Chaowu Wen
Papers
1
Total Citations
15
H-Index
1
About
Chaowu Wen is a leading researcher in agricultural robotics and computer vision, with a primary focus on developing intelligent systems for precision agriculture. His most impactful work centers on deep learning-based fruit detection and recognition in complex field environments, where he has made significant contributions to real-time object detection algorithms. Wen’s seminal 2021 paper, “Fast recognition method for citrus under complex environments based on improved YOLOv3,” has garnered 15 citations and introduced the Improved-YOLOv3 algorithm—a modified multi-scale YOLO network that enhances the darknet-53 backbone with residual modules. This innovation enables rapid, accurate identification of citrus fruit under challenging conditions like variable lighting and occluded foliage, directly addressing critical bottlenecks in automated harvesting. By optimizing the trade-off between speed and precision, Wen’s work has advanced the practical deployment of vision-guided agricultural robots, reducing computational load while maintaining high detection rates. His research not only pushes the boundaries of deep learning in agriculture but also provides scalable solutions for real-world crop monitoring and yield estimation, making him a pivotal figure in the intersection of artificial intelligence and sustainable farming.
Research Focus
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Top Papers
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