Liang-Liang YANG

Papers

Total Citations

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About

Liang-Liang Yang is a researcher whose work focuses on the intersection of computer vision, deep learning, and agricultural technology, with a particular emphasis on object detection for specialty crops. His most cited paper, "Research on Winter Jujube Object Detection Based on Optimized Yolov5s" (2023, 23 citations), addresses a critical challenge in precision agriculture: accurately detecting small-sized winter jujube fruits using machine learning. Yang’s major contribution lies in optimizing the YOLOv5s architecture to overcome the limitations of both traditional machine learning (low accuracy for small objects) and deep learning approaches, significantly improving detection performance for this high-value fruit. This work has direct implications for automated harvesting and yield estimation, showcasing his ability to bridge advanced AI techniques with real-world agricultural needs. By tackling the specific problem of small object detection in dense, natural environments, Yang’s research demonstrates a practical, impactful application of computer vision, earning recognition among peers working in smart farming and agricultural robotics.