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Swinburne University of Technology Sarawak Campus

Dr Lee Sue Han

Lecturer
BEng (Hons)(MMU), MSc(Nagano, Japan), PhD(UM)

Faculty of Engineering, Computing and Science

Fax No:+60 82 260 813
Email: shlee@swinburne.edu.my

Biography

Lee Sue Han received her Bachelor of Engineering (Hons) in Electronic from Multimedia University, Cyberjaya in year 2011 and her M.Sc. in Electrical and Electronic Engineering from Shinshu University in Japan in 2014. She received her Ph.D. in Computer Science and Information Technology from University of Malaya in 2018. After her Ph.D. graduation, she worked as a post-doctoral fellow in the interdisciplinary team of Pl@ntNet, in collaboration with The French agricultural research and international cooperation organization (CIRAD) and the France National Institute for Research in Digital Science and Technology (INRIA) ZENITH team for the project of automated plant health monitoring system. In April 2020, she joined Swinburne University of Technology as a full-time lecturer.

 Her research interest focuses on the areas of computer vision, machine learning and deep learning. She works on learning algorithms that provide solutions to scientific problems for social and environmental goods. With her passion for bridging the gap between biology and the Artificial Intelligence era, she explores the potential of machine learning in plant biology. For example, she has demonstrated how discriminatory characteristics of leaves learned through deep learning correspond to hierarchical botanical definitions of leaf characteristics. She is also working closely with plant disease experts to study the links between human visual perception and machine vision when identifying discriminatory characteristics of infected plants

Research Interests

  • Computer vision
  • Artificial intelligence
  • Machine Learning
  • Image processing
  • Video processing

  • Publications

Publications

  • Journal paper Lee, S.H., Goëau, H., Bonnet, P. and Joly, A., 2020. New perspectives on plant disease characterization based on deep learning.  Computers and Electronics in Agriculture170, p.105220.
  • Journal paper Lee, S.H., Chan, C.S. and Remagnino, P., 2018. Multi-organ plant classification based on convolutional and recurrent neural networks.  IEEE Transactions on Image Processing27(9), pp.4287-4301.
  • Journal paper Lee, S.H., Chan, C.S., Mayo, S.J. and Remagnino, P., 2017. How deep learning extracts and learns leaf features for plant classification.  Pattern Recognition71, pp.1-13.
  • Conference paper Lee, S.H., Bonnet, P. and Goeau, H., 2018, August. Plant Classification Based on Gated Recurrent Unit. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France, September 10-14, 2018, Proceedings(Vol. 11018, p. 169). Springer.
  • Conference paper Lee, S.H., Chang, Y.L., Chan, C.S. and Remagnino, P., 2017, September. HGO-CNN: hybrid generic-organ convolutional neural network for multi- organ plant classification. In 2017 IEEE international conference on image processing (ICIP)(pp. 4462-4466). IEEE.
  • Conference paper Lee, S.H., Chan, C.S., Wilkin, P. and Remagnino, P., 2015, September. Deep-plant: Plant identification with convolutional neural networks. In 2015 IEEE international conference on image processing (ICIP)(pp. 452-456). IEEE.
  • Conference paper Lee, S.H., Cheong, S.N., Ooi, C.P. and Siew, W.H., 2011, July. Real time FPGA implementation of hand gesture recognizer system. In  Proceedings of the 15th WSEAS international conference on Computers(pp. 217-222). World Scientific and Engineering Academy and Society (WSEAS).
  • Working notes Lee, S.H., Chang, Y.L., Chan, C.S. and Remagnino, P., 2016. Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification Task. In CLEF (Working Notes)(pp. 502-510).
  • Working notes Lee, S.H., Chang, Y.L. and Chan, C.S., 2017. LifeClef 2017 Plant Identification Challenge: Classifying Plants using Generic-Organ Correlation Features. In CLEF (Working Notes).