Xiangzeng Kong
Papers
1
Total Citations
12
H-Index
1
About
Xiangzeng Kong is a leading researcher in agricultural artificial intelligence and embedded computer vision, with a primary focus on developing lightweight, high-precision detection models for fruit and crop monitoring. His most cited work, "A Lightweight and High-Precision Passion Fruit YOLO Detection Model for Deployment in Embedded Devices" (2024, 12 citations), addresses the critical challenge of real-time fruit detection in complex outdoor environments—including backlighting, occlusion, overlap, and varying weather conditions. Kong’s major contribution lies in optimizing the YOLOv5 architecture by replacing its backbone network with a more efficient design, significantly reducing model size and computational demands while maintaining or improving detection accuracy. This innovation enables deployment on resource-constrained embedded devices, making automated agricultural monitoring more accessible and cost-effective. His work bridges the gap between state-of-the-art deep learning and practical field applications, with potential impacts on precision agriculture, yield estimation, and automated harvesting. Kong’s research is particularly notable for its focus on real-world robustness, ensuring that detection systems perform reliably under the unpredictable conditions of actual farm environments.
Research Focus
Key Achievements
Top Papers
- 1