Fenghua Wang

Kunming University of Science and Technology

Papers

Total Citations

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About

Fenghua Wang is a leading researcher in agricultural robotics and computer vision, specializing in deep learning-based fruit detection for automated harvesting systems. Her work focuses on overcoming the significant challenges of real-time target detection in complex orchard environments, particularly for the Xiaomila green pepper (Capsicum frutescens L.). Wang’s major contributions include developing lightweight, high-accuracy detection algorithms that address issues of dense fruit distribution, occlusion, and variable lighting conditions. Her most cited paper, “Xiaomila Green Pepper Target Detection Method under Complex Environment Based on Improved YOLOv5s” (2022, 20 citations), pioneered a solution for accurate detection in challenging field conditions. She further advanced the field with “Rapid detection of Yunnan Xiaomila based on lightweight YOLOv7 algorithm” (2023, 18 citations), which significantly reduced computational costs while improving detection precision for occluded targets. These innovations are critical for enabling efficient, real-time operation of harvesting robots. Wang’s work has garnered attention for its practical impact on agricultural automation, directly supporting the development of intelligent picking systems that can operate reliably in unstructured, real-world environments.