Yang Gan
Papers
3
Total Citations
48
H-Index
3
About
Yang Gan is a rising researcher in agricultural artificial intelligence, specializing in deep learning-based computer vision for precision agriculture. His work focuses on developing advanced object detection models to address the critical challenge of automated strawberry ripeness identification in complex, real-world farmland environments. Gan’s major contributions include pioneering the application of state-of-the-art transformer architectures, such as upgrading the Swin-B Transformer with a task-aligned one-stage detection mechanism, achieving high-accuracy ripe strawberry recognition. He has also advanced practical deployment by creating a real-time lightweight YOLO11-based framework suitable for edge computing platforms, bridging the gap between research and on-field application. His 2024 study on distinguishing difficulty imbalances in strawberry ripeness instances introduced a novel hybrid attention mechanism and a partial convolution-based compact inverted block, significantly improving precision and reducing miss rates in cluttered scenes. With his most-cited paper accumulating 37 citations shortly after publication, Gan’s work is gaining traction for its direct impact on agricultural automation, offering scalable solutions for yield estimation and harvesting robotics.
Research Focus
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Top Papers
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