Liujun Li

University of Idaho, Chinese University of Hong Kong

Papers

Total Citations

H-Index

About

Liujun Li is a researcher whose work spans precision agriculture, financial risk modeling, and advanced infrastructure inspection. His most impactful contribution to date is the development of BCTNet, a deep learning architecture for detecting apple leaf diseases in unconstrained environments. This paper, published in 2023, has already garnered 53 citations, reflecting its practical value for automated crop health monitoring and sustainable agriculture. Li’s work addresses a critical need for robust, real-time disease identification under variable field conditions, offering a scalable solution for precision farming. Beyond agriculture, Li has contributed to the intersection of finance and longevity risk, co-authoring the 2022–2023 update on longevity risk and capital markets, a niche but vital area for pension and insurance industries. He has also explored aerial nondestructive testing and evaluation (aNDT&E), focusing on drone-based bridge inspections. His research highlights the limitations of manual drone operations for long-span bridges and proposes automated solutions to improve safety, efficiency, and data consistency. With a growing citation record and diverse applications—from crop protection to infrastructure safety—Liujun Li demonstrates a commitment to solving real-world problems through interdisciplinary innovation.