Wenxin Xue
Papers
1
Total Citations
57
H-Index
1
About
Wenxin Xue is a leading researcher at the intersection of precision agriculture and computer vision, with a primary focus on deep learning for weed and crop segmentation. Her most influential work, the 2023 paper "Cross-domain transfer learning for weed segmentation and mapping in precision farming using ground and UAV images," has garnered 57 citations, establishing a new benchmark for robust, cross-platform plant identification. Xue’s key contribution lies in developing transfer learning frameworks that enable segmentation models trained on ground-level images to perform effectively on UAV-captured data—a critical advance for scalable, real-time weed mapping. This work directly addresses the domain shift problem that has long hindered the deployment of AI in variable field conditions. By bridging the gap between different imaging platforms, Xue’s research empowers farmers with precise, actionable maps for targeted herbicide application, reducing chemical use and environmental impact. Her achievements demonstrate a rare ability to translate complex deep learning architectures into practical agricultural solutions, making her a pivotal figure in the movement toward data-driven, sustainable farming.
Research Focus
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Top Papers
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