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About

Kunjie Chen is a leading researcher in agricultural robotics and computer vision, with a focused expertise in deep learning-based detection and perception for specialty crop harvesting. His primary research areas include object detection under unstructured field environments, lightweight neural network design for real-time robotic perception, and generative data augmentation for agricultural applications. Chen’s most impactful contribution is the development of the TC-YOLO model for tea chrysanthemum detection, which has garnered 60 citations and established a benchmark for accurate flower detection under challenging conditions such as occlusion, overlapping, and variable illumination. He further advanced the field by introducing the F-YOLO model for early flowering stage detection—a critical capability for selective harvesting robots—and the MC-LCNN model for real-time medicinal chrysanthemum detection. His innovative use of generative adversarial networks and edge computing to overcome high-resolution dataset limitations demonstrates his commitment to practical, deployable solutions. Most recently, Chen has pioneered vision transformer-based robotic perception for flower counting, addressing the global feature extraction limitations of traditional CNNs. His work directly enables the development of intelligent selective harvesting robots, with cumulative citations exceeding 95, marking him as a key innovator at the intersection of computer vision and precision agriculture.