Papers
2
Total Citations
231
H-Index
2
About
Fang Deng is a leading researcher at the intersection of agricultural robotics and computer vision, with a primary focus on intelligent harvesting systems. His most impactful work centers on automating the delicate process of high-quality tea plucking, a task traditionally reliant on skilled human labor. Deng’s major contribution lies in developing deep learning-based visual recognition systems that enable robots to identify and precisely locate tender tea shoots. His seminal 2019 paper, cited over 125 times, introduced an improved YOLO-V3 model that achieves rapid, accurate detection of picking points, effectively solving the challenge of distinguishing young shoots from mature leaves. Building on this, his 2021 work, with over 106 citations, integrated this vision system with a Delta parallel manipulator, creating a complete, high-speed robotic plucker. This achievement demonstrates a practical end-to-end solution for automated tea harvesting, significantly advancing the field of precision agriculture. Deng’s research not only addresses a critical labor shortage in the tea industry but also sets a benchmark for applying computer vision to complex, non-destructive crop harvesting.
Research Focus
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Top Papers
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