Yongda Lin
Papers
2
Total Citations
16
H-Index
2
About
Dr. Yongda Lin is a rising researcher at the intersection of precision agriculture, computer vision, and autonomous robotics. His work focuses on developing intelligent perception systems for agricultural unmanned ground vehicles (UGVs), with a particular emphasis on weed detection and navigation in complex orchard environments. Lin’s most-cited paper, “Multi-task deep convolutional neural network for weed detection and navigation path extraction” (2024, 14 citations), introduces a novel architecture that simultaneously identifies weeds and extracts drivable paths from visual data—a dual-task approach that significantly improves the efficiency and autonomy of weeding robots. His subsequent work, “Low-altitude remote sensing and deep learning-based canopy detection method for the navigation of orchard unmanned ground vehicles” (2025, 2 citations), extends this capability by leveraging aerial imagery for robust canopy detection, enabling UGVs to navigate under tree canopies with greater accuracy. Though early in his career, Lin’s contributions are already shaping the next generation of smart farming technologies, offering scalable solutions for reducing herbicide use and enhancing crop management. His research holds promise for transforming labor-intensive agricultural tasks into automated, data-driven operations.
Research Focus
Key Achievements
Top Papers
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- 2