Peiliang Guo
Papers
6
Total Citations
179
H-Index
4
About
Peiliang Guo is an emerging researcher at the forefront of agricultural robotics and computer vision, specializing in deep learning-based navigation systems for smart farming applications. His work centers on developing advanced algorithms that enable autonomous agricultural robots to navigate crop fields with precision, with a particular focus on corn and maize cultivation. Guo's most significant contributions lie in adapting and improving state-of-the-art deep learning architectures — notably YOLOv8s and UNet — for real-world agricultural challenges. His 2023 navigation line extraction algorithm for corn spraying robots garnered 73 citations within its first year, reflecting the immediate relevance of his approach to the agricultural robotics community. Complementing this, his improved UNet-based maize crop row recognition algorithm accumulated 60 citations, establishing him as a key contributor in visual navigation research. A particularly noteworthy achievement is his ST-YOLOv8s network, which addresses the complex challenge of crop row recognition across different growth stages — a problem that has long hindered practical deployment of field robots. With nearly 180 cumulative citations across his focused body of work, Guo's research is accelerating the integration of artificial intelligence into precision agriculture, making autonomous field operations increasingly viable for modern farming.
Research Focus
Key Achievements
Top Papers
- 1
- 2Maize crop row recognition algorithm based on improved UNet network60 citations · 2023
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- 5Maize Crop Row Recognition Algorithm Based on Improved Unet Network3 citations · 2022
- 6