Yuanhua Lv

Papers

Total Citations

H-Index

About

Yuanhua Lv is a researcher at the forefront of agricultural robotics and computer vision, with a primary focus on developing intelligent systems for precision fruit harvesting. His most notable contribution is the introduction of the YOLO-OBB-based approach for citrus fruit stem pose estimation, a breakthrough that addresses the critical challenge of enabling robots to accurately locate picking points in complex, unstructured orchard environments. This work, published in 2025, tackles the persistent problems of stem occlusion by branches and leaves, as well as interference from overlapping fruits—issues that have long hindered automated harvesting efficiency. By integrating oriented bounding boxes (OBB) with the YOLO detection framework, Lv's method significantly enhances the precision of robotic picking operations, directly improving yield and reducing fruit damage. Although his most cited paper currently holds 4 citations, its recency and practical relevance signal strong potential for future impact in the fields of precision agriculture and robotic manipulation. Lv's research stands out for its direct application to real-world agricultural challenges, bridging the gap between advanced computer vision algorithms and practical farming needs.