Papers
3
Total Citations
138
H-Index
3
About
Xiangyu Qi is a leading researcher in precision agriculture and intelligent robotics, specializing in computer vision and deep learning for plant and livestock phenotyping. Their work focuses on developing automated, non-invasive methods to monitor crop growth and animal health, significantly advancing smart farming technologies. Qi's major contributions include creating the YOLO-Deepsort network for tracking and counting tomatoes at different growth stages, which integrates ShuffleNetv2 and CBAM attention mechanisms to improve accuracy for inspection robots. This work has garnered 76 citations, underscoring its impact on yield prediction. Additionally, Qi authored a comprehensive review on computer vision-based measurement techniques for livestock body dimension and weight, cited 45 times, highlighting the shift from manual, stress-inducing methods to automated systems. Their innovative neural network with attention mechanisms and feature fusion layers for tomato flowering detection, cited 17 times, further supports pollination robots in complex greenhouse environments. Through these achievements, Qi is driving the integration of AI and robotics into agriculture, enhancing efficiency and sustainability.
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