Lihong Zheng

Associate Professor Lihong Zheng

Data Scientist

Computing, Mathematics and Engineering


A/Prof Lihong Zheng is with the School of Computing, Maths and Engineering, Charles Sturt University (CSU). She has broad expertise spanning from Artificial Intelligence & Image Processing, and data analytics, to Robotics, Wireless Sensor Networks, Internet of Things (IoT) and Edge computing. Dr. Zheng's research focus is to design, model, develop or apply image processing, computer vision, computational intelligence, machine learning techniques toward solving real-world problems in various domains, including agriculture, viticulture, and the environment. She has secured $7.4 million industry grants since 2018. Her diverse range of projects includes developing number plate recognition technology for highway monitoring systems; building IoT-based solutions to support agriculture; making farms more cyber secure, etc. She was a national winner of the Academia Award as part of the Women in IT Awards, Cisco in 2019, She also received two Research Awards at CSU. She has contributed to an increased public awareness of STEM skills through promoting, organising and mentoring a variety of national and international events and competitions. She led a CSU team who won a Runner-up award in the Internet of Things (IoT) Spartans Challenge 2017, among 250 universities globally. In addition to her active contribution to the research field of machine learning, computer vision, robotics, and biometrics identification through high-quality publications, A/Prof. Lihong Zheng is actively serving the various IEEE committees by organising international conferences and serving as an invited advisory committee member and session chair, the editorial advisory board of journals, and frequently reviewing papers for international journals and conferences.

  • Machine learning and Robotics
  • Computer vision
  • Image analysis
  • Human action recognition
  • Document/Character analysis
  • Network security
Full publications list on CRO

Recent publications

  • Dulal, R., Zheng, L., Kabir, A., McGrath, S., Medway, J., Swain, D., & Swain, W. (2023). Automatic cattle identification using YOLOv5 and Mosaic Augmentation: A comparative analysis. In The International Conference on Digital Image Computing: Techniques and Applications (DICTA) IEEE.
  • Amarasingam, N., Hamilton, M., Kelly, J. E., Zheng, L., Sandino, J., Gonzalez, F., Dehaan, R. L., & Cherry, H. (2023). Autonomous detection of mouse-ear hawkweed using drones, multispectral imagery and supervised machine learning. Remote Sensing, 15(6), [1633].
  • Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2023). Image segmentation for MR brain tumor detection using machine learning: A review. IEEE Reviews in Biomedical Engineering, 16, 70-90.
  • Zheng, L., Oczkowski, A., Soomro, T. A., & Wu, H. (Accepted/In press). Rapid on-site weed identification with machine learning. In Image and Video Technology: 10th Pacific-Rim Symposium, PSIVT 2022, Bintan Island, Indonesia, November 12–14, 2022, Proceedings (Vol. 13763). (Lecture Notes in Computer Sciences; Vol. 13763). Springer.
  • Zheng, L., Rahaman, M., Hamilton, M. A., Dehaan, R., Gonzalez, F., Kelly, J., & Cherry, H. (Accepted/In press). Remote Tiny Weeds Detection. In Lecture Notes in Computer Science: 10th Pacific-Rim Symposium, PSIVT 2022, Virtual Event, November 12–14, 2022, Proceedings (Vol. 13763).