Max Robotics — Global Service Robot Directory + US Certification

About

Xiangpeng Fan is a leading researcher in precision agriculture and intelligent robotics, specializing in deep learning for crop and weed detection. His major contributions lie in developing lightweight, high-accuracy computer vision models for real-time agricultural applications, particularly in cotton fields and orchards. His most influential work, "Deep learning based weed detection and target spraying robot system at seedling stage of cotton field" (2023, 104 citations), pioneered an integrated robotic system that combines YOLO-based detection with precision spraying, significantly reducing herbicide use. Fan further advanced weed detection efficiency with "YOLO-WDNet: A lightweight and accurate model for weeds detection in cotton field" (2024, 78 citations), demonstrating that compact neural networks can achieve state-of-the-art performance on edge devices. His research extends to fruit crop automation, where he addressed the challenging problem of plum stalk segmentation under heavy leaf occlusion using an improved DeepLabv3+ architecture, and developed the YOLOv8n-CRS model for detecting overlapping fruits in complex orchard environments. With over 189 total citations and a clear trajectory toward practical, deployable solutions, Fan’s work is shaping the future of smart farming—enabling autonomous robots to see, decide, and act with unprecedented precision.