers
1
Total Citations
5
H-Index
1
About
Cheng Lin is an emerging researcher specializing in agricultural robotics, autonomous navigation, and deep learning-based computer vision. Their work sits at the intersection of precision agriculture and artificial intelligence, focusing on developing intelligent systems that enable agricultural robots to operate with greater autonomy and reliability in complex field environments. Lin's most notable contribution to date is a deep learning-driven navigation path extraction method utilizing the Res2net50 segmentation model, designed specifically for inter-ridge navigation in agricultural settings. This work directly addresses longstanding practical challenges in the field, including poor real-time performance and susceptibility to lighting interference — two critical obstacles that have historically limited the deployment of autonomous agricultural robots. By leveraging advanced neural network architectures, Lin's approach offers a more robust and efficient solution for ridge navigation route identification, representing a meaningful step forward in making precision agriculture systems more dependable under real-world conditions. Though early in their research career, with this 2023 publication having accumulated 5 citations, Lin is establishing a focused research identity in an increasingly vital domain. As global demand for agricultural automation continues to grow, their contributions to intelligent navigation systems position them as a researcher to watch in the smart farming and agri-robotics community.
Research Focus
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Top Papers
- 1