Keyin Chen

Papers

Total Citations

179

H-Index

About

Keyin Chen is a leading researcher in agricultural robotics and computer vision, with a primary focus on intelligent fruit harvesting systems. His work centers on developing deep learning models for the detection and semantic segmentation of litchi fruits and branches in complex field environments. Chen’s major contributions include pioneering the use of the DeepLabV3+ model for semantic segmentation of litchi branches, a critical advancement for robotic harvesting that addresses the challenge of precisely locating small, easily damaged picking points. His 2020 paper on this topic has garnered 162 citations, underscoring its impact on the field. Additionally, Chen improved the YOLOv3 model for robust litchi detection under challenging conditions such as variable illumination, complex backgrounds, and occlusion, achieving 17 citations. His notable work is inspired by residual network architectures, enhancing recognition rates for picking robots in real-world orchard settings. Chen’s research directly advances precision agriculture, enabling more efficient and damage-free automated harvesting. His innovative application of state-of-the-art convolutional neural networks to agricultural problems marks him as a key figure in bridging computer vision and robotics for sustainable farming solutions.