Rok-Yeun Hwang

Papers

Total Citations

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About

Rok-Yeun Hwang is a researcher at the forefront of agricultural robotics, specializing in autonomous navigation and computer vision for precision farming. His work centers on developing deep learning-based path detection systems that enable agricultural machinery to travel autonomously through complex orchard environments. Hwang’s most impactful contribution is his 2020 paper on path detection using patch-based convolutional neural networks (CNNs), which has garnered 68 citations and established a practical framework for real-time navigation in citrus orchards. This research addresses a critical bottleneck in farming automation: enabling robots to reliably detect crop rows and navigate uneven, unstructured terrain without human intervention. Hwang’s approach combines efficient deep learning architectures with robust image processing, offering a scalable solution that reduces labor demands while increasing operational precision. His work has been instrumental in bridging the gap between theoretical computer vision and field-ready agricultural robots, with applications extending to fruit detection, yield estimation, and automated harvesting. By tackling the fundamental challenge of autonomous traveling in orchards, Hwang is helping to shape the next generation of intelligent farming systems that promise greater efficiency and sustainability.