Ziling Chen
Papers
2
Total Citations
38
H-Index
2
About
Ziling Chen is a pioneering researcher in agricultural robotics and precision phenotyping, with a focus on developing automated systems for in-field plant analysis. Their major contributions lie in creating robotic platforms that bridge the gap between high-resolution sensing and practical field deployment. Chen's most cited work, "Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform" (2021, 36 citations), introduced a novel approach to capturing detailed spectral data directly in agricultural fields, enabling non-destructive assessment of plant health and stress. This work has been foundational for advancing high-throughput phenotyping in row crops. More recently, Chen has pushed the boundaries of field robotics with "PhenoBee: Drone-based robot for advanced field in vivo contact-based phenotyping in agriculture" (2025), which combines aerial mobility with contact-based sensors—a significant step toward autonomous, multi-modal data collection in complex environments. By integrating robotics, computer vision, and plant science, Chen's research directly addresses critical challenges in sustainable agriculture, offering scalable tools for crop monitoring and breeding. Their work continues to shape the future of precision agriculture and digital phenotyping.
Research Focus
Key Achievements
Top Papers
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