Jinyi Xie
Papers
1
Total Citations
48
H-Index
1
About
Jinyi Xie is a researcher at the forefront of precision agriculture and computer vision, with a primary focus on developing intelligent systems for automated crop monitoring and harvesting. Their most cited work, "Greenhouse tomato detection and pose classification algorithm based on improved YOLOv5" (2023, 48 citations), represents a significant contribution to agricultural robotics. In this study, Xie enhanced the YOLOv5 deep learning model to accurately detect greenhouse tomatoes and classify their spatial orientation—a critical step for robotic harvesting in complex, occluded environments. By addressing challenges like varying lighting, overlapping fruits, and real-time processing constraints, Xie’s algorithm achieved high precision and recall, demonstrating practical viability for smart farming applications. This work has garnered attention from both agricultural engineers and computer vision researchers, as it bridges the gap between state-of-the-art object detection and real-world agricultural needs. Xie’s research not only advances the field of automated fruit detection but also supports sustainable agriculture by reducing labor dependency and improving harvest efficiency. Their contributions highlight a growing intersection of AI and agritech, positioning them as a key innovator in developing scalable, intelligent solutions for modern greenhouse operations.
Research Focus
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Top Papers
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