Kui Cheng
Papers
1
Total Citations
22
H-Index
1
About
Kui Cheng is a researcher at the forefront of computer vision and agricultural automation, with a primary focus on developing efficient, real-time object detection systems for complex environments. Their most notable contribution is an improved YOLOv5s model that integrates feature concatenation with an attention mechanism, specifically designed for real-time fruit detection and counting. This work, published in 2023 and already garnering 22 citations, addresses the critical challenge of accurate detection in cluttered, natural settings. By refining the network architecture to 122 layers while maintaining a compact model size of 4.4 × 10 parameters, Cheng achieved a balance between computational efficiency and detection precision. This innovation not only advances precision agriculture—enabling automated yield estimation and harvesting—but also demonstrates a scalable approach for deploying deep learning on resource-constrained devices. Cheng’s research bridges the gap between theoretical model optimization and practical field application, making a tangible impact on smart farming technologies. Their work is essential reading for students and researchers interested in lightweight neural networks, attention mechanisms, and the intersection of AI with agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1