Jiapan Guo
Papers
2
Total Citations
21
H-Index
2
About
Jiapan Guo is a leading researcher at the intersection of agricultural robotics and computer vision, with a primary focus on automating the harvesting of high-value crops such as winter jujubes. His work addresses critical challenges in precision agriculture, where declining labor availability and rising costs demand robust robotic solutions. Guo’s major contributions include the development of MLG-YOLO, a state-of-the-art deep learning model for real-time, accurate detection and localization of winter jujubes in complex, unstructured orchard environments. Achieving localization errors as low as 3.90 mm, this model provides essential technical support for autonomous harvesting robots, with its foundational paper already garnering 19 citations. Building on this, he pioneered an adaptive path planning method using an improved RRT-Connect algorithm, enabling robotic arms to navigate the dynamic, obstacle-laden conditions of real orchards. His work directly tackles the core challenges of agricultural automation—perception and motion planning—offering scalable solutions to enhance agricultural competitiveness. Guo’s research is not only highly cited but also practically impactful, bridging the gap between laboratory algorithms and deployable field robots.
Research Focus
Key Achievements
Top Papers
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