Defang Xu
Papers
2
Total Citations
26
H-Index
2
About
Defang Xu is a rising researcher at the forefront of agricultural robotics and computer vision, specializing in intelligent detection systems for greenhouse horticulture. Their work centers on automating critical tasks in muskmelon cultivation, from ripeness assessment to robotic pollination. Xu’s most influential contribution is the YOLO-RFEW model, a lightweight deep learning architecture that achieves accurate, real-time detection of muskmelon ripeness—directly addressing the need for optimized harvest timing and reduced labor costs. This work has already garnered 16 citations since its 2024 publication. Building on this, Xu’s 2025 study on target detection and 3D localization for pollination robots tackles the pressing challenge of declining manual pollination labor, proposing integrated models that enable robots to precisely identify and locate flowers in complex greenhouse environments. With 10 citations in under a year, this research underscores Xu’s impact on precision agriculture. By bridging advanced AI with practical agronomic needs, Defang Xu is helping to shape a future where intelligent robots enhance both crop quality and farming sustainability.
Research Focus
Key Achievements
Top Papers
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