Yifeng Wen
Papers
2
Total Citations
46
H-Index
2
About
Yifeng Wen is a researcher at the forefront of precision agriculture and computer vision, specializing in real-time object detection for complex orchard environments. His work addresses the critical challenge of accurately identifying and localizing green citrus fruits—a task notoriously difficult due to occlusion, variable lighting, and the fruit’s natural camouflage against foliage. Wen’s major contributions include the development of a multi-scale feature adaptive fusion model, which achieves robust real-time detection in dynamic citrus orchards, and a data-driven Bayesian Gaussian mixture model that optimizes anchor box design for enhanced detection efficiency. These innovations have garnered significant attention, with his most-cited paper accumulating 35 citations and a complementary study reaching 11 citations within a year of publication. By integrating adaptive feature fusion with probabilistic optimization, Wen’s research bridges the gap between theoretical computer vision models and practical agricultural automation, offering scalable solutions for yield estimation and robotic harvesting. His work is particularly notable for its focus on green fruit detection—a domain where traditional methods often fail—and represents a vital step toward fully autonomous orchard management.
Research Focus
Key Achievements
Top Papers
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