Haikun Zheng

Papers

Total Citations

H-Index

About

Haikun Zheng is a researcher at the forefront of applying computer vision and deep learning to agricultural automation, with a particular focus on intelligent poultry management. His most impactful work, "A detection method for dead caged hens based on improved YOLOv7" (2024), has already garnered 36 citations, underscoring its immediate relevance to the field. Zheng's major contribution lies in enhancing the YOLOv7 algorithm to accurately and efficiently identify deceased hens in cage environments—a critical task for improving animal welfare, reducing disease spread, and optimizing farm labor. By integrating attention mechanisms and lightweight network modifications, his method achieves high detection precision while maintaining real-time performance, addressing a longstanding challenge in precision livestock farming. This work not only demonstrates Zheng's expertise in object detection and edge computing but also highlights his commitment to solving practical, high-impact problems in agriculture. His research bridges the gap between state-of-the-art AI techniques and the pressing needs of modern farming, offering scalable solutions that can be deployed on resource-constrained devices. For students and researchers, Zheng's work exemplifies how deep learning can be tailored to niche, real-world applications, making him a notable figure in the growing intersection of artificial intelligence and sustainable agriculture.