Jiong Mu
Papers
2
Total Citations
22
H-Index
2
About
Dr. Jiong Mu is a leading researcher in agricultural artificial intelligence, with a focused expertise in deep learning for precision agriculture and automated crop management. His major contributions lie in developing lightweight, real-time object detection models tailored for complex, unstructured field environments. Dr. Mu’s work directly addresses the critical bottleneck of deploying AI in practical agriculture: balancing high accuracy with computational efficiency for resource-constrained robotic systems. His highly cited paper, "PeachYOLO" (2024, 13 citations), introduces a novel algorithm for precise peach detection in cluttered orchard settings, a foundational step toward viable automated harvesting robots. Building on this, his "YOLO-SW" model (2025, 9 citations) tackles the equally challenging task of weed detection in soybean fields, integrating Swin Transformer architectures with RT-DETR to distinguish weeds from visually similar crop backgrounds. These contributions are pivotal for reducing herbicide usage and enhancing crop yield. Dr. Mu’s work is notable for its practical, deployment-ready approach, bridging the gap between state-of-the-art computer vision and the real-world demands of sustainable, automated agriculture.
Research Focus
Key Achievements
Top Papers
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