Guojun Chen

Institute of Software, Nanjing Institute of Technology

Papers

Total Citations

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About

Guojun Chen is an emerging researcher at the intersection of artificial intelligence, computer vision, and intelligent robotics, with a particular focus on precision agriculture and bionic systems. His most recognized contribution is the development of YOLOv8-CML, a lightweight target detection model designed to identify ripeness stages in color-changing melons within smart agricultural environments. By introducing innovations such as the Faster-Block architecture, Chen addressed critical real-world challenges including slow detection speeds and high deployment costs on agricultural hardware — work that has garnered over 40 citations across related publications. This research directly supports the advancement of autonomous robotic harvesting systems, bridging the gap between cutting-edge deep learning and practical field deployment. Beyond agriculture, Chen has extended his expertise into biomimetic robotics, contributing to the design and implementation of an independent-drive bionic dragonfly robot, demonstrating his broader interest in safe, flexible unmanned systems suited for complex environments. While still early in his research career, Chen's interdisciplinary approach — combining lightweight neural network design with real-world robotic applications — signals a promising trajectory in intelligent automation and agricultural technology.