Max Robotics — Global Service Robot Directory + US Certification

About

Junzhe Feng is a leading researcher in agricultural robotics, specializing in the automated detection and harvesting of winter jujube—a high-value fruit prized for its vitamin C content. Feng’s work addresses critical challenges in precision agriculture, particularly the accurate detection of small, occluded fruits in complex orchard environments. Their first major contribution, an optimized YOLOv5s model for winter jujube detection (23 citations), significantly improved detection accuracy for small targets. Building on this, Feng developed MLG-YOLO (19 citations), a real-time model that achieves precise localization with error margins as low as 3.90 mm, providing essential technical support for harvesting robots. In a third highly cited work (18 citations), Feng introduced an optimized Informed-RRT* algorithm for robotic arm motion planning, reducing path length and planning time while maintaining smooth, stable movement in dynamic settings. Together, these contributions form a comprehensive pipeline from vision to manipulation, advancing the feasibility of autonomous fruit harvesting. Feng’s research sits at the intersection of computer vision, deep learning, and robotics, with direct applications to labor-intensive agricultural tasks.