Papers
13
Total Citations
397
H-Index
8
About
Jianneng Chen is a leading researcher in agricultural robotics and precision automation, with a specialization in intelligent harvesting systems for tea cultivation and broader applications in field robotics. His work sits at the intersection of computer vision, deep learning, and robotic engineering, tackling real-world challenges in automating crop detection, localization, and harvesting. Chen has made significant contributions to tea shoot detection, developing novel RGB-D camera-based systems, compressed deep learning models, and improved algorithms such as pruned YOLOv3-SPP and enhanced YOLOv5 frameworks that enable accurate, real-time performance in complex outdoor environments. His 2021 paper on in-field tea shoot detection and 3D localization has garnered 94 citations, reflecting the field's enthusiasm for his practical innovations. Beyond detection, Chen has advanced full harvesting pipelines, including a high-quality robotic tea plucking system, soft robotic gripper design, and agricultural robot navigation in GNSS-denied environments. His research extends to apple-picking robotics, demonstrating versatile expertise across fruit and specialty crop automation. With a cumulative citation footprint exceeding 380 across his top works, Chen's contributions are shaping the future of intelligent, autonomous agricultural systems worldwide.
Research Focus
Key Achievements
Top Papers
- 1In-field tea shoot detection and 3D localization using an RGB-D camera94 citations · 2021
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