Papers
1
Total Citations
22
H-Index
1
About
Pinlan Chen is a rising researcher in agricultural robotics and precision agriculture, with a primary focus on computer vision and deep learning for crop monitoring and harvesting. Her most-cited work, “Detection and Localization of Tea Bud Based on Improved YOLOv5s and 3D Point Cloud Processing” (2023, 22 citations), addresses a critical challenge in automated tea harvesting: accurately identifying and localizing small, morphologically diverse tea buds in complex, unstructured field environments. By enhancing the YOLOv5s architecture and integrating 3D point cloud data, Chen’s method significantly improves detection precision and spatial localization, offering a practical solution for robotic picking in dense foliage. This contribution has been recognized for its potential to reduce labor costs and increase harvest efficiency in tea plantations. Chen’s research bridges the gap between advanced AI models and real-world agricultural applications, demonstrating a strong commitment to developing robust, field-deployable systems. Her work is particularly valuable for students and researchers exploring the intersection of deep learning, point cloud processing, and agricultural automation, providing a clear example of how algorithmic improvements can solve domain-specific problems.
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Top Papers
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