Papers
2
Total Citations
105
H-Index
1
About
Meili Sun is a leading researcher in agricultural computer vision, specializing in deep learning solutions for precision horticulture. Their work focuses on developing robust detection and segmentation algorithms tailored to the complex, unstructured environments of real-world orchards. Sun’s most influential contribution is the novel green apple segmentation algorithm based on ensemble U-Net, published in 2020, which has garnered 104 citations. This work addresses the critical challenge of accurately identifying fruit against cluttered backgrounds of leaves, branches, and varying illumination, enabling automated yield estimation and robotic harvesting. More recently, Sun introduced Mrtic Det, a structure-aware detection framework designed for the difficult task of identifying thinning-stage fruit, demonstrating a continued commitment to solving practical, high-impact problems in agricultural automation. By pioneering ensemble deep learning architectures for fruit detection, Sun has significantly advanced the field of smart agriculture, providing foundational tools that help bridge the gap between computer vision research and real-world farming applications.
Research Focus
Key Achievements
Top Papers
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