Huajian Liu

Dr Huajian Liu

Grant-Funded Researcher (B)

School of Agriculture, Food and Wine

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

In 2018, I finished my PhD study of machine vision for invertebrate detection on crops at the University of South Australia. In the same year, I joined The Plant Accelerator, the Adelaide node of the Australian Plant Phenomic Facility, located at the Waite campus of The University of Adelaide, as a post-doctoral researcher. Since then, I have been specialising in machine vision and machine learning for plant phenotyping and precision agriculture, especially for hyperspectral imaging-based plant phenotyping. I am currently a grant-funded researcher and my research interests include plant nutrient estimation, plant disease detection, drought and salt stress tolerance, plant growing status estimation, invertebrate pest detection, machine learning and deep learning, optical sensing system design, bio-inspired machine vision system, bird vision, insect vision, high-dimensional colour space,  3D model reconstruction and object recognition.  

  • Journals
    Year Citation
    2024 Xie, Y., Plett, D., Evans, M., Garrard, T., Butt, M., Clarke, K., & Liu, H. (2024). Hyperspectral imaging detects biological stress of wheat for early diagnosis of crown rot disease. Computers and Electronics in Agriculture, 217, 15 pages.
    DOI Scopus5
    2024 Tzeis, S., Gerstenfeld, E. P., Kalman, J., Saad, E. B., Sepehri Shamloo, A., Andrade, J. G., . . . Trines, S. A. (2024). 2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 26(4), 921-1072.
    DOI Scopus77 Europe PMC10
    2024 Liu, H., Perry, K. D., Lu, T. F., Wu, T., & Buhl, C. (2024). Machine vision solutions for monitoring pest snails in australian no-till cropping fields: An exploration of spectral characteristics and detectability. Journal of Agriculture and Food Research, 16, 12 pages.
    DOI
    2024 Hume, R., Marschner, P., Mason, S., Schilling, R. K., Liu, H., Jewell, N., . . . Mosley, L. M. (2024). Assessing wheat responses to soil acidification and liming using hyperspectral imaging. Plant and Soil, 19 pages.
    DOI
    2023 Wu, T., Dai, J., Shen, P., Liu, H., & Wei, Y. (2023). Seedscreener: A novel integrated wheat germplasm phenotyping platform based on NIR-feature detection and 3D-reconstruction. Computers and Electronics in Agriculture, 215, 15 pages.
    DOI Scopus2
    2023 Liu, H., & Xu, Z. (2023). Editorial: Machine vision and machine learning for plant phenotyping and precision agriculture. Frontiers in Plant Science, 14, 1331918.
    DOI Scopus2 Europe PMC1
    2022 Xie, Y., Plett, D., & Liu, H. (2022). Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning. AgriEngineering, 4(1), 141-155.
    DOI Scopus8 WoS2
    2022 Ball, K. R., Liu, H., Brien, C., Berger, B., Power, S. A., & Pendall, E. (2022). Hyperspectral imaging predicts yield and nitrogen content in grass-legume polycultures. PRECISION AGRICULTURE, 23(6), 2270-2288.
    DOI Scopus8 WoS2
    2021 Liu, H., & Chahl, J. S. (2021). Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images. Artificial Intelligence in Agriculture, 5, 13-23.
    DOI Scopus23 WoS8
    2021 Xie, Y., Plett, D., & Liu, H. (2021). The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat. AgriEngineering, 3(4), 924-941.
    DOI Scopus11 WoS4
    2020 Liu, H., Bruning, B., Garnett, T., & Berger, B. (2020). Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing. Computers and Electronics in Agriculture, 175, 13 pages.
    DOI Scopus83 WoS41
    2020 Bruning, B., Berger, B., Lewis, M., Liu, H., & Garnett, T. (2020). Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat. The Plant Phenome Journal, 3(1), 22 pages.
    DOI Scopus29
    2020 Liu, H., Bruning, B., Garnett, T., & Berger, B. (2020). The performances of hyperspectral sensors for proximal sensing of nitrogen levels in wheat. Sensors (Switzerland), 20(16), 1-21.
    DOI Scopus21 WoS11 Europe PMC7
    2019 Bruning, B., Liu, H., Brien, C., Berger, B., Lewis, M., & Garnett, T. (2019). The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum).. Frontiers in plant science, 10, 1380.
    DOI Scopus58 WoS33 Europe PMC11
    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 Scopus35 WoS17 Europe PMC1
    2018 Liu, H., Lee, S. -H., & Chahl, J. S. (2018). Registration of multispectral 3D points for plant inspection. Precision Agriculture, 19(3), 513-536.
    DOI Scopus22 WoS14
    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 Scopus52 WoS37 Europe PMC3
    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 Scopus9 WoS7
    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 Scopus32 WoS19
    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 Scopus17 WoS15 Europe PMC1
    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 Scopus24
    2013 Liu, H., Saunders, C., & Lee, S. H. (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 Scopus12

  • Conference Papers
    Year Citation
    2018 Chahl, J., & Liu, H. (2018). Bioinspired invertebrate pest detection on standing crops. In Bioinspiration, Biomimetics, and Bioreplication VIII Vol. 10593 (pp. 105930B-1-105930B-14). online: SPIE.
    DOI Scopus2 WoS1
    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 Scopus4 WoS5
    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
    2019 Liu, H., Bruning, B., Berger, B., & Garnett, T. (2019). Green plant segmentation in hyperspectral images using SVM and hyper-hue. Poster session presented at the meeting of Proceedings: 7th International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS 2019). Lyon, France.
    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.

  • Datasets
    Year Citation
    - Liu, H., Berger, B., Garnett, T., & Bruning, B. (n.d.). public data for wheat_n experiment.
    DOI
    - Liu, H. (n.d.). The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat.
    DOI
    - Liu, H. (n.d.). Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning.
    DOI
    - Liu, H., Ball, K., & Brien, C. (n.d.). Hyperspectral imaging predicts yield and nitrogen content in grass-legume polyculturesem.
    DOI

 

Dates Roles Grants
2023-2026 CI In-field high-throughput phenotyping for barley net blotch, $830,000
2022-2024 CI GRDC project (PROC-9176394) "More effective control of pest molluscs (snails and slugs) in Australian grain crops (RFT)", AUD$2, 831, 392.
2021-2022 CI Research Roadmap "Improving detection and monitoring of biosecurity threats using drones, field robots and machine learning", AUD$43, 330.
2020-2021 Lead CI Yitpi Foundation Awards “Hyperspectral phenotyping for the rapid identification and quantification of crown rot in wheat”, AUD$18,150.
2019 CI GRDC Project (1977340) "New methods for snail control ", AUD$140,250.

 

  • Position: Grant-Funded Researcher (B)
  • Phone: 83131102
  • Email: huajian.liu@adelaide.edu.au
  • Campus: Waite
  • Building: The Plant Accelerator, floor Ground
  • Org Unit: Agricultural Science
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