Enze Duan

Jiangsu Academy of Agricultural Sciences

Papers

Total Citations

H-Index

About

Enze Duan is a researcher at the forefront of precision livestock farming, with a primary focus on developing non-invasive, automated monitoring technologies for animal welfare and production management. His most impactful work centers on computer vision and deep learning, particularly for estimating body weight in captive rabbits. Duan’s major contribution is the creation of an improved Mask R-CNN model that enables accurate, contactless weight estimation, significantly reducing the stress and injury associated with manual handling in breeding operations. This innovation directly addresses the need for intelligent, automated management in meat rabbit production, enhancing both efficiency and animal welfare. His flagship paper on this method has garnered 9 citations since 2023, reflecting its timely relevance in agricultural automation. By integrating advanced neural networks with animal science, Duan is helping to pave the way for more humane, data-driven livestock systems, making his work a valuable reference for students and researchers exploring the intersection of AI and sustainable agriculture.