Huajian Liu

Huajian Liu

School of Agriculture, Food and Wine

Faculty of Sciences

Research direction: plant phenotyping, applications of machine learning, deep learning, optical sensing system design, bio-inspired machine vision system, bird vision, insect vision, high-dimensional colour space, multispectral and hyperspectral image processing, 3D model reconstruction and object recognition, insect/plant detection, machine vision for agricultural applications

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  • Journals
    Year Citation
    2018 Liu, H., Lee, S. -H., & Chahl, J. (2018). Registration of multispectral 3D points for plant inspection. Precision Agriculture, 19(3), 513-536.
    DOI Scopus3
    2018 Liu, H., & Chahl, J. (2018). A multispectral machine vision system for invertebrate detection on green leaves. Computers and Electronics in Agriculture, 150, 279-288.
    DOI Scopus4
    2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). A review of recent sensing technologies to detect invertebrates on crops. Precision Agriculture, 18(4), 635-666.
    DOI Scopus11
    2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). An evaluation of the contribution of ultraviolet in fused multispectral images for invertebrate detection on green leaves. Precision Agriculture, 18(4), 667-683.
    DOI Scopus4
    2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). A multispectral 3-D vision system for invertebrate detection on crops. IEEE Sensors Journal, 17(22), 7502-7515.
    DOI
    2017 Liu, H., Lee, S. -H., & Chahl, J. S. (2017). Transformation of a high-dimensional color space for material classification. Journal of the Optical Society of America A, 34(4), 523.
    DOI
    2014 Liu, H., Lee, S. H., & Saunders, C. (2014). Development of a machine vision system for weed detection during both of off-season and in-season in broadacre no-tillage cropping lands. American Journal of Agricultural and Biological Sciences, 9(2), 174-193.
    DOI
    2013 Liu. (2013). DEVELOPMENT OF A PROXIMAL MACHINE VISION SYSTEM FOR OFF-SEASON WEED MAPPING IN BROADACRE NO-TILLAGE FALLOWS. Journal of Computer Science, 9(12), 1803-1821.
    DOI

  • Conference Papers
    Year Citation
    2019 Liu, H., Bruning, B., Berger, B., & Garnett, T. (2019). Green plant segmentation in hyperspectral images using SVM and hyper-hue. In Proceedings: 7th International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS 2019) (pp. 35-36). Lyon, France.
    2018 Chahl, J., & Liu, H. (2018). Bioinspired invertebrate pest detection on standing crops. In Bioinspiration, Biomimetics, and Bioreplication VIII (pp. 105930B-1-105930B-14). Denver.
    DOI
    2015 Liu, H., & Lee, S. (2015). Stitching of video sequences for weed mapping. In 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) (pp. 441-444). Online: IEEE.
    DOI
    2013 Liu, H. (2013). Development of a green plant image segmentation method of machine vision system for no-tillage fallow weed detection. In innovative agricultural technologies for a sustainable futur. Mandurah, WA, Australia.

  • Conference Items
    Year Citation
    2018 Liu, H. (2018). Invertebrate pest detection on crops using multispectral images and 3D vision. Poster session presented at the meeting of The 5th International Plant Phenotyping Symposium. Adelaide.

  • Theses
    Year Citation
    2018 Liu, H., Lee, S. -H., & Chahl, J. (2018). MACHINE VISION FOR DETECTION OF INVERTEBRATES ON CROPS.
    2015 Liu, H., Saunder, C., & Lee, S. -H. (2015). Development of the Algorithms and Mechanism of a Machine Vision System for Both Summer and In-season Weed Mapping in Broadacre No-till Cropping Lands.
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