Chang Qiu

Nanjing Agricultural University, Southeast University

Papers

Total Citations

H-Index

About

Chang Qiu is a leading researcher in agricultural robotics and computer vision, with a focus on enabling autonomous operations in complex orchard environments. Their work centers on developing deep learning-based detection systems that allow robots to perceive and interact with natural, unstructured agricultural settings. Qiu’s major contribution includes pioneering the use of improved YOLO architectures for real-time, high-accuracy detection tasks in orchards. Their most cited paper, "Grape Maturity Detection and Visual Pre-Positioning Based on Improved YOLOv4" (2022, 28 citations), presents an algorithm that not only recognizes and classifies grape clusters by maturity but also provides spatial positioning, directly guiding robotic harvesting. Building on this, Qiu’s recent work, "A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7" (2023), tackles the critical challenge of autonomous navigation by enabling robots to detect tree-based markers for precise row-end turning. These contributions are foundational for the next generation of intelligent orchard management, reducing reliance on manual labor and increasing efficiency in precision agriculture.