Mingxuan Li

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About

Mingxuan Li is an emerging researcher specializing in intelligent agricultural systems, computer vision, and robotics, with a particular focus on applying deep learning architectures to precision farming challenges. Li's work addresses one of the most pressing global concerns — the need to enhance agricultural productivity in the face of growing populations and shrinking arable land — by developing smart, automated solutions that bridge the gap between traditional farming practices and modern artificial intelligence. Li's most impactful contribution, "CNN-MLP-Based Configurable Robotic Arm for Smart Agriculture" (2024), has garnered 29 citations, demonstrating rapid recognition within the research community for its innovative approach to automating large-scale agricultural tasks through configurable robotic systems. Building on this foundation, Li's more recent work, "AFBF-YOLO" (2025), advances object detection capabilities by refining the YOLO11n algorithm to tackle real-world greenhouse complexities such as occlusion, cluster overlap, and nuanced ripeness variation in cherry tomato harvesting. Though early in their career, Li's research reflects a coherent and ambitious vision: merging cutting-edge neural network methodologies with practical robotics to create scalable, intelligent agricultural systems capable of transforming modern food production.