Song Mei

Ministry of Agriculture and Rural Affairs, Nanjing Institute of Agricultural Mechanization

Papers

Total Citations

H-Index

About

Song Mei’s research bridges the critical intersection of agricultural robotics and sustainable manufacturing. Her primary contributions lie in developing lightweight, high-accuracy computer vision systems for fruit detection in harvesting robots, and in designing real-time energy monitoring solutions for intelligent industrial workshops. Her most impactful work, a 2025 paper on dragon fruit detection, tackles the persistent challenges of variable lighting and occlusion in natural environments. By proposing YOLOv10n-CGD, she achieved a detection method that is both accurate and deployable on resource-constrained edge devices—a vital step for practical agricultural automation. This work has already garnered 7 citations, reflecting its immediate relevance. In parallel, her 2023 research addresses China’s “carbon peaking and carbon neutrality” goals by developing a real-time energy consumption sensing system for Surface Mount Technology (SMT) workshops. This work demonstrates how low-cost, high-performance monitoring can empower the manufacturing sector to reduce its environmental footprint. Together, Song Mei’s research showcases a commitment to solving real-world problems—from feeding a growing population to greening industrial production—using efficient, deployable AI and IoT technologies.