Papers
1
Total Citations
12
H-Index
1
About
Jing-Yun Ke is a pioneering researcher in agricultural robotics and intelligent automation, with a focus on deep learning-driven navigation and precision agriculture. His most-cited work, "Robust Guidance and Selective Spraying Based on Deep Learning for an Advanced Four-Wheeled Farming Robot" (2023, 12 citations), addresses critical challenges in vision-guided farming—specifically, the difficulty of detecting guidance paths under complex farmland backgrounds and variable lighting. Ke proposed a robust line extraction method that enables reliable robot navigation, coupled with a selective spraying system that optimizes resource use. This work has direct implications for sustainable farming, reducing chemical waste while improving crop management efficiency. Ke’s contributions lie at the intersection of computer vision, robotics, and agrotechnology, demonstrating how deep learning can overcome real-world environmental variability. His research is particularly notable for its practical deployment on four-wheeled platforms, bridging the gap between theoretical algorithms and field-ready solutions. With growing interest in autonomous agriculture, Ke’s work is gaining traction among researchers and engineers seeking to enhance the intelligence and adaptability of farming robots.
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Top Papers
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